Edited WMS layer titles (#372)

services/ows_refactored/elevation/ows_elevation_cfg.py

@@ -106,7 +106,7 @@
 }
 
 layer_nasadem = {
-    "title": "NASADEM (30 m)",
+    "title": "NASA Digital Elevation Model (30 m)",
     "name": "nasadem",
     "abstract": """
 NASADEM provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the Shuttle Radar Topography Mission (SRTM).

services/ows_refactored/land_cover/ows_io_lulc_cfg.py

@@ -88,7 +88,7 @@
 }
 
 layer = {
-    "title": "IO Land Use/Land Cover 2020",
+    "title": "Impact Observatory Land Use/Land Cover 2020",
     "name": "io_lulc",
     "abstract": """
 Global estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution.

services/ows_refactored/prod_af_ows_root_cfg.py

@@ -109,7 +109,7 @@
             "title": "Digital Earth Africa - OGC Web Services",
             "abstract": "Digital Earth Africa OGC Web Services",
             "layers": [
-                # Hierarchical list of layers.  May be a combination of unnamed/unmappable folder-layers or named mappable layers.
+                # Hierarchical list of layers.  May be a combination of unnamed/unmappable folder-layers or named mappable layers. Influences structure of Terria catalogue in production.
                 {
                     "title": "Satellite images",
                     "abstract": """Satellite images""",
@@ -231,8 +231,8 @@
                     "abstract": """Surface water""",
                     "layers": [
                         {
-                            "title": "Daily water",
-                            "abstract": """Daily water""",
+                            "title": "Daily surface water",
+                            "abstract": """Daily surface water""",
                             "layers": [
                                 {
                                     "include": "ows_refactored.wofs.ows_wofs_ls_cfg.layer",
@@ -241,8 +241,8 @@
                             ]
                         },
                         {
-                            "title": "Annual water",
-                            "abstract": """Annual water""",
+                            "title": "Annual surface water",
+                            "abstract": """Annual surface water""",
                             "layers": [
                                 {
                                     "include": "ows_refactored.wofs.ows_wofs_ls_annual_cfg.layer",
@@ -251,8 +251,8 @@
                             ]
                         },
                         {
-                            "title": "All-time water",
-                            "abstract": """All-time water""",
+                            "title": "All-time surface water",
+                            "abstract": """All-time surface water""",
                             "layers": [
                                 {
                                     "include": "ows_refactored.wofs.ows_wofs_ls_alltime_cfg.layer",
@@ -272,31 +272,19 @@
                         },
                     ],
                 },
-                {
-                    "title": "Elevation",
-                    "abstract": """Digital elevation model""",
-                    "layers": [
-                        {
-                            "include": "ows_refactored.elevation.ows_elevation_cfg.layer_srtm",
-                            "type": "python",
-                        },
-                        {
-                            "include": "ows_refactored.elevation.ows_elevation_cfg.layer_nasadem",
-                            "type": "python",
-                        },
-                        {
-                            "include": "ows_refactored.elevation.ows_elevation_cfg.layer_cop_30",
-                            "type": "python",
-                        },
-                    ],
-                },
                 {
                     "title": "Vegetation",
                     "abstract": """Vegetation""",
                     "layers": [
                         {
-                            "include": "ows_refactored.vegetation.ows_fc_cfg.layer",
-                            "type": "python",
+                            "title": "Daily vegetation",
+                            "abstract": """Daily vegetation""",
+                            "layers": [
+                                {
+                                    "include": "ows_refactored.vegetation.ows_fc_cfg.layer",
+                                    "type": "python",
+                                },
+                            ],
                         },
                         {
                             "include": "ows_refactored.vegetation.ows_gmw_cfg.layer",
@@ -306,7 +294,7 @@
                 },
                 {
                     "title": "Land cover",
-                    "abstract": """Land Cover""",
+                    "abstract": """Land cover""",
                     "layers": [
                         {
                             "include": "ows_refactored.land_cover.ows_io_lulc_cfg.layer",
@@ -327,7 +315,25 @@
                             "type": "python"
                         }
                     ]
-                }
+                },
+                {
+                    "title": "Elevation",
+                    "abstract": """Digital elevation model""",
+                    "layers": [
+                        {
+                            "include": "ows_refactored.elevation.ows_elevation_cfg.layer_srtm",
+                            "type": "python",
+                        },
+                        {
+                            "include": "ows_refactored.elevation.ows_elevation_cfg.layer_nasadem",
+                            "type": "python",
+                        },
+                        {
+                            "include": "ows_refactored.elevation.ows_elevation_cfg.layer_cop_30",
+                            "type": "python",
+                        },
+                    ],
+                },
             ],
         }
     ],

services/ows_refactored/radar_backscatter/ows_alos_cfg.py

@@ -119,7 +119,7 @@
 }
 
 layer = {
-    "title": "Radar Backscatter Annual Mosaic (ALOS/PALSAR)",
+    "title": "Annual mosaic (ALOS/PALSAR)",
     "name": "alos_palsar_mosaic",
     "abstract": """
 Synthetic Aperture Radar (SAR) data have been shown to provide different and complementary information to the more common optical remote sensing data. Radar backscatter response is a function of topography, land cover structure, orientation, and moisture characteristics—including vegetation biomass—and the radar signal can penetrate clouds, providing information about the earth’s surface where optical sensors cannot. Digital Earth Africa provides access to Normalized Radar Backscatter data, for which Radiometric Terrain Correction (RTC) has been applied so data acquired with different imaging geometries over the same region can be compared.

services/ows_refactored/radar_backscatter/ows_jers_cfg.py

@@ -39,7 +39,7 @@
 }
 
 layer = {
-    "title": "Radar Backscatter Annual Mosaic (JERS)",
+    "title": "Annual mosaic (JERS)",
     "name": "jers_sar_mosaic",
     "abstract": """
 Synthetic Aperture Radar (SAR) data have been shown to provide different and complementary information to the more common optical remote sensing data. Radar backscatter response is a function of topography, land cover structure, orientation, and moisture characteristics—including vegetation biomass—and the radar signal can penetrate clouds, providing information about the earth’s surface where optical sensors cannot. Digital Earth Africa provides access to Normalized Radar Backscatter data, for which Radiometric Terrain Correction (RTC) has been applied so data acquired with different imaging geometries over the same region can be compared.

services/ows_refactored/radar_backscatter/ows_sentinel1_cfg.py

@@ -85,7 +85,7 @@
 
 
 layer = {
-    "title": "Normalized Radar Backscatter Sentinel-1",
+    "title": "Normalised radar backscatter (Sentinel-1)",
     "name": "s1_rtc",
     "abstract": """
 

services/ows_refactored/surface_reflectance/ows_gm_ls5_ls7_annual_cfg.py

@@ -4,7 +4,7 @@
 
 
 layer = {
-    "title": "Surface Reflectance Annual GeoMAD Landsat 5 & Landsat 7",
+    "title": "Annual GeoMAD (Landsat 5 & Landsat 7)",
     "name": "gm_ls5_ls7_annual",
     "abstract": """
 Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create 'summary' images or 'composites' that combine multiple images into one image to reveal the median or 'typical' appearance of the landscape for a certain time period.

services/ows_refactored/surface_reflectance/ows_gm_ls8_annual_cfg.py

@@ -4,7 +4,7 @@
 
 
 layer = {
-    "title": "Surface Reflectance Annual GeoMAD Landsat 8",
+    "title": "Annual GeoMAD (Landsat 8)",
     "name": "gm_ls8_annual",
     "abstract": """
 Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create 'summary' images or 'composites' that combine multiple images into one image to reveal the median or 'typical' appearance of the landscape for a certain time period.

services/ows_refactored/surface_reflectance/ows_gm_s2_annual_cfg.py

@@ -4,7 +4,7 @@
 
 
 layer = {
-    "title": "Surface Reflectance Annual GeoMAD Sentinel-2",
+    "title": "Annual GeoMAD (Sentinel-2)",
     "name": "gm_s2_annual",
     "abstract": """
 Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create 'summary' images or 'composites' that combine multiple images into one image to reveal the median or 'typical' appearance of the landscape for a certain time period.

services/ows_refactored/surface_reflectance/ows_gm_s2_semiannual_cfg.py

@@ -3,7 +3,7 @@
 from ows_refactored.surface_reflectance.style_sr_cfg import styles_gm_list
 
 layer = {
-    "title": "Surface Reflectance Semiannual GeoMAD Sentinel-2",
+    "title": "Semiannual GeoMAD (Sentinel-2)",
     "name": "gm_s2_semiannual",
     "abstract": """
 Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create 'summary' images or 'composites' that combine multiple images into one image to reveal the median or 'typical' appearance of the landscape for a certain time period.

services/ows_refactored/surface_reflectance/ows_lsc2_sr_cfg.py

@@ -6,7 +6,7 @@
     styles_ls8c2_sr_list, styles_lsc2_sr_list)
 
 layer_ls8 = {
-    "title": "Surface Reflectance Landsat 8 (USGS Collection 2)",
+    "title": "Surface reflectance (Landsat 8)",
     "name": "ls8_sr",
     "abstract": """
 Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface.
@@ -42,7 +42,7 @@
 }
 
 layer_ls7 = {
-    "title": "Surface Reflectance Landsat 7 (USGS Collection 2)",
+    "title": "Surface reflectance (Landsat 7)",
     "name": "ls7_sr",
     "abstract": """
 Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface.
@@ -78,7 +78,7 @@
 }
 
 layer_ls5 = {
-    "title": "Surface Reflectance Landsat 5 (USGS Collection 2)",
+    "title": "Surface reflectance (Landsat 5)",
     "name": "ls5_sr",
     "abstract": """
 Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface.

services/ows_refactored/surface_reflectance/ows_s2_cfg.py

@@ -3,7 +3,7 @@
 from ows_refactored.surface_reflectance.style_sr_cfg import styles_s2_list
 
 layer = {
-    "title": "Surface Reflectance Sentinel-2",
+    "title": "Surface reflectance (Sentinel-2)",
     "name": "s2_l2a",
     "abstract": """
 Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface.

services/ows_refactored/surface_temperature/ows_lsc2_st_cfg.py

@@ -128,7 +128,7 @@
 
 
 layer_ls8 = {
-    "title": "Surface Temperature Landsat 8 (USGS Collection 2)",
+    "title": "Surface temperature (Landsat 8)",
     "name": "ls8_st",
     "abstract": """
 Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects.