[Production] v0.16.0

.env

@@ -8,10 +8,10 @@ APP_DESCRIPTION=Visualization, Exploration, and Data Analysis (VEDA)
 [email protected]
 
 # Endpoint for the Tiler server. No trailing slash.
-API_RASTER_ENDPOINT='https://staging-raster.delta-backend.com'
+API_RASTER_ENDPOINT='https://openveda.cloud/api/raster'
 
 # Endpoint for the STAC server. No trailing slash.
-API_STAC_ENDPOINT='https://staging-stac.delta-backend.com'
+API_STAC_ENDPOINT='https://openveda.cloud/api/stac'
 API_XARRAY_ENDPOINT='https://prod-titiler-xarray.delta-backend.com/tilejson.json'
 
 MAPBOX_STYLE_URL='mapbox://styles/covid-nasa/ckb01h6f10bn81iqg98ne0i2y'
@@ -25,3 +25,5 @@ GOOGLE_FORM = 'https://docs.google.com/forms/d/e/1FAIpQLSfGcd3FDsM3kQIOVKjzdPn4f
 
 # Google analytics tracking code
 GOOGLE_ANALYTICS_ID='G-CQ3WLED121'
+
+FEATURE_NEW_EXPLORATION = 'TRUE'

.github/workflows/checks.yml

@@ -14,7 +14,7 @@ on:
     - 'main'
 
 env:
-  NODE: 16
+  NODE: 20
 
 jobs:
   prep:

.github/workflows/deploy-prod.yml

@@ -12,7 +12,7 @@ on:
     - 'main'
 
 env:
-  NODE: 16
+  NODE: 20
   DOMAIN_PROD: https://www.earthdata.nasa.gov/dashboard
   DEPLOY_BUCKET_PROD: climatedashboard
   DEPLOY_BUCKET_PROD_REGION: us-east-1

.nvmrc

@@ -1 +1 @@
-16
+20

datasets/CMIP-winter-median-pr.data.mdx

@@ -12,6 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - EIS
+infoDescription: |
+  ::markdown
+    Future changes to precipitation are expected to alter the volume and timing of snow water resources. Here, we present the projected percent-change to Western US cumulative winter precipitation at quarter-degree spatial resoutions across 20-year time periods between 2016 and 2095. Projections are averaged from an ensemble of 23 downscaled climate models from the CMIP6 NASA Earth Exchange Global Daily Downscaled Projections.
 layers:
   - id: CMIP245-winter-median-pr
     stacCol: CMIP245-winter-median-pr
@@ -48,6 +51,11 @@ layers:
       - "#A0CBE4"
       - "#5EA4D1"
       - "#207BBD"
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Percent Difference
   - id: CMIP585-winter-median-pr
     stacCol: CMIP585-winter-median-pr
     name: 'Percent-change to winter cumulative precipitation, SSP5-8.5'
@@ -83,6 +91,11 @@ layers:
       - "#A0CBE4"
       - "#5EA4D1"
       - "#207BBD"
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Percent Difference
 ---
 <Block>
 <Prose>

datasets/CMIP-winter-median-ta.data.mdx

@@ -12,6 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - EIS
+infoDescription: |
+  ::markdown
+    Future changes to air temperature are expected to influence the phase of winter precipitation (snowfall or rainfall) and the timing and amount of snowmelt and streamflow. Here, we present the projected percent-change to Western US average winter temperature at quarter-degree spatial resoutions across 20-year time periods between 2016 and 2095. Projections are averaged from an ensemble of 23 downscaled climate models from the [CMIP6 NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).
 layers:
   - id: CMIP245-winter-median-ta
     stacCol: CMIP245-winter-median-ta
@@ -49,6 +52,11 @@ layers:
       - "#F2B089"
       - "#DE6158"
       - "#CA171C"
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Percent Difference
   - id: CMIP585-winter-median-ta
     stacCol: CMIP585-winter-median-ta
     name: 'SSP5-8.5, Change to winter average air temperature'
@@ -85,6 +93,11 @@ layers:
       - "#F2B089"
       - "#DE6158"
       - "#CA171C"
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Percent Difference
 ---
 <Block>
 <Prose>

datasets/FLDAS-soilmoisture-anomalies.data.mdx

@@ -14,6 +14,15 @@ taxonomy:
   - name: Source
     values:
       - NASA GES DISC
+infoDescription: |
+  ::markdown
+    - **Temporal Extent:** January 1982 - June 2023
+    - **Temporal Resolution:** Monthly
+    - **Spatial Extent:** Quasi-Global ( -180.0,-60.0,180.0,90.0)
+    - **Spatial Resolution:** 10 km x 10 km
+    - **Data Units:** Fraction Soil moisture anomaly (mm3/mm3) difference from 1982-2016 monthly mean
+    - **Data Type:** Research
+    - **Data Latency:** Monthly
 layers:
   - id: SoilMoi00_10cm_tavg
     stacCol: fldas-soil-moisture-anomalies
@@ -49,6 +58,11 @@ layers:
         - "#d1e5f0"
         - "#4393c3"
         - "#053061"
+    info:
+      source: NASA
+      spatialExtent: Quasi-Global
+      temporalResolution: Monthly
+      unit: mm3/mm3
 ---
 
 <Block type='wide'>

datasets/aerosol-difference.data.mdx

@@ -12,6 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - Air Quality
+infoDescription: |
+  ::markdown
+    This dataset comes from the two decadal COGs that displayed mean Aerosol Optical Depth for 2000-2009 and for 2010-2019. Those tiffs were subtracted to display the differences between the two decades.
 layers:
   - id: houston-aod-diff
     stacCol: houston-aod-diff
@@ -47,9 +50,11 @@ layers:
         - "#fee090"
         - "#fc8d59"
         - "#d73027"
-
-
-
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Percent Difference
 ---
 
 <Block>

datasets/bangladesh-landcover-2001-2020.data.mdx

@@ -12,6 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - EIS
+infoDescription: |
+  ::markdown
+     The annual land cover maps of 2001 and 2021 were captured using combined Moderate Resolution Imaging Spectroradiometer (MODIS) Annual Land Cover Type dataset (MCD12Q1 V6, dataset link: [https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)).  The actual data product provides global land cover types at yearly intervals (2001-2020) at 500 meters with six different types of land cover classification. Among six different schemes, The International Geosphere–Biosphere Programme (IGBP) land cover classification selected and further simplified to dominant land cover classes (water, urban, cropland, native vegetation) for two different years to illustrate the changes in land use and land cover of the country.
 layers:
   - id: bangladesh-landcover-2001-2020
     stacCol: bangladesh-landcover-2001-2020
@@ -46,6 +49,11 @@ layers:
         ::js ({ dateFns, datetime, compareDatetime }) => {
           if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
         }
+    info:
+      source: NASA
+      spatialExtent: Bangladesh
+      temporalResolution: Annual
+      unit: Categorical
 ---
 
 <Block>

datasets/barc-thomasfire.data.mdx

@@ -12,6 +12,10 @@ taxonomy:
   - name: Topics
     values:
       - EIS
+infoDescription: |
+  ::markdown
+    Burn Area Reflectance Classification (BARC) from the Burned Area Emergency Response (BAER) program for the Thomas Fire of 2017.
+
 layers:
   - id: barc-thomasfire
     stacCol: barc-thomasfire
@@ -38,6 +42,11 @@ layers:
           label: "Moderate"
         - color: "#971d2b"
           label: "High"
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Categorical
 
 ---
 

datasets/caldor-fire-characteristics-burn-severity.data.mdx

@@ -12,6 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - EIS
+infoDescription: |
+  ::markdown
+    This dataset describes the progression and active fire behavior of the 2021 Caldor Fire in California, as recorded by the algorithm detailed in https://www.nature.com/articles/s41597-022-01343-0. It includes an extra layer detailing the soil burn severity (SBS) conditions provided by the [Burned Area Emergency Response](https://burnseverity.cr.usgs.gov/baer/) team.
 layers:
   - id: caldor-fire-behavior
     stacCol: caldor-fire-behavior
@@ -36,6 +39,11 @@ layers:
         - "#BB3754"
         - "#781D6D"
         - "#34095F"
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Categorical
   - id: caldor-fire-burn-severity
     stacCol: caldor-fire-burn-severity
     name: Burn Severity
@@ -59,7 +67,11 @@ layers:
         - "#BB3754"
         - "#781D6D"
         - "#34095F"
-
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Categorical
 ---
 
 <Block>

datasets/camp-fire-albedo-wsa-diff.data.mdx

@@ -12,8 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - EIS
-
-
+infoDescription: |
+  ::markdown
+    In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the Albedo WSA difference portion of that analysis.
 layers:
   - id: modis-albedo-wsa-diff-2015-2022
     stacCol: campfire-albedo-wsa-diff
@@ -42,10 +43,11 @@ layers:
         - "#fee090"
         - "#fc8d59"
         - "#d73027"
-
-
-
-
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Percent Difference
 ---
 
 <Block>

datasets/camp-fire-lst-day-diff.data.mdx

@@ -12,8 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - EIS
-
-
+infoDescription: |
+  ::markdown
+     In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the LST Day difference portion of that analysis.
 layers:
   - id: modis-lst-day-diff-2015-2022
     stacCol: campfire-lst-day-diff
@@ -44,10 +45,11 @@ layers:
         - "#fee090"
         - "#fc8d59"
         - "#d73027"
-
-
-
-
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Percent Difference
 ---
 
 <Block>

datasets/camp-fire-lst-night-diff.data.mdx

@@ -12,8 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - EIS
-
-
+infoDescription: |
+  ::markdown
+    In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the LST Night difference portion of that analysis.
 layers:
   - id: modis-lst-night-diff-2015-2022
     stacCol: campfire-lst-night-diff
@@ -44,10 +45,11 @@ layers:
         - "#fee090"
         - "#fc8d59"
         - "#d73027"
-
-
-
-
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Percent Difference
 ---
 
 <Block>

datasets/camp-fire-ndvi-diff.data.mdx

@@ -12,8 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - EIS
-
-
+infoDescription: |
+  ::markdown
+    In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the NDVI difference portion of that analysis.    
 layers:
   - id: modis-ndvi-diff-2015-2022
     stacCol: campfire-ndvi-diff
@@ -42,10 +43,11 @@ layers:
         - "#fee090"
         - "#fc8d59"
         - "#d73027"
-
-
-
-
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Percent Difference
 ---
 
 <Block>

datasets/camp-fire-nlcd.data.mdx

@@ -12,8 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - EIS
-
-
+infoDescription: |
+  ::markdown
+    We utilized the National Land Cover Database (NLCD), which provides a classification of land cover categories at 30m spatial resolution over geographical locations within the Continental United States (CONUS). The NLCD is derived from Landsat satellite sensors data and is available at approximately three-year time intervals. We used the NLCD maps for the years 2016 and 2019 to examine changes in land cover type resulting from the Camp Fire event, to examine LULC before and after the Camp Fire. This analysis shows that the dominant vegetation cover type that was present within the region per-wildfire are evergreen forest and shrub/scrub cover, while post-wildfire are grasslands and herbaceous vegetation.
 layers:
   - id: campfire-nlcd
     stacCol: campfire-nlcd
@@ -83,9 +84,11 @@ layers:
     compare:
         datasetId: campfire-nlcd
         layerId: campfire-nlcd
-
-
-
+    info:
+      source: NASA
+      spatialExtent: Regional
+      temporalResolution: Annual
+      unit: Percent Difference 
 ---
 
 <Block>