[Content] Initial Hog Farm Push

.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://openveda.cloud/api/raster'
+API_RASTER_ENDPOINT='https://staging.openveda.cloud/api/raster'
 
 # Endpoint for the STAC server. No trailing slash.
-API_STAC_ENDPOINT='https://openveda.cloud/api/stac'
+API_STAC_ENDPOINT='https://staging.openveda.cloud/api/stac'
 API_XARRAY_ENDPOINT='https://prod-titiler-xarray.delta-backend.com/tilejson.json'
 
 MAPBOX_STYLE_URL='mapbox://styles/covid-nasa/ckb01h6f10bn81iqg98ne0i2y'

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

@@ -11,7 +11,14 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Precipitation
+      - Snow
+      - Climate
+      - Climate Model
+  - name: Source
+    values:
+      - NASA EIS
+      - CMIP6
 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.

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

@@ -11,7 +11,15 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Temperature
+      - Precipitation
+      - Snow
+      - Climate
+      - Climate Model
+  - name: Source
+    values:
+      - NASA EIS
+      - CMIP6
 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).

datasets/aerosol-difference.data.mdx

@@ -12,6 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - Air Quality
+  - name: Source
+    values:
+      - MODIS
 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.

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

@@ -11,10 +11,13 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Land Cover
+  - name: Source
+    values:
+      - MODIS
 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.
+     The annual land use - land cover maps for 2001 and 2021 were captured using the combined Moderate Resolution Imaging Spectroradiometer (MODIS) Annual Land Cover Type dataset ([MCD12Q1 V6](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

datasets/barc-thomasfire.data.mdx

@@ -11,7 +11,10 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+  - name: Source
+    values:
+      - NASA EIS
 infoDescription: |
   ::markdown
     Burn Area Reflectance Classification (BARC) from the Burned Area Emergency Response (BAER) program for the Thomas Fire of 2017.

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

@@ -11,7 +11,10 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+  - name: Source
+    values:
+      - NASA 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.

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

@@ -11,7 +11,12 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+      - Disasters
+      - Land Cover
+  - name: Source
+    values:
+      - MODIS
 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.

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

@@ -11,7 +11,12 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+      - Disasters
+      - Temperature
+  - name: Source
+    values:
+      - MODIS
 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.

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

@@ -11,7 +11,12 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+      - Disasters
+      - Temperature
+  - name: Source
+    values:
+      - MODIS
 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.

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

@@ -11,7 +11,12 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+      - Disasters
+      - Land Cover
+  - name: Source
+    values:
+      - MODIS
 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.    

datasets/camp-fire-nlcd.data.mdx

@@ -11,7 +11,10 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Land Cover
+  - name: Source
+    values:
+      - Landsat
 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.

datasets/cmip6-tas.data.mdx

@@ -1,6 +1,6 @@
 ---
 id: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO
-name: 'CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (demo subset)'
+name: 'CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (demo subset; 1950-2014)'
 featured: false
 description: "Daily near-surface air temperature from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) Project."
 media:
@@ -13,11 +13,16 @@ taxonomy:
   - name: Topics
     values:
       - Climate
+      - Temperature
+  - name: Source
+    values:
+      - CMIP6
+
 infoDescription: |
   ::markdown
     * Format: [kerchunk (metadata)](https://fsspec.github.io/kerchunk/) for netCDF4
     * Spatial Coverage: 180° W to 180° E, 60° S to 90° N
-    * Temporal: 1950-01-01 to 1951-12-31
+    * Temporal: 1950-01-01 to 2014-12-31
       * _As noted below, this dataset is a subset all available data. The full dataset includes data from 1950 to 2100._
     * Data Resolution:
       * Latitude Resolution: 0.25 degrees (25 km)
@@ -26,7 +31,7 @@ infoDescription: |
 layers:
   - id: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO
     stacCol: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO
-    name: CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (demo subset)
+    name: CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (demo subset; 1950-2014)
     type: zarr
     tileApiEndpoint: 'https://prod-titiler-xarray.delta-backend.com/tilejson.json'
     description: "Historical (1950-2014) daily-mean near-surface (usually, 2 meter) air temperature in Kelvin."
@@ -72,7 +77,7 @@ NEX-GDDP-CMIP6 is comprised of global downscaled climate scenarios derived from
 
 * Format: [kerchunk (metadata)](https://fsspec.github.io/kerchunk/) for netCDF4
 * Spatial Coverage: 180° W to 180° E, 60° S to 90° N
-* Temporal: 1950-01-01 to 1951-12-31
+* Temporal: 1950-01-01 to 2014-12-31
   * _As noted below, this dataset is a subset all available data. The full dataset includes data from 1950 to 2100._
 * Data Resolution:
   * Latitude Resolution: 0.25 degrees (25 km)

datasets/co2.data.mdx

@@ -12,7 +12,11 @@ taxonomy:
   - name: Topics
     values:
       - Air Quality
-      - EIS
+      - COVID 19
+  - name: Source
+    values:
+      - GOSAT
+
 infoDescription: |
   ::markdown
     The Impact of the COVID-19 Pandemic on Atmospheric CO2

datasets/conus-reach.data.mdx

@@ -10,9 +10,9 @@ media:
     url: https://www.nasa.gov
 pubDate: 2023-03-03
 taxonomy:
-  - name: Topics
+  - name: Source
     values:
-      - EIS
+      - NASA EIS
 infoDescription: |
   ::markdown
     This dataset describes the Stream network across the Contiguous United States delineated using Soil and Water Assessment Tool

datasets/damage-probability-ian.data.mdx

@@ -12,6 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - Disasters
+  - name: Source
+    values:
+      - HLS
 layers:
   - id: damage_probability_2022-10-03
     stacCol: damage_probability_2022-10-03

datasets/darnah-flood.data.mdx

@@ -11,10 +11,12 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Disasters
+      - Precipitation
   - name: Source
     values:
-      - UAH
+      - HLS
+      - GPM
 
 layers:
   - id: darnah-flood
@@ -37,6 +39,8 @@ layers:
         ::js ({ dateFns, datetime, compareDatetime }) => {
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;        
         }
+    metadata:
+      source: HLS
 
   - id: darnah-gpm-daily
     stacCol: darnah-gpm-daily
@@ -67,7 +71,9 @@ layers:
         - '#52076c'
         - '#f57c16'
         - '#f7cf39'
-
+
+    metadata:
+      source: GPM
 ---
 
 <Block>
@@ -143,4 +149,4 @@ Environmental Aspects: When interpreting the data, it is crucial to consider the
 * [The Deadliest Flood of the 21st Century](https://www.earthdata.nasa.gov/dashboard/stories/darnah-flood)
 
 </Prose>
-</Block>
+</Block>

datasets/disalexi-etsuppression.data.mdx

@@ -9,9 +9,9 @@ media:
     name: Mike Newbry
     url: https://unsplash.com/photos/DwtX9mMHBJ0
 taxonomy:
-  - name: Topics
+  - name: Source
     values:
-      - EIS
+      - NASA EIS
 infoDescription: |
   ::markdown
     Impact of fires on changes in evapotranspiration, obtained OpenET observations (DisALEXI model) for 2017-20 fires

datasets/ecco-surface-height-change.data.mdx

@@ -9,9 +9,9 @@ media:
     name: Lance Asper
     url: https://unsplash.com/photos/3P3NHLZGCp8
 taxonomy:
-  - name: Topics
+  - name: Source
     values:
-      - EIS
+      - NASA EIS
 infoDescription: |
   ::markdown
     Gridded global sea-surface height change from 1992 to 2017 from the Estimating the Circulation and Climate of the Ocean (ECCO) ocean state estimate. The dataset was calculated as the difference between the annual means over 2017 and 1992, from the 0.5 degree, gridded monthly mean data product available on [PO.DAAC](https://podaac.jpl.nasa.gov/dataset/ECCO_L4_SSH_05DEG_MONTHLY_V4R4).

datasets/entropy-difference-ian.data.mdx

@@ -11,7 +11,10 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Disasters
+  - name: Source
+    values:
+      - HLS
 layers:
   - id: hls-entropy-difference
     stacCol: hls-entropy-difference
@@ -79,4 +82,4 @@ This work has been supported by the USGS-NASA Landsat Science Team (LST) Program
 [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
 
 </Prose>
-</Block>
+</Block>