Added ingested data, waiting for prod

datasets/planet-indices.data.mdx

@@ -17,105 +17,267 @@ taxonomy:
     values:
       - Community Contributed     
 layers:
-  - id: marsh-ida
-    stacCol: marsh-ida
-    name: Salt Marsh
+  - id: ida-ndvi
+    stacCol: ida-ndvi
+    name: NDVI
     type: raster
-    description: 'Salt Marsh Classification Pre-Ida (Southern Louisiana)'
+    description: 'NDVI Pre-Ian (Southern Louisiana)'
     initialDatetime: newest
     zoomExtent:
       - 0
       - 20
     sourceParams:
-      colormap_name: reds
-      nodata: 0
+      colormap_name: rdylgn
       rescale:
         - 0
         - 1
     legend:
-      type: categorical
+      type: gradient
+      min: "-1"
+      max: "1"
       stops:
-        - color: "#ffffff"
-          label: Non-Salt Marsh
-        - color: "#d73027"
-          label: Salt Marsh
+      - "#a50026"
+      - "#f46d43"
+      - "#fee08b"
+      - "#d9ef8b"
+      - "#66bd63"
+      - "#006837"
     compare:
-      datasetId: marsh-ida
-      layerId: marsh-ida
+      datasetId: planet-indices
+      layerId: ida-ndvi
       mapLabel: |
         ::js ({ dateFns, datetime, compareDatetime }) => {
           return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
         }
     info:
-      source: UNEP-WCMC
+      source: PlanetScope
       spatialExtent: Southern Louisiana
       temporalResolution: Monthly 
       unit: Binary
 
-  - id: marsh-difference
-    stacCol: marsh-difference
-    name: Salt Marsh Difference
+  - id: ida-ndwi
+    stacCol: ida-ndwi
+    name: NDWI
     type: raster
-    description: "Difference in Salt Marshes Pre- and Post-Ida"
+    description: 'NDWI Pre-Ian (Southern Louisiana)'
     initialDatetime: newest
     zoomExtent:
       - 0
       - 20
     sourceParams:
-      colormap_name: bwr
-      nodata: 0
+      colormap_name: rdylbu
+      rescale:
+        - 0
+        - 1
+    legend:
+      type: gradient
+      min: "0"
+      max: "1"
+      stops:
+      - "#a50026"
+      - "#f46d43"
+      - "#fee08b"
+      - "#d9ef8b"
+      - "#66bd63"
+      - "#006837"
+    compare:
+      datasetId: planet-indices
+      layerId: ida-ndwi
+      mapLabel: |
+        ::js ({ dateFns, datetime, compareDatetime }) => {
+          return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
+        }
+    info:
+      source: PlanetScope
+      spatialExtent: Southern Louisiana
+      temporalResolution: Monthly 
+      unit: Binary
+
+  - id: ida-ndwi-difference
+    stacCol: ida-ndwi-difference
+    name: NDWI Difference
+    type: raster
+    description: 'NDWI Difference Pre-Ian (Southern Louisiana)'
+    initialDatetime: newest
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rdbu
       rescale:
         - -1
         - 1
     legend:
-      type: categorical
+      type: gradient
+      min: "-1"
+      max: "1"
       stops:
-        - color: "#FF0000"
-          label: Loss of Marsh
-        - color: "#0000FF"
-          label: Gain of Marsh
+      - "#67001f"
+      - "#d6604d"
+      - "#fddbc7"
+      - "#d1e5f0"
+      - "#4393c3"
+      - "#053061"
+    info:
+      source: PlanetScope
+      spatialExtent: Southern Louisiana
+      temporalResolution: Monthly 
+      unit: Binary
+
+  - id: ida-ndvi-difference
+    stacCol: ida-ndvi-difference
+    name: NDVI Difference
+    type: raster
+    description: 'NDVI Difference Pre-Ian (Southern Louisiana)'
+    initialDatetime: newest
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rdbu
+      rescale:
+        - -1
+        - 1
+    legend:
+      type: gradient
+      min: "-1"
+      max: "1"
+      stops:
+      - "#67001f"
+      - "#d6604d"
+      - "#fddbc7"
+      - "#d1e5f0"
+      - "#4393c3"
+      - "#053061"
+    info:
+      source: PlanetScope
+      spatialExtent: Southern Louisiana
+      temporalResolution: Monthly 
+      unit: Binary
 ---
 
 
-<Block type='wide'>
-<Prose>
-Harmonized Landsat Sentinel-2 (HLS) project from NASA is designed to integrate and harmonize data from multiple satellite sources, specifically the Operation Land Imager (OLI) on Landsat-8/9 and the Mult-Spectral Instrument (MSI) on Sentinel-2A/B satellites. This project aims to create a seamless surface reflectance record that is essential for various Earth Observation and monitoring tasks.
+<Block>
+  <Prose>
+  ## Dataset Details
+  - **Temporal Extent:** August 23 - September 9, 2021
+  - **Temporal Resolution:** Inconsistent
+  - **Spatial Extent:** Southern Louisiana
+  - **Spatial Resolution:** 3 meters
+  - **Data Units:** N/A
+  - **Data Type:** Research
+  - **Data Latency:** N/A
+  </Prose>
+<Figure>
+  <Map
+    datasetId='marsh-ida'
+    layerId='marsh-ida'
+    dateTime='2021-08-23'
+    zoom={13}
+    compareDateTime='2021-09-09'
+  />
+  <Caption
+    attrAuthor='NASA'
+    attrUrl='https://nasa.gov/'
+  >
+    Planet satellite imagery classification of salt marshes pre- and post-Ida for southern Louisiana.
+  </Caption>
+</Figure>
+</Block>
 
-- **Temporal Extent:** Landsat-9 2021-10-31; Sentinel-2B 2017-07-06
-- **Temporal Resolution:** ~3 days
-- **Spatial Extent:** Global with the exception of Antarctica
-- **Spatial Resolution:** 30 m x 30 m 
-- **Data Units:** Surface Reflectance 
-- **Data Type:** Research
-- **Data Latency:** 2 to 3 days
+<Block>
+  <Prose>
 
+  ### About
 
-**Scientific Details:**  
-HLS project incorporates several advanced scientific methodologies and technologies to harmonize data from the Landsat and Sentinel-2 satellites such as atmospheric correction, geographic co-registration and common gridding, bidirectional reflectance distribution normalization, and cloud-shadow masking. To calculate the Normalized Difference Vegetation Index (NDVI) from the HLS-2 Dataset, we utilize the red and near-infrared bands to assess vegetation health by applyin the formula NDVI = NIR - Red / NIR + Red, where 'NIR' refers to the near-infrared surface reflectance, and 'Red' denotes the red light surface reflectance, both harmonized from the Landsat and Sentinel satellites.
-</Prose>
+  Planet Labs’ SmallSat imagery, captured by the PlanetScope Dove satellite constellation, is a highly valuable commercial satellite remote sensing product, frequently leveraged for rapid damage assessment and environmental monitoring. Known for its frequent overpasses, with revisit times on the order of a couple of days, and an impressive spatial resolution of 3 meters, this imagery offers exceptional capabilities for monitoring changes in landscapes and infrastructure. The PlanetScope constellation has near-global coverage daily across the visible and near-infrared channels, providing extensive data for timely and precise analysis of up to 140-150 million square kilometers.
+  
+  </Prose>
 </Block>
 
 <Block>
-<Prose>
-## Source Data Product Citation
-Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C.,   Skakun, S. V., & Justice, C. (2018). The Harmonized Landsat and Sentinel-2   surface reflectance data set. Remote Sensing of Environment, 219, 145-161.
-## Disclaimer
-All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)). Careful quality checks are used to ensure data transformation has been performed correctly.
+
+  <Prose>
+
+  ### What PlanetScope Data Offers
 
-## Key Publications
-Su Ye, John Rogan, Zhe Zhu, J. Ronald Eastman, A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection, Remote Sensing of Environment, Volume 252, 2021,112167, ISSN 0034-4257, (https://doi.org/10.1016/j.rse.2020.112167)[https://doi.org/10.1016/j.rse.2020.112167]. 
+  * High-Resolution Imagery: With a 3-meter spatial resolution, the PlanetScope Dove satellite imagery provides detailed views of landscapes, enabling precise assessments of building and vegetation damage.
+
+  * Frequent Revisit Times: The satellite constellation’s ability to capture imagery on the order of every couple of days makes it highly effective for monitoring rapid changes, such as those caused by natural disasters.
+
+  * Extensive Coverage: Collecting up to 2 million square kilometers of imagery daily, the Dove constellation ensures broad coverage across visible and near-infrared channels.
 
+  * Building and Vegetation Damage Assessment: The high-resolution and frequent imagery facilitate quick identification and analysis of damage to infrastructure and vegetation, aiding in damage determination and response efforts.
 
-Su Ye, Zhe Zhu, Guofeng Cao, Object-based continuous monitoring of land disturbances from dense Landsat time series, Remote Sensing of Environment, Volume 287, 2023, 113462, ISSN 0034-4257, (https://doi.org/10.1016/j.rse.2023.113462)[https://doi.org/10.1016/j.rse.2023.113462].
+  </Prose>
+</Block>
+
+<Block>
+  <Prose>
+
+  ### Access the Data
 
-### Other Relevant Publications
-Ye, S., Zhu, Z., & Suh, J. W. (2024). Leveraging past information and machine learning to accelerate land disturbance monitoring. Remote Sensing of Environment, 305, 114071. 
+  Visit Planet's [home page](https://www.planet.com) to explore options for data access. This data was made available through the NASA [Commercial Satellite Data Acquisition (CSDA) Program](https://earthdata.nasa.gov/about/csda/vendor-planet). You can access the CDSA data explorer [**HERE**](https://csdap.earthdata.nasa.gov).
+
+  </Prose>
+</Block>
+
+<Block>
+  <Prose>
 
- ## Acknowledgment
-This work has been supported by the USGS-NASA Landsat Science Team (LST) Program for Toward Near Real-time Monitoring and Characterization of Land Surface Change for the Conterminous US (140G0119C0008)
+  ### Citing this Dataset
+
+  Image © 2024 Planet Labs PBC. Planet Application Program Interface: In Space for Life on Earth. https://api.planet.com.
+
+  </Prose>
+</Block>
 
-## License
-[Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
+<Block>
+  <Prose>
+
+  ## Disclaimer
+
+  All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.  
+
+  </Prose>
+</Block>
+
+<Block>
+  <Prose>
+
+  ### Key Publications
+
+  Marshall, W and C. Boshuizen, 2013: Planet Labs' Remote Sensing Satellite System. Proc. of the 2013 Small Satellite Conference, Utah State University. https://digitalcommons.usu.edu/smallsat/2013/all2013/7/
+
+  </Prose>
+</Block>
+
+<Block>
+  <Prose>
+
+  ### Other Publications
+
+  Molthan, A., L. A. Schultz, K. M. McGrath, J. E. Burks, J. P. Camp, K. Angle, J. R. Bell, and G. J. Jedlovec, 2020: Earth Remote Sensing in NWS Severe Weather Damage Assessments. *Bull. Amer. Meteor. Soc.*, **101**, 221–226. https://www.jstor.org/stable/27028125 
+
+  </Prose>
+</Block>
+
+<Block>
+  <Prose>
+
+  ## Data Stories Using This Dataset
+
+  **<Link to={"/stories/wetland-impacts"}>How Hurricane Ida’s Impact on Wetlands Endangers Inland Communities</Link>**
+
+  </Prose>
+</Block>
+
+<Block>
+  <Prose>
+
+  ## License
+
+  [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
 
-</Prose>
+  </Prose>
 </Block>