configuring marsh datasets

datasets/marsh-ida.data.mdx

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+---
+id: marsh-ida
+name: "Salt Marsh Distribution from UNEP-WCMC (WILL ADD MORE INFO)"
+description: "ADD INFO"
+media:
+  src: ::file ../stories/ian_goes_cover.jpg
+  alt: Hurricane Ian as seen from space as it makes landfall with the state of Florida. NASA Earth Observatory image.
+  author:
+    name: Joshua Stevens, using GOES 16 imagery courtesy of NOAA and the National Environmental Satellite, Data, and Information Service (NESDIS)
+    url: https://visibleearth.nasa.gov/images/150408/hurricane-ian-reaches-florida
+taxonomy:
+  - name: Topics
+    values:
+      - Natural Disasters
+      - Tropical
+  - name: Source
+    values:
+      - Community Contributed     
+layers:
+  - id: marsh-ida
+    stacCol: marsh-ida
+    name: Salt Marsh
+    type: raster
+    description: "Salt Marsh Classification Pre-Ida (Southern Louisiana)"
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: binary-salt-marsh
+      rescale: 0,1  # Assuming binary data (0 for non-salt marsh, 1 for salt marsh)
+    legend:
+      type: discrete
+      items:
+        - color: "#FF0000"  # Red for Salt Marsh
+          label: "Salt Marsh"
+        - color: "#0000FF"  # Blue for Non-Salt Marsh
+          label: "Non-Salt Marsh"
+    compare:
+      datasetId: marsh-ida
+      layerId: marsh-ida
+layers:
+  - id: marsh-difference
+    stacCol: marsh-difference
+    name: Salt Marsh Difference
+    type: raster
+    description: "Difference in Salt Marshes Pre- and Post-Ida"
+    initialDatetime: newest
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: binary-difference-salt-marsh
+      rescale: -1,1  # -1 for loss, 0 for no change, 1 for gain
+    legend:
+      type: discrete
+      items:
+        - color: "#FF0000"  # Red for Loss of Salt Marsh
+          label: "Loss of Salt Marsh"
+        - color: "#FFFFFF"  # White for No Change
+          label: "No Change"
+        - color: "#0000FF"  # Blue for Gain of Salt Marsh
+          label: "Gain of Salt Marsh"
+---
+
+<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.
+
+- **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
+
+
+**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>
+</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.
+
+## 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]. 
+
+
+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].
+
+### 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. 
+
+ ## 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)
+
+## License
+[Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
+
+</Prose>
+</Block>