Merge pull request #306 from NASA-IMPACT/hurricane-ian

Initial Hurricane Ian Push

datasets/damage-probability-ian.data.mdx

@@ -0,0 +1,87 @@
+---
+id: damage_probability_2022-10-03
+name: "NRT HLS-Derived Damage Probability Index"
+description: "Near real-time monitoring of land disturbances for CONUS based on the 30-m Harmonized Landsat Sentinel-2 (HLS) dataset."
+media:
+  src: ::file ./HLS_Damage_Probability_Cover_Image_FL.jpg
+  alt: Satellite imagery over Florida showing damage probability (using Viridis color ramp, with yellow being high probability and purple being low probability) for Oct 3, 2023.
+  author:
+    name: CONUS Disturbance Watcher
+    url: https://gers.users.earthengine.app/view/nrt-conus
+taxonomy:
+  - name: Topics
+    values:
+      - Disasters
+layers:
+  - id: damage_probability_2022-10-03
+    stacCol: damage_probability_2022-10-03
+    name: Damage Probability
+    type: raster
+    description: "DPI values from 0 to 99. 0: no damage; 99: damage mostly likely"
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: magma
+      rescale:
+        - 0
+        - 99
+    legend:
+      type: gradient
+      min: "0"
+      max: "99"
+      stops:
+        - '#000004'
+        - '#180f3d'
+        - '#440f76'
+        - '#721f81'
+        - '#9e2f7f'
+        - '#cd4071'
+        - '#f1605d' 
+        - '#fd9668'
+        - '#fec287'
+        - '#f0f921'
+---
+
+<Block type='wide'>
+<Prose>
+The contiguous United States (CONUS) Disturbance Watcher system was built based on time-series analysis and machine learning techniques. The system consisted of two components. The first component, the retrospective disturbance analysis, extracted disturbance predictors from historical open access disturbance products and satellite datasets and built a series of machine learning models respectively for different lag stages. The second component, namely the near real-time monitoring, recursively updates per-pixel time-series models and outputted current vectors of disturbance predictor using Stochastic Continuous Change Detection (S-CCD) algorithm, and then applied the offline machine learning models from the first component to map disturbance probabilities and patches. The system could be updated regularly in an interval of one week, or updated as an user-defined interval, such as one day that was used for Hurricane Ian monitoring. The per-day updating interval provided the quickest mapping response and the most extensive damage region as some would be recovered after days, while the highest computational resources were needed.   
+
+- **Temporal Extent:** January 1, 2022 - October 3, 2022 
+- **Temporal Resolution:** Daily 
+- **Spatial Extent:** Continental United States
+- **Spatial Resolution:** 30 m x 30 m 
+- **Data Units:** Disturbance probability from 0 to 1 
+- **Data Type:** Research
+- **Data Latency:** Daily 
+
+
+**Scientific Details:**  
+The Hurricane Ian disturbance probability layer shows the likelihood impacted by Hurricane Ian. The data came from the map production of [CONUS Disturbance Watcher system](https://gers.users.earthengine.app/view/nrt-conus) on Oct. 3th, 2022, when the first post-hurricane Harmonized Landsat Sentinel-2 (HLS) 2.0 dataset on Sep. 30th, 2022 became available for downloading. The [HLS dataset](https://hls.gsfc.nasa.gov/) produce harmonized surface reflectance products from four satellite sensors, i.e., Landsat-8, Landsat-9,  Sentinel-2A and Sentinel-2B, providing observation once every 2-3 days. The probability layer (scaled by 100) describes the probability produced by machine learning models. The higher probability reveals the high spectral similarity to the historical disturbance.
+</Prose>
+</Block>
+
+<Block>
+<Prose>
+## Source Data Product Citation
+Ye, S, Zhu, Z, 2023, Near Real-time Hurricane Ian Disturbance Probability Map:  Global Environmental Remote Sensing Laboratory release
+
+## 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
+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, [https://doi.org/10.31223/X5WT2H](https://doi.org/10.31223/X5WT2H) .
+
+### Other Relevant Publications
+Ye, S., Rogan, J., Zhu, Z., & Eastman, J. R. (2021). A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection. Remote Sensing of Environment, 252, 112167, [https://doi.org/10.1016/j.rse.2020.112167](https://doi.org/10.1016/j.rse.2020.112167). 
+
+Ye, S., Zhu, Z., & Cao, G. (2023). Object-based continuous monitoring of land disturbances from dense Landsat time series. Remote Sensing of Environment, 287, 113462, [https://doi.org/10.1016/j.rse.2023.113462](https://doi.org/10.1016/j.rse.2023.113462).
+
+ ## 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>

datasets/entropy-difference-ian.data.mdx

@@ -0,0 +1,82 @@
+---
+id: hls-entropy-difference
+name: "Harmonized Landsat and Sentinel-2 Entropy Difference"
+description: "Using Harmonized Landsat and Sentinel-2 to Analyze the Aftermath of Hurricane Ian in Lee and Charlotte Counties in Florida."
+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:
+      - EIS
+layers:
+  - id: hls-entropy-difference
+    stacCol: hls-entropy-difference
+    name: Entropy Difference
+    type: raster
+    description: "Bitemporal Different with higher values indicating higher likelihood of change from before to after Ian."
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: bwr
+      rescale: -1,1
+    legend:
+      type: gradient
+      min: "-1"
+      max: "1"
+      stops:
+        - '#0000ff'
+        - '#6666ff'
+        - '#ccccff'
+        - '#ffffff'
+        - '#ffcccc'
+        - '#ff6666'
+        - '#ff0000'
+---
+
+<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. The calculation of entropy can be accomplished using the 'entropy' function from the 'scipy.stats' module, which computes the entropy of a distribution for given probability values. This function calculates the Shannon entropy. This metric quantifies the randomness or unpredictability inherent in the dataset's distribution from one time point to another.
+</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>

datasets/hls-ndvi-ian.data.mdx

@@ -0,0 +1,108 @@
+---
+id: hls-ndvi-ian
+name: "Harmonized Landsat and Sentinel-2 Normalized Difference Vegetation Index"
+description: "Using Harmonized Landsat and Sentinel-2 to Analyze the Aftermath of Hurricane Ian in Lee and Charlotte Counties in Florida."
+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:
+      - Hurricane
+      - UAH
+layers:
+  - id: hls-ndvi
+    stacCol: hls-ndvi
+    name: NDVI
+    type: raster
+    description: "NDVI: 0 to 1; 0 = little to no vegetation; 1 = heavy vegetation"
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rdylgn
+      rescale: -1,1
+    legend:
+      type: gradient
+      min: "-1"
+      max: "1"
+      stops:
+      - "#a50026"
+      - "#f46d43"
+      - "#fee08b"
+      - "#d9ef8b"
+      - "#66bd63"
+      - "#006837"
+    compare:
+        datasetId: hls-ndvi-ian
+        layerId: hls-ndvi
+  - id: ndvi_difference
+    stacCol: hls-ndvi-difference
+    name: NDVI Difference
+    type: raster
+    description: "NDVI Difference: -1 to 1; -1 = decrease in vegetation; 1 = increase in vegetation"
+    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"
+---
+
+<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>