diff --git a/.env b/.env index 8694cf5c8..0aeb58068 100644 --- a/.env +++ b/.env @@ -8,10 +8,10 @@ APP_DESCRIPTION=Visualization, Exploration, and Data Analysis (VEDA) APP_CONTACT_EMAIL=email@example.org # 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' diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index aa8b320e9..d23172fc6 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -14,7 +14,7 @@ on: - 'main' env: - NODE: 16 + NODE: 20 jobs: prep: diff --git a/.github/workflows/deploy-prod.yml b/.github/workflows/deploy-prod.yml index 7504852d1..930e3a96f 100644 --- a/.github/workflows/deploy-prod.yml +++ b/.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 diff --git a/.nvmrc b/.nvmrc index 19c7bdba7..2edeafb09 100644 --- a/.nvmrc +++ b/.nvmrc @@ -1 +1 @@ -16 \ No newline at end of file +20 \ No newline at end of file diff --git a/.veda/ui b/.veda/ui index 8c27cb77b..4531c975c 160000 --- a/.veda/ui +++ b/.veda/ui @@ -1 +1 @@ -Subproject commit 8c27cb77b1ccf09b5648eff46a6242e0c966e0e7 +Subproject commit 4531c975cc954400b0cbd3cc53abb6a8d2c820b0 diff --git a/datasets/CMIP-winter-median-pr.data.mdx b/datasets/CMIP-winter-median-pr.data.mdx index f33f41b99..c1efdc2b7 100644 --- a/datasets/CMIP-winter-median-pr.data.mdx +++ b/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 --- diff --git a/datasets/CMIP-winter-median-ta.data.mdx b/datasets/CMIP-winter-median-ta.data.mdx index 63dee81e7..5a1c52249 100644 --- a/datasets/CMIP-winter-median-ta.data.mdx +++ b/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 --- diff --git a/datasets/FLDAS-soilmoisture-anomalies.data.mdx b/datasets/FLDAS-soilmoisture-anomalies.data.mdx index 870a4850f..6d35dc63f 100644 --- a/datasets/FLDAS-soilmoisture-anomalies.data.mdx +++ b/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 --- diff --git a/datasets/HLS_Damage_Probability_Cover_Image_FL.jpg b/datasets/HLS_Damage_Probability_Cover_Image_FL.jpg new file mode 100644 index 000000000..98f836129 Binary files /dev/null and b/datasets/HLS_Damage_Probability_Cover_Image_FL.jpg differ diff --git a/datasets/aerosol-difference.data.mdx b/datasets/aerosol-difference.data.mdx index ab54f0f31..7e82eed78 100644 --- a/datasets/aerosol-difference.data.mdx +++ b/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 --- diff --git a/datasets/bangladesh-landcover-2001-2020.data.mdx b/datasets/bangladesh-landcover-2001-2020.data.mdx index b4a92a63b..1ed510d48 100644 --- a/datasets/bangladesh-landcover-2001-2020.data.mdx +++ b/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 --- diff --git a/datasets/barc-thomasfire.data.mdx b/datasets/barc-thomasfire.data.mdx index 5c951361e..83dfe414c 100644 --- a/datasets/barc-thomasfire.data.mdx +++ b/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 --- diff --git a/datasets/caldor-fire-characteristics-burn-severity.data.mdx b/datasets/caldor-fire-characteristics-burn-severity.data.mdx index 49fc51d7b..e4cbec8d4 100644 --- a/datasets/caldor-fire-characteristics-burn-severity.data.mdx +++ b/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 --- diff --git a/datasets/camp-fire-albedo-wsa-diff.data.mdx b/datasets/camp-fire-albedo-wsa-diff.data.mdx index 121ef80c4..343d37803 100644 --- a/datasets/camp-fire-albedo-wsa-diff.data.mdx +++ b/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 --- diff --git a/datasets/camp-fire-lst-day-diff.data.mdx b/datasets/camp-fire-lst-day-diff.data.mdx index bae42dc19..6baa80d77 100644 --- a/datasets/camp-fire-lst-day-diff.data.mdx +++ b/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 --- diff --git a/datasets/camp-fire-lst-night-diff.data.mdx b/datasets/camp-fire-lst-night-diff.data.mdx index 4ead02a1b..8995f3d9a 100644 --- a/datasets/camp-fire-lst-night-diff.data.mdx +++ b/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 --- diff --git a/datasets/camp-fire-ndvi-diff.data.mdx b/datasets/camp-fire-ndvi-diff.data.mdx index 97a6c69b5..5b7e3237e 100644 --- a/datasets/camp-fire-ndvi-diff.data.mdx +++ b/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 --- diff --git a/datasets/camp-fire-nlcd.data.mdx b/datasets/camp-fire-nlcd.data.mdx index 76923c23f..b82e63c9d 100644 --- a/datasets/camp-fire-nlcd.data.mdx +++ b/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 --- diff --git a/datasets/cmip6-tas.data.mdx b/datasets/cmip6-tas.data.mdx index 272ed430c..18f9963b9 100644 --- a/datasets/cmip6-tas.data.mdx +++ b/datasets/cmip6-tas.data.mdx @@ -13,6 +13,16 @@ taxonomy: - name: Topics values: - Climate +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 + * _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) + * Longitude Resolution: 0.25 degrees (25 km) + * Temporal Resolution: daily layers: - id: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO stacCol: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO @@ -43,6 +53,11 @@ layers: - '#f2cbb7' - '#ee8468' - '#b40426' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: Kelvin --- diff --git a/datasets/co2.data.mdx b/datasets/co2.data.mdx index bf6711eb9..5050b4440 100644 --- a/datasets/co2.data.mdx +++ b/datasets/co2.data.mdx @@ -13,6 +13,9 @@ taxonomy: values: - Air Quality - EIS +infoDescription: | + ::markdown + The Impact of the COVID-19 Pandemic on Atmospheric CO2 layers: - id: co2-mean stacCol: co2-mean @@ -47,6 +50,11 @@ layers: - "#fee090" - "#fc8d59" - "#d73027" + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: ppm - id: co2-diff stacCol: co2-diff name: Difference CO2 @@ -77,6 +85,11 @@ layers: - "#f39779" - "#db5c48" - "#b50021" + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: ppm --- diff --git a/datasets/conus-reach.data.mdx b/datasets/conus-reach.data.mdx index 5ab22469b..a66c77dd1 100644 --- a/datasets/conus-reach.data.mdx +++ b/datasets/conus-reach.data.mdx @@ -13,6 +13,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + This dataset describes the Stream network across the Contiguous United States delineated using Soil and Water Assessment Tool layers: - id: conus-reach stacCol: conus-reach @@ -28,7 +31,11 @@ layers: - 1 - 1 nodata: 65535 - + info: + source: NASA + spatialExtent: Contiguous US + temporalResolution: Annual + unit: N/A --- diff --git a/datasets/damage-probability-ian.data.mdx b/datasets/damage-probability-ian.data.mdx new file mode 100644 index 000000000..6717d40e1 --- /dev/null +++ b/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' +--- + + + +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. + + + + + +## 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) + + + diff --git a/datasets/darnah-flood-background.jpg b/datasets/darnah-flood-background.jpg new file mode 100644 index 000000000..ce16934ea Binary files /dev/null and b/datasets/darnah-flood-background.jpg differ diff --git a/datasets/darnah-flood.data.mdx b/datasets/darnah-flood.data.mdx new file mode 100644 index 000000000..d0f03ac57 --- /dev/null +++ b/datasets/darnah-flood.data.mdx @@ -0,0 +1,146 @@ +--- +id: darnah-flood +name: 'Darnah, Libya Flood' +description: "HLS (SWIR FalseColor composites) imagery supporting the Darnah Flood Story" +media: + src: ::file ./darnah-flood-background.jpg + alt: Aerial view over the Wadi Darnah River post-flood in Derna, Libya on September 14, 2023. + author: + name: Marwan Alfaituri (Reuters) + url: https://abcnews.go.com/International/casualties-libya-floods-avoided-world-meteorological-organization-chief/story?id=103200104 +taxonomy: + - name: Topics + values: + - EIS + - name: Source + values: + - UAH + +layers: + - id: darnah-flood + stacCol: darnah-flood + name: HLS SWIR FalseColor Composite + type: raster + description: 'HLS falsecolor composite imagery using S30 Bands 12, 8A, and 4, over Darnah, Libya.' + zoomExtent: + - 0 + - 20 + sourceParams: + rescale: + - 0,5000 + resampling: bilinear + asset_bidx: cog_default|1,2,3 + compare: + datasetId: darnah-flood + layerId: darnah-flood + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + + - id: darnah-gpm-daily + stacCol: darnah-gpm-daily + name: GPM Accumulated Rainfall + type: raster + description: 'Accumulated Rainfall (mm) over the eastern Mediterranean Sea from Medicane Daniel (4 - 16 September, 2023).' + initialDatetime: newest + zoomExtent: + - 0 + - 20 + + sourceParams: + colormap_name: inferno + nodata: 0 + resampling: bilinear + bidx: 1 + rescale: + - 0.1 + - 500 + + legend: + type: gradient + min: "0.1 mm" + max: "500 mm" + stops: + - '#08041d' + - '#1f0a46' + - '#52076c' + - '#f57c16' + - '#f7cf39' + +--- + + + +## Overview + +On Monday, September 11, 2023, the city of Darnah, Libya experienced the [deadliest flood disaster of the 21st century](https://www.google.com/url?q=https://www.aa.com.tr/en/environment/floods-in-libya-s-derna-worst-disaster-in-21st-century/2992617&sa=D&source=docs&ust=1709231595507737&usg=AOvVaw3MuRygRSSxtExzI_shVddG), and Africa’s deadliest flood ever recorded. A storm in the Mediterranean Sea dubbed ‘Medicane Daniel’ moved over northeastern Libya on the evening of the 10th, dumping prolific rain over the desert the morning of the 11th. A record 16” of rainfall was measured in 24 hours at the city of Al-Bayda, Libya (just west of Derna) from ‘Medicane’ Daniel. Two dams upstream of Darnah collapsed during the heavy rains leading to approximately [25% of the city being destroyed](https://www.google.com/url?q=https://www.reuters.com/world/africa/more-than-1000-bodies-recovered-libyan-city-after-floods-minister-2023-09-12/&sa=D&source=docs&ust=1709231595509452&usg=AOvVaw083l0kMybsbbwT18u4SVTm). The first dam broke around 3:00 AM local time on September 11th, and the second followed suit shortly thereafter, which exacerbated the death toll greatly. The International Committee of the Red Cross (ICRC) reported that proceeding the dam bursts, a wave as high as 23 feet (7 meters) rushed towards the city. With a population of 120,000, the major city of Darnah saw massive destruction, with entire districts of the city being washed away.Nearly 1,000 buildings are estimated to have been completely destroyed as well as 5 major bridges that connect the west and east sides of the city. The United Nations Office for the Coordination of Humanitarian Affairs initially reported a death toll currently sits at 11,300 with another 10,100 reported missing. This estimate was later revised to [3,958 fatalities](https://www.aljazeera.com/news/2023/9/18/libya-floods-conflicting-death-tolls-greek-aid-workers-die-in-crash) on September 18. + + + + + + + +## Scientific Research + + +The primary dataset employed in analyzing the Darnah flood, alongside Integrated Multi-Satellite Retrivals for the Global Precipitation Measurement Mission (GPM IMERG) data, is a three-band HLS composite image created from the shortwave infrared, narrow near-infrared, and red bands of pre and post-flood HLS data, supplemented with total rainfall data from GPM taken from 5 to 16 September 2023. These scenes were acquired on September 7 and 22, 2023. The SWIR false color composite visually illustrates the extent of the greenup resulting from heavy rainfall leading to the flood, while GPM's total rainfall data provides insight into precipitation patterns associated with Medicane Daniel over the eastern Mediterranean region. + +These datasets support ongoing scientific research and analysis of the Darnah flood and its aftermath. They aid in assessing the flood's impact on local land cover, vegetation extent, sediment loading, and precipitation patterns. Furthermore, they facilitate the monitoring of long-term environmental recovery and ecosystem resilience, as well as evaluating the effectiveness of flood mitigation and dam rebuilding efforts upstream of Darnah. + + + +
+ + + SWIR False color HLS imagery showing the destroyed area of Darnah, Libya along the Wadi Darnah River on 22 September 2023. + +
+ + + + + +## Interpreting the Data + +The HLS and GPM datasets concerning the Darnah Flood should be interpreted with careful consideration of temporal, spatial, and environmental factors. + +Temporal Aspects: The HLS SWIR FalseColor composite images were taken at 10:30 AM LST on September 7 and 22, 2023, corresponding to pre- and post-flood times. The GPM total rainfall data is a summed daily rainfall accumulation product that spans from September 5 to 16, 2023. + +Spatial Aspects: All three bands used from the HLS dataset are at 30-meter resolution, providing detailed spatial information. GPM's total rainfall data covers the entire eastern Mediterranean region, offering insights into the broader spatial distribution of precipitation associated with Medicane Daniel, and is at 0.1 by 0.1 degree spatial resolution. + +Environmental Aspects: When interpreting the data, it is crucial to consider the local topography and land cover. Darnah, situated along the southern coast of the Mediterranean Sea, experiences rapidly rising terrain to its south and lies within the Saharan Desert region. + + + + + + +## Additional Resources + +* [Harmonized Landsat-Sentinel](https://hls.gsfc.nasa.gov/) + +* [European Commission Report 9/13/2023](https://upload.wikimedia.org/wikipedia/commons/2/2c/ECDM_20230913_FL_Libya.pdf) + + + + + + + +## Data Story + +* [The Deadliest Flood of the 21st Century](https://www.earthdata.nasa.gov/dashboard/stories/darnah-flood) + + + \ No newline at end of file diff --git a/datasets/disalexi-etsuppression.data.mdx b/datasets/disalexi-etsuppression.data.mdx index f71b6198d..d68c6d8b3 100644 --- a/datasets/disalexi-etsuppression.data.mdx +++ b/datasets/disalexi-etsuppression.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Impact of fires on changes in evapotranspiration, obtained OpenET observations (DisALEXI model) for 2017-20 fires layers: - id: disalexi-etsuppression stacCol: disalexi-etsuppression @@ -46,6 +49,11 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Difference - id: mtbs-burn-severity stacCol: mtbs-burn-severity name: MTBS Burn Severity @@ -70,6 +78,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/ecco-surface-height-change.data.mdx b/datasets/ecco-surface-height-change.data.mdx index 4cc9be96c..d2ed42f41 100644 --- a/datasets/ecco-surface-height-change.data.mdx +++ b/datasets/ecco-surface-height-change.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - 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). layers: - id: ecco-surface-height-change stacCol: ecco-surface-height-change @@ -34,7 +37,11 @@ layers: - "#EF8A62" - "#F7F7F7" - "#67A9CF" - + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: meters --- diff --git a/datasets/emit-landfill.data.mdx b/datasets/emit-landfill.data.mdx index 8cb54a7b8..cb62a63ff 100644 --- a/datasets/emit-landfill.data.mdx +++ b/datasets/emit-landfill.data.mdx @@ -16,6 +16,14 @@ taxonomy: - name: Source values: - NASA EMIT +infoDescription: | + ::markdown + - **Temporal Extent:** June 22, and August 25, 2023 + - **Temporal Resolution:** Variable (based on ISS orbit, solar illumination, and target mask) + - **Spatial Extent:** Stockton, CA and Dallas, TX + - **Spatial Resolution:** 60 m + - **Data Units:** Parts per million-meter (ppm m) + - **Data Type:** Research layers: - id: landfill-emit stacCol: landfill-emit @@ -63,6 +71,11 @@ layers: - '#fdac33' - '#fdc527' - '#f8df25' + info: + source: NASA + spatialExtent: Stockton, CA and Dallas, TX + temporalResolution: Annual + unit: ppm m --- diff --git a/datasets/entropy-difference-ian.data.mdx b/datasets/entropy-difference-ian.data.mdx new file mode 100644 index 000000000..2df23b35c --- /dev/null +++ b/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' +--- + + + +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. + + + + + +## 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) + + + \ No newline at end of file diff --git a/datasets/epa-agriculture.data.mdx b/datasets/epa-agriculture.data.mdx index bfa30c6c5..89f871f62 100644 --- a/datasets/epa-agriculture.data.mdx +++ b/datasets/epa-agriculture.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_4b_manure_management stacCol: EPA-annual-emissions_4B_Manure_Management @@ -42,6 +45,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_4b_manure_management stacCol: EPA-monthly-emissions_4B_Manure_Management name: Manure Management (monthly) @@ -68,6 +76,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² - id: epa-annual-emissions_4c_rice_cultivation stacCol: EPA-annual-emissions_4C_Rice_Cultivation name: Rice Cultivation @@ -94,6 +107,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_4c_rice_cultivation stacCol: EPA-monthly-emissions_4C_Rice_Cultivation name: Rice Cultivation (monthly) @@ -120,6 +138,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² - id: epa-annual-emissions_4a_enteric_fermentation stacCol: EPA-annual-emissions_4A_Enteric_Fermentation name: Enteric Fermentation @@ -148,6 +171,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_4f_field_burning stacCol: EPA-annual-emissions_4F_Field_Burning name: Field Burning @@ -174,6 +202,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_4f_field_burning stacCol: EPA-monthly-emissions_4F_Field_Burning name: Field Burning (monthly) @@ -200,6 +233,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² --- diff --git a/datasets/epa-coal-mines.data.mdx b/datasets/epa-coal-mines.data.mdx index 79918a883..f867717a9 100644 --- a/datasets/epa-coal-mines.data.mdx +++ b/datasets/epa-coal-mines.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_1b1a_coal_mining_underground stacCol: EPA-annual-emissions_1B1a_Coal_Mining_Underground @@ -42,6 +45,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1b1a_coal_mining_surface stacCol: EPA-annual-emissions_1B1a_Coal_Mining_Surface name: Surface Coal Mines @@ -68,6 +76,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1b1a_abandoned_coal stacCol: EPA-annual-emissions_1B1a_Abandoned_Coal name: Abandoned Coal Mines @@ -94,7 +107,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' - + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² --- diff --git a/datasets/epa-natural-gas-systems.data.mdx b/datasets/epa-natural-gas-systems.data.mdx index 985f670cb..d241f778c 100644 --- a/datasets/epa-natural-gas-systems.data.mdx +++ b/datasets/epa-natural-gas-systems.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_1b2b_natural_gas_processing stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Processing @@ -42,6 +45,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1b2b_natural_gas_production stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Production name: Natural Gas Production @@ -68,6 +76,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_1b2b_natural_gas_production stacCol: EPA-monthly-emissions_1B2b_Natural_Gas_Production name: Natural Gas Production (monthly) @@ -96,6 +109,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² - id: epa-annual-emissions_1b2b_natural_gas_transmission stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Transmission name: Natural Gas Transmission @@ -122,6 +140,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1b2b_natural_gas_distribution stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Distribution name: Natural Gas Distribution @@ -148,7 +171,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' - + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² --- diff --git a/datasets/epa-other.data.mdx b/datasets/epa-other.data.mdx index 3ed980e90..69f9afab7 100644 --- a/datasets/epa-other.data.mdx +++ b/datasets/epa-other.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_2b5_petrochemical_production stacCol: EPA-annual-emissions_2B5_Petrochemical_Production @@ -42,6 +45,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_2c2_ferroalloy_production stacCol: EPA-annual-emissions_2C2_Ferroalloy_Production name: Ferroalloy Production @@ -68,6 +76,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1a_combustion_mobile stacCol: EPA-annual-emissions_1A_Combustion_Mobile name: Mobile Combustion @@ -96,6 +109,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1a_combustion_stationary stacCol: EPA-annual-emissions_1A_Combustion_Stationary name: Stationary Combustion @@ -126,6 +144,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_1a_combustion_stationary stacCol: EPA-monthly-emissions_1A_Combustion_Stationary name: Stationary Combustion (monthly) @@ -156,6 +179,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² - id: epa-annual-emissions_5_forest_fires stacCol: EPA-annual-emissions_5_Forest_Fires name: Forest Fires @@ -182,6 +210,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-daily-emissions_5_forest_fires stacCol: EPA-daily-emissions_5_Forest_Fires name: Forest Fires (daily) @@ -208,7 +241,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' - + info: + source: EPA + spatialExtent: United States + temporalResolution: Daily + unit: Mg 1/a km² --- diff --git a/datasets/epa-petroleum-systems.data.mdx b/datasets/epa-petroleum-systems.data.mdx index 870a2e688..5273c0552 100644 --- a/datasets/epa-petroleum-systems.data.mdx +++ b/datasets/epa-petroleum-systems.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_1b2a_petroleum stacCol: EPA-annual-emissions_1B2a_Petroleum @@ -44,6 +47,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_1b2a_petroleum stacCol: EPA-monthly-emissions_1B2a_Petroleum name: Petroleum (monthly) @@ -72,7 +80,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' - + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² --- diff --git a/datasets/epa-waste.data.mdx b/datasets/epa-waste.data.mdx index 6c7e1b96b..5e6fd8cab 100644 --- a/datasets/epa-waste.data.mdx +++ b/datasets/epa-waste.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_6b_wastewater_treatment_domestic stacCol: EPA-annual-emissions_6B_Wastewater_Treatment_Domestic @@ -42,6 +45,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_6b_wastewater_treatment_industrial stacCol: EPA-annual-emissions_6B_Wastewater_Treatment_Industrial name: Industrial Wastewater Treatment @@ -70,6 +78,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_6a_landfills_industrial stacCol: EPA-annual-emissions_6A_Landfills_Industrial name: Industrial Landfills @@ -98,6 +111,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_6a_landfills_municipal stacCol: EPA-annual-emissions_6A_Landfills_Municipal name: Municipal Landfills @@ -126,6 +144,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_6d_composting stacCol: EPA-annual-emissions_6D_Composting name: Composting @@ -152,7 +175,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' - + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² --- diff --git a/datasets/fb_population.ej.data.mdx b/datasets/fb_population.ej.data.mdx index 73a11905c..0417424b2 100644 --- a/datasets/fb_population.ej.data.mdx +++ b/datasets/fb_population.ej.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - Meta +infoDescription: | + ::markdown + In partnership with the Center for International Earth Science Information Network (CIESIN) at Columbia University, Meta [formerly known as Facebook] used census data and computer vision techniques (Convolutional Neural Networks) to identify buildings from publicly accessible mapping services to create population density datasets. These high-resolution maps estimate the number of individuals living within 30-meter grid tiles on a global scale. The population estimates are based on data from the Gridded Population of the World (GPWv4) data collection. layers: - id: facebook_population_density stacCol: facebook_population_density @@ -31,6 +34,11 @@ layers: rescale: - 0 - 69 + info: + source: Meta + spatialExtent: Global + temporalResolution: Annual + unit: Units of People per Square Meter legend: type: gradient min: "0" diff --git a/datasets/fire.data.mdx b/datasets/fire.data.mdx index 98ecdd94e..2f2a3e7d8 100644 --- a/datasets/fire.data.mdx +++ b/datasets/fire.data.mdx @@ -16,6 +16,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Fire perimeter data is generated by the FEDs algorithm. The FEDs algorithm tracks fire movement and severity by ingesting observations from the VIIRS thermal sensors on the Suomi NPP and NOAA-20 satellites. This algorithm uses raw VIIRS observations to generate a polygon of the fire, locations of the active fire line, and estimates of fire mean Fire Radiative Power (FRP). The VIIRS sensors overpass at ~1:30 AM and PM local time, and provide estimates of fire evolution ~ every 12 hours. The data produced by this algorithm describe where fires are in space and how fires evolve through time. This CONUS-wide implementation of the FEDs algorithm is based on [Chen et al 2020’s algorithm for California.](https://www.nature.com/articles/s41597-022-01343-0) layers: - id: eis_fire_perimeter stacCol: eis_fire_perimeter @@ -25,6 +28,11 @@ layers: zoomExtent: - 5 - 20 + info: + source: NASA + spatialExtent: Contiguous United States + temporalResolution: Daily + unit: N/A --- diff --git a/datasets/frp-max-thomasfire.data.mdx b/datasets/frp-max-thomasfire.data.mdx index 40c93fdb7..0070893d6 100644 --- a/datasets/frp-max-thomasfire.data.mdx +++ b/datasets/frp-max-thomasfire.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Maximum Fire Radiative Power recorded by the Suomi NPP VIIRS sensor per 12hr fire line segment for the Thomas Fire of 2017 layers: - id: frp-max-thomasfire stacCol: frp-max-thomasfire @@ -45,6 +48,11 @@ layers: - "#BB3754" - "#781D6D" - "#34095F" + info: + source: NASA + spatialExtent: Thomas Fire Area + temporalResolution: Annual + unit: Watts - id: barc-thomasfire stacCol: barc-thomasfire name: Burn Area Reflectance Classification for Thomas Fire @@ -70,7 +78,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" - + info: + source: NASA + spatialExtent: Thomas Fire Area + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/geoglam.data.mdx b/datasets/geoglam.data.mdx index 234dfb7c9..46fcaa082 100644 --- a/datasets/geoglam.data.mdx +++ b/datasets/geoglam.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - GEOGLAM +infoDescription: | + ::markdown + The Group on Earth Observation's Global Agricultural Monitoring Initiative (GEOGLAM) Global Crop Monitor uses remote sensing data like global precipitation and soil moisture measurements to help reduce uncertainty, promote market transparency, and provide early warning for crop failures through multi-agency collaboration. layers: - id: geoglam stacCol: geoglam @@ -49,6 +52,11 @@ layers: label: "Out of season" - color: "#804115" label: "No data" + info: + source: GEOGLAM + spatialExtent: Global + temporalResolution: Monthly + unit: Categorical --- diff --git a/datasets/global-reanalysis-da.data.mdx b/datasets/global-reanalysis-da.data.mdx index 97cab95d5..f8d3e6625 100644 --- a/datasets/global-reanalysis-da.data.mdx +++ b/datasets/global-reanalysis-da.data.mdx @@ -13,6 +13,10 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + The reanalysis product is created using the [NASA Land Information System](https://lis.gsfc.nasa.gov/) modeling framework to merge land surface model simulations with observations from satellites through data assimilation. The team uses the Noah-MP land surface model and assimilates soil moisture from the European Space Agency’s Climate Change Initiative Program (ESA CCI), leaf area index from the Moderate Resolution Imaging Spectroradiometer (MODIS), and terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment and the follow-on missions (GRACE/GRACE-FO). + layers: - id: lis-global-da-evap stacCol: lis-global-da-evap @@ -49,7 +53,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' - + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: kg m-2 s-1 - id: lis-global-da-gpp stacCol: lis-global-da-gpp name: 'Gross Primary Productivity' @@ -85,6 +93,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: g m-2 s-1 - id: lis-global-da-gws stacCol: lis-global-da-gws @@ -121,6 +134,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: mm - id: lis-global-da-swe stacCol: lis-global-da-swe @@ -158,6 +176,11 @@ layers: - "#4A98C9" - "#2164AB" - "#0E316B" + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: mm - id: lis-global-da-streamflow stacCol: lis-global-da-streamflow @@ -194,6 +217,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: m3 s-1 - id: lis-global-da-qs stacCol: lis-global-da-qs @@ -230,6 +258,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: kg m-2 s-1 - id: lis-global-da-qsb stacCol: lis-global-da-qsb @@ -266,6 +299,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: kg m-2 s-1 - id: lis-global-da-tws stacCol: lis-global-da-tws @@ -302,6 +340,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: mm - id: lis-global-da-totalprecip stacCol: lis-global-da-totalprecip @@ -339,6 +382,11 @@ layers: - "#4A98C9" - "#2164AB" - "#0E316B" + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: kg m-2 s-1 --- diff --git a/datasets/grdi-v1.data.mdx b/datasets/grdi-v1.data.mdx index 4471310c6..fb2c7d426 100644 --- a/datasets/grdi-v1.data.mdx +++ b/datasets/grdi-v1.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - NASA CIESIN +infoDescription: | + ::markdown + The Global Gridded Relative Deprivation Index (GRDI), Version 1 (GRDIv1) dataset characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. GRDIv1 is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components to produce the final index raster. Inputs were selected from the best-available data that either continuously vary across space or have at least administrative level 1 (provincial/state) resolution, and which have global spatial coverage. layers: - id: grdi-cdr-raster stacCol: grdi-cdr-raster @@ -39,6 +42,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-filled-missing-values-count stacCol: grdi-filled-missing-values-count name: GRDI Constituent Inputs @@ -62,6 +71,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-imr-raster stacCol: grdi-imr-raster name: GRDI Infant Mortality Rate @@ -85,6 +100,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-shdi-raster stacCol: grdi-shdi-raster name: GRDI Subnational Human Development Index @@ -108,6 +129,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-v1-built stacCol: grdi-v1-built name: GRDI v1 built-up area @@ -131,6 +158,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-v1-raster stacCol: grdi-v1-raster name: GRDI v1 raster @@ -154,6 +187,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-vnl-raster stacCol: grdi-vnl-raster name: GRDI VNL Constituent raster @@ -177,6 +216,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-vnl-slope-raster stacCol: grdi-vnl-slope-raster name: GRDI VNL Slope Constituent raster @@ -200,6 +245,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + --- @@ -302,4 +353,4 @@ layers: - World Bank. (2020). Poverty and Shared Prosperity 2020: Reversals of Fortune - Frequently Asked Questions. World Bank. https://www.worldbank.org/en/research/brief/poverty-and-shared-prosperity-2020-reversals-of-fortune-frequently-asked-questions - \ No newline at end of file + diff --git a/datasets/hls-events.ej.data.mdx b/datasets/hls-events.ej.data.mdx index 806d687fb..fdd6da615 100644 --- a/datasets/hls-events.ej.data.mdx +++ b/datasets/hls-events.ej.data.mdx @@ -19,6 +19,9 @@ taxonomy: - name: Topics values: - Environmental Justice +infoDescription: | + ::markdown + Input data from Landsat 8/9 and Sentinel-2A/B is reprojected and Sentinel-2 data adjusted so that the output data products, HLSL30 (Landsat-derived) and HLSS30 (Sentinel-2-derived) can be used interchangeably. The harmonization of the Optical Land Imager (OLI) on Landsat 8/9 and Multispectral Imager (MSI) on Sentinel-2A/B increases the time series density of plot-scale observations such that data is available every 2-4 days over a given location. layers: - id: hls-l30-002-ej stacCol: hls-l30-002-ej-reprocessed @@ -41,6 +44,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; } + info: + source: NASA + spatialExtent: Puerto Rico + temporalResolution: Daily + unit: N/A + - id: hls-s30-002-ej stacCol: hls-s30-002-ej-reprocessed name: HLS Sentinel-2 SWIR @@ -55,6 +64,12 @@ layers: - B12 - B8A - B04 + info: + source: NASA + spatialExtent: Puerto Rico + temporalResolution: Daily + unit: N/A + --- diff --git a/datasets/hls-ndvi-ian.data.mdx b/datasets/hls-ndvi-ian.data.mdx new file mode 100644 index 000000000..be50b3cb7 --- /dev/null +++ b/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" +--- + + + +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. + + + + + +## 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) + + + \ No newline at end of file diff --git a/datasets/is2sitmogr4.data.mdx b/datasets/is2sitmogr4.data.mdx index 0cf7cd7ca..6150da084 100644 --- a/datasets/is2sitmogr4.data.mdx +++ b/datasets/is2sitmogr4.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + This data set reports monthly, gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10), Version 5 data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading. layers: - id: IS2SITMOGR4-cog stacCol: IS2SITMOGR4-cog @@ -38,6 +41,11 @@ layers: - '#cc4778' - '#f89540' - '#f89540' + info: + source: NASA + spatialExtent: Polar + temporalResolution: Monthly + unit: Meters --- diff --git a/datasets/lahaina-fire.data.mdx b/datasets/lahaina-fire.data.mdx index 3bcf2f45d..b12527eb9 100644 --- a/datasets/lahaina-fire.data.mdx +++ b/datasets/lahaina-fire.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - UAH +infoDescription: | + ::markdown + On August 8th, 2023, a devastating wildfire rapidly spread through the city of Lahaina, Hawai’i, which is located on the island of Maui and home to over 13,000 residents. This destructive wildfire was initially ignited by a downed powerline on Lahainaluna Road and was later fueled by intense wind gusts that persisted throughout the day. The National Weather Service recorded wind gusts as high as 67 mph in the area, contributing to the rapid spread of the wildfire across much of Lahaina during the afternoon hours of August 8th. layers: - id: hls-bais2-v2 stacCol: hls-bais2-v2 @@ -48,6 +51,11 @@ layers: - "#fee090" - "#f46d43" - "#a50026" + info: + source: NASA + spatialExtent: Hawaii + temporalResolution: Annual + unit: N/A - id: hls-swir-falsecolor-composite stacCol: hls-swir-falsecolor-composite @@ -69,6 +77,11 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { return `${dateFns.format(datetime, 'DD LLL yyyy')}`; } + info: + source: NASA + spatialExtent: Hawaii + temporalResolution: Annual + unit: N/A - id: landsat-nighttime-thermal stacCol: landsat-nighttime-thermal @@ -106,7 +119,11 @@ layers: - '#52076c' - '#f57c16' - '#f7cf39' - + info: + source: NASA + spatialExtent: Hawaii + temporalResolution: Annual + unit: N/A --- @@ -204,4 +221,4 @@ Environmental Aspects: When interpreting the data, it is essential to consider t * [The Devastating August 8th, 2023 Lahaina, Hawai'i Wildfire](https://www.earthdata.nasa.gov/dashboard/stories/lahaina-fire) - \ No newline at end of file + diff --git a/datasets/lis-etsuppression.data.mdx b/datasets/lis-etsuppression.data.mdx index 6b8990943..9b252466c 100644 --- a/datasets/lis-etsuppression.data.mdx +++ b/datasets/lis-etsuppression.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Change in ET for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model. Change is calculated as the difference of ET in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire ET and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in ET. layers: - id: lis-etsuppression stacCol: lis-etsuppression @@ -46,6 +49,12 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Percentage Diff + - id: mtbs-burn-severity stacCol: mtbs-burn-severity name: MTBS Burn Severity @@ -70,6 +79,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/lis-tvegsuppression.data.mdx b/datasets/lis-tvegsuppression.data.mdx index 3f9b6a388..61d747ade 100644 --- a/datasets/lis-tvegsuppression.data.mdx +++ b/datasets/lis-tvegsuppression.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Change in vegetation transpiration for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model. Change is calculated as the difference of transpiration in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire transpiration and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in transpiration. layers: - id: lis-tvegsuppression stacCol: lis-tvegsuppression @@ -46,6 +49,12 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Percent Diff + - id: mtbs-burn-severity stacCol: mtbs-burn-severity name: MTBS Burn Severity @@ -70,6 +79,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/lis.da.trend.data.mdx b/datasets/lis.da.trend.data.mdx index 633d242c6..ac951bb4c 100644 --- a/datasets/lis.da.trend.data.mdx +++ b/datasets/lis.da.trend.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Realistic estimates of water and energy cycle variables are necessary for accurate understanding of earth system processes. We develop a 10 km global reanalysis product of water, energy, and carbon fluxes by assimilating satellite observed surface soil moisture, leaf area index, and terrestrial water storage anomalies into a land surface model within NASA Land Information System Framework. We applied a seasonal and trend decomposition algorithm to get the trend estimates for terrestrial water storage and gross primary production. The method can better help to deal with [nonstationarities](https://github.com/Earth-Information-System/sea-level-and-coastal-risk/blob/main/AMS_2023_Wanshu_Nie_for_VEDA_Discovery.pdf) and seasonal shifts and provide a more robust estimate of trends. layers: - id: lis-global-da-tws-trend stacCol: lis-global-da-tws-trend @@ -49,6 +52,12 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: mm/yr + - id: lis-global-da-gpp-trend stacCol: lis-global-da-gpp-trend name: 'LIS DA GPP Trend' @@ -78,6 +87,12 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: mm/yr + --- diff --git a/datasets/mo_npp_vgpm.data.mdx b/datasets/mo_npp_vgpm.data.mdx index c50d1a4d4..74f2cc720 100644 --- a/datasets/mo_npp_vgpm.data.mdx +++ b/datasets/mo_npp_vgpm.data.mdx @@ -19,6 +19,9 @@ taxonomy: - name: Topics values: - Water Quality +infoDescription: | + ::markdown + Find information at the [Ocean Productivity website](https://sites.science.oregonstate.edu/ocean.productivity/index.php) layers: - id: MO_NPP_npp_vgpm stacCol: MO_NPP_npp_vgpm @@ -44,6 +47,11 @@ layers: - "#ffff00" - "#fa0000" - "#800000" + info: + source: Oregon State University + spatialExtent: Global + temporalResolution: Monthly + unit: Mg C/m²/day --- diff --git a/datasets/modis-aerosol-dataset.data.mdx b/datasets/modis-aerosol-dataset.data.mdx index 008098964..366aaa002 100644 --- a/datasets/modis-aerosol-dataset.data.mdx +++ b/datasets/modis-aerosol-dataset.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Air Quality +infoDescription: | + ::markdown + The MCD19A2 product represents a dataset that offers insights into aerosol optical thickness over land surfaces, grounded in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Originating from both the Terra and Aqua MODIS satellites, this dataset is remarkable for its fusion of information from multiple satellite platforms. Generated daily, the data has a high spatial resolution of 1 km per pixel, allowing detailed observiations. layers: - id: houston-aod stacCol: houston-aod @@ -43,6 +46,11 @@ layers: compare: datasetId: houston-aod layerId: houston-aod + info: + source: NASA + spatialExtent: Houston, Texas + temporalResolution: Annual + unit: Unitless --- diff --git a/datasets/mtbs-burn-severity.data.mdx b/datasets/mtbs-burn-severity.data.mdx index 58b95bf62..69dd05639 100644 --- a/datasets/mtbs-burn-severity.data.mdx +++ b/datasets/mtbs-burn-severity.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + MTBS is an interagency program whose goal is to consistently map the burn severity and extent of large fires across all lands of the United States from 1984 to present. This includes all fires 1000 acres or greater in the western United States and 500 acres or greater in the eastern Unites States. The extent of coverage includes the continental U.S., Alaska, Hawaii and Puerto Rico. layers: - id: mtbs-burn-severity stacCol: mtbs-burn-severity @@ -44,7 +47,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" - + info: + source: Interagency + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/nceo_africa_2017.data.mdx b/datasets/nceo_africa_2017.data.mdx index aaa06cf9f..b6fa95599 100644 --- a/datasets/nceo_africa_2017.data.mdx +++ b/datasets/nceo_africa_2017.data.mdx @@ -19,6 +19,9 @@ taxonomy: - name: Topics values: - Biomass +infoDescription: | + ::markdown + The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data. This product was developed by the UK’s National Centre for Earth Observation (NCEO) through the Carbon Cycle and Official Development Assistance (ODA) programmes. For more information see [CEOS biomass](https://ceos.org/gst/biomass.html). layers: - id: nceo_africa_2017 stacCol: nceo_africa_2017 @@ -48,6 +51,11 @@ layers: - '#1f567b' - '#080e74' - '#000000' + info: + source: UK + spatialExtent: Africa + temporalResolution: Annual + unit: N/A --- diff --git a/datasets/nighttime-lights.data.mdx b/datasets/nighttime-lights.data.mdx index b820b1dec..24b9aaebf 100644 --- a/datasets/nighttime-lights.data.mdx +++ b/datasets/nighttime-lights.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Covid 19 +infoDescription: | + ::markdown + Nightlights data are collected by the [Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB)](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite. The images are produced by [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) products suite. All data are calibrated daily, corrected, and validated with ground measurements for science-ready analysis. layers: - id: nightlights-hd-monthly stacCol: nightlights-hd-monthly @@ -44,6 +47,11 @@ layers: - '#52076c' - '#f57c16' - '#f7cf39' + info: + source: NOAA & NASA + spatialExtent: Global + temporalResolution: Monthly + unit: N/A --- @@ -121,4 +129,4 @@ Black Marble data courtesy of [Universities Space Research Association (USRA) Ea * [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) * [Suomi National Polar-orbiting Partnership (Suomi NPP)](https://www.nasa.gov/mission_pages/NPP/main/index.html) - \ No newline at end of file + diff --git a/datasets/nighttime-lights.ej.data.mdx b/datasets/nighttime-lights.ej.data.mdx index 03aa4ad4f..df0e19464 100644 --- a/datasets/nighttime-lights.ej.data.mdx +++ b/datasets/nighttime-lights.ej.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Environmental Justice +infoDescription: | + ::markdown + Nightlights data are collected by the [Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB)](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite. The images are produced by [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) products suite. All data are calibrated daily, corrected, and validated with ground measurements for science-ready analysis. layers: - id: nightlights-hd-1band stacCol: nightlights-hd-1band @@ -30,6 +33,11 @@ layers: compare: datasetId: nighttime-lights-ej layerId: nightlights-hd-1band + info: + source: NOAA & NASA + spatialExtent: Puerto Rico + temporalResolution: Monthly + unit: N/A --- diff --git a/datasets/nlcd-urbanization.data.mdx b/datasets/nlcd-urbanization.data.mdx index d2ea062ca..7709431e8 100644 --- a/datasets/nlcd-urbanization.data.mdx +++ b/datasets/nlcd-urbanization.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + The National Land Cover Database (NLCD) stands as a paramount dataset offering an in-depth overview of the land cover characteristics in the United States. Spearheaded by the Earth Resources Observation and Science (EROS) Center, this database is renewed every two to three years to provide updated and accurate data for the nation. layers: - id: houston-urbanization stacCol: houston-urbanization @@ -35,7 +38,11 @@ layers: label: No Data - color: "#d73027" label: Urbanization - + info: + source: EROS + spatialExtent: Houston + temporalResolution: Annual + unit: Binary --- diff --git a/datasets/no2.data.mdx b/datasets/no2.data.mdx index 732fb4b4e..a77167464 100644 --- a/datasets/no2.data.mdx +++ b/datasets/no2.data.mdx @@ -21,6 +21,9 @@ taxonomy: values: - Air Quality - Covid 19 +infoDescription: | + ::markdown + OMI, which launched in 2004, preceded TROPOMI, which launched in 2017. While TROPOMI provides higher resolution information, the longer OMI data record provides context for the TROPOMI observations. layers: - id: no2-monthly stacCol: no2-monthly @@ -55,6 +58,12 @@ layers: - "#E4EEF3" - "#FDDCC9" - "#DD7059" + info: + source: NASA + spatialExtent: Global + temporalResolution: Monthly + unit: N/A + - id: no2-monthly-diff stacCol: no2-monthly-diff name: Nitrogen Dioxide (monthly difference) @@ -87,6 +96,12 @@ layers: - "#E4EEF3" - "#FDDCC9" - "#DD7059" + info: + source: NASA + spatialExtent: Global + temporalResolution: Monthly + unit: N/A + - id: OMI_trno2-COG stacCol: OMI_trno2-COG name: Nitrogen Dioxide Total and Tropospheric Column (NASA OMI/Aura) @@ -111,6 +126,12 @@ layers: - '#fdd1bf' - '#e02d26' - '#67000c' + info: + source: NASA + spatialExtent: Global + temporalResolution: Monthly + unit: N/A + --- diff --git a/datasets/ps_blue_tarp_detections.ej.data.mdx b/datasets/ps_blue_tarp_detections.ej.data.mdx index 15bf42b05..5e78ae97a 100644 --- a/datasets/ps_blue_tarp_detections.ej.data.mdx +++ b/datasets/ps_blue_tarp_detections.ej.data.mdx @@ -12,6 +12,15 @@ taxonomy: - name: Topics values: - Environmental Justice +infoDescription: | + ::markdown + Planetscope provides 3-band RGB imagery at 3-meter ground resolution which + can support building-scale analysis of the land surface. In the aftermath of + natural disasters associated with high wind speeds, homes with damaged roofs + typically are covered with blue tarps to protect the interior of the home + from further damage. Using machine learning, blue tarps can be detected from + the Planetscope imagery using pre-event cloud free images to detect blue + pixels and potential impacts after a natural disaster. layers: - id: blue-tarp-detection stacCol: blue-tarp-detection @@ -28,6 +37,12 @@ layers: rescale: - 0 - 400 + info: + source: NASA + spatialExtent: Puerto Rico + temporalResolution: Sub-Annual + unit: N/A + - id: blue-tarp-planetscope stacCol: blue-tarp-planetscope name: Planetscope input RGB imagery used for blue tarp detection @@ -42,6 +57,12 @@ 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: Puerto Rico + temporalResolution: Sub-Annual + unit: N/A + --- diff --git a/datasets/snow-projections-diff.data.mdx b/datasets/snow-projections-diff.data.mdx index ff86c62b5..c95d895e7 100644 --- a/datasets/snow-projections-diff.data.mdx +++ b/datasets/snow-projections-diff.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Snow water equivalent (SWE) is defined as the amount of water in the snow. Here, we present the projected percent-change to projected snow in future periods, relative to the historical period (1995 - 2014). layers: - id: snow-projections-diff-scenario-245 stacCol: snow-projections-diff-245 @@ -45,6 +48,12 @@ layers: - "#D1E5F0" - "#4393C3" - "#0D2F60" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Percent Diff + - id: snow-projections-diff-scenario-585 stacCol: snow-projections-diff-585 name: 'SWE Losses, SSP5-8.5' @@ -77,6 +86,12 @@ layers: - "#D1E5F0" - "#4393C3" - "#0D2F60" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Percent Diff + --- diff --git a/datasets/snow-projections-median.data.mdx b/datasets/snow-projections-median.data.mdx index 7a32de071..43c923e91 100644 --- a/datasets/snow-projections-median.data.mdx +++ b/datasets/snow-projections-median.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Snow water equivalent (SWE) is defined as the amount of water in the snow. It is expressed as a height (in millimeters), representative of the height of water that would exist if snow was only in a liquid state. layers: - id: snow-projections-median-scenario-245 stacCol: snow-projections-median-245 @@ -46,6 +49,12 @@ layers: - "#4A98C9" - "#2164AB" - "#0E316B" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: mm + - id: snow-projections-median-scenario-585 stacCol: snow-projections-median-585 name: 'SWE, SSP5-8.5' @@ -79,6 +88,12 @@ layers: - "#4A98C9" - "#2164AB" - "#0E316B" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: mm + --- diff --git a/datasets/so2.data.mdx b/datasets/so2.data.mdx index 51d40103b..f1b82c179 100644 --- a/datasets/so2.data.mdx +++ b/datasets/so2.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Air Quality +infoDescription: | + ::markdown + The OMI Sulfur Dioxide (SO2) Total Column layer indicates the column density of sulfur dioxide and is measured in Dobson Units (DU). Sulfur Dioxide and Aerosol Index products are used to monitor volcanic clouds and detect pre-eruptive volcanic degassing globally. This information is used by the Volcanic Ash Advisory Centers in advisories to airlines for operational decision layers: - id: OMSO2PCA-COG stacCol: OMSO2PCA-COG @@ -47,6 +50,11 @@ layers: - "#fee090" - "#f46d43" - "#a50026" + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: N/A ---
diff --git a/datasets/sport-lis.data.mdx b/datasets/sport-lis.data.mdx index 9bf949d9a..1e123c041 100644 --- a/datasets/sport-lis.data.mdx +++ b/datasets/sport-lis.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications. layers: - sourceParams: resampling: bilinear @@ -47,6 +50,11 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'MMM yyyy')} VS ${dateFns.format(compareDatetime, 'MMM yyyy')}`; } + info: + source: NASA + spatialExtent: Contiguous United States + temporalResolution: Sub-Annual + unit: cm --- diff --git a/datasets/svi_household.ej.data.mdx b/datasets/svi_household.ej.data.mdx index 0fe82da17..72220faf4 100644 --- a/datasets/svi_household.ej.data.mdx +++ b/datasets/svi_household.ej.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - ATSDR +infoDescription: | + ::markdown + The Household Composition & Disability Score (HCDS) is one of the four themes used in determining a community’s social vulnerability. This dataset can be used to create a community evacuation plan accounting for individuals who have special needs, the elderly, and/or families with young children. In the event of a disaster, this data can also help responders determine the number of emergency personnel required for special household cases (accessibility assistance), the type of supplies needed based on age, and the amount of supplies, food, and other restorative resources needed¹. The HCDS SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC)was provided under the U.S. National Aeronautics and Space Administration (NASA)². layers: - id: social-vulnerability-index-household stacCol: social-vulnerability-index-household @@ -49,6 +52,12 @@ layers: - "#f3701b" - "#c54102" - "#7f2704" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + - id: social-vulnerability-index-household-nopop stacCol: social-vulnerability-index-household-nopop name: Household and Disability Score (No Pop) @@ -82,6 +91,12 @@ layers: - "#f3701b" - "#c54102" - "#7f2704" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + --- diff --git a/datasets/svi_housing.ej.data.mdx b/datasets/svi_housing.ej.data.mdx index 8ae33bb5b..f2e9fdb7e 100644 --- a/datasets/svi_housing.ej.data.mdx +++ b/datasets/svi_housing.ej.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - ATSDR +infoDescription: | + ::markdown + The Housing Type & Transportation Score (HTTS) is one of the four themes used in determining a community’s social vulnerability, examining it against housing structure/type and vehicle access. As with the other SVI thematic areas, in the event of a disaster, or to better prepare for one, this dataset can help emergency personnel create an evacuation plan for individuals without vehicles, allocate emergency preparedness funding by community need, and identify areas in need of emergency shelters¹. It can also be used for local governments to identify areas needing more robust public transportation, areas of overcrowding, and local housing vulnerability. The HTTS SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC)was provided under the U.S. National Aeronautics and Space Administration (NASA)². layers: - id: social-vulnerability-index-housing stacCol: social-vulnerability-index-housing @@ -53,6 +56,12 @@ layers: - "#4a98c9" - "#1764ab" - "#08306b" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + - id: social-vulnerability-index-housing-nopop stacCol: social-vulnerability-index-housing-nopop name: Housing Type and Transportation Score - Masked for No Population @@ -86,6 +95,12 @@ layers: - "#4a98c9" - "#1764ab" - "#08306b" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + --- diff --git a/datasets/svi_minority.ej.data.mdx b/datasets/svi_minority.ej.data.mdx index 5ab3dd35a..b1477f83d 100644 --- a/datasets/svi_minority.ej.data.mdx +++ b/datasets/svi_minority.ej.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - ATSDR +infoDescription: | + ::markdown + The Minority Status & Language Score (MSLS), as with the other SVI thematic areas, is used to calculate a community’s social vulnerability. This data set can be used to prepare emergency plans for communities with lower English-proficiency levels¹, and has helped contribute to efforts such as the Minority Health SVI and its related Dashboard. The Minority Health SVI is an extension of the CDC/ATSDR Social Vulnerability Index (CDC/ATSDR SVI), which is a database that helps emergency response planners and public health officials identify, map, and plan support for communities that will most likely need support before, during, and after a public health emergency². The MSLS SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC) was provided under the U.S. National Aeronautics and Space Administration (NASA)³. layers: - id: social-vulnerability-index-minority stacCol: social-vulnerability-index-minority @@ -49,6 +52,12 @@ layers: - "#8683bd" - "#61409b" - "#3f007d" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + - id: social-vulnerability-index-minority-nopop stacCol: social-vulnerability-index-minority-nopop name: Minority Status and Language Score - Masked for No Population @@ -82,6 +91,12 @@ layers: - "#8683bd" - "#61409b" - "#3f007d" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + --- diff --git a/datasets/svi_overall.ej.data.mdx b/datasets/svi_overall.ej.data.mdx index e7fd873f9..3322f42d3 100644 --- a/datasets/svi_overall.ej.data.mdx +++ b/datasets/svi_overall.ej.data.mdx @@ -22,6 +22,9 @@ taxonomy: - name: Source values: - ATSDR +infoDescription: | + ::markdown + The SVI Overall Score provides the overall, summed social vulnerability score for a given tract. The Overall Score SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC) was provided under the U.S. National Aeronautics and Space Administration (NASA)¹. layers: - id: social-vulnerability-index-overall stacCol: social-vulnerability-index-overall @@ -56,6 +59,12 @@ layers: - "#2498c1" - "#234da0" - "#081d58" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + - id: social-vulnerability-index-overall-nopop stacCol: social-vulnerability-index-overall-nopop name: Overall (NoPop) Social Vulnerability - Percentile Ranking @@ -89,6 +98,12 @@ layers: - "#2498c1" - "#234da0" - "#081d58" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + --- diff --git a/datasets/svi_socioeconomic.ej.data.mdx b/datasets/svi_socioeconomic.ej.data.mdx index 0e429a907..2903c2be8 100644 --- a/datasets/svi_socioeconomic.ej.data.mdx +++ b/datasets/svi_socioeconomic.ej.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - ATSDR +infoDescription: | + ::markdown + The Economic Status Score, like the three other themes, is used in observing a community’s social vulnerability. As with other SVI scores, the economic status score can help local officials and teams identify communities that will need continued support to recover following an emergency or natural disaster¹. The Economic Status SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC) was provided under the U.S. National Aeronautics and Space Administration (NASA)². layers: - id: social-vulnerability-index-socioeconomic stacCol: social-vulnerability-index-socioeconomic @@ -49,6 +52,12 @@ layers: - "#4bb062" - "#157f3b" - "#00441b" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + - id: social-vulnerability-index-socioeconomic-nopop stacCol: social-vulnerability-index-socioeconomic-nopop name: Socioeconomic (No Pop) Vulnerability Score @@ -82,6 +91,12 @@ layers: - "#4bb062" - "#157f3b" - "#00441b" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + --- diff --git a/datasets/twsanomaly.data.mdx b/datasets/twsanomaly.data.mdx index 5d0ead4f1..51bfc86f9 100644 --- a/datasets/twsanomaly.data.mdx +++ b/datasets/twsanomaly.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water. layers: - id: lis-tws-anomaly stacCol: lis-tws-anomaly @@ -47,6 +50,12 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: N/A + --- diff --git a/datasets/twsnonstationarity.data.mdx b/datasets/twsnonstationarity.data.mdx index c6f8e466f..d9286260e 100644 --- a/datasets/twsnonstationarity.data.mdx +++ b/datasets/twsnonstationarity.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water. layers: - id: lis-tws-nonstationarity-index stacCol: lis-tws-nonstationarity-index @@ -40,6 +43,11 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: N/A --- diff --git a/datasets/twstrend.data.mdx b/datasets/twstrend.data.mdx index 8b93c0c09..1f91d2eb8 100644 --- a/datasets/twstrend.data.mdx +++ b/datasets/twstrend.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water. layers: - id: lis-tws-trend stacCol: lis-tws-trend @@ -40,6 +43,11 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: N/A --- diff --git a/datasets/urban-heating.data.mdx b/datasets/urban-heating.data.mdx index ad0f99b21..d773d73c4 100644 --- a/datasets/urban-heating.data.mdx +++ b/datasets/urban-heating.data.mdx @@ -8,6 +8,9 @@ media: author: name: Arto Marttinen url: https://unsplash.com/photos/6xh7H5tWj9c +infoDescription: | + ::markdown + Terra MODIS has been instrumental in capturing LST data. This platform, orbiting Earth, scans our planet in multiple spectral bands, allowing for a detailed analysis of LST values. The data periods 2000-20009 and 2010-2019 form this satellite have been particularly enlightening, revealing distinct shifts in Houston’s urban heat profile. layers: - sourceParams: resampling: bilinear @@ -42,6 +45,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } + info: + source: NASA + spatialExtent: Houston + temporalResolution: Annual + unit: N/A + - sourceParams: resampling: bilinear bidx: 1 @@ -75,6 +84,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } + info: + source: NASA + spatialExtent: Houston + temporalResolution: Annual + unit: N/A + - sourceParams: resampling: bilinear bidx: 1 @@ -108,6 +123,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } + info: + source: NASA + spatialExtent: Houston + temporalResolution: Annual + unit: N/A + - sourceParams: resampling: bilinear bidx: 1 @@ -181,6 +202,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } + info: + source: NASA + spatialExtent: Houston + temporalResolution: Annual + unit: Categorical + - sourceParams: resampling: bilinear bidx: 1 @@ -203,6 +230,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } + info: + source: NASA + spatialExtent: Houston + temporalResolution: Annual + unit: N/A + --- @@ -236,4 +269,4 @@ layers: Houston’s LST data, meticulously captured by Terra MODIS, serves as a crucial pointer for urban planners, environmentalists, and policymakers. By understanding the nexus of urban heat, infrastructure, and socio-economics, we can shape urban features that are not only sustainable but also equitable. As Houston continues its urban journey, armed with this data, it has the potential to redefine urban resilience in the face of escalating heat challenges. - \ No newline at end of file + diff --git a/package.json b/package.json index 932195765..af6bd1759 100644 --- a/package.json +++ b/package.json @@ -1,7 +1,7 @@ { "name": "veda-config", "description": "Configuration for Veda", - "version": "0.15.2", + "version": "0.16.0", "source": "./.veda/ui/app/index.html", "license": "Apache-2.0", "scripts": { @@ -13,9 +13,15 @@ "local-cms": "npx netlify-cms-proxy-server", "test": "NODE_ENV=test .veda/veda test" }, + "targets": { + "veda-app": { + "source": "./.veda/ui/app/index.html", + "context": "browser" + } + }, "browserslist": "> 0.5%, last 2 versions, not dead", "engines": { - "node": "16.x" + "node": "20.x" }, "devDependencies": { "@parcel/packager-raw-url": "2.7.0", diff --git a/stories/comp_damage_sanibel.jpg b/stories/comp_damage_sanibel.jpg new file mode 100644 index 000000000..cea3061a4 Binary files /dev/null and b/stories/comp_damage_sanibel.jpg differ diff --git a/stories/comp_fort_meyers_dmg.jpg b/stories/comp_fort_meyers_dmg.jpg new file mode 100644 index 000000000..6e6932d05 Binary files /dev/null and b/stories/comp_fort_meyers_dmg.jpg differ diff --git a/stories/comp_sanibel_causeway.jpg b/stories/comp_sanibel_causeway.jpg new file mode 100644 index 000000000..549ddeea2 Binary files /dev/null and b/stories/comp_sanibel_causeway.jpg differ diff --git a/stories/comp_surge_track_figure.jpg b/stories/comp_surge_track_figure.jpg new file mode 100644 index 000000000..0fd7b1fce Binary files /dev/null and b/stories/comp_surge_track_figure.jpg differ diff --git a/stories/darnah-building-footprint.jpg b/stories/darnah-building-footprint.jpg new file mode 100644 index 000000000..ca2876faa Binary files /dev/null and b/stories/darnah-building-footprint.jpg differ diff --git a/stories/darnah-daniel-satellite.jpg b/stories/darnah-daniel-satellite.jpg new file mode 100644 index 000000000..e79ef48c4 Binary files /dev/null and b/stories/darnah-daniel-satellite.jpg differ diff --git a/stories/darnah-flood-background.jpg b/stories/darnah-flood-background.jpg new file mode 100644 index 000000000..ba428ef47 Binary files /dev/null and b/stories/darnah-flood-background.jpg differ diff --git a/stories/darnah-flood-extent.jpg b/stories/darnah-flood-extent.jpg new file mode 100644 index 000000000..874aeeeea Binary files /dev/null and b/stories/darnah-flood-extent.jpg differ diff --git a/stories/darnah-flood.stories.mdx b/stories/darnah-flood.stories.mdx new file mode 100644 index 000000000..0a4f0714f --- /dev/null +++ b/stories/darnah-flood.stories.mdx @@ -0,0 +1,305 @@ +--- +id: darnah-flood +name: The Deadliest Flood of the 21st Century +description: "An Overview of the September 11, 2023 Darnah, Libya Flood" +media: + src: ::file ./darnah-flood-background.jpg + alt: Aerial view over the Wadi Darnah River post-flood in Derna, Libya on September 14, 2023. + author: + name: Marwan Alfaituri (Reuters) + url: https://abcnews.go.com/International/casualties-libya-floods-avoided-world-meteorological-organization-chief/story?id=103200104 +pubDate: 2024-07-22 +taxonomy: + - name: Topics + values: + - Flood +--- + + + + + Authors: Andrew Blackford1, Trent Cowan1, Udaysankar Nair1\ + 1 The University of Alabama in Huntsville + + + + + + + +
+ Estimated extent of floodwaters over Darnah, Libya. + + Satellite-derived extent of floodwaters in Darnah, as determined from the Humanitarian Data Exchange. + + +
+ + + ## Overview + 🚧 This Data Story presents work in progress and not peer-reviewed results! 🚧 + + On Monday, September 11, 2023, the city of Darnah, Libya experienced the [deadliest flood disaster of the 21st century](https://www.google.com/url?q=https://www.aa.com.tr/en/environment/floods-in-libya-s-derna-worst-disaster-in-21st-century/2992617&sa=D&source=docs&ust=1709231595507737&usg=AOvVaw3MuRygRSSxtExzI_shVddG), and Africa’s deadliest flood ever recorded. A storm in the Mediterranean Sea dubbed ‘Medicane Daniel’ moved over northeastern Libya on the evening of the 10th, dumping prolific rain over the desert the morning of the 11th. A record 16” of rainfall was measured in 24 hours at the city of Al-Bayda, Libya (just west of Derna) from ‘Medicane’ Daniel. Two dams upstream of Darnah collapsed during the heavy rains leading to approximately [25% of the city being destroyed](https://www.google.com/url?q=https://www.reuters.com/world/africa/more-than-1000-bodies-recovered-libyan-city-after-floods-minister-2023-09-12/&sa=D&source=docs&ust=1709231595509452&usg=AOvVaw083l0kMybsbbwT18u4SVTm). The first dam broke around 3:00 AM local time on September 11th, and the second followed suit shortly thereafter, which exacerbated the death toll greatly. The International Committee of the Red Cross (ICRC) reported that proceeding the dam bursts, a wave as high as 23 feet (7 meters) rushed towards the city. With a population of 120,000, the major city of Darnah saw massive destruction, with entire districts of the city being washed away.Nearly 1,000 buildings are estimated to have been completely destroyed as well as 5 major bridges that connect the west and east sides of the city. The United Nations Office for the Coordination of Humanitarian Affairs initially reported a death toll currently sits at 11,300 with another 10,100 reported missing. This estimate was later revised to [3,958 fatalities](https://www.aljazeera.com/news/2023/9/18/libya-floods-conflicting-death-tolls-greek-aid-workers-die-in-crash) on September 18. + + + + + + + ## Medicane Daniel + [Medicanes](https://www.google.com/url?q=https://www.easa.europa.eu/community/topics/medicanes&sa=D&source=docs&ust=1709231595506704&usg=AOvVaw3IE4-Z1b9q1j7MKe7dE4YH) have similar properties to hurricanes, as they are warm-core and quasi-tropical in nature, but are not technically designated as hurricanes. Storm Daniel formed in the Ionian Sea on September 4th, 2023, and drifted to the south underneath an Omega Block (a high pressure system stuck in place between two low-pressure systems). As the cyclone moved out of the Ionian Sea and into the central Mediterranean Sea on September 5th, it transitioned into a medicane. Daniel began to drift southeastward towards the northern Libyan coast on September 9th before making landfall near the city of Benghazi on September 10th. Satellite-derived measurements from the ASCAT (Advanced Scatterometer) recorded wind speeds of up to 85 km/hour before Daniel made landfall. The medicane then dissipated on September 12th over northwestern Egypt after causing massive destruction from floods in northern Libya and Greece. A record 16” (414 mm) of rainfall was measured in 24 hours at the city of Al-Bayda, Libya, with satellite-derived measurements suggesting locally higher totals near Tolmeita and Battah. Medicane Daniel’s impact resulted in it being the deadliest medicane in history, and the costliest tropical cyclone on record outside of the North Atlantic Ocean. Over $21.14 billion (2023 USD) in damages was reported from Daniel, and over 4,361 fatalities have been confirmed with unofficial estimates of 20,000, and over 7,000 were injured. + + +
+ Medicane Daniel VIIRS imagery, 9 September 2023. + + Satellite imagery of Daniel over the Mediterranean. (VIIRS imagery from NASA Worldview) + + +
+ + + + + + + ## TIMELINE + ## September 4th + + Low pressure system forms over the Ionean Sea off the western coast of Greece. + + + + ## September 5th + + The system was officially designated Storm Daniel. + + Impactful rainfall and flooding occurred in Greece, with 43" measured in Zagora and 34.8" in Portaria. + + + + ## September 6th + + Northwestern Turkey deals with heavy flooding, which inflicts 5 fatalities. + + + + ## September 6th + + Heavy flooding occurs in southern Bulgaria, with a national record 13" of rainfall recorded in Tsarevo in 24 hours, and 12.2" in Kosti. + + + + ## September 7th + + Over 800 flood rescue operations occur in Thessaly, Greece. The Pinelos River crested at 9.5 meters, compared to a typical river height of 4 meters. The Sentinel-1 satellite measured a flood inundation of 180,000 acres, which is the region's worst flooding since 1930. + + + + ## September 10th, 10 PM + + A curfew was issued as a precaution in the city of Darnah, Libya as Daniel began making landfall southwest of the city. A state of emergency was issued for the entire country of Libya on September 9th. + + ## September 11th, 3 AM + + Two upriver dams (The Belad and Abu Mansour Dams) burst, and locals reported hearing loud explosions. 25% of the city of Darnah was swept away under an ~7 meter tall wave of water. Four bridges were washed away, and over 2,200 buildings were impacted (1,500 destroyed). The official death toll stands at 3,958 in Darnah alone as of September 18, 2023. + + + + + ## September 11th + + A national rainfall record was set at Al Bayda, Libya, where 16" of rain fell in 24 hours due to Daniel. Significant flooding occurred here, with ~200 fatalities reported. Over 5,000 homes were impacted by floodwaters. + + + + + ## September 11th + + The city of Al Abraq, Libya measured 6.7" of rainfall and experienced upwards of 10 feet of flood inundation. + + + + + ## September 12th + + Storm Daniel dissipates over northern Egypt after causing moderate rainfall in the region. + + + + + ## September 13th + + Storm Daniel's remnants reach Israel, where locally heavy rain causes sinkholes to open. These are the last notable impacts from this storm. + + + + + ## September 16th + + The death toll in Greece rises to 17 due to prolonged flooding caused by Daniel. + + + + + + +
+ + + Harmonized Landsat Sentinel-2 false color composite imagery of Darnah before (September 7, 2023) and after the fire (September 22, 2023). + +
+ + + ## Satellite Analysis + NASA's Harmonized Landsat-Sentinel-2 datasets were acquired to showcase the before-and-after scenes of the Darnah, Libya area. The utilization of false-color composite imagery allowed for an exploration of the flood damage evident in the city. Instead of employing the conventional red, green, and blue (RGB) wavelength 'TrueColor' imagery, a false-color composite utilizing the near-infrared (IR) and shortwave IR channels was chosen. This involved substituting the traditional RGB composite with shortwave IR, near-IR, and red bands, respectively. As a result, areas most affected by the flood exhibited a distinct contrast to unaffected regions, with displaced sediments like sand becoming apparent in Darnah's streets through this band combination. While this specific band combination is commonly associated with wildfire detection due to its effectiveness in revealing darkened burned areas, it also proves capable of highlighting greenery after heavy rain events. This characteristic played a crucial role in enhancing the contrast of flood damage in and around Darnah. + + + + + + ## Building Footprint Analysis + Google's OpenBuildings dataset, spanning vast regions including Africa, South Asia, South-East Asia, Latin America, and the Caribbean with 1.8 billion building detections, holds significant potential for assessing urban challenges, especially in the aftermath of natural disasters like the devastating flood in Darnah. This dataset provides a detailed map of building footprints and their distribution, allowing for a broad understanding of the urban landscape. In the case of disaster response, such as identifying damaged buildings, the dataset's confidence scores can help prioritize areas for intervention. While it doesn't delve into specifics like building types or addresses, its comprehensive coverage and detailed information comprise a valuable resource for evaluating the impact of events on the built environment. Leveraging this data alongside other disaster response tools could greatly enhance our ability to assess and respond effectively to the aftermath of such events in Darnah and similar regions. + + The [Humanitarian Data Exchange](https://experience.arcgis.com/experience/970d0cacd0c24b39b08d844b99a797ae/page/UNOSAT/) (HDX) generated a dataset of impacted buildings in the aftermath of the Darnah flood through satellite imagery building detection. A high-resolution overpass from the Pleiades satellite on September 13, 2023, with a spatial resolution of 0.5 meters, was employed for this purpose. This dataset established point locations for buildings identified as damaged or destroyed during the Darnah flood on September 11, 2023. These specific point locations were then cross-referenced with the OpenBuildings dataset for Darnah. The result is a comprehensive map highlighting which buildings were likely impacted by the floodwaters, providing valuable insights into the extent of the damage in the region. + + + + + + + +
+ Google OpenBuildings Footprint over Darnah, Libya. + +Building footprint detection over Darnah, Libya. Red-colored buildings were heavily impacted by floodwaters, orange-colored buildings were slightly impacted by floodwaters, and green-colored buildings were left largely undisturbed. + + +
+ + + + + + ## Aftermath and Implications + As of September 14th, the United Nations reported that as many as 884,000 people are in need of assistance in Libya, including nearly 300,000 children. Over 10,000 people remain missing, and portions of Darnah have been washed out into the Mediterranean Sea. At the time of Medicane Daniel’s impact, the Belad and Abu Mansour Dams were in subpar condition, lacking consistent upkeep. The two dams were first built in the mid 1970s to control the impacts of flooding, but had not been maintained regularly since 2002. The dams were damaged from a storm in 1986, with cracks observed as early as in 1998. A Turkish construction firm claims they were hired to replace the dams in 2007, having concluded preliminary work in 2012, but the Libyan government claims they ceased working in 2011 at the outbreak of the Libyan Civil War. Research as recent as 2022 warned of a high risk of flooding should the dams fail, pointing back to four previous major floods since 1942. European meteorological agencies also warned Libya of the impending potential major impacts from Daniel days in advance, and the World Meteorological Organization said that many of the casualties would have been prevented had Libya set up a functional weather service. + + + + + + + ## Additional Resources + [Omega Block Definition](https://glossarytest.ametsoc.net/wiki/Blocking_high) + + [Medicane Definition](https://skybrary.aero/articles/medicane) + + [Rainfall in Greece](https://www.cbsnews.com/news/greece-historic-flooding-more-than-2-feet-of-rain-in-just-a-few-hours/) + + [Rainfall in Libya](https://earthobservatory.nasa.gov/images/151826/torrential-rain-wreaks-havoc-in-libya) + + [Rainfall in Turkey and Bulgaria](https://www.bbc.com/news/av/world-europe-66728709) + + [European Commission Report 9/13/2023](https://upload.wikimedia.org/wikipedia/commons/2/2c/ECDM_20230913_FL_Libya.pdf) + + [Yale Research on Libya Flood](https://yaleclimateconnections.org/2023/09/the-libya-floods-a-climate-and-infrastructure-catastrophe/) + + + + + + + ## References + Ashoor, Abdelwanees A. R (26 July 2022). "Estimation of the surface runoff depth of Wadi Derna Basin by integrating the geographic information systems and Soil Conservation Service (SCS-CN) model" (SCS-CN) نموذج عمق الجريان السطحي لحوض وادي درنة بالتكامل بين تقنيات نظم املعلومات الجغر افية و تقدير (PDF). Journal of Pure & Applied Sciences (in Arabic). Sebha University Press. 21 (2): 90–100. doi:10.51984/jopas.v21i2. ISSN 2521-9200 + + [Jr, Roger Pielke (13 September 2023). "Trends in Flooding in Africa". The Honest Broker.](https://rogerpielkejr.substack.com/p/trends-in-flooding-in-africa) + + + + diff --git a/stories/darnah_daniel_satellite.jpg b/stories/darnah_daniel_satellite.jpg new file mode 100644 index 000000000..e79ef48c4 Binary files /dev/null and b/stories/darnah_daniel_satellite.jpg differ diff --git a/stories/dpi.jpg b/stories/dpi.jpg new file mode 100644 index 000000000..4510f51ac Binary files /dev/null and b/stories/dpi.jpg differ diff --git a/stories/entropy_difference.jpg b/stories/entropy_difference.jpg new file mode 100644 index 000000000..041c4c665 Binary files /dev/null and b/stories/entropy_difference.jpg differ diff --git a/stories/fort-myers.jpeg b/stories/fort-myers.jpeg new file mode 100644 index 000000000..60605a794 Binary files /dev/null and b/stories/fort-myers.jpeg differ diff --git a/stories/hurricane-ian.stories.mdx b/stories/hurricane-ian.stories.mdx new file mode 100644 index 000000000..3b0962ca1 --- /dev/null +++ b/stories/hurricane-ian.stories.mdx @@ -0,0 +1,247 @@ +--- +id: hurricane-ian +name: Hurricane Ian and Impacts on Vegetation +description: "A in-depth look on how hurricane Ian impacted vegetation on Sanibel Island near Fort Myers." +media: + src: ::file ./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 +pubDate: 2024-07-22 +taxonomy: + - name: Topics + values: + - UAH + - Hurricanes +--- + + + + ## Introduction + Trent Cowan1, Andrew Blackford1, Udaysankar Nair1, and Ashley Riddle1 + + 1: University of Alabama in Huntsville + + Disclaimer: This research is ongoing and is not yet peer-reviewed. + + Atlantic hurricane season stretches from June 1 to November 30, and generally reaches a peak in August and September. On September 28, 2022, Hurricane Ian made landfall in southwest Florida as a Category 4 hurricane (based on the Saffir-Simpson Wind Scale), with estimated wind speeds of 150 MPH. The toll was high, leaving 158 people dead and producing $110 billion in damage in Florida alone. The hurricane also left a devastating impact on the landscape in portions of southwest Florida near Fort Myers. A storm surge reached unprecedented levels of 12 to 18 feet around the regions of Cape Coral and Fort Myers. A storm surge is the sudden rise in ocean water above normal tide levels during a storm such as a tropical cyclone or other strong low pressure system as wind from the storm pushes water ashore. An analysis in Figure 1 from the U.S. National Hurricane Center’s review of Hurricane Ian shows the storm surge’s heavy impact on the southwest Florida coast. The storm surge is attributed as the primary cause of fatalities along the coast of southwestern Florida. + + + + + + + This work aims to provide a comprehensive analysis of Hurricane Ian’s devastating environmental impact on southwestern Florida and explore the critical role of remote sensing in monitoring and mitigating the effects of natural disasters using advanced remote sensing techniques. Parameters such as Normalized Difference Vegetation Index (NDVI) and entropy changes offer insights into Ian’s effects on vegetation and surface textures. The HLS (Harmonized Landsat and Sentinel-2) project serves as a great tool in this analysis by providing essential data for response and recovery; moreover, this article introduces the UCONN Geographic Environmental Remote Sensing (GERS) lab’s CONUS Disturbance Water to highlight the importance of near real-time land disturbance detection. + + ## Inland Effects of Ian + On September 24, Hurricane Ian became a named storm southeast of Jamaica. Ian quickly intensified into a hurricane and made landfall in western Cuba near Havana as a Category 3 storm, where it caused catastrophic damage and widespread power outages. After exiting Cuba, the storm quickly intensified into a massive Category 5 storm near the southwestern coast of Florida with sustained wind speeds in excess of 158 mph. As Ian continued moving north and west, the storm made landfall on Florida’s Cayo Costa Island as a strong Category 4 hurricane, with maximum sustained winds of 149 mph. Ian weakened to a tropical storm as it moved further inland of the Florida Peninsula before exiting into the Atlantic. + + + + + + The damage from Hurricane Ian was particularly catastrophic on Pine Island and Sanibel Island just off the coast of Cape Coral. The scrollytelling cards shows images from the Associated Press encompassing Fort Myers Beach and Sanibel Island and illustrating the destruction Hurricane Ian left in its wake. Most homes along this portion of Fort Myers beach were moved due to the storm surge and were either destroyed or severely damaged. A section of the Sanibel Causeway (the causeway that connects Sanibel Island to the mainland of Florida) was taken out by Hurricane Ian leaving residents stranded on Sanibel Island. The damage to houses, roads, and other structures was catastrophic, and the hurricane also inflicted severe harm on the area’s vegetation. + + +
+ + + Figure 1: National Hurricane Center analysis of the storm surge from Hurricane Ian. Black line indicates Ian’s path. Red/magenta colors indicate areas of highest storm surge. Credit: National Hurricane Center. + +
+ + + +
+ + + Figure 2 and 3: HLS NDVI pre/post-Ian over Sanible Island, Cape Coral, and portions of Fort Myers. + +
+ + ## NDVI & Entropy Analysis + + Normalized Difference Vegetation Index (NDVI) serves as a fundamental metric for understanding the vitality and abundance of vegetation within a specific geographical region. In this data story, we compare NDVI data of the area derived from HLS data for two different days pre- and post-hurricane. The primary vegetation in this portion of Florida includes grasslands and forested regions. NDVI is a key metric used to assess the health and density of vegetation in a specific area based on satellite imagery. Figure 2 (left portion of the NDVI slider) depicts the NDVI around Cape Coral a month prior to Hurricane Ian, while Figure 3 (right portion of the NDVI slider) shows the same landscape the week following the storm. In Figure 2, darker green colors indicate a relatively high abundance of healthy vegetation at a particular location. The light green, yellow, and red colors indicate areas with little to no healthy vegetation. + + Pine and Sanibel Islands, east of Cape Coral, are particularly notable because the hurricane inflicted so much damage. The post-Ian image (Figure 3) shows a significant change in the vegetation landscape. Storm surge inundated much of the access points to the islands and washed away or damaged much of the vegetation in these locations. In addition to statistical analyses, the notable transformation of vegetation in these dispersed areas holds significant scientific importance, as it provides compelling evidence of the formidable impacts of this catastrophic storm. Hurricanes can have a massive and varied impact on nature, demonstrating why it is crucial to understand their wider environmental effects. + + + + + +
+ + + Figure 4: HLS NDVI Difference for Sanibel Island, Cape Coral, and portions of Fort Myers. Red colors indicate an increase in vegetation and blue indicates a decrease in vegetation. + +
+ + Figure 4 shows a significant change in the post-hurricane vegetation landscape by tracking the difference between pre- and post-Ian NDVI. The blue colors show a negative change in NDVI in the difference image, suggesting there was a substantial loss in vegetation after Ian. Ian caused widespread devastation in coastal areas, with many places experiencing severe damage or complete flooding. The storm surge washed away much of the natural vegetation, severely impacting local ecosystems and communities. This highlights the broader issue of the destructive power of hurricanes on natural environments near and along the coast, underscoring the need for preparedness and resilience strategies. + + + + + + + Entropy, in the context of remote sensing and image analysis, is a measure of the randomness or disorder in an image. More specifically, we can see how surface textures such as vegetation and buildings changed after the hurricane. A high entropy value suggests a higher level of complexity and texture, while a lower value indicates uniformity and smoothness. By analyzing the entropy change associated with Hurricane Ian using the same HLS data as was used in the NDVI examination above, we analyze alterations in surface patterns and characteristics. + + Depicted in Figure 5, the observed increase in entropy in certain coastal regions post-hurricane suggests that the once smooth and uniform beach areas became more complex in texture. This complexity could be attributed to the catastrophic storm surge that pushed coastal sand further inland. This not only altered the coastal terrain but also created a lot of land surface changes further inland, where homes and businesses that were once present are now either destroyed or heavily damaged. The amount of sand and debris scattered throughout the area of interest can be seen within our entropy change image from Hurricane Ian. These differences are highlighted in Figure 8, where areas in light to dark red represent locations where the highest change occurred. + + +
+ + + Figure 5. HLS entropy difference for Sanibel Island, Cape Coral, and portions of Fort Myers. The red indicates areas with a significant increase in complexity and texture following the hurricane. + +
+ + + + + ## Damage Probability Predictions + + The UCONN GERS lab’s CONUS Disturbance Watcher offers a pioneering approach to near real-time land disturbance detection utilizing the HLS dataset. By harnessing the capabilities of the Stochastic Continuous Change Detection (S-CCD), the dataset establishes a reference image from sustained observation of the land surface and is triggered after a natural disaster to identify land surface disturbance features in post-event satellite imagery. S-CCD is a remote sensing technique used to monitor and detect ongoing changes in land surface characteristics. It highlights areas of disturbances or alteration by comparing new images against a reference baseline. Once these features are defined, the system predicts damage probability by comparing a post-disaster image against a reference image for a given location. In the context of Hurricane Ian, this damage probability dataset serves as a valuable complement to evaluating vegetation and coastline changes associated with wind and storm surge. + + While our primary assessments, including entropy changes, provide direct insights into Ian’s impact, the CONUS Disturbance Watcher augments our understanding by delivering probabilistic insights into areas most likely affected by disturbances. The damage probability index primarily highlights changes in forested regions. To provide a more comprehensive analysis, we have combined vegetation and entropy indices in this study to encompass both the impact on vegetation and changes to human-influenced land cover. By integrating these data and combining direct observation with predictive analytics to determine areas of potential vulnerability and change, we gain a more comprehensive view of Ian’s aftermath. + + + + + +
+ + + Figure 6: Damage Probability from UCONN GERS lab for Lee and Charlotte counties in Florida. This product is on a scale of 0 to 99 with 0 indicating 0 probability of damage and 99 indicating the highest probability of damage. + +
+ + + + + ## Damage on the Sanibel Causeway + + The devastating impact of Hurricane Ian resulted in the collapse of the Sanibel Causeway, a critical link connecting Sanibel Island to Florida's mainland. This destruction stranded numerous residents, necessitating boat-based evacuations and delivery of vital supplies and underscoring the hurricane's profound effects on offshore communities. The area around Punta Rassa, where the causeway commences, suffered extensive damage, further illustrating the severe consequences of Hurricane Ian on these coastal regions. + +
+ + + Damage to the Sanibel Causeway. Credit to the Wilfredo Lee - Associated Press. + +
+ + + ## Fort Myers Beach + + On the mainland Florida coast, high storm surge led to major inundation of Fort Myers Beach. Most of this area is developed; however, wetlands several miles inland experienced major vegetation damage from the water. Homes and businesses were mostly damaged or destroyed in this region. + +
+ + + Damage to a portion of Fort Myers Beach. Credit to Kevin Fogarty - REUTERS. + +
+ + + ## Sanibel Island + + Overall, the most significant change in vegetation as a result of Hurricane Ian occurred on Sanibel Island. The highest winds and energy occurred in this area, resulting in some of the highest storm surge and inundation from the other locations mentioned. + +
+ + + Damage to a portion of Sanibel Island. Credit to Wilfredo Lee - Associated Press. + +
+ + + + + + ## Conclusion + + Hurricane Ian left a trail of devastation across southwestern Florida, highlighting the urgent need for enhanced disaster preparedness and resilience strategies. The catastrophic impact on life, property, and the environment, evidenced through detailed NDVI and entropy analyses using HLS data, underscores the challenges communities face in recovery. The use of advanced remote sensing tools like the CONUS Disturbance Watcher emphasizes the importance of real-time data in disaster response. + + + + + + + ## Supplemental Resources + + [National Hurricane Center (NHC)](https://www.nhc.noaa.gov/) + + [Hurricane Ian Overview](https://www.nhc.noaa.gov/data/tcr/AL092022_Ian.pdf) + + [University of Connecticut - Global Environmental Remote Sensing Laboratory (GERs Lab)](https://gerslab.uconn.edu/) + + Author of Cover Photo - [Joshua Stevens](https://earthobservatory.nasa.gov/about/joshua-stevens) + + NESDIS - [National Environmental Satellite, Data, and Information Service](https://www.nesdis.noaa.gov/) (NESDIS) + + + + diff --git a/stories/ian_goes_cover.jpg b/stories/ian_goes_cover.jpg new file mode 100644 index 000000000..c486149b6 Binary files /dev/null and b/stories/ian_goes_cover.jpg differ diff --git a/stories/lahaina-fire.stories.mdx b/stories/lahaina-fire.stories.mdx index 00ccc3278..b4d66d51d 100644 --- a/stories/lahaina-fire.stories.mdx +++ b/stories/lahaina-fire.stories.mdx @@ -17,7 +17,7 @@ taxonomy: - Authors: Trent Cowan[1], Andrew Blackford[1], Udaysankar Nair[1]\ + Authors: Andrew Blackford[1], Trent Cowan[1], Udaysankar Nair[1]\ [1] University of Alabama in Huntsville(UAH) ## Introduction diff --git a/stories/ndvi_after.jpg b/stories/ndvi_after.jpg new file mode 100644 index 000000000..468ffab35 Binary files /dev/null and b/stories/ndvi_after.jpg differ diff --git a/stories/ndvi_before.jpg b/stories/ndvi_before.jpg new file mode 100644 index 000000000..0cdd667f3 Binary files /dev/null and b/stories/ndvi_before.jpg differ diff --git a/stories/ndvi_difference.jpg b/stories/ndvi_difference.jpg new file mode 100644 index 000000000..bb81fc720 Binary files /dev/null and b/stories/ndvi_difference.jpg differ diff --git a/stories/urban-heating.stories.mdx b/stories/urban-heating.stories.mdx index 7117b1a82..daf40daff 100644 --- a/stories/urban-heating.stories.mdx +++ b/stories/urban-heating.stories.mdx @@ -27,10 +27,16 @@ taxonomy:
- + + + Authors: Andrew Blackford[1], Trent Cowan[1], Udaysankar Nair[1]\ + [1] University of Alabama in Huntsville(UAH) + ## Implications for Heat Stress + 🚧 This Discovery presents work in progress and not peer-reviewed results! 🚧 + Heat stress includes a series of conditions where the body undergoes stress due to overheating– typically from exposure to hot weather. It is a natural hazard that causes a large number of fatalities globally. Those at a greatest risk of heat stress include children, the elderly, and people with medical conditions, however, even young and healthy individuals can experience heat stress when exposed to intense heat or when conducting strenuous activities in hotter conditions. In the coming decades, climate change may cause more frequent heat waves, which could exacerbate heat-induced health issues. Future urban growth could also strengthen the urban heat island effect–in which infrastructure absorbs and re-emit the sun's heat at rates higher than natural landscapes–causing more severe heat events in urban populations. In urban areas a variety of individual, social, and geographic factors determine an individual's heat risk (Reckien et al. 2018). Social structures and segregation in urban areas can increase the risk of unequal heat exposure. This discovery will be using NASA Earth observations to explore heat stress inequalities throughout areas of urban growth in Houston, Texas over the past 20 years.