United States of America environment data

NOTE: Due to the higher resolution of this data, it may be slow to load or require the user to zoom to a smaller area of interest. Date of Images: Post-Event: 10/24/2025, 10/23/2025, 10/21/2025, 10/20/2025, 10/18,2025 Pre-Event: 9/10/2025, 9/13/2025, 9/14/2025, 9/15/2025, 9/20/2025 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space and may show damage caused by severe weather. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Satellite/Sensor: PlanetScope Resolution: 3 Meters Esri REST Endpoint: See URL section on right side of page

Date of Image(s): 1/10/25; UTC: 223109 1/10/25; UTC: 222449 Summary: The images are false-color “quick looks” generated from the NASA AVIRIS-3 (Airborne Visible/Infrared Imaging Spectrometer -3). They are generated from un-atmospherically-corrected, at-sensor radiance data. Specifically: AVIRIS 3 L1B calibrated spectral radiance (units: uW nm-1 cm-2 sr-1). The images have been orthorectified. Given that these images were derived from solely calibrated, at-sensor radiance data, they have not undergone atmospheric correction and do not quantitatively represent surface reflectance. Suggested Usage: Despite the rawer form of this quick-look product, it is still useful for burned area mapping. Sensor: Airborne Visible/Infrared Imaging Spectrometer 3rd-Generation (AVIRIS-3) AV320250110t223109 Resolution: 3.70 x 3.70 m pixels Dimensions for AV320250110t223109: X: 6210 Y: 3068 Bands: 3 AV320250110t222449 Resolution: 3.40 x 3.40 m pixels Dimensions for AV320250110t222449: X: 6593 Y: 3465 Bands: 3 The AVIRIS-3 False color RGB wavelengths are 1660nm, 850nm, and 560nm for (R, G, B) respectively. Credits: Rob Green (JPL), Phil Brodrick (JPL), David Thompson (JPL), John Chapman (JPL), Michael Eastwood (JPL), and Adam Chlus (JPL). Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=843bb9103d94417bac6724a8d90a27ce WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_aviris_QuickLook/ImageServer/WMSServer

Date of Images: Post-Event: June 26, 2025 at 00:59 UTC (about 6:59PM MT) Pre-Event: June 20, 2025 Summary: We developed a rapid-response workflow to map flooded areas during this emergency event using multi-temporal Sentinel-1 SAR data. Sentinel-1’s SAR backscatter characteristics are particularly effective for detecting surface water: water surfaces typically exhibit low backscatter, whereas non-water areas show higher backscatter. Leveraging this physical property, we distinguished between water and non-water surfaces from both pre- and post-flood observations. To highlight flood-induced surface changes, we computed the differenced co-polarized VV backscatter (dVV = VV_post – VV_pre). This change detection technique enhances the visibility of newly inundated areas. We then applied a simple threshold to the dVV image to separate flooded from non-flooded areas. Suggested Use: The final output is a binary raster product, where pixels with a value of 1 represent flooded areas (dark blue color). Satellite/Sensor: Sentinel-1 /Synthetic Aperture Radar (SAR) Resolution: 10 meters Credits: Dr. Khuong H. Tran (Khuong.tran@nasa.gov) Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=87d3edc8e19d437da1336481506ff377 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202506_FIRE_NM/2506_sentinel1_flood/ImageServer/WMSServer

Date of Image: Pre-Event: August 2024 Post-Event: September 28, 2024 Summary: These Black Marble High-Definition (BMHD) images were created by the NASA Black Marble Science team, with directed funding the NASA-Google Partnership program. The images map the impact of Hurricane Helene on electric grids. The baseline image is from August 2024, a cloud-free, moon-free composite, and the “after" image is from September 28. There is a layer to display where clouds are present in the "after" images. This comparison between the images is meant as a visual assessment of outage impacts from the extreme heat to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NOTE: Black Marble HD images are downscaled from NASA’s Black Marble nighttime lights product (VNP46), and as such are a “modelled” or “best guess” estimate of how lights are distributed at a 30m resolution. These images should be used for visualization purposes, not for quantitative analysis. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Satellite/Sensor: The primary data source, NASA’s Black Marble nighttime lights product suite (VNP46), utilized to generate this product is derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP) along with high resolution base layers - Landsat derived normalized index products (NDVI and NDWI) and OpenStreetMap (OSM) derived road layer Resolution: Scaled resolution of 30 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Esri REST Endpoint: See URL section on right side of page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202409_HURRICANE_HELENE/BMHD/ImageServer/WMSServer

Dates of Images: Post-Event: June 22, 2025; June 21, 2025 Pre-Event: June 19, 2024 Date of Next Image: Unknown Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input images were generated from the Harmonized Landsat Sentinel-2 (HLS) dataset at 30m resolution. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. dNBR is computed by the difference between the pre-fire NBR and the post-fire NBR. More information on dNBR can be found here: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio. dNBR data may be computed while the fire is in progress. This is intentionally done to prioritize rapid data availability for proactive disaster response but means data can change over the course of the fire. Check fire containment and image dates for further context on image timing. Suggested Use: NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. Since the dNBR considers the condition of the scene before the fire occurred, the resulting value has been used as a proxy for burn severity. Higher dNBR values represent a proxy for greater burn severity. Negative dNBR values may represent a re-greening of or growth of vegetation in between pre and post imagery. The use of this dNBR product as a quantitative metric of burn severity at the time of posting this dataset should be strongly caveated. This is due to several dNBR limitations: The spectral band selections used for dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure). This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with representative conditions for each scene; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Satellite/Sensor: Harmonized Landsat Sentinel-2 data: https://www.earthdata.nasa.gov/data/projects/hls Resolution: 30 meters Credits: NASA LP DAAC: https://www.earthdata.nasa.gov/data/projects/hls Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=42dc8b50e387435ea4dc80a13c4ca9ca WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202506_FIRE_NM/2506_hls_dnbr/ImageServer/WMSServer

Dates of Images Post-Event: 3/26/2026, 3/23/2026 Summary Skysat is a high-resolution satellite constellation operated by Planet and allows for multiple viewing opportunities of any location on Earth daily. Skysat collects 4-band imagery which allows for multiple derived products to be produced. The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. The Color Infrared composite is created using the near-infrared, red, and green channels. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Resolution ~1 m Credits NASA Disasters Program, Processing from MSFC, Planet. For derived products: “Includes copyrighted material of Skysat. All rights reserved.” This work utilized data made available through the NASA Commercial Satellite Data Acquisition (CSDA) program.

Fire Hazard Severity Zones (FHSZ) Fire Hazard Severity Zone classes from the map dated September 29, 2023, as adopted on January 31, 2024 and implemented on April 1, 2024 This layer was acquired/downloaded from the CalFire OSFM. Below is the original metadata that came with the layer:   The State Fire Marshal shall classify lands within state responsibility areas into fire hazard severity zones. Each zone shall embrace relatively homogeneous lands and shall be based on fuel loading, slope, fire weather, and other relevant factors present, including areas where winds have been identified by the department as a major cause of wildfire spread.  The California laws and regulations that require Fire Hazard Severity Zones include California Public Resource code 4201-4204 and California Code of Regulations Title 19, Section 2201.  Effective April 1, 2024 this regulatory adoption repeals Section 1280.1 of Title 14 California Code of Regulations and adopts similar regulatory provisions in Section 2201 of Title 19 California Code of Regulations.   A prior version of this file and data was released as part of the data package accompanying the final Public Comment period for Fire Hazard Severity Zones (September 29, 2023).  This previous version and this version are the same geographic representation of Fire Hazard Severity Zones and are now part of the formal regulation as adopted. While under normal workflow any revision to a public data file would get reflected in the filename, in this case the file name remains identical as it is part of the adopted regulation.  The only changes reflect edits to the metadata describing timing of adoption and effective date for the regulation.   Calistoga Revisions (2025) Revisions were made within Calistoga city limits by request of Calistoga City Fire (Jed Matcham). Fire Hazard Severity Zones were expanded to cover all parcels they intersect. Each parcel was assigned the highest severity zone it overlapped with.  Steps taken to update Fire Hazard Severity Zones within Calistoga Fire Hazard Severity Zones within Calistoga City Limits were erased from the original layer downloaded from Calfire.  Join Calistoga Parcels with original FHSZ layer by spatial intersect. If parcel intersects with multiple FHSZs, duplicate parcels with different FHSZ zones exist. Per parcel, only keep parcel with highest severity Dissolve parcel polygons from Calistoga_FHSZ by severity zone. This will combine features that have the same severity zone.  Clip ROW FHSZ Zones within Calistoga (They will be appended back in later). This is done so when I append them to the FHSZ_Erase layer, all polygons within Calistoga end at the city boundaries.   Append Calistoga_FHSZ_Dissolve polygon layer and Calistoga_ROW_Clipped to FHSZ_Erased layer. Merge ROW polygons of the same Fire Hazard Severity Class with Parcel Polygons of the same Fire Hazard Severity Class where they are adjacent.  Cleaned up the SRA / LRA divide to follow the Calistoga City Limits in North central Part of town

Dates of Images: Post-Event: May 19, 2026 Pre-Event: May 11, 2026 Date of Next Image: Unknown Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input images were generated from the Landsat datasets at 30m resolution. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. dNBR is computed by the difference between the pre-fire NBR and the post-fire NBR. More information on dNBR can be found here: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio. dNBR data may be computed while the fire is in progress. This is intentionally done to prioritize rapid data availability for proactive disaster response but means data can change over the course of the fire. Check fire containment and image dates for further context on image timing. Suggested Use: NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. Since the dNBR considers the condition of the scene before the fire occurred, the resulting value has been used as a proxy for burn severity. Higher dNBR values represent a proxy for greater burn severity. Negative dNBR values may represent a re-greening of or growth of vegetation in between pre and post imagery. The use of this dNBR product as a quantitative metric of burn severity at the time of posting this dataset should be strongly caveated. This is due to several dNBR limitations: The spectral band selections used for dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure). This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with representative conditions for each scene; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Service URL: See individual Layers. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202605_FIRE_KS/202608_landsat_dnbr/ImageServer/WMSServer

Dates of Images: Post-Event: May 16, 2026 Pre-Event: May 11, 2026 Date of Next Image: Updated images are expected to be available for additional analysis as needed up to every 6 days. For lower-latency information regarding fire extent and estimated burn area, please see the Fire Events Data Suite (FEDS) web map. Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input images were generated from the Sentinel-2 dataset at 10m resolution. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. dNBR is computed by the difference between the pre-fire NBR and the post-fire NBR. More information on dNBR can be found here: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio. dNBR data may be computed while the fire is in progress. This is intentionally done to prioritize rapid data availability for proactive disaster response but means data can change over the course of the fire. Check fire containment and image dates for further context on image timing. Suggested Use: dNBR is commonly used as a proxy to identify fire-affected areas. Higher dNBR value are statistically correlated with greater burn severity, where negative dNBR values may represent a re-greening of or growth of vegetation in between pre and post-vegetation. The use of this dNBR product as a quantitative metric of burn severity at the time of posting this dataset should be strongly caveated. This is due to several dNBR limitations: The spectral band selections used for dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR will not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure), and the interpretation of dNBR values may vary widely based on the type of vegetation burned. This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with representative conditions for each scene; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Satellite/Sensor: Copernicus Sentinel-2 Resolution: 10 meters Credits: NASA/MSFC, ESA Copernicus Service URL: See URL to the bottom right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202605_FIRE_KS/202608_sentinel2_dnbr/ImageServer/WMSServer

Date of Images: 10/9/2025 - 10/26/2025 Please Note: Images with majority cloud cover are not shown in the Map Viewer, but are available in the service. Summary: These Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30. The baseline image is from September 2025, generated from all daily atmospheric and lunar-corrected data from that month. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights.  Observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 500 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=de7a99b66cca4a20bfc91b27ea84cd4d WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_FLOOD_AK/2510_BlackMarble_brdf/ImageServer/WMSServer

Dates of Images: Post-Event: May 16, 2026 Pre-Event: May 11, 2026 Date of Next Image: Unknown Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input images were generated from the Harmonized Landsat Sentinel-2 (HLS) dataset at 30m resolution. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. Check fire containment and image dates for further context on image timing. Suggested Use: NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. This dataset has not been validated by independent burn severity assessments. Satellite/Sensor: Harmonized Landsat Sentinel-2 data: https://www.earthdata.nasa.gov/data/projects/hls Resolution: 30 meters Credits: NASA/MSFC, USGS, ESA Copernicus Service URL: See individual Layers.

Full Metadata This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The layers in this web service depict ecosystem services at the census block group level for all EnviroAtlas communities. These layers illustrate the ecosystems and natural resources that are associated with clean air, clean and plentiful water, natural hazard mitigation, climate stabilization, recreation, culture, and aesthetics, and biodiversity conservation, and factors that place stress on those resources. EnviroAtlas allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the conterminous United States as well as detailed metrics for select communities. Additional descriptive information about each attribute in this web service can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This page provides data for the Carbon Neutrality performance measure.   The City of Tempe is committed to protecting the environment. Carbon emissions are a significant contributor to the pollution of our atmosphere. Sources of carbon emissions include the energy used in city buildings and facilities, and in the fuel used in transit, city vehicles, and employee commuting. This performance measure puts the City on a path to being a carbon-neutral city. The municipal carbon footprint includes buildings, streetlights, water treatment, electricity, and fuel usage for fleet, transit, solid waste, and employee commute. The performance measure dashboard is available at 4.19 Carbon Neutrality     Data Dictionary   Additional Information   Source: City pisions (is this word correct?): fleet, WUD, solid waste. APS, SRP, MAG, SHROG, and Valley Metro Contact:  Grace Kelly Contact E-Mail: Grace_Kelly@tempe.gov Data Source Type:  CSV Preparation Method:  It is part of a larger data set and uses proprietary software. Publish Frequency:  Every 5 years Publish Method: Manual

Date of Images: Post-Event: 8/13/2026, 8/19/2026 Pre-Event: July 2026 Summary: These Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images are corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30. The baseline image is from July 2026, generated from all daily atmospheric and lunar-corrected data from that month. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. Grey indicates cloud cover. Observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 15 arc-seconds (~500 meters) Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Data Access: REST URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=a97341f5bb9143039769267a1f385dd6 WMS: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202608_FLOOD_OH/VIIRS_BRDF_cloudmask/ImageServer/WMSServer

Full Metadata This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The layers in this web service depict ecosystem services at the census block group level for all EnviroAtlas communities. These layers illustrate the ecosystems and natural resources that are associated with clean air, clean and plentiful water, natural hazard mitigation, climate stabilization, recreation, culture, and aesthetics, and biodiversity conservation, and factors that place stress on those resources. EnviroAtlas allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the conterminous United States as well as detailed metrics for select communities. Additional descriptive information about each attribute in this web service can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Date of Images: 10/9/2025 - 10/28/2025 Please Note: Images with majority cloud cover are not shown in the Map Viewer, but are available in the service. Summary: These Black Marble Day-Night Band (BRDF-Corrected) cloud masks were created by the NASA Black Marble Science team. This is the layer to displaying where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. Grey represents cloud cover. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 500 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=961f83b5aef64f75bc4caa9c4610f4ed WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_FLOOD_AK/2510_BlackMarble_brdf_cloud/ImageServer/WMSServer

Dates of Images Sentinel-1 and Sentinel-2 pre-fire collection: January 2018 to November 2024 Sentinel-1 post-fire image: 21st January 2025 Sentinel-2 post-fire image: 12th January 2025 Summary Fire severity refers to the degree of change caused by fire in the state of vegetation, encompassing both aboveground and belowground impacts. It reflects the extent of fuel consumption, vegetation mortality, and structural damage caused by the fire. Accurately mapping fire severity is critical for understanding ecological impacts, informing post-fire recovery strategies, and supporting emergency response efforts. The 2025 Southern California fire episode marked one of the most severe global fire events in recent history. It resulted in extensive ecological and structural damage, particularly within the wildland-urban interface (WUI), where anthropogenic development interacts with fire-prone vegetation. The Palisades and Eaton fires were among the most severe, driven by prolonged drought, anomalously high temperatures, and strong synoptic-scale wind patterns. Given the large-scale ecosystem disturbances and infrastructural losses, an independent fire severity assessment for each fire is essential to quantify post-fire ecological impacts and inform resilience strategies. Traditionally, fire severity has been assessed both qualitatively and quantitatively through field surveys conducted by forest professionals. These assessments provide detailed on-the-ground observations but are often constrained by logistical challenges, such as less frequency of visit and limited spatial coverage. As a result, satellite remote sensing has become the preferred approach for fire severity mapping due to its ability to provide high-frequency, high-resolution, and continuous data over large spatial extents. To address this need, this study employs the “OptiSAR” framework, a multi-sensor remote sensing approach, for enhanced fire severity assessment by integrating Synthetic Aperture Radar (SAR; Sentinel-1) and multispectral optical imagery (Sentinel-2). By leveraging SAR’s all-weather and day/night capability and its sensitivity to structural changes in vegetation, along with optical indices' ability to capture spectral responses of burned landscapes, the “OptiSAR” enables a comprehensive and multi-dimensional assessment of fire-induced transformations. Satellite/Sensor & Resolution Sentinel-1/ C-Band /10m spatial resolution. Sentinel-2/ MSI /resampled spatial resolution from 20m to 10m. Methodology Independent fire severity analyses were conducted for the Palisades and Eaton fires due to their distinct vegetation characteristics and fire dynamics. The Palisades and Eaton fires burned under different conditions, each with unique fire behavior and impacts. The Palisades fire spread rapidly due to strong coastal winds and dry fuels, leading to high-intensity burns. In contrast, the Eaton fire, with more varied terrain and denser fuel accumulation, burned at a different pace, resulting in distinct structural and spectral fire effects. The study area consists of chaparral, and coastal sage scrub, with chaparral being the dominant fire-prone vegetation type. It comprises dense, drought-adapted shrubs which accumulate dry biomass, contributing to high fire intensity. Coastal sage scrub and oak woodlands are also present, with varying degrees of fire adaptation. The fire severity was divided into five classes and color-coded for intuitive visualization. Pixels in green show ‘unburnt’ representing undisturbed vegetation, yellow depict ‘low’ indicating areas with surface fuel depletion but an intact canopy. Orange represents ‘moderate’, signifying partial canopy scorch with some structural damage. Red denotes ‘high’, where complete canopy scorch and significant structural damage have occurred. Black indicates ‘extreme’, describing areas with total canopy consumption and collapse, resulting in extensive structural destruction. To quantify changes in vegetation, two advanced change detection indices were employed: the RADAR-Vegetation Structural Perpendicular Index (RADAR-VSPI; Chhabra et al., 2022) and the Vegetation Structural Perpendicular Index (VSPI; Massetti et al., 2019). These indices represent a significant advancement over conventional vegetation characterization and burn severity indices by providing more detailed insights into disturbance-induced transformations as a function of their undisturbed state from the pre-fire period. To compute RADAR-VSPI and VSPI, a pixel-wise baseline was established using a time series of Sentinel-1 and Sentinel-2 data from January 2018 to November 2024. This baseline represented the spatiotemporal characteristics of undisturbed vegetation, identified from periods with no observed disturbances in both SAR and optical datasets. Post-fire vegetation changes were then quantified as an orthogonal distance from this baseline. Independent fire severity classifications were performed separately on the post-fire RADAR-VSPI (21st January 2025) and VSPI (12th January 2025) acquisitions using K-means clustering. This unsupervised classification method was chosen due to its ability to objectively segment the data into distinct severity levels based on natural groupings within the distribution of RADAR-VSPI and VSPI values. Unlike threshold-based approaches, K-means clustering allows for a data-driven classification, reducing subjective biases in defining severity categories while ensuring adaptability across different fire-affected landscapes. From the clustering results, the severity class ranges for RADAR-VSPI and VSPI were derived, establishing quantitative thresholds that delineate the gradient of fire-induced vegetation and structural changes. Higher negative decibel values of RADAR-VSPI indicate greater structural destruction, whereas higher values in VSPI indicate greater vegetation damage. To systematically classify fire severity based on the synergies between RADAR-VSPI and VSPI, a high-level decision-tree algorithm was developed, enabling a data-driven approach to delineate fire impacts across varying burn intensities. Table 1: Fire severity classification ranges for the Palisades fire Fire Severity Class RADAR-VSPI [dB] VSPI [-] Unburnt (green) 0.107 (-68.804) Low (yellow) (-1.027) 142.064 Moderate (orange) (-1.992) 406.705 High (red) (-3.138) 700.930 Extreme (black) (-5.472) 920.930 Table 2: Fire severity classification ranges for the Eaton fire Fire Severity Class RADAR-VSPI [dB] VSPI [-] Unburnt (green) 0.895 (-49.594) Low (yellow) (-0.834) 116.854 Moderate (orange) (-2.227) 392.053 High (red) (-3.503) 572.801 Extreme (black) (-5.945) 794.519 Suggested Use The Palisades Fire severity classification map highlights widespread ‘high’ to ‘extreme’ severity burns across the Santa Monica Mountains and surrounding areas, particularly in Topanga State Park, Temescal Gateway Park, and Monte Nido. These areas, dominated by chaparral and coastal sage scrub, experienced intense fire behavior, resulting in significant vegetation loss. The western sections, including areas near Saddle Peak Trailhead, also show severe burns following ridgelines and steep slopes, reinforcing the role of topography in fire intensity. In contrast, ‘moderate’ and ‘low’ severity burns appear in scattered patches, particularly near Topanga, Sylvia Park, and portions of Mandeville and Rustic Canyons, where surface fuels burned but some canopy remained intact. Additionally, vegetation in green ‘unburnt’ is mapped along the Pacific Coast Highway, near Malibu, Pacific Palisades, and Brentwood, suggesting fire suppression efforts or natural firebreaks may have limited spread in these areas. The Eaton Fire severity classification map shows extensive ‘high’ to ‘extreme’ severity burns in the San Gabriel Mountains, particularly around Mount Wilson Observatory, Big Santa Anita Canyon, Sturtevant Falls, and Echo Mountain. These regions, characterized by steep terrain and mixed chaparral-woodland vegetation, experienced severe fire effects, leading to complete canopy consumption. The northwestern portions, including Dawn Mine and sections of the San Gabriel Mountains, also exhibit intense burns along ridgelines, aligning with expected fire spread dynamics in mountainous landscapes. In contrast, ‘moderate’ and ‘low’ severity burns are more scattered, particularly in Kinneloa Mesa and sections of Santa Anita Canyon, where some structural elements of the vegetation were retained despite surface fuel consumption. At the southern edge, near Altadena, Hastings Ranch, and Sierra Madre, ‘unburnt’ vegetation is observed, marking areas that remained unaffected within the fire perimeter. Terms of Use & Limitations This product is only relevant to fire impacts to vegetation and should not be used to understand impacts to built infrastructure. A key limitation of the fused fire severity product is its dependence on the quality of pre-disturbance baseline data. Inconsistencies or gaps in the baseline—particularly in areas with frequent vegetation changes or limited temporal coverage—can impact classification accuracy. Additionally, in highly mixed vegetation landscapes such as chaparral shrublands, RADAR-VSPI’s structural sensitivity may dominate, potentially masking subtle spectral changes captured by VSPI. These challenges emphasize the need for ongoing refinement, improved temporal consistency, and ground-truth validation to enhance classification reliability. Despite its limitations, the fire severity classification provides a general assessment of fire perimeters and vegetation damage. It can aid in post-fire recovery planning, such as soil stabilization in affected areas. The severity map also distinguishes between areas of structural collapse and regions where surface burns occurred while vegetation structure remained intact, offering insights for targeted mitigation efforts. Additionally, it may support long-term monitoring in the WUI, helping assess ecosystem recovery and fire behavior trends over time. This enhanced fire severity product should be used with caution as a qualitative guide rather than an absolute measure of fire impacts. The classification is based on satellite-derived indices and may be influenced by factors such as sensor limitations, baseline data availability, and local vegetation variability. NASA data and products are freely available to federal, state, public, non-profit, and commercial users. However, these datasets are primarily experimental or research-grade and may not be suitable for operational applications requiring real-time accuracy or consistency. The NASA Disasters Mapping Portal and associated data products are designed to support decision-making and enhance situational awareness, but availability and updates are not guaranteed on a routine basis. Credits 1. Aakash Chhabra (NASA Ames/NEX; aakash.chhabra7489@gmail.com), 2. Taejin Park (NASA Ames/NEX), 3. Ian Brosnan (NASA Ames/NEX) Data Sources a . Sentinel-1 (GRD pre-processed product): https://developers.google.com/earthengine/datasets/catalog/COPERNICUS_S1_GRD#description b. Sentinel-2 (2A atmospherically corrected product): https://developers.google.com/earthengine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED c. ESA Landcover: https://developers.google.com/earthengine/datasets/catalog/ESA_WorldCover_v200 d. Fire Perimeter: https://www.nifc.gov/fire-information/maps Service URL https://gis.earthdata.nasa.gov/portal/home/item.html?id=d5cfe94588854455a443980c5bd52e3a WMS Endpoint https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_optisar_FireSeverity/ImageServer/WMSServer

Date of Image(s): 9/5/24, 1/16/25 Summary: The normalized burn ratio difference (dNBR) are generated from the NASA AVIRIS-3 (Airborne Visible/Infrared Imaging Spectrometer -3). They are generated from differencing images from September 5, 2024 and January 16, 2025 over Palisades and Eaton. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. dNBR is computed by the difference between the pre-fire NBR and the post-fire NBR. More information on dNBR can be found here: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio. Suggested Usage: NBR is commonly used as a proxy to indicate areas which have charred vegetation. The use of this dNBR product as a quantitative metric of burn severity at the time of posting this dataset should be strongly caveated. This is due to several dNBR limitations: The spectral band selections used for dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure). This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with representative conditions for each scene; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Sensor: Airborne Visible/Infrared Imaging Spectrometer 3rd-Generation (AVIRIS-3) The AVIRIS-3 wavelengths are 866nm and 2198nm. Credits: Rob Green (JPL), Phil Brodrick (JPL), David Thompson (JPL), John Chapman (JPL), Michael Eastwood (JPL), and Adam Chlus (JPL). Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=61f84f97df684f8ba5446c82d881b960 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_aviris_dnbr_color/ImageServer/WMSServer

NOTE: The Disasters Team is currently revising the legend on this dataset. Visualization properties are not finalized. Please consult the metadata on this page for reference. Date of Images Pre-event: 2025-10-08, 2025-10-10 Co and Post-event: 2025-10-12, 2025-10-13, 2025-10-15 Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at NASA's Jet Propulsion Laboratory (JPL) and the California Institute of Technology produced maps showing areas affected by flooding in western Alaska in October 2025 using the OPERA Dynamic Surface Water Extent from Sentinel-1 (DSWx-S1) product. The flooding was triggered by the remnants of Typhoon Halong, which brought hurricane-force winds and record-breaking storm surge to the Yukon-Kuskokwim Delta region. The event caused catastrophic damage across multiple Alaska Native coastal villages, including Kipnuk and Kwigillingok, where floodwaters swept homes from their foundations, destroyed infrastructure, and disrupted access to essential services. These maps provide an initial view of the surface water extent observed following the flood event. The results presented here are preliminary and unvalidated, intended primarily to support situational awareness and field response efforts. The ARIA-Share platform emphasizes rapid dissemination of satellite-based products for disaster response. All information is provisional and should be used under emergency response guidelines. These data are provided without warranty of any kind. Use at your own risk. OPERA DSWx-S1 The OPERA DSWx-S1 was used to identify surface water for the flooding event. The images provided are a mosaic of OPERA DSWx-S1 products, producing a composite image for each date. The mosaicking applied prioritized pixels classified as open surface water to preserve water pixels in region of product granule overlap. The images are provided as GeoTIFF files. Water (WTR) Layer Description: Masked interpreted water classification layer. This represents pixel-wise classification into one of three water classes (not water, open water and inundated vegetation), masks (HAND mask and layover/shadow mask), or no data classes. Water (WTR) Layer Values: 0: Not Water - an area with valid data that is not open water (class 1), inundated vegetation (class 3), height above nearest drainage masked (class 250), or layover/shadow masked (class 251). Masking can result in "not water" (class 0) where land cover masking is applied (suggested color: #ffffff) 1: Open Water - an area that is entirely water and unobstructed to the sensor, including obstructions by vegetation, terrain, and buildings. (suggested color: #0000ff) 3: Inundated vegetation - an area that is considered inundated, extracted from the high value in dual polarization ratio and the wetland class in land cover map. (suggested color: #00ff00) 250: Height Above Nearest Drainage (HAND) masked - an area where topographic height is higher than the HAND threshold (suggested color: #c8c8c8) 251: Layover/shadow masked - an area identified as layover or shadow computed from the geometry of the digital elevation model and sensor. The area is directly copied from input burst RTC-S1 products (suggested color: #808080) 255: No data (suggested color: transparent) Satellite/Sensor: Synthetic Aperture Radar (SAR) instrument on European Space Agency's (ESA) Sentinel-1A satellite. Resolution: 30 meters OPERA DSWx-S1 data availability The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20251012_Alaska_Floods/DSWx-S1/. The OPERA DSWx-S1 products have been in production since September 2024, are freely distributed to the public via NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC), and can be downloaded through NASA's Earthdata search. For more information about the Dynamic Surface Water eXtent product suite, please refer to the DSWx Product page: https://www.jpl.nasa.gov/go/opera/products/dswx-product-suite. For more information about the Caltech-JPL ARIA project, visit https://aria.jpl.nasa.gov. For more information about the JPL OPERA project, visit https://www.jpl.nasa.gov/go/opera/. REST Endpoint: See URL on the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_FLOOD_AK/202510_opera_dswx_s1/ImageServer/WMSServer Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (mary.grace.p.bato@jpl.nasa.gov) Ines Fenni (ines.fenni@jpl.nasa.gov) Alexander Handwerger (alexander.handwerger@jpl.nasa.gov) Renato Frasson (renato.prata.de.moraes.frasson@jpl.nasa.gov) Credits: Sentinel-1 data were accessed through the Copernicus Open Hub and the Alaska Satellite Facility server. The product contains modified Copernicus Sentinel data (2025), processed by the European Space Agency and analyzed by the NASA-JPL/Caltech ARIA and OPERA team. The products are produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group, and managed by NASA's Jet Propulsion Laboratory. NASA JPL-Caltech ARIA/OPERA Team ==================

Holdings of the Sonoma County Agricultural Preservation and Open Space District. Includes conservation easements, open space easements conveyed to Ag + Open Space through the County of Sonoma planning process, and Ag + Open Space fee properties. This dataset represents Ag + Open Space's current portfolio of protected lands. Superseded projects (e.g. former fee properties since transferred, easements since amended or rescinded and replaced) are housed in a separate dataset.

Researchers working with the Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at NASA's Jet Propulsion Laboratory, Pasadena, California, created the OPERA Surface Disturbance Alert from Sentinel-1 (DIST-S1-ALERT) prototype product suite to create maps of surface disturbance due to ongoing wildfires in Los Angeles County in January 2025. The OPERA DIST-S1 prototype results posted here are preliminary results, primarily intended to aid the field response and people who wanted to have a rough first look area impacted by wildfire. The ARIA-share website has always focused on posting preliminary results as fast as possible for urgent response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk. Suggested Use: The DIST-S1 product shows disturbance of each pixel with a corresponding level of confidence: 0: No disturbance 1: Moderate Confidence Disturbance (more than 2.5 standard deviations from mean estimated from imagery prior to event) 2: High Confidence Disturbance (more than 4.5 standard deviations from mean estimated from imagery prior to event) Date of most recent image used: Ascending track 64: 2025-01-09T01:50:51 (UTC) or 2025-01-08T17:50:51 (PST). Descending track 71: 2025-01-09T13:53:03 (UTC) or 2025-01-09 05:53:03 (PST) Dates of other used images to define pre-fire baseline: 2024-09-11, 2024-09-23, 2024-10-05, 2024-10-17, 2024-10-29, 2024-11-10, 2024-11-22, 2024-12-04, 2024-12-16, 2024-12-28 Processing Details: The OPERA DIST-S1-ALERT prototype product provides a picture of surface disturbance (e.g., vegetation loss, structure change, damage) outside a historical norm using Sentinel-1A images. The products are distributed as GeoTIFFs. Pixels capturing disturbances are further differentiated as moderate confidence disturbance (pixel value = 1) and high confidence disturbance (pixel value = 2). These labels represent pixels that deviate more than 2.5 and 4.5 standard deviations from the mean, respectively, estimated from the 10 SAR acquisitions collected prior to the event. Statistics are estimated using a deep-learning model with spatiotemporal encoding (via the vision transformer). Files: disturbance_track64_2025-01-09_LA.tif(geotiff) shows surface disturbance as of January 9, 2025 2025-01-09T01:50:51 (UTC) or 2025-01-08T17:50:51 (PST). Note there are artifacts in this file that result from issue with Sentinel-1 burst overlap areas. Further investigation is needed in these areas. disturbance_track71_2025-01-09_LA.tif(geotiff) shows surface disturbance as of January 9, 2025 2025-01-09T13:53:03 (UTC) or 2025-01-09 05:53:03 (PST) Released January 13, 2025. --------------------------------- Data Availability: The OPERA DIST-S1 products produced by ARIA/OPERA - JPL, contain modified Copernicus data (2024). The products are available to download at https://aria-share.jpl.nasa.gov/20250113-GreaterLosAngeles_Fires/ Product POCs: Alexander L. Handwerger (alexander.handwerger@jpl.nasa.gov) Charlie Marshak (charlie.z.marshak@jpl.nasa.gov) Harris Hardiman-Mostow (hhm@math.ucla.edu) Steven Chan (steventsz.k.chan@jpl.nasa.gov) David Bekaert (david.bekaert@jpl.nasa.gov) References/Acknowledgement: https://aria.jpl.nasa.gov/ (ARIA project) https://www.jpl.nasa.gov/go/opera/ (OPERA Project) This prototype product was developed by Harris Hardiman-Mostow (UCLA) under the guidance of Charlie Marshak and Al Handwerger. Further product development has been led by the JPL OPERA DIST-S1 team (Charlie Marshak, Talib Oliver Cabrera, Jungkyo Jung, Richard West) Satellite/Sensor: Sentinel-1A Resolution: 30 meters Disclaimer: The California Institute of Technology (''Caltech'') makes these data available to the public ''as is'' for informational purposes only. The methods used and data collected have not been reviewed, cleared, or validated. Caltech, including its operating division the Jet Propulsion Laboratory, its employees, and agents make no representation or warranty, express or implied, as to the merchantability, non-infringement, or fitness for a particular purpose. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=24269306876346f689860d3e7a67da0a WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_opera_dist_s1/ImageServer/WMSServer Data Download: https://aria-share.jpl.nasa.gov/20250113-GreaterLosAngeles_Fires/

Date of Images: Post-Event: 8/13/2026, 8/19/2026 Pre-Event: July 2026 Summary: These Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images are corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30. The baseline image is from July 2026, generated from all daily atmospheric and lunar-corrected data from that month. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 15 arc-seconds (~500 meters) Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Data Access: REST URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=a83088fd0a0940cda0770b46e9222acf WMS: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202608_FLOOD_OH/202620_VIIRS_BRDF/ImageServer/WMSServer

Date of Image(s): January 11, 2025 Summary: This product shows the char/ash fraction of each pixel based on airborne imaging spectroscopy measurements from NASA JPL AVIRIS-3. The surface reflectance was first estimated using ISOFIT, after which a Spectral Unmixing algorithm was used to determine the amount of char and ash within each pixel, based on the MESMA approach. See credits for citations and more information on ISOFIT, Spectral Unmixing, and the MESMA approach. The char/ash fractional cover value is displayed as a single band pseudocolor grayscale, with thresholds at minimum detectable bounds of 50% and 80% char/ash fractional cover for Palisades and Eaton Fires, respectively. Suggested Use: The product describes the relative amount of char and ash (0-1) within each pixel. In order to deliver a product with low latency, the endmember library used was constrained to that common to a Southern California urban environment, and manual masking was also used. Locations that are heavily shaded may have errant solutions. The product also only indicates the char/ash fraction of the exposed surface - not what lies underneath. Areas covered by ash, for example, may express as having a fraction despite having only a thin coating of ash on top. This is a research grade product with known false positives and false negatives. Improvements to product accuracy are ongoing and linear spectral mixture model was used to approximate char/ash fractional cover; however, due to multiple scattering effects of complex surfaces, actual the relationship between observed char/ash reflectance and actual char/ash abundance may be non-linear. Resolution: The product ground spatial resolution is approximately 2.8 m, and flights occurred between 19:40 and 21:00 UTC on Jan 11th 2025. Raw data are available for download here: https://popo.jpl.nasa.gov/pub/LA_Fires/working/eaton_1.tif Point of Contact: Philip G. Brodrick (JPL) and David R. Thompson (JPL) Credits: AVIRIS-3 Radiance Data: Eckert, R., D.R. Thompson, A.M. Chlus, J.W. Chapman, M. Eastwood, M. Bernas, S. Geier, M. Helmlinger, D. Keymeulen, E. Liggett, S. Nadgauda, L.M. Rios, L.A. Shaw, W. Olson-Duvall, P.G. Brodrick, and R.O. Green. 2024. AVIRIS-3 L1B Calibrated Radiance, Facility Instrument Collection. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2356 AVIRIS-3 Reflectance Data: Brodrick, P.G., A.M. Chlus, U.N. Bohn, E. Greenberg, J. Montgomery, J.W. Chapman, M. Eastwood, S.R. Lundeen, R. Eckert, W. Olson-Duvall, D.R. Thompson, and R.O. Green. 2025. AVIRIS-3 L2A Orthocorrected Surface Reflectance, Facility Instrument Collection. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2357 ISOFIT: Thompson, D.R., Natraj, V., Green, R.O., Helmlinger, M.C., Gao, B.C. and Eastwood, M.L., 2018. Optimal estimation for imaging spectrometer atmospheric correction. Remote sensing of environment, 216, pp.355-373. Philip G Brodrick, David R Thompson, Niklas Bohn, Nimrod Carmon, Regina Eckert, James Montgomery, Kevin Wurster, & Nicholas Vaughn. (2024). isofit (v3.2.2). Zenodo. https://doi.org/10.5281/zenodo.14135462 Spectral Unmixing: https://github.com/emit-sds/SpectralUnmixing MESMA Approach: Quintano, C., Fernández-Manso, A. and Roberts, D.A., 2013. Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries. Remote Sensing of Environment, 136, pp.76-88. Service URL:https://gis.earthdata.nasa.gov/portal/home/item.html?id=f08f896fe8ce488fa66e6ab25b1963be WMS Endpoint:https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_aviris_CharAsh/ImageServer/WMSServer

Several weeks of storms in January 2023 have caused significant damage across California, with mudslides and flooding resulting in at least 19 dead and widespread damage to infrastructure. Drone experts at GLOBHE have activated drone operators in the region, capturing critical data in most affected areas and making it accessible to all the authorities, NGOs, and volunteers who now scramble to assist. The donated high-quality drone data is available to all entities working to support the region, easily accessible here. The datasets includes one high-resolution orthomosaic and six 360° panoramas to show the impact of floodings in the area. “Our thoughts are with everyone affected by the storms and all those who work hard to rescue, repair, and rebuild across the affected areas,” says Napoleon Bergström, COO at GLOBHE. As a global leader in connecting local drone operators with organizations in need of accurate drone data, GLOBHE works together with leading organizations to prevent and respond to natural disasters, often in hard-to-access areas across the world. “Access to high-quality imaging can give critical data on damage to housing, infrastructure, and nature, and make it easier to make the right decisions and help those affected,” says Napoleon Bergström, COO at GLOBHE. “That is why our team will work hard to provide this data.” From January 24, the data of affected areas in California is freely available here. About GLOBHE: GLOBHE is leading organizations to make better decisions through drone data. Through a single platform, the company offers organizations accurate and actionable drone data from anywhere in the world. GLOBHE's platform is a leading curated marketplace connecting local drone operators with organizations with the need for drone data of the highest quality. By creating the sharpest view of reality, Globhe enriches our communities and impacts our planet. For more information, please visit https://globhe.com/. MORE CROWDDRONING BY GLOBHE Web: https://globhe.com/ Facebook: https://www.facebook.com/Crowddroning Twitter: https://twitter.com/globhedrones Instagram: https://www.instagram.com/globhedrones/ LinkedIn: https://www.linkedin.com/company/globhedrones/

Building damage analysis of the Spokane wildfires in August 2026.

FEMA special flood hazard areas (100-year floodplain) in the City of Los Angeles. Why it matters: Flood insurance zones and where storm capture reduces flood risk. This is the tile layer: fast cached map display from county (1:2,311,162, level 8) to neighborhood scale (1:18,056, level 15). Query the data through the paired feature layer. Part of: LATTC Watershed Demo Study - Water and Wetlands (code C15). Processing: copied from the source on October 19, 2026, kept to Los Angeles County (layers over 2000 features clipped to the county land boundary), Web Mercator. Source: FEMA National Flood Hazard Layer, as published by the City of Los Angeles https://maps.lacity.org/lahub/rest/services/Stormwater_Information/MapServer/7

# USA California Wildfires: Damage Proxy Maps, Jan 2025 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for USA California Wildfires (Jan 2025) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: California, USA, Eaton Fire, 19 Jan 2025, v0.9 ** - **EOS-RS Damage Proxy Map: California, USA, Wildfires, 9 Jan 2025, v2.5 ** - **EOS-RS Damage Proxy Map: California, USA, Wildfires, 9 Jan 2025, v0.9 ** (superseded) **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Original data ALOS-2 PALSAR-2 Product - JAXA (2023-2025)

# EOS-RS Flood Proxy Map: Tampa Bay, Florida, USA, Hurricane Helene Floods, 26 Sep 2024, v0.9 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in Tampa Bay, Florida, USA due to heavy rainfall and storm surges from Hurricane Helene on 26 Sep 2024. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (29 Jan 2024 to 14 Sep 2024) and during (26 Sep 2024) the event. The map covers an area indicated by the white polygon. The preliminary map shows areas which are likely flooded (shown by light blue pixels of approximately 30m in size) due to the heavy rainfall and storm surges. Preliminary validation was conducted using news reports and ground-level videos and imagery in selected areas. This map should be used as a guide to identify areas that are likely flooded, and may be less reliable over urban and vegetated areas. This product contains modified Copernicus Sentinel data (2024), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2024)

# Hawaii Wildfires: Damage Proxy Maps, Aug 2023 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Hawaii Wildfires (Aug 2023) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Maui, Hawaii, Wildfires, 16 Aug 2023, v0.9 wbd** (superseded) - **EOS-RS Damage Proxy Map: Maui, Hawaii, Wildfires, 16 Aug 2023, v0.9** - **EOS-RS Damage Proxy Map: Maui, Hawaii, Wildfires, 13 Aug 2023, v0.9 wbd** (superseded) - **EOS-RS Damage Proxy Map: Maui, Hawaii, Wildfires, 13 Aug 2023, v0.9** **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2023),Validated with FirstLook Imagery © 2023 Maxar and Copernicus Sentinel-2

# Hawaii Mauna Loa Volcano: Damage Proxy Maps and Interferograms, Nov 2022 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps and interferograms for Hawaii Mauna Loa Volcano (Nov 2022) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Hawaii, USA, Mauna Loa Volcano Eruption, 29 Nov 2022, v0.5** - **EOS-RS Damage Proxy Map: Hawaii, USA, Mauna Loa Volcano Eruption, 29 Nov 2022, v0.5** - **EOS-RS Interferogram (unwrapped): Hawaii, USA, Mauna Loa Volcano Eruption, 2022/10/18-2022/11/29, v0.1** - **EOS-RS Interferogram (wrapped): Hawaii, USA, Mauna Loa Volcano Eruption, 2022/10/18-2022/11/29, v0.1** **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Original data ALOS-2 PALSAR-2 Product - JAXA (2022).

# EOS-RS Damage Proxy Map: Montana (Yellowstone National Park), USA, Floods, 15 Jun 2022, v0.9 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in the areas in Montana, United States due to floods caused by heavy rainfall and snowmelt. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA) before (15 Feb 2022 - 3 Jun 2022) and after (15 Jun 2022) the event. The image covers an area indicated by the large white polygon. Each pixel measures about 30 meters across. The colour variation from yellow to red indicates increasingly more significant surface change. Preliminary validation was done by comparing with high-resolution optical imagery and media reports. This map could be used as a guidance to identify damaged areas, and may be less reliable over vegetated areas (such as landslides in vegetated slopes). Scattered pixels over vegetated areas may be false positives, and a lack of colored pixels over vegetated areas may not mean no damage. Sentinel-1 data were accessed through the Copernicus Open Hub. This map contains modified Copernicus Sentinel data (2022), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) in collaboration with NASA-JPL and Caltech. **More map details and files:** **More map details and files:**

# ARIA DPM Sentinel-1 v1.0, Hurricane Ida The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology in Southern California, in collaboration with the Earth Observatory of Singapore (EOS), created this Damage Proxy Map (DPM) depicting areas that are likely damaged by the Hurricane Ida (August 29, 2021) in central and western Louisiana, United States. The map was derived from synthetic aperture radar (SAR) images on Sep 03, 2021 by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA). The pre-event images were taken before (Aug 10, 2021 and Aug 22, 2021) the hurricane hit land. The image covers an area of 337 by 250 kilometers (210 by 155 miles), shown by the large red polygon. Each pixel measures about 30 meters across. The color variation from yellow to red indicates increasingly more significant surface change. Preliminary validation was done by comparing with the Media reports. This damage proxy map should be used as guidance to identify damaged areas, and may be less reliable over vegetated or flooded areas. For example, the scattered colored pixels over vegetated areas and flooded fields may be false positives, and the lack of colored pixels over vegetated areas does not necessarily mean no damage. Some additional false positives in the middle of the image are likely due to the stitching of Sentinel-1 frames as a thin line running across the map at a ENE-WSW angle. This map is most sensitive to building damage, but small-scale change or partial structural damage may not be detected by this map. Sentinel-1 data were accessed through the Copernicus Open Hub and the Alaska Satellite Facility server. The product contains modified Copernicus Sentinel data (2021), processed by ESA and analyzed by the NASA-JPL/Caltech ARIA team and EOS team. Part of the funding was provided by NASA's Earth Applied Sciences Disasters Program. The products (KMZ and GeoTIFF files) are available to download at For more information about ARIA, visit: For more information about EOS, visit: **More map details and files:** **NOTE:** If you are loading the KMZ file in Google Earth Pro, to avoid slight color degradation, please make sure to uncheck the "Compress" option in the Preference > 3D View > Texture Colors (for Mac) or Tools > Options > 3D View > Texture Colors (for Windows). Google Earth may need to be restarted afterwards.

# Louisiana Hurricane Laura: Damage Proxy Maps, Aug 2020 The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and Caltech, in collaboration with the Earth Observatory of Singapore (EOS), produced these damage proxy maps for Louisiana Hurricane Laura (Aug 2020) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **ARIA-SG Damage Proxy Map: Louisiana, USA, Hurricane Laura, 1 Sep 2020, v0.4 ** - **ARIA-SG Damage Proxy Map: Louisiana, USA, Hurricane Laura, 1 Sep 2020, v0.4 ** **More map details and files:**

The Federal Flood Risk Management Standard (FFRMS) floodplain was developed by the Federal Emergency Management Agency (FEMA) to improve the resilience of communities and federal assets against the impacts of flooding. Through a series of Federal executive orders, the FFRMS was established (EOs 11988 and 13690); cancelled in 2017 (EO 13807); re-established in 2021 (EO 14030) and also mapped as the FFRMS floodplain; and then cancelled again in 2025 (EO 14148). This FFRMS floodplain data set was provided by FEMA at the request of Maryland Department of the Environment and is the same data available through the Flood Standard Support Tool (https://floodstandard.climate.gov/).3-meter DEM resolution terrain resolution used for this mapping. In some cases, 1-meter DEM data may have been re-sampled to a 3-meter DEM resolution. FEMA did this to create a consistent FFRMS data set nationwide. The primary sources of data used by FEMA to develop the FFRMS floodplain were USGS’s The National Map for terrain and FEMA’s National Flood Hazard Layer for regulatory water surface elevations. In Riverine areas, the FFRMS floodplain is derived from the National Flood Hazard Layer using the methodology developed by the Maryland Department of the Environment for production of the Riverine CRAB using a linear interpolation applied between two riverine cross sections from the National Flood Hazard Layer (FEMA 100-year floodplain). A freeboard (2-feet for the 2-foot freeboard map, and 3-feet for the 3-foot freeboard map) was added to the 100-year floodplain water surface elevation at each riverine cross section in the National Flood Hazard Layer data set. The new elevations were then interpolated to create a freeboard elevation gradient. This elevation gradient was used to map the horizontal extents of the FFRMS floodplain boundary based on topographic data. Additional details can be found here: https://www.fema.gov/sites/default/files/documents/fema_ffrms-data-methodology.pdfThe methodology to develop the coastal FFRMS floodplain (water surface) raster utilized the coastal static Base Flood Elevation values from the National Flood Hazard Layer as the water surface. To create an expanded surface, these water surface values were collected at uniformly spaced points along the boundary of the effective coastal 1%-annual-chance flood mapping and expanded inland utilizing Thiessen polygons. The Thiessen polygons were converted to a raster matching the spatial resolution of the terrain DEM, and the difference between the water surface elevation raster and the terrain was computed. Areas where the water surface raster value was higher than the terrain raster value indicate the area of flooding. Each freeboard value (2-foot and 3-foot) was mapped independently in whole foot increments utilizing the prior, lower freeboard value boundary of flooding to establish an expanded surface. One limitation of this methodology is that wave runup was modeled for the 100-year floodplain but not remodeled at the higher water surface elevation conditions.Please note that the CRAB data set shows inundation depths derived as the height difference between the DEM and the water surface elevation, whereas the FFRMS shows water surface elevations only. Their horizontal extents may be compared, but users should be careful comparing any color coding or depth values provided in the two applications.

These data represent a subset of the data available on the paper Flood Insurance Rate Maps (FIRM) as provided by the Federal Emergency Management Agency (FEMA). The Q3 flood data were developed to support floodplain management and planning activities but do not replace the official paper FIRMs. These data are not suitable for engineering applications or site work nor can the data be used to determine absolute delineation of flood boundaries. Instead the data should be used to portray zones of uncertainty and possible risks associated with flooding. ** This service includes data only for areas for which the newer National Flood Hazard Layer are not available.  ** Feature service is also available.

National Flood Hazard Layer