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Temporal coverage: Current calendar year - Today Data Sources: VIIRS Sensor on NOAA-20 Time Interval: Every 12 Hours Data Latency: ~ 9 - 12 hours from satellite overpass Projection: EPSG:4326 Spatial Extent: Contiguous United States, Alaska, and Canada. Portions of Alaska west of 169 Degrees west and portions of Canada north of 75 degrees north are excluded. Resolution: Vector data derived from the 375 m nadir resolution of the VIIRS sensors Purpose / use case: The FEDS algorithm is an “event-based” approach that tracks the growth and intensity of individual fires every 12 hours and treats them as discrete objects that persist, expand, and evolve together. This tracking strategy allows for detailed monitoring of large fires in space and time as they spread across a landscape. The algorithm clusters VIIRS active fire observations into specific fire events, and then estimates a fire perimeter polygon, the active segment of the perimeter, and metrics for each fire, such as the total estimated area (km^2) and length of the active fire front (km). Each 12-hour increment also includes information on a fire’s mean Fire Radiative Power (FRP), which is a metric that describes fire intensity as estimated by the satellite sensor. This dataset is best used for near real-time situational awareness and retrospective scientific analysis of fire behavior. Notes on Interpretation of the Data: As a satellite-based data product, FEDS has lower spatial resolution and accuracy than fire perimeters derived in operational contexts, such as those from airborne imagers. However, FEDS can provide information when other sources are unavailable, and the algorithm runs automatically in fixed 12-hr intervals for all clusters of fire activity greater than 4 km^2. The FEDS algorithm inherits the same uncertainties associated with the VIIRS active fire product. These include potential geolocation errors that can be relevant for interpreting fire locations and fire spread. For example, hot plumes of smoke can generate false positive detections that overestimate the size of the fire at that time step. In addition, active fire detections may be clustered into events that do not align directly with operational definitions of fire events (i.e., FEDS may group multiple agency-designated fires into a single fire event, or consider a single agency-designated fire to be multiple fire events). Finally, clouds or thick smoke may lead to the omission of VIIRS active fire detections, leading to an underestimate of fire activity and fire spread. More information on the interpretation of the VIIRS Active Fire Dataset for fires can be found in the NASA Fire Information for Resource Management System (FIRMS) FAQ. Source information/ Program Information: This dataset was developed as part of the NASA Earth Information Systems (EIS) project. This dataset was generated through the Wildfire Tracking Lab –a collaboration between scientists at NASA Goddard, NASA Langley, NASA Marshall, University of California Irvine, and University of Maryland. Citation: Chen et al. “California wildfire spread derived using VIIRS satellite observations and an object-based tracking system.” Scientific data 9.1 (2022): 249 https://doi.org/10.1038/s41597-022-01343-0. Repository: The underlying algorithm that generates these data layers is hosted and developed at the Fire Events Data Suite repository. Parameters: Column Name: t Description: Time of VIIRS detection. Data collected during approximately 1:30 AM and 1:30 PM local solar time overpasses. The time component of the datetime is assigned a local time of 00:00:00 for 1:30 AM overpasses, and 12:00:00 for 1:30 PM overpasses. Unit is Datetime. yyyy-mm-ddThh:mm:ss. Time component is in local solar time. Date component is the UTC date. UTC date will match local date for all of CONUS and Canada, but will not match for Alaska. Column Name: region Description: Descriptive name of larger region where tracking is occurring. Column Name: fireid Description: Fire ID. Unique for each fire within a given region. Unit is Numeric ID Column: geometry Description: The location of the pixels. Unit is Geometry. CRS is EPSG:4326. Column: Primary Key Description: Globally unique identifier for a given layer entry. This key is formatted by [region]|[fireid]|[timestamp]. Unitless. Alternate Forms Of Data Access: This data can also be accessed through an API. Documentation for the API can be found here. The API distributes data in open source data formats. Contact: For inquiries about service performance please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum. For questions about the data, please contact the science team with this form.
Dates of Images: July 9, 2025 Date of Next Image: None Expected Summary: These maps show the results of a machine learning classification applied to UAVSAR data to identify open water flooding and inundation under tree canopy. Colored areas in the image show detected flooding, where the color shows the type of landcover which was flooded (see legend below). Non-flooded areas are transparent. The classification uses a U-Net machine learning algorithm which was trained using UAVSAR data from previous flood events. UAVSAR provides polarimetric synthetic aperture radar data from which different types of scattering mechanisms can be observed, which provides information on the type of flooding. For example, flooding underneath vegetation produces strong double-bounce scattering from the water surface and tree trunks. However, strong double-bounce scattering from urban areas aligned with the radar viewing direction, such as in Austin, TX, can produce erroneous classifications. Non-flooded vegetation is generally dominated by volume scattering from the forest canopy. In comparison, open water flooding exhibits weak radar returns in all polarizations. Suggested Use: Classified Images: Blue = Open Water Yellow = Flooded Developed Green = Flooded Vegetation Orange = Flooded Crop Satellite/Sensor: UAVSAR airborne L-band synthetic aperture radar (SAR) aboard a NASA Gulfstream C-20A jet. Resolution: 10 meters Credits: UAVSAR data courtesy of NASA/JPL-Caltech Esri REST Endpoint: See URL section on right side of the page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202507_FLOOD_TX/202507_UAVSAR/MapServer/WMSServer Data Download: https://uavsar.jpl.nasa.gov/cgi-bin/download-files.pl
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
Date of Image: 4/14/2026, 4/16/2026, 4/18/2026 Summary ICEYE US Backscatter is derived from scaling factors provided with ICEYE Single Look Complex (SLC) and/or Ground Range Detected (GRD) products. In the SLC product all original sensor data is retained and offers full resolution in the azimuth and range directions. Data in the GRD product is oriented in the natural range-azimuth orientation and projected to the ground surface using an Earth ellipsoid mode and may be multi-looked to reduced speckle. The units for this product are decibels (dB). Resolution: Sub 1 meter. Credits NASA Disasters Program, Processing from MSFC, ICEYE US. For derived products: “Includes copyrighted material of ICEYE US. All rights reserved.” This work utilized data made available through the NASA Commercial Satellite Data Acquisition (CSDA) program.
This table describes each metric in the Brisbane City Council dataset called Flood — Awareness — Property Parcel Metrics. Definitions for this dataset: AEP – Annual Exceedance Probability (AEP) is the probability of a flood event of a given size occurring in any one year, usually expressed as a percentage annual chance. FWPR – Floodwise Property Report (FWPR), Council's FloodWise Property Reports provide property specific flood information to enable you to plan and build in accordance with the correct requirements. Holding – A property holding is a Council-defined and managed information entity. Its boundaries are generally based on land parcels. A property holding may consist of one or multiple land parcels. Parcel – Land Parcels are the building blocks of Council properties. Land parcels (also called lots) are mapped and the title details shown on a Plan of Subdivision. The parcel is a graphical representation of surveyed boundaries together with identifiers such as Lot/Plan description and house numbers. Information about flood types can be found in open data by using this search on the Brisbane City Council open data website and opening the required dataset.
Storm surge hazard is modeled only along the coastline. The process involves generating a grid along the coastline and 5km inland. In fact, no backwater effects were modeled, this is only due to the continent’s contour map (shapefile) version used to create the grid mentioned on the storm surge hazard map indicates the mean run-up height on the coast for seven return periods: 10, 25, 50, 100, 250, 500, 1000 years. The map has coastal coverage with points in the countries with storm surge hazard coastline. The data were classified into three hazard categories—low, moderate, and high—based on storm surge run-up heights for a 250-year return period, a widely used standard for coastal hazard zoning and infrastructure risk assessment. Run-up values below 0.1 meters are considered as “Low”; run-up values between 0.1 meters and 2 meters (inclusive) are considered as “Moderate”; run-up values greater than 2 meters are considered as “High”. In this project Mediterranean tropical-like cyclones were not included in the hazard assessment process. We considered only tropical cyclones as storms that develop in areas of low pressure over tropical oceans, and that are associated with several hazards such as strong winds and storm surge. These two hazards were assessed at a global level but given that although tropical cyclones are formed along the North Atlantic Ocean but do not occur in Europe, neither strong wings nor storm surge were modeled for European countries. Background paper: Multi-hazard Disaster Risk Model of Infrastructure and Buildings at the Global Level (2023). Cardona, O.D., Bernal, G.A., Villegas, C.P., Molina, J.F., Herrera, S.A., Marulanda, M.C., Rincón, D.F., Grajales, S., Marulanda, P.M., Gonzalez, D., Maskrey, A. (2023). https://giri.unepgrid.ch/sites/default/files/2023-11/2.4-INGENIAR-CDRI-Background-Report-Risk-model.pdf Release date: Fri Jun 30 2023 The layer was clipped to the geographic boundaries of 1) IDB Borrowing Member Countries, 2) Caribbean Development Bank (CDB) Borrowing Countries, 3) Organization of Eastern Caribbean States (OECS) Member Countries.
Current and recent droughts alerts from the Global Disaster Alert and Coordination System (GDACS), a cooperation of the United Nations and the European Commission, with alert level, severity and affected population.
Current and recent wildfires alerts from the Global Disaster Alert and Coordination System (GDACS), a cooperation of the United Nations and the European Commission, with alert level, severity and affected population.
Current and recent volcanic eruptions alerts from the Global Disaster Alert and Coordination System (GDACS), a cooperation of the United Nations and the European Commission, with alert level, severity and affected population.
Update Frequency:Twice a day Summary:The global Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to inform a wide range of users about landslide hazards. LHASA version 2.1 uses machine learning to show where and when landslides are most probable. Suggested Use:LHASA 2.1 was created to enable global monitoring of landslide hazard and is not a substitute for local site investigations. Red indicates high hazard and blue indicates low hazard. LHASA nowcast "tomorrow": This map shows the relative probability of landslide occurrence tomorrow, based on rainfall estimates from GPM and forecasts from GEOS-FP. Satellite/Sensor:GPM, SMAP Resolution:30 arcseconds (~1 kilometer) Credits:NASA GSFC Users are requested to cite the following paper:Stanley, T. A., D. B. Kirschbaum, G. Benz, et al. 2021. "Data-Driven Landslide Nowcasting at the Global Scale." Frontiers in Earth Science, 9: [10.3389/feart.2021.640043]
Date of Images: 05/18/2024 Summary: The visible-wavelength true color RGB digital camera images were taken by astronauts on board the International Space Station showing flooding in southeastern Texas. These images were manually georeferenced by members of the Earth Science and Remote Sensing Unit at NASA Johnson Space Center. Suggested Use: This image provides regional context, and may be useful for visualization of the ongoing event. Higher spatial resolution images may be suitable for spatial analysis to support decision making or research applications. Sensor: Nikon Z9 Electronic Still Camera. Additional Information about the astronaut photography from the ISS. Credits: International Space Station Program, NASA Johnson Space Center Earth Science and Remote Sensing Unit, ARES Division, Exploration Architecture, Integration & Science Directorate Data Download: Full datasets are available for download at the Gateway to Astronaut Photography of Earth. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=c9bb2094473649b2a3802305f4fc8eb1 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_TX/2408_iss/ImageServer/WMSServer
This collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the gNATSGO Rasters Collection. The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase. SSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods. The gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years. See the official documentation
This collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the gNATSGO Tables Collection. Many items have commonly used values encoded in additional raster assets. The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase. SSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods. The gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years. See the official documentation
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.
Monaghan County Council commissioned a Landscape Character Assessment of the County in 2008 with the purpose of providing the foundation for policy formation and decision making for landscape management. The Landscape Character Assessment identified 31 landscape character types in County Monaghan which represent geographical areas with a particular landscape type or types. The Landscape Character Assessment identified the main forces for landscape change in the present age as, changing agricultural practices, forestry, rural housing, infrastructural developments, power lines, telecommunications masts, wind farms, quarrying and tourism/recreation. This data is in ITM Projection. Monaghan County Council provides this information with the understanding that it is not guaranteed to be accurate, correct or complete. Monaghan County Council accepts no liability for any loss or damage suffered by those using this data for any purpose.
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
Monaghan County Council commissioned a Landscape Character Assessment of the County in 2008 with the purpose of providing the foundation for policy formation and decision making for landscape management. The Landscape Character Assessment identified 9 landscape character areas in County Monaghan which represent geographical areas with a particular landscape type or types. The Landscape Character Assessment identified the main forces for landscape change in the present age as, changing agricultural practices, forestry, rural housing, infrastructural developments, power lines, telecommunications masts, wind farms, quarrying and tourism/recreation. This data is in ITM Projection. Monaghan County Council provides this information with the understanding that it is not guaranteed to be accurate, correct or complete. Monaghan County Council accepts no liability for any loss or damage suffered by those using this data for any purpose.
The National Flood Hazard Layer (NFHL) dataset represents the current effective flood risk data for those parts of the country where maps have been modernized by the Federal Emergency Management Agency (FEMA). The data in this service was published by FEMA as of July 18, 2025 (Latest Study Effective Date = 07/08/2025; Latest LOMR Effective Date = 07/09/2025). Also see the Feature Service and Tile service. See full details.
1. Descripción Componente oficial del Geoportal IDE Ambiente con las Reservas de Biósfera. Dentro de ECU_MOD se conservan Podocarpus-El Cóndor, Macizo del Cajas, Yasuní y Sumaco. 2. Fuente Geoportal IDE Ambiente; diccionario HC002_RESERVA_BIOSFERA.pdf; UNESCO Man and the Biosphere Programme. 3. Procedimiento gportal Fuente HC002: 8 reservas nacionales; 4 intersectan ECU_MOD. Se mantienen individualmente. Se eliminaron código interno y escala, se normalizaron nombres/provincias, se conservan fecha de creación e informe periódico tal como figuran en la fuente, y se recalculó Area_ha tras el recorte. La fecha 2024-11-10 consignada para Sumaco se preserva como dato fuente y se documenta como discrepancia frente a UNESCO, que registra Sumaco como reserva designada en 2000. 4. Control y observaciones Registros originales: 8 Registros finales: 4 Dissolve: No aplicado Discrepancia Sumaco: La fuente consigna 2024-11-10; UNESCO registra designación en 2000. Se preserva el dato fuente y se documenta. CRS final: WGS 84 (EPSG:4326) 5. Referencias https://ide.ambiente.gob.ec/mapainteractivo/ https://www.unesco.org/en/mab https://www.unesco.org/en/mab/yasuni https://www.unesco.org/en/mab/podocarpus-el-condor
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
Temporal coverage: Current calendar year - Today Data Sources: VIIRS Sensor on NOAA-20 Time Interval: Every 12 Hours Data Latency: ~ 9 - 12 hours from satellite overpass Projection: EPSG:4326 Spatial Extent: Contiguous United States, Alaska, and Canada. Portions of Alaska west of 169 Degrees west and portions of Canada north of 75 degrees north are excluded. Resolution: Vector data derived from the 375 m nadir resolution of the VIIRS sensors Purpose / use case: The FEDS algorithm is an “event-based” approach that tracks the growth and intensity of individual fires every 12 hours and treats them as discrete objects that persist, expand, and evolve together. This tracking strategy allows for detailed monitoring of large fires in space and time as they spread across a landscape. The algorithm clusters VIIRS active fire observations into specific fire events, and then estimates a fire perimeter polygon, the active segment of the perimeter, and metrics for each fire, such as the total estimated area (km^2) and length of the active fire front (km). Each 12-hour increment also includes information on a fire’s mean Fire Radiative Power (FRP), which is a metric that describes fire intensity as estimated by the satellite sensor. This dataset is best used for near real-time situational awareness and retrospective scientific analysis of fire behavior. Notes on Interpretation of the Data: As a satellite-based data product, FEDS has lower spatial resolution and accuracy than fire perimeters derived in operational contexts, such as those from airborne imagers. However, FEDS can provide information when other sources are unavailable, and the algorithm runs automatically in fixed 12-hr intervals for all clusters of fire activity greater than 4 km^2. The FEDS algorithm inherits the same uncertainties associated with the VIIRS active fire product. These include potential geolocation errors that can be relevant for interpreting fire locations and fire spread. For example, hot plumes of smoke can generate false positive detections that overestimate the size of the fire at that time step. In addition, active fire detections may be clustered into events that do not align directly with operational definitions of fire events (i.e., FEDS may group multiple agency-designated fires into a single fire event, or consider a single agency-designated fire to be multiple fire events). Finally, clouds or thick smoke may lead to the omission of VIIRS active fire detections, leading to an underestimate of fire activity and fire spread. More information on the interpretation of the VIIRS Active Fire Dataset for fires can be found in the NASA Fire Information for Resource Management System (FIRMS) FAQ. Source information/ Program Information: This dataset was developed as part of the NASA Earth Information Systems (EIS) project. This dataset was generated through the Wildfire Tracking Lab –a collaboration between scientists at NASA Goddard, NASA Langley, NASA Marshall, University of California Irvine, and University of Maryland. Citation: Chen et al. “California wildfire spread derived using VIIRS satellite observations and an object-based tracking system.” Scientific data 9.1 (2022): 249 https://doi.org/10.1038/s41597-022-01343-0. Repository: The underlying algorithm that generates these data layers is hosted and developed at the Fire Events Data Suite repository. Parameters: Column Name: t Description: Time of VIIRS detection. Data collected during approximately 1:30 AM and 1:30 PM local solar time overpasses. The time component of the datetime is assigned a local time of 00:00:00 for 1:30 AM overpasses, and 12:00:00 for 1:30 PM overpasses. Unit is Datetime. yyyy-mm-ddThh:mm:ss. Time component is in local solar time. Date component is the UTC date. UTC date will match local date for all of CONUS and Canada, but will not match for Alaska. Column Name: region Description: Descriptive name of larger region where tracking is occurring. Column Name: fireid Description: Fire ID. Unique for each fire within a given region. Unit is Numeric ID Column Name: pixden Description: Number of total pixels (n_pixels) divided by area of fire perimeter. Unit is pixels/Km^2 Column Name: duration Description: Number of days since first observation of fire. Fires with a single observation have a duration of zero. Unit is Days. Column Name: flinelen Description: Length of active fire segment, based on new pixels. If no new pixels are detected, flinelen is set to zero. Unit is Km. Column: fperim Description: Length of fire perimeter. Unit is Km. Column: farea Description: Area within fire perimeter. Unit is Km^2 Column Name: meanfrp Description: Mean fire radiative power for detected active fire pixels. The sum of the fire radiative power divided by the number of newly detected pixels at each time step. If no new pixels are detected, meanfrp is set to zero for that timestep. Units are MW/pixel. Column: n_newpixels Description: Number of pixels newly detected since last overpass. Unit is pixels. Column: n_pixels Description: Number of total pixel-detections in history of fire. Unit is pixels. Column: isactive Description: Boolean indicator of recent fire activity. If no new detections occur within N days, this is marked as “False” The number of days is defined regionally, and has a default of 5. See repository information. Unit is Boolean. Column: geometry Description: The shape of the perimeter. Unit is Geometry. Can be a Polygon or Multipolygon. CRS is EPSG:4326. Column: Primary Key Description: Globally unique identifier for a given layer entry. This key is formatted by [region]|[fireid]|[timestamp]. Unitless. Alternate Forms Of Data Access: This data can also be accessed through an API. Documentation for the API can be found here. The API distributes data in open source data formats. Contact: For inquiries about service performance please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum. For questions about the data, please contact the science team with this form.
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: October 29, 2025; October 30, 2025; October 31, 2025; November 1, 2025; November 2, 2025; November 4, 2025; November 7, 2025; November 8, 2025; November 9, 2025. Pre-Event: September 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. REST Endpoint: See URL on the right side of the page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_HURRICANE_MELISSA/202511_blackmarble_brdf_cloud/ImageServer/WMSServer
Update Frequency:Twice a day Summary:The global Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to inform a wide range of users about landslide hazards. LHASA version 2.1 uses machine learning to show where and when landslides are most probable. Suggested Use:LHASA 2.1 was created to enable global monitoring of landslide hazard and is not a substitute for local site investigations. Red indicates high hazard and blue indicates low hazard. LHASA nowcast "yesterday": This map shows the relative probability of landslide occurrence within the last day, based on rainfall estimates from GPM. Satellite/Sensor:GPM, SMAP Resolution:30 arcseconds (~1 kilometer) Credits:NASA GSFC Users are requested to cite the following paper:Stanley, T. A., D. B. Kirschbaum, G. Benz, et al. 2021. "Data-Driven Landslide Nowcasting at the Global Scale." Frontiers in Earth Science, 9: [10.3389/feart.2021.640043]
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.
Polygon features marking architectural conservation areas, published by Galway City Council. Each feature records an area with its computed area and perimeter length.
Line features representing views and prospects, each labelled as a view and recording its length. Published by Galway City Council and covering Ireland.
Date of Images: During Event: 5/6/2024 Pre-Event: 4/21/2024 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 and California Institute of Technology derived the surface water extent flood maps using the OPERA Dynamic Surface Water Extent (DSWx) from NASA Harmonized Landsat Sentinel-2 (HLS) products. The results posted here are preliminary and unvalidated results, primarily intended to aid the field response and people who wanted to have a rough first look at the inundation extent. ARIA/OPERA flood map derived from DSWx-HLS The ARIA/OPERA flood map is derived from two OPERA DSWx-HLS images taken on April 21, 2024 and May 06, 2024. These maps depict areas of new water detection that is interpreted as flood. The flood map was created by reclassifying the DSWx-HLS data into two new classes (1) water and (2) not water then taking the difference between the two images. The new water class includes DSWx-HLS classes for open water, partial surface water, and HLS snow/ice. We note the HLS snow/ice mask often misclassified sediment rich water as snow/ice. This reclassification was necessary to capture flood extent. The new not water class includes DSWx-HLS classes not water and HLS cloud/cloud shadow. OPERA DSWx-HLS OPERA DSWx-HLS data was used to identify surface water using the B01_WTR layer. Two images were examined 1) April 21, 2024 and 2) May 6, 2024. Each image consists of multiple MGRS tiles that were merged together for a composite image saved as a GeoTIFF file. OPERA DSWx-HLS data availability The post-processed products are available to download at https://aria-share.jpl.nasa.gov/202405-RioGrandeSul_Brazil-floods/. The OPERA DSWx-HLS products have been in production since April 2023, 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 OPERA project and other products, visit https://www.jpl.nasa.gov/go/opera. 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 Suggested Use: The OPERA DSWx-HLS Water product classifies the Harmonized Landsat Sentinel-2 (HLS) input imagery into "not water", "open surface water", and “partial surface water”. The "HLS cloud/cloud shadow" and "HLS snow/ice" layers are direct inputs from the HLS FMask. Areas with "open water" are dark blue and "partial surface water" are light blue in the OPERA WTR layer. Areas with clouds or cloud shadows are light gray. An area identified as cloud, cloud shadow, or adjacent to cloud/cloud shadow according to input HLS quality assurance (QA) data. Areas with no water detected are white. An area with valid data that is not water, snow/ice, cloud/cloud shadow, or ocean masked. This layer is meant to provide users with a quick view for water/no-water. Invalid data classes (cloud/cloud shadow along with adjacent to cloud/cloud shadow) are also provided to indicate areas in which the classification does not provide water/no-water classification. Note: Sediment rich water is sometimes misclassified as snow/ice by the HLS QA mask. For more information about how the OPERA DSWx-HLS Water product classifies data: https://d2pn8kiwq2w21t.cloudfront.net/documents/ProductSpec_DSWX_URS309746.pdf Satellite/Sensor: Harmonized Landsat Sentinel-2 (HLS) MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 30 meters Credits: NASA JPL-Caltech ARIA Team, NASA, NASA Disasters Program Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_BRAZIL/SENTINEL2/ImageServer/WMSServer Data Download: https://aria-share.jpl.nasa.gov/202405-RioGrandeSul_Brazil-floods/DSWx-HLS/
Current and recent tropical cyclones alerts from the Global Disaster Alert and Coordination System (GDACS), a cooperation of the United Nations and the European Commission, with alert level, severity and affected population.
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 Post-event: 2026-06-21, 2026-06-28 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 Louisiana using the OPERA Dynamic Surface Water Extent from Sentinel-1 (DSWx-S1) product. 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) and Binary Water (BWTR) 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. 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 (2026), 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 ==================