United States of America environment data
＋ Follow new data141 datasets about environment in United States of America.
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 are derived from a dataset produced by Forest Trends' Ecosystem Marketplace containing points and polygons depicting the location of market-based programs and projects addressing ecosystem services protection and their enabling conditions in the United States. This dataset contains polygons and points representing the location of markets and projects primarily targeting forest and land-use carbon-related ecosystem services per Ecosystem Marketplace research. Polygons for projects represent the area of primary impact. Polygons for markets represent the entire region that shares market infrastructure and rules, or a larger program encompassing many smaller projects. Points represent the centroids of forest and land-use carbon markets and lat/lon locations of forest and land-use carbon projects with the exception of national markets, whose points represent administrative headquarters locations.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).
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 are derived from a dataset produced by Forest Trends' Ecosystem Marketplace containing points and polygons depicting the location of market-based programs and projects addressing ecosystem services protection and their enabling conditions in the United States. This dataset contains polygons and points representing the location of markets and projects primarily targeting forest and land-use carbon-related ecosystem services per Ecosystem Marketplace research. Polygons for projects represent the area of primary impact. Polygons for markets represent the entire region that shares market infrastructure and rules, or a larger program encompassing many smaller projects. Points represent the centroids of forest and land-use carbon markets and lat/lon locations of forest and land-use carbon projects with the exception of national markets, whose points represent administrative headquarters locations.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).
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 are derived from a dataset produced by Forest Trends' Ecosystem Marketplace containing points and polygons depicting the location of market-based programs and projects addressing ecosystem services protection and their enabling conditions in the United States. 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).
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 are derived from a dataset produced by Forest Trends' Ecosystem Marketplace containing points and polygons depicting the location of market-based programs and projects addressing ecosystem services protection and their enabling conditions in the United States. This dataset contains polygons and points representing the location of markets and projects primarily targeting forest and land-use carbon-related ecosystem services per Ecosystem Marketplace research. Polygons for projects represent the area of primary impact. Polygons for markets represent the entire region that shares market infrastructure and rules, or a larger program encompassing many smaller projects. Points represent the centroids of forest and land-use carbon markets and lat/lon locations of forest and land-use carbon projects with the exception of national markets, whose points represent administrative headquarters locations.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).
FEMA provides access to the National Flood Hazard Layer through a Web Map Service. The National Flood Hazard Layer is a computer database that contains FEMA's flood hazard map data. The data depict flood hazard information and supporting data used to develop the information. The primary hazard classifications are the 1 percent-annual-chance flood event, the 0.2 percent-annual-chance flood event, and areas of minimal flood hazard. Flood hazard and supporting data are developed using specifications for horizontal control consistent with 1:12,000-scale mapping. If you plan to display maps from the National Flood Hazard Layer with other map data for official purposes, ensure that the other information meets FEMA's standards for map accuracy. The NFHL data are from FEMA's Digital Flood Insurance Rate Map (DFIRM) databases. New data are added continually. The NFHL also contains changes to DFIRM data made by Letters of Map Revision (LOMRs). The service provides map images geo-referenced to the earth's surface using the North American Datum of 1983, Geodetic Reference System 80 coordinate system. The service also provides information that complements the National Flood Hazard Layer. In areas for which data are not available from the National Flood Hazard Layer, an older set of flood hazard data, named the "Q3" data, may be available. The service also provides information about the availability of flood data and maps, the national map panel scheme, river subbasins, and point locations for Letters of Map Amendment (LOMA) and Letters of Map Revision Based on Fill (LOMR-Fs). These complementary data are positioned less accurately than are the National Flood Hazard Layer data. The service organizes the data in layers. The layers display information at map scales appropriate for the data. Status information and river subbasins are displayed at map scales smaller than 1:300,000, regional overviews at map scales between 1:300,000 and 1:32,000, and detailed flood hazard maps at map scales of 1:32,000 and larger. The "Scalehint" item in the Capabilities file for the Web Map Service encodes the scale range for maps for a layer. Flood hazard areas identified on the Flood Insurance Rate Map are identified as a Special Flood Hazard Area (SFHA). SFHA are defined as the area that will be inundated by the flood event having a 1-percent chance of being equaled or exceeded in any given year. The 1-percent annual chance flood is also referred to as the base flood or 100-year flood. SFHAs are labeled as Zone A, Zone AO, Zone AH, Zones A1-A30, Zone AE, Zone A99, Zone AR, Zone AR/AE, Zone AR/AO, Zone AR/A1-A30, Zone AR/A, Zone V, Zone VE, and Zones V1-V30. Moderate flood hazard areas, labeled Zone B or Zone X (shaded) are also shown on the FIRM, and are the areas between the limits of the base flood and the 0.2-percent-annual-chance (or 500-year) flood. The areas of minimal flood hazard, which are the areas outside the SFHA and higher than the elevation of the 0.2-percent-annual-chance flood, are labeled Zone C or Zone X (unshaded). Flood Zone Definitions from the FEMA Flood Map Service Center. Data Download: https://www.floodmaps.fema.gov/NFHL/status.shtml
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://www.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/
**ADAM ID: 1001000\_7** Cyclone (tropical storm) during the period Aug 26 2023-Sep 02 2023 in United States. It impacted 494630 people.
The NFHL is a nationwide dataset containing FEMA’s effective flood hazard map data, designed to support risk analysis and decision-making. It integrates information from FEMA’s Flood Insurance Rate Maps (FIRMs), Flood Insurance Studies (FIS), and Letters of Map Change (LOMCs). This service includes: Flood Hazard Zones – Identifies areas classified by flood risk, including Special Flood Hazard Areas (SFHAs), regulatory floodways, and moderate-to-low-risk flood zones. Base Flood Elevations (BFE) – Displays elevation levels for the 1% annual chance flood event. Coastal Flood Hazard Areas – Represents zones affected by coastal flooding and wave action. LOMC Data – Includes Letters of Map Amendment (LOMA) and Letters of Map Revision (LOMR) that refine floodplain boundaries. The NFHL service is updated as new flood hazard data becomes available, ensuring that users have access to the most current flood risk information. The data is essential for urban planning, floodplain management, infrastructure development, and emergency preparedness within the City of Fair Oaks Ranch.
The visible-wavelength true color RGB digital camera images were taken by astronauts aboard the International Space Station showing areas affected by the severe storms in April 2025 in Arkansas and Mississippi. Date of Images: April 15, 2025 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. Focal Length: 78mm: 1001007, 1001013 & 1001016 170mm: 1001022, 1001025 & 1001028 380mm: 1001034 500mm: 1001037 - 1001079 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 Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=62c685fdb65347e6a3847c8a9a9cf55e WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202504_SEVEREWX_SOUTHEASTUS/2505_iss/ImageServer/WMSServer Data Download: Full datasets are available for download at the Gateway to Astronaut Photography of Earth. Download for this specific data can be found at: https://eol.jsc.nasa.gov/WebShare/WebShare.pl?folder=NASADisastersProgram%2FNDP_TornadoesSouthernUS_15APRIL2025
Date of Image(s): Post-Event: July 8,2025; July 9, 2025; July 10, 2025; July 31, 2025; August 3, 2025 Pre-Event: June 2, 2025; June 16, 2025; June 17, 2025 Date of Next Image: Unknown Summary: Normalized Differential Vegetation Index (NDVI) maps are produced by converting pixel values to NDVI = (Red – NIR)/(Red + NIR), NIR indicates the near infrared band. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. NDVI change was created by subtracting the cloud-masked post-event NDVI from the cloud-masked pre-event NDVI to show areas of potentially impacted vegetation as negative values (darker shades) and healthy/unimpacted vegetation in positive values (lighter shades). NDVI change can give insights to impacts from an event (like the scouring of vegetation from the riverbeds) but can also show changes in unrelated areas like the clearing of agriculture fields. This layer is a binary NDVI change polygon (threshold of areas where NDVI decreased by -0.25 or more) for July 8 and 9 as compared to Pre-event imagery on June 17. Suggested Use: NDVI: Dark green colors are areas with a lot of green leaf growth which indicates the presence of chlorophyll. Chlorophyll reflects more infrared light and less visible light. Areas with some green leaf growth are in light greens, and areas with little to no vegetation growth are even lighter greens. NDVI change: Commonly used to distinguish healthy/active vegetation (positive values) from potentially unhealthy/impacted vegetation (negative values). Gaps in the dataset indicate clouds in the Planet imagery used to create this product. Satellite/Sensor: PlanetScope. This product includes copyrighted material of Planet Labs PBC. All rights reserved. Resolution: 3 meters Credits: NASA Disasters Program, NASA Marshall Space Flight Center (MSFC). PlanetScope: Includes copyrighted material of Planet Labs PBC. All rights reserved. Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://maps.disasters.nasa.gov/ags03/services/texas_flood_202507/planet_binaryNDVI/MapServer/WMSServer Data Download: https://maps.disasters.nasa.gov/download/gis_products/event_specific/2025/texas_flood_202507/NDVIchange/
Dates of Images: Post-Event: May 20, 2026; May 19, 2026 Pre-Event: May 7, 2026; April 20, 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 Sentinel2- 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: 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: Copernicus Sentinel-2 Resolution: 10 meters Credits: NASA/MSFC, USGS Service URL: See REST Endpoint to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/gis05/services/DISASTERS_202605_FIRE_CA/202609_sentinel2_nbr/ImageServer/WMSServer
Wells are defined by 21 DCMR Chapter 18 as any test hole, shaft, or soil excavation created by any means including, but not limited to, drilling, coring, boring, washing, driving, digging, or jetting, for purposes including, but not limited to, locating, testing, diverting, artificially recharging, or withdrawing fluids, or for the purpose of underground injection. The Department of Energy & Environment's Regulatory Review Division reviews permit applications to install wells in private and public space through the Department of Licensing and Consumer Protection and the District Department of Transportation.
Rasters/SoilLoss_ExistingLandCover
Dates of Images: Post-Event: June 29, 2026; July 2, 2026; July 4, 2026 Pre-Event: June 19, 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 Sentinel2- 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: 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: Copernicus Sentinel-2 Resolution: 10 meters Credits: NASA/MSFC, USGS Service URL: See individual Layers. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202607_FIRE_CO/sentinel2_dnbr/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 are derived from a dataset produced by Forest Trends' Ecosystem Marketplace containing points and polygons depicting the location of market-based programs and projects addressing ecosystem services protection and their enabling conditions in the United States. 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).
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: WMS Endpoint: Data Download: https://aria-share.jpl.nasa.gov/20250113-GreaterLosAngeles_Fires/
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 Table 2: Fire severity classification ranges for the Eaton fire 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 WMS Endpoint
Summary: Researchers working with the Global Land Analysis & Discovery (GLAD) at University of Maryland, College Park, Maryland and 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 maps of surface disturbance due to ongoing wildfires in eastern and central New Mexico in June 2025 using the OPERA Disturbance Alert from Harmonized Landsat Sentinel-2 (DIST-ALERT-HLS) products. The results posted here are preliminary results, primarily intended to aid the field response and people who wanted to have a rough first look at the 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. OPERA DIST-ALERT-HLS: The Disturbance product (DIST) maps per pixel vegetation disturbance (specifically, vegetation cover loss) from the Harmonized Landsat Sentinel-2 (HLS) scenes. We provide the (1) maximum vegetation anomaly value (VEG-ANOM-MAX) layer and (2) vegetation disturbance status (VEG-DIST-STATUS). All provided layers have been filtered to include only disturbance detected following the start of the Seven Springs Fire, near Mescalero, NM (starting June 17, 2025). DIST-HLS-ALERT data provided were generated on June 30, 2025 and are derived from data acquired by the Copernicus Sentinel-2C satellite on June 26, 2025 (Seven Springs) and June 24, 2025 (Trout). The images are provided as GeoTIFF files. VEG-ANOM-MAX: Difference between historical and current year observed vegetation cover at the date of maximum decrease (vegetation loss of 0-100%). This layer can be used to threshold vegetation disturbance per a given sensitivity (e.g. disturbance of >=20% vegetation cover loss). The sum of the historical percent vegetation and the anomaly value will be the vegetation cover estimate for the current year. VEG-DIST-STATUS: Indication of vegetation cover loss (vegetation disturbance). The status label is based on the maximum anomaly value, confidence level, and whether it is ongoing or finished. ''First'' means the pixel has had an anomaly detection but no subsequent observations whether anomalous or not. ''Provisional'' means there have been two consecutive disturbance detections but not yet with high confidence. ''Confirmed'' means that vegetation disturbance is detected with high confidence. The label ''finished'' is applied to confirmed disturbances that have had two consecutive no-anomaly observations or one 15 days or more after the last anomaly detection. If a new disturbance is detected, it will overwrite those in a ''finished'' state. These labels are reported for both above and below the 50% disturbance threshold based on the maximum anomaly value. Suggested Use: VEG-DIST-STATUS Layer values: 0: No disturbance (suggested color: #121212) 1: first 2: provisional 3: confirmed 4: first >=50% (suggested color: #008888) 5: provisional >=50% (suggested color: #e48727) 6: confirmed >=50% (suggested color: #e01b07) 7: confirmed 8: confirmed >=50%, finished (suggested color: #dddddd) 255: No data (suggested color: transparent) VEG-ANOM-MAX Layer values: 0-100: Maximum loss of percent vegetation (suggested color: white to red gradient) 255: No data (suggested color: transparent) The DIST-ALERT-HLS products included here have these flags: 255 represents ''No Data'' and is based on the Fmask layer of the source HLS granule. OPERA DIST-ALERT-HLS data availability: The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20250630_NewMexico_Fires/DIST-HLS/ The OPERA DIST-HLS products have been in production since January 2022, are freely distributed to the public via NASA's Land Processes Distributed Active Archive Center (LP DAAC), and can be downloaded through NASA's Earthdata search. For more information about the Surface Disturbance product suite, please refer to the DIST Product page: https://www.jpl.nasa.gov/go/opera/products/dist-product-suite/ For more information about the GLAD project, visit https://glad.umd.edu/ 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/ Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/B/C satellites and Operational Land Imager 2 (OLI-2) on NASA's Landsat 8/9 satellite. Resolution: 30 meters Credits: The product contains modified Copernicus Sentinel data (2025) and is produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group. Managed by NASA's Jet Propulsion Laboratory, OPERA funds and manages the DIST-ALERT-HLS product developed and produced by the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland. NASA JPL-Caltech-UMD ARIA/OPERA Team Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (mary.grace.p.bato@jpl.nasa.gov) Alexander L. Handwerger (alexander.handwerger@jpl.nasa.gov) Zhen Song (zhensong@umd.edu) Matt Hansen (mhansen@umd.edu) Steven Chan (steventsz.k.chan@jpl.nasa.gov) David Bekaert (david.bekaert@jpl.nasa.gov) ================== Service URL: WMS Endpoint: Data Download: Seven Springs Fire: OPERA-DIST-ALERT-HLS-VEG-ANOM-MAX_20250626.tif: June 26, 2025 showing the maximum vegetation anomaly. OPERA-DIST-ALERT-HLS-DIST-STATUS_20250626.tif: June 26, 2025 showing the status anomaly. Trout Fire: OPERA-DIST-ALERT-HLS-VEG-ANOM-MAX_20250624.tif: June 24, 2025 showing the maximum vegetation anomaly. OPERA-DIST-ALERT-HLS-DIST-STATUS_20250624.tif: June 24, 2025 showing the status anomaly. Last Update 30 June 2025
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 are derived from a dataset produced by Forest Trends' Ecosystem Marketplace containing points and polygons depicting the location of market-based programs and projects addressing ecosystem services protection and their enabling conditions in the United States. 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).
Dates of Images: Post-Event: 7/2/2026, 7/12/2026, 7/18/2026 Pre-Event: 6/10/2026. 6/12/2026 Date of Next Image: Unknown Summary: True Color: 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. Color Infrared: The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted from the fires. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Normalized Burn Ratio (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: True Color: provides a product of how the surface would look to the naked eye from space. 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. Color Infrared: depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. 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 8 Operational Land Imager (OLI), Landsat 9 Operational Land Imager (OLI-2) Resolution: 30 meters Esri REST Endpoint: See Layers below. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/rest/services/DISASTERS_202607_FIRE_CO/202614_landsat_nbr/ImageServer
"The FIRM is the basis for floodplain management, mitigation, and insurance activities for the National Flood Insurance Program (NFIP). Insurance applications include enforcement of the mandatory purchase requirement of the Flood Disaster Protection Act, which "... requires the purchase of flood insurance by property owners who are being assisted by Federal programs or by Federally supervised, regulated or insured agencies or institutions in the acquisition or improvement of land facilities located or to be located in identified areas having special flood hazards, " Section 2 (b) (4) of the Flood Disaster Protection Act of 1973. In addition to the identification of Special Flood Hazard Areas (SFHAs), the risk zones shown on the FIRMs are the basis for the establishment of premium rates for flood coverage offered through the NFIP. The FIRM Database presents the flood risk information depicted on the FIRM in a digital format suitable for use in electronic mapping applications. The FIRM Database serves to archive the information collected during the Flood Risk Project." FEMA National Flood Hazard Layer Metadata. The layers contained wherein are a merge between Hudson County's FIRM and Bergen County's FIRM. Area's of 'minimal flood risk' or those outside of the 100 and 500 year floodplain have been omitted.
THIS LAYER IS HOSTED BY FEMA, not the City of Pleasanton CA. This item references FEMA's live map service The National Flood Hazard Layer (NFHL) is a geospatial database that contains current effective flood hazard data. FEMA provides the flood hazard data to support the National Flood Insurance Program (NFIP). Data is updated by FEMA on an ongoing basis as studies and Letters of Map Change (LOMCs) go effective — this item always reflects FEMA's current live data, since it is not a static copy. You can view this information in FEMA's standalone viewer here: https://hazards-fema.maps.arcgis.com/apps/webappviewer/index.html?id=8b0adb51996444d4879338b5529aa9cd REST URL: https://hazards.fema.gov/arcgis/rest/services/public/NFHL/MapServer For more information, visit FEMA's NFHL page: https://www.fema.gov/flood-maps/national-flood-hazard-layer
Dates of Images: Post-Event: 6/29/2026, 7/01/2026, 7/02/2026, 7/04/2026, 7/6/2026, 7/9/2026, 7/11/2026, 7/14/2026 Pre-Event: 6/16/2026, 6/19/2026 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. 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. These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input images were generated from the Sentinel2- 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. Check fire containment and image dates for further context on image timing. Federal permitted grazing land maps from the US Forest Service and Department of Interior - Bureau of Land Management (BLM) are shown in the web map. For more information: https://data-usfs.hub.arcgis.com/datasets/usfs::range-unit-allotment-3/about & https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-colorado-grazing-allotments/about Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space. 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. A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. 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: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: True Color: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See layers below. WMS Endpoint: See individual layer pages.
Rasters/SoilLoss_NaturalVegRemoved
URL to Flood Hazard layer from FEMA's National Flood Hazard Layer (NFHL) Viewer.
The visible-wavelength true color RGB digital camera images were taken by astronauts aboard the International Space Station showing areas affected by tornadoes in the US such as the cities of Nashville, TN, Murfreesboro, TN, Huntsville, AL, and Bowling Green, KY. These images were manually georeferenced by members of the Earth Science and Remote Sensing Unit at NASA Johnson Space Center. Date of Images: March 17, 2025 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. Focal Length: 200mm. 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. Download for this specific data can be found at: https://eol.jsc.nasa.gov/WebShare/WebShare.pl?folder=NASADisastersProgram/NDP_TornadoesMidwesternUS_17MAR2025
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:
Researchers working with the Global Land Analysis & Discovery (GLAD) at University of Maryland, College Park, Maryland and 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 maps of surface disturbance due to ongoing wildfires in Los Angeles County in January 2025 using the OPERA Disturbance Alert from Harmonized Landsat Sentinel-2 (DIST-ALERT-HLS) products. The results posted here are preliminary results, primarily intended to aid the field response and people who wanted to have a rough first look at the 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. OPERA DIST-ALERT-HLS The Disturbance product (DIST) maps per pixel vegetation disturbance (specifically, vegetation cover loss) from the Harmonized Landsat Sentinel-2 (HLS) scenes. We provide the (1) maximum vegetation anomaly value (VEG-ANOM-MAX) layer and (2) vegetation disturbance status (VEG-DIST-STATUS). All provided layers have been filtered to include only disturbance detected following the start of the Greater Los Angeles wildfires (starting Jan. 7, 2025). Images are provided from January 12 and January 14, 2025. The images are provided as GeoTIFF files. VEG-ANOM-MAX: Difference between historical and current year observed vegetation cover at the date of maximum decrease (vegetation loss of 0-100%). This layer can be used to threshold vegetation disturbance per a given sensitivity (e.g. disturbance of >=20% vegetation cover loss). The sum of the historical percent vegetation and the anomaly value will be the vegetation cover estimate for the current year. VEG-DIST-STATUS: Indication of vegetation cover loss (vegetation disturbance). The status label is based on the maximum anomaly value, confidence level, and whether it is ongoing or finished. "First" means the pixel has had an anomaly detection but no subsequent observations whether anomalous or not. ''Provisional'' means there have been two consecutive disturbance detections but not yet with high confidence. ''Confirmed'' means that vegetation disturbance is detected with high confidence. The label ''finished'' is applied to confirmed disturbances that have had two consecutive no-anomaly observations or one 15 days or more after the last anomaly detection. If a new disturbance is detected, it will overwrite those in a ''finished'' state. These labels are reported for both above and below the 50% disturbance threshold based on the maximum anomaly value. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/B satellites and Operational Land Imager 2 (OLI-2) on NASA's Landsat 8/9 satellite. Resolution: 30 meters The DIST-ALERT-HLS products included here have these flags: 255 represents ''No Data'' and is based on the Fmask layer of the source HLS granule. OPERA DIST-ALERT-HLS data availability: The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20250113-GreaterLosAngeles_Fires/DIST-HLS/ The OPERA DIST-HLS products have been in production since January 2022, are freely distributed to the public via NASA's Land Processes Distributed Active Archive Center (LP DAAC), and can be downloaded through NASA's Earthdata search. For more information about the Surface Disturbance product suite, please refer to the DIST Product page: https://www.jpl.nasa.gov/go/opera/products/dist-product-suite/ For more information about the GLAD project, visit https://glad.umd.edu/ 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/ Suggested Use for DIST-HLS: VEG-DIST-STATUS: Layer values: 0: No disturbance (suggested color: #121212) 1: first 2: provisional 3: confirmed 4: first >=50% (suggested color: #008888) 5: provisional >=50% (suggested color: #e48727) 6: confirmed >=50% (suggested color: #e01b07) 7: confirmed 8: confirmed >=50%, finished (suggested color: #dddddd) 255: No data (suggested color: transparent) VEG-ANOM-MAX: Layer values: 0-100: Maximum loss of percent vegetation (suggested color: white to red gradient) 255: No data (suggested color: transparent) Product POCs: Alexander L. Handwerger (alexander.handwerger@jpl.nasa.gov) Zhen Song (zhensong@umd.edu) Matt Hansen (mhansen@umd.edu) Steven Chan (steventsz.k.chan@jpl.nasa.gov) David Bekaert (david.bekaert@jpl.nasa.gov) Credits: The product contains modified Copernicus Sentinel data (2024) and is produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group. Managed by NASA's Jet Propulsion Laboratory, OPERA funds and manages the DIST-ALERT-HLS product developed and produced by the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland. NASA JPL-Caltech-UMD ARIA/OPERA Team ================== Files: OPERA-DIST-ALERT-HLS-VEG-ANOM-MAX_01-14-2025.tif: January 14, 2025 showing the maximum vegetation anomaly. OPERA-DIST-ALERT-HLS-VEG-DIST-STATUS_01-14-2025.tif: January 14, 2025 showing the status anomaly. OPERA-DIST-ALERT-HLS-VEG-ANOM-MAX_20250112.tif: January 12, 2025 showing the maximum vegetation anomaly. OPERA-DIST-ALERT-HLS-VEG-DIST-STATUS_20250112.tif: January 12, 2025 showing the status anomaly. Last Update 14 January 2025 Service URL: WMS Endpoint: Data Download: https://aria-share.jpl.nasa.gov/20250113-GreaterLosAngeles_Fires/DIST-HLS/
This hosted tile layer represents a modeled flood inundation depth grid for the Neffs Canyon alluvial fan, developed using FEMA standard methodologies and the National Flood Hazard Layer (NFHL). Designed to support macro-level hazard analysis, the raster dataset maps simulated water depths across the fan footprint to illustrate potential surface flow paths and spatial extent during a designated flood scenario. As a core spatial input for HAZUS 7.1 risk modeling, this layer provides the underlying depth values required to calculate structural damage ratios, direct economic losses, and community vulnerability metrics.
Dates of Images: 9/19/2018 Date of Next Image: Varies by region, typically 12 days since previous pass. Set time slider to most recent interval and click on area of interest to identify date of last pass. Summary: The Alaska Satellite Facility has developed false color Red, Green, Blue (RGB) and Radiometrically Terrain-Correct (RTC) composites and surface water extent products of the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument which assigns the co- and cross-polarization information to a channel in the composite. When used to support a flooding event, areas in blue denotes water present at the time of the satellite overpass before or after the start of the flooding event. Sentinel-1 RGB Decomposition of RTC VV and VH imagery over United States coastlines. Blue areas have low returns in VV and VH (smooth surfaces such as calm water, but also frozen/crusted soil or dry sand), Green areas have high returns in VH (volume scatterers such as vegetation or some types of snow/ice), and Red areas have relatively high VV returns and relatively low VH returns (such as urban or sparsely vegetated areas). To identify the date of an image either set the time slider to a 1 day interval or zoom in to AOI, set time slider to desired range and click on the imagery. The name will contain a full date and time in a format like this example: S1A_IW_20210705T231453_DVR_RTC30_G_gpufed_27F4 The date and time would be 07/05/2021 at 23:14:53 UTC. Additional SAR Information Suggested Use: In this image, water appears in blue, vegetated areas in shades of green and urban areas in bright orange. It is recommended to use this product with ancillary information to derive flooded areas. Satellite/Sensor: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A/B satellite NOTE: Sentinel-1B is no longer acquiring data and is only available into December 2021 Resolution: 30 meters Credits: Sentinel data used in this derived product, contains modified Copernicus Sentinel data (2019-2022), processed by ESA, Alaska Satellite Facility. Esri REST Endpoint: See URL to the bottom right. WMS Endpoint:
Contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data.