United States of America GIS data

ACS 2019 Legal and Statistical Entities Web Map Service; January 1, 2019 vintage

ACS 2019 Legal and Statistical Entities Web Map Service; January 1, 2019 vintage

ACS 2012 Legal and Statistical Entities Web Map Service; January 1, 2012 vintage

CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.

uav imagery by Garrett Jobes, acquired 2026-05-29 to 2026-05-29, 13.0 cm per pixel, published on OpenAerialMap.

Census 2020 Legal and Statistical Entities Web Map Service; January 1, 2020 vintage

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2021 vintage; Generalized

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

Census Current (2026) WMS Legal and Statistical Entities Web Map Service; January 1, 2026 vintage

Detailed financial data encompassing 2021-2022 actual costs, 2023-2024 Adopted Budget, and 2025-2026 Proposed Budget broken out by Fund, Department, Program, and Cost Category. Note that revenues are shown as negative (-) values to properly balance with expenses. For explanations of significant budget changes between the 2023-2024 and 2025-2026 bienniums, please consult the 2025-2026 Budget Book.

Legal and Statistical Entities Web Map Service; January 1, 2010 vintage

Date of Image(s): 12/18/2024; UTC: 183709 1/12/25; UTC: 183731 Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input imagers were generated from the ESA’s Sentinel-2’s Multi-Spectral imager. They were created from the Level 2A products which have been atmospherically corrected and orthorectified. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. For Sentinel 2, NIR = Band 8 and SWIR = Band 12. Using these definitions, the 20-meter resolution Band 8 and Band 12 images were used to generate these products. Sentinel-2 NBR methods: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/nbr/ Finally dNBR is simply the difference between the pre-fire NBR and the post-fire NBR. Thus, we calculated dNBR as dNBR = NBR(12/18/2024) - NBR(01/12/2025). 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. More positive values in the dNBR analysis represent a proxy for greater burn severity. Negative values in dNBR 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 of the Los Angeles Fires at the time of posting this dataset should be strongly caveated. This is due to several dNBR Limitations: The spectral band selections used in the NBR and 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 the shortest time difference possible in the image pair; 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: ESA Copernicus Sentinel-2 Resolution: 20 m in infrared bands Credits: Copernicus Sentinel-2 data was downloaded from the Sentinel-2 data explorer (https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2) Data processed by Kyle Kabasares (ARC; kyle.k.kabasares@nasa.gov) References: Normalize Burn Ratio theory: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=3bc872886af24b799acd2ef881691a6a WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_sentinel2_dnbr/ImageServer/WMSServer

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage

Congressional Districts, Voting Districts, State Legislative Districts - Upper, State Legislative Districts - Lower; January 1, 2019 vintage; Generalized

Legal and Statistical Entities Web Map Service; January 1, 2010 vintage

Address Residential Units. This dataset contains residential units and attributes of Address points, created as part of the Master Address Repository (MAR) for the D.C. Residential units can be condominiums or also apartments. Office of the Chief Technology Officer (OCTO) and DC Department of Consumer and Regulatory Affairs . It contains the addresses in the District of Columbia which are typically placed on the buildings. More information on the MAR can be found at https://opendata.dc.gov/pages/addressing-in-dc.

County Subdivisions, Subbarrios, Consolidated Cities, Incorporated Places, Census Designated Places, and Economic Places; January 1, 2012 vintage; Generalized

Tribal Census Tracts, Tribal Block Groups; January 1, 2020 vintage

Records municipal boundary modifications in Utah, United States, published by the Utah Automated Geographic Reference Center. Each polygon feature represents a change such as an annexation, adjustment, or disconnection, with the municipality name, county number, FIPS code, update type, in and out dates, and the associated document name and location.

Boundaries and dates of the Google imagery flight blocks

Legal and Statistical Entities Web Map Service; January 1, 2020 vintage

Household type, Education, Disability, Language, Computer/Internet Use, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: Census Tracts. Current Vintage: 2020-2024. ACS Table(s): DP02. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: March 11, 2026. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black) This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases. Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.   Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

This dataset comes from the Annual Community Survey questions that relate to this performance measure, if they answered "Yes" to being a victim of crime in the past 6 months: “Were the police informed that your household had been burglarized, or did they find out about this incident in any way?” and "Were the police informed that you were robbed, physically assaulted, or sexually assaulted, or did they find out about this incident in any way?" Respondents are asked to provide their answer as “Yes” or “No” (without “don’t know” as an option). The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level. This page provides data for the Victim Not Reporting Crime to Police performance measure.  The performance measure dashboard is available at 1.06 Crime Reporting   Data Dictionary   Additional Information   Source: Community Attitude Survey Contact:  Wydale Holmes Contact E-Mail: Wydale_Holmes@tempe.gov Data Source Type:  CSV Preparation Method:  Data received from vendor and entered into CSV Publish Frequency:  Annual Publish Method: Manual

The Kentucky SPARROW (SPAtially Referenced Regressions On Watershed Attributes) model estimates annual nitrogen and phosphorus loads (concentrations) for stream reaches throughout the state based on data from 40 monitored stations in Kentucky and Tennessee. Approximately 300 land use, municipal, and natural resource variables were evaluated in order to identify the significant contributing factors of nitrogen and phosphorus to our streams. The SPARROW model is a non-linear model that estimates the magnitude and significance of source and delivery variables for the downstream end of each stream segment (reach) based on measured data at monitored sites. The model then sums the nutrient load within each reach with those from the upstream reaches. The results presented here are from an adjusted model - this means that measured data for monitored reaches were included in the final model and used to adjust the model at each monitored reach and thereby improve the accuracy of the estimated nutrient concentrations. Consequently, the model is most accurate immediately downstream of a monitored reach. Conversely, the largest accumulated error is in reaches immediately upstream of a monitored reach. Data are reported with 90% lower and upper confidence bounds. For nitrogen, the sources were identified as agricultural land use (pasture plus cultivated land areas - NLCD 1992), atmospheric deposition of ammonium and nitrate (NADP), and wastewater (Hoos, 2007). Delivery of the atmospheric deposition is controlled by how water moves through the soil (soil permeability - STATSGO). In addition, there is depletion of stream nitrogen in the state's reservoirs (as estimated by the model). This nitrogen model has a yield R2 (accounting for the size of each drainage area) of 0.89. For phosphorus, the sources were identified as pasture (NLCD), soil phosphorus abundance (Karathanasis, 2006), and wastewater (Hoos, 2007). Delivery of phosphorus from pasture lands is related to a function of infiltration excess overland flow (Wolock, 2003). This phosphorus model has a yield R2 of 0.65. Questions? tnwillia@usgs.gov Download Link: https://ky.box.com/v/kymartian-nitrogen-phosporus

Census Current (2026) WMS Legal and Statistical Entities Web Map Service; January 1, 2026 vintage

This dataset was generated to publish a geospatial inventory of Shopping Centers in Mecklenburg County, NC. This data serves to support and assist Housing & Neighborhood Services.

Tribal Census Tracts, Tribal Block Groups; January 1, 2022 vintage; Generalized

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows demographic context for emergency response efforts. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   This layer is symbolized to show the percentage of households who do not have access to internet. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B01001, B08201, B09021, B16003, B16004, B17020, B18101, B25040, B25117, B27010, B28001, B28002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).   The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico   Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

EnviroAtlas image service of HistFallPrecip for time slider

ACS 2026 Legal and Statistical Entities Web Map Service; January 1, 2026 vintage

uav imagery by NMFRD, acquired 2026-03-25 to 2026-03-25, 1.6 cm per pixel, published on OpenAerialMap.

Congressional Districts, Voting Districts, State Legislative Districts - Upper, State Legislative Districts - Lower; January 1, 2020 vintage; Generalized

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

County Subdivisions, Subbarrios, Consolidated Cities, Incorporated Places, and Census Designated Places; January 1, 2015 vintage; Generalized

ACS 2018 Legal and Statistical Entities Web Map Service; January 1, 2018 vintage