United States of America GIS data

ACS 2014 Legal and Statistical Entities Web Map Service; January 1, 2014 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, 2015 vintage; Generalized

Alaska Native Regional Corporations (ANRC), American Indian Reservations (AIR), 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, 2015 vintage; Generalized

Date of Image(s) Sentinel-1 Synthetic Aperture Radar (SAR): 12/28/2024 (Pre-fire) 01/09/2025 (Called post-fire, but fires may still burning) Sentinel-2 MultiSpectral Instrument (MSI): 01/02/2025 (Pre-fire) 01/12/2025 (Called post-fire, but fires may still burning) Ancillary data ESA Global Land Cover in 2021 01/23/2025 Summary The burned areas and burn severity levels for vegetation and urban areas were generated separately based on an integration of Sentinel-2 and Sentinel-1 data. Specifically, the Sentinel-2 MSI data provide large changes in vegetation reflectance after burning due to changes in near-infrared (NIR) and shortwave-infrared (SWIR) bands, but changes in these spectral ranges were less obvious or nonexistent in affected urban environments. In a complementary sense, Sentinel-1 synthetic aperture radar (SAR) is sensitive to surface roughness changes, a signal which is more apparent (usually rougher or higher backscatter) due to the fire destruction of built infrastructure. Thus, we leveraged these two key physical characteristics together to distinguish the urban-burned areas and vegetation-burned areas. Note that while the magnitude of calculated changes in NIR, SWIR, and SAR backscatter in pre-and post-event imagery imply a range of burn severity, these metrics are still considered proxies for actual burn severity and should be interpreted as such. We first calculated the differenced Normalized Burn Ratio (dNBR = NBR_pre – NBR_post) from Sentinel-2 data in which the NBR = (NIR – SWIR)/(NIR+SWIR). This provides critical changes in vegetation due to burning. Second, we calculated the differenced vertical/horizontal polarization (VH) backscatter (dVH = VH_post – VH_pre) from Sentinel-1 SAR data. The ESA Land Cover classification map and the relationship between dNBR and dVH were used to distinguish urban and vegetation-burned areas. The generated dNBR was used to divide the vegetation-burned areas into 4 groups: Low (values ≤0.25), Moderate (0.250.45). The generated dVH was used to divide the urban-burned areas into 3 groups: Low (values ≤1), Moderate (14). Notes that these thresholds were defined for contrast visualization of severity levels across four southern California fires and may not reflect other regions. All analysis was performed in Google Earth Engine. From these layers, we can clearly see the vegetation and urban-burned areas and their burn levels. The structure damage could be overlayed on top of burn maps generated by the integration of Sentinel-1 and -2. The maps were preliminary results only and no ground-validation has been considered at the time of posting. To separate vegetation and urban-burned areas, we applied the empirical thresholds of Sentinel-2 dNBR and Sentinel-1 dVH, which could be imperfect in some locations on a large scale. Sentinel-1 SAR data was subject to signal noise in some areas likely due to terrain and speckle noise, which could lead to incorrect severity levels in urban-burned areas to some extents. Refinement of the results is in progress. Suggested Usage Despite the rawer form of this quick-look product, it is still useful for distinguishing vegetation and urban-burned areas and severity assessment. Resolution Sentinel-1A SAR C-band, 10m spatial resolution Sentinel-2 MSI, 10-60m spatial resolution Credits Khuong Tran (ARC), Taejin Park (ARC), Aakash Chhabra (ARC), Weile Wang (ARC), Kyle Kabasares (ARC). Service URL https://gis.earthdata.nasa.gov/portal/home/item.html?id=88fe789c74f9459392d293931ac7aac5 WMS Endpoint https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_s1_s2_burnseverity/ImageServer/WMSServer

ACS 2014 Legal and Statistical Entities Web Map Service; January 1, 2014 vintage

Date of Images: Pre-Landfall: HLS: 9/27/2024 - 10/7/2024 Sentinel-1: 10/3/2024, 10/8/2024 Post-Landfall: Sentinel-1: 10/10/2024, 10/11/2024 Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at NASA's Jet Propulsion Laboratory and California Institute of Technology derived the surface water extent maps from the OPERA Dynamic Surface Water Extent (DSWx) from NASA Harmonized Landsat Sentinel-2 (HLS) and Sentinel-1 (DSWx-S1) products. The results posted here are preliminary and unvalidated results, primarily intended to aid the field response and people who wanted to have a rough first look at the inundation extent. The ARIA-share website has always focused on posting preliminary results as fast as possible for disaster response. OPERA DSWx-S1 The OPERA DSWx-S1 data identifies surface water and inundated vegetation. We provide the Water (WTR) layers. Images are provided from 1) October 3, 2024, 2) October 8, 2024, 3) October 10, 2024, and 4) October 11, 2024. Each image consists of multiple MGRS tiles that were merged together for a composite image saved as a GeoTIFF file. ARIA/OPERA water change map derived from OPERA DSWx-S1 The ARIA/OPERA water change map is derived from two OPERA DSWx-S1 Binary Water (BWTR) images. Change maps are made for images taken on October 3, 2024 and October 11, 2024 and for October 8, 2024, and October 11, 2024. The BWTR combines inundated vegetation and open water into a single water class. These maps depict areas of new water detection (or loss). The change map includes values of: (0) indicate no change between images, (1) absence of water pre-event, presence of water during-event, and (-1) presence of water pre-event, absence of water during-event. OPERA DSWx-HLS The OPERA DSWx-HLS was used to identify surface water using the WTR layer. 378 images were examined from 2024-09-27 to 2024-10-07, prioritizing pixels classified as open surface water. Pixels flagged as snow/ice in the HLS Fmask were reclassified to water. The mosaic image consists of multiple MGRS tiles that were merged together for a composite image saved as a GeoTIFF file. Suggested Use: OPERA DSWx-S1 The OPERA DSWx-S1 products classifies the OPERA Radiometric Terrain Corrected SAR backscatter from Sentinel-1 (RTC-S1) input imagery into: not water, water, and inundated vegetation with the masks such as layover/shadow mask and HAND mask. The WTR layer includes all classes. Open water and inundated vegetation are represented in blue and green in WTR. Areas with masks are gray. The masks include the layover/shadow mask and HAND mask. Areas with no water detected are transparent. Transparent: Not Water Blue: Water Green: Inundated Vegetation Gray: Layover/Shadow/Hand Masks ARIA/OPERA water change map derived from OPERA DSWx-S1 The ARIA/OPERA water extent change map classifies water extent into change/no change. Transparent (0): No Change Red (1): increased water extent Gray (-1): decreased water extent OPERA DSWx-HLS-WTR The OPERA DSWx-HLS Water product classifies the Harmonized Landsat Sentinel-2 (HLS) input imagery into "not water", "open surface water", and "partial surface water". The "HLS cloud/cloud shadow" and "HLS snow/ice" layers are direct inputs from the HLS FMask. Transparent (0): No Water Blue (1): Open Water Light Blue (2): Partial Surface Water Gray (253): HLS Cloud/Cloud Shadow This layer is meant to provide users with a quick view for water/no-water. Invalid data classes (snow/ice and cloud/cloud shadow along with adjacent to cloud/cloud shadow) are also provided to indicate areas in which the water classification does not provide water/no-water classification. Note: Sediment rich water is sometimes misclassified as snow/ice by the HLS Fmask. As such, we have reclassified any of the snow/ice pixels to water for these images. For more information about how the OPERA DSWx-HLS Water product classifies data: https://d2pn8kiwq2w21t.cloudfront.net/documents/ProductSpec_DSWX_URS309746.pdf Satellite/Sensor: Synthetic Aperture Radar (SAR) instrument on European Space Agency's (ESA) Sentinel-1A satellite Harmonized Landsat Sentinel-2 (HLS): MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites and Operational Land Imager (OLI) on the NASA/USGS Landsat 8 satellite Resolution: 30 meters The DSWx-S1 products have these flags: 250 (light gray) and 251 (dark gray) represent HAND and layover/shadow masks, respectively. HAND mask (light gray, value 250) delineates regions where the terrain's elevation exceeds a specified threshold relative to the height above the nearest drainage point, indicating areas less likely to be subject to direct inundation. Layover/shadow mask (dark gray, value 251) identifies zones that are either occluded by topographic features taller than the surrounding landscape (layover) or are not illuminated by the radar signal due to obstruction by these elevated features (shadow), leading to potential data voids in SAR imagery. Credits: NASA JPL-Caltech ARIA/OPERA Team Sentinel-1 data were accessed through the Copernicus Open Hub and the Alaska Satellite Facility server. The product contains modified Copernicus Sentinel data (2024), 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. HLS data was accessed through NASA EarthData Search. The product contains modified Copernicus Sentinel data (2024), processed by the European Space Agency, NASA/USGS Landsat 8 data, 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. The OPERA DSWx 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. Data Download: https://aria-share.jpl.nasa.gov/20241010-Hurricane_Milton/DSWx/ Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=84fe3b39b6ac482e8e8de954eb4677fe WMS: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202410_HURRICANE_MILTON/2428_opera_dswx_hls/ImageServer/WMSServer

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

ACS 2014 Legal and Statistical Entities Web Map Service; January 1, 2014 vintage

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 tenure (owner or renter) by education level of householder. 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 by the overall homeownership rate. 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): B25013Data 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 HistFallTempMax for time slider

Alaska Native Regional Corporations (ANRC), American Indian Reservations (AIR), 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, 2015 vintage; Generalized

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

Dates of Images: May 18- 25, 2026 Summary This product provides the PM2.5 impacts at the surface from the Sandy Fire by calculating the total number of daytime hours when surface fine particulate matter (PM2.5) exceeded the 35 µg/m³ health threshold for sensitive groups (USG). Surface concentrations of PM2.5 Level 4 (L4) files were generated from Tropospheric Emissions: Monitoring of Pollution (TEMPO) Satellite and Advanced Baseline Imager (ABI) aerosol retrievals. The aerosol optical depth (AOD) and PM2.5 products were created at NOAA/NESDIS. The data covers the daytime observation windows during the active fire period from May 18 to May 25, 2026. Because TEMPO relies on sunlight, these observations represent approximately 10 to 12 hours of sun-lit coverage per calendar day rather than a full 24-hour cycle. For emergency managers, this highlights total cumulative exposure durations, allowing officials to efficiently target health impacts in heavily affected communities. Caveats and Limitations Since the TEMPO instrument only makes observations during daylight hours, communities likely experienced significant additional nighttime smoke exposure that is not captured in these products. Furthermore, dense cloud cover can block satellite measurements, and these values may underestimate the true duration of smoke exposure in persistently cloudy areas. TEMPO data also exhibits more noise at the edges of clouds. Suggested Use While hours when surface PM2.5 was greater than 35 µg/m³ represents the health standard referred to as "Unhealthy for Sensitive Groups," this daytime-only product is developed only for rapid disaster response and should not be used for official EPA regulatory compliance, which requires a full 24-hour average. Red colors in the image represent more hours when the surface pM2.5 concentrations were higher than 35 µg/m³. Satellite/Sensor/Resolution The surface PM2.5 product is estimated using data from two primary instruments: the TEMPO spectrometer aboard the Intelsat-40e commercial satellite and the Advanced Baseline Imager (ABI) on the GOES-R Series satellites (GOES-East and GOES-West). The final product is derived using a geographically weighted regression (GWR) algorithm that combines satellite aerosol optical depth (AOD) and aerosol layer height (ALH) with hourly data from AIRNOW surface monitors. While the raw TEMPO data has a native resolution of 2.0 km by 4.75 km, it is re-scaled to match the ABI's fixed grid, resulting in a final product with a spatial resolution of 2 km. Credits AOD and PM2.5 were provided by NOAA/NESDIS. Data analysis and cumulative exposure maps were created by Dr. Aishwarya Raman (BAERI/NASA ARC). Point of contact for TEMPO PM2.5 data - Amy Huff (Science and Technology Corporation (STC) at NOAA) and Shobha Kondragunta (NOAA). Esri REST Endpoint: See URL to the right. Data Download For data, please contact Amy Huff (Science and Technology Corporation (STC) at NOAA) and Shobha Kondragunta (NOAA).

ACS 2014 Legal and Statistical Entities Web Map Service; January 1, 2014 vintage

ACS 2014 Legal and Statistical Entities Web Map Service; January 1, 2014 vintage

ACS 2014 Legal and Statistical Entities Web Map Service; January 1, 2014 vintage

ACS 2014 Legal and Statistical Entities Web Map Service; January 1, 2014 vintage

ACS 2023 Legal and Statistical Entities Web Map Service; January 1, 2023 vintage

ACS 2014 Legal and Statistical Entities Web Map Service; January 1, 2014 vintage

ACS 2014 Legal and Statistical Entities Web Map Service; January 1, 2014 vintage

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

Legal and Statistical Entities Web Map Service; January 1, 2000 vintage

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

Census Regions, Census Divisions; January 1, 2020 vintage

Alaska Native Regional Corporations (ANRC), American Indian Reservations (AIR), 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, 2015 vintage; Generalized

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage

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

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage