United States of America GIS data

The dataset contains locations and attributes of owner lines with dimensions. The tax information (attribution) comes from the Office of Tax and Revenue's Public Extract file. The creation of this layer is automated, occurs weekly, and uses the most currently available tax information. The date of the extract can be found in the EXTRACTDAT field in this layer.

Congressional Districts, Voting Districts, State Legislative Districts - Upper, State Legislative Districts - Lower; January 1, 2021 vintage; Generalized

Census Regions, Census Divisions; January 1, 2020 vintage

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

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

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

Metro Facility Benchmarking data is included in this data set including 3 elements: 1. EUI (Energy Use Intensity) values for Metro Nashville Government facilities for Calendar Year 2021 and 2022. EUI is an indicator of the amount of energy consumed on a property annually, in relation to the size of the building. It is a metric that is used to indicate if a building is energy efficient. 2. Metro facility energy consumption for Calendar Year 2021 and 2022. 3. Metro facility energy costs for Calendar Year 2021 and 2022. Source Link: https://www.nashville.gov/departments/general-servicesMetadata Document: Metro Energy Use Intensity Benchmarking Metadata.pdfContact Data Owner: opendata@nashville.gov

Data source is the Office of Tax and Revenue’s Computer-Assisted Mass Appraisal (CAMA) system. The CAMA system is used by the Assessment Division (AD) within the Real Property Tax Administration to value real estate for ad valorem real property tax purposes. The intent of this data is to provide a sale history for active properties listed among the District of Columbia’s real property tax assessment roll. This data is updated daily. The AD constantly maintains sale data, adding new data and updating existing data. Daily updates represent a “snapshot” at the time the data was extracted from the CAMA system, and data is always subject to change.

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 poverty status 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. Poverty status is based on income in past 12 months of survey.  This layer is symbolized to show the percentage of the population whose income falls below the Federal poverty line. 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): B17020, C17002Data 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 U.S. 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 U.S. 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.

The purpose of this feature class is to illustrate the location of proposed water points in public water systems in the Commonwealth of Kentucky as collected by Kentucky’s Area Development Districts. The locations of proposed water points along with the basic attribute data concerning those features, are used by the Kentucky Infrastructure Authority, Area Development Districts, public water systems, and other state/local agencies for the purpose of water infrastructure planning, project development, state and federal funding applications, Kentucky State Clearinghouse, capital improvement plans, data analysis, and for any other purpose that will improve the potable water infrastructure and service in the Commonwealth of Kentucky.

Dates of Images: 4/30/2024, 5/7/2024 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 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). 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 satellite Resolution: 30 meters Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=e893329c61b6428bbe903d3452a8ba0a WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_TX/2408_sentinel1/ImageServer/WMSServer

ACS 2013 Legal and Statistical Entities Web Map Service; January 1, 2013 vintage

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

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

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

ACS 2013 Legal and Statistical Entities Web Map Service; January 1, 2013 vintage

ACS 2013 Legal and Statistical Entities Web Map Service; January 1, 2013 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 youth (age 16-19) school enrollment and employment status. This is shown by tract, county, and state centroids. 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. Estimates here for 'disconnected youth' differ from estimates of 'idle youth' on Census Bureau's website because idle youth includes those unemployed (actively looking for work).  This layer is symbolized by the count of total youth and the percentage of youth who were disconnected. 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): B14005Data 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.

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

ACS 2013 Legal and Statistical Entities Web Map Service; January 1, 2013 vintage

Dates of Images: Post-Event: 3/14/2026 Pre-Event: 1/25/2026 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. 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. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202603_FLOOD_HI/2605_landsat/ImageServer/WMSServer

uav imagery by NetApex, acquired 2026-02-24 to 2026-02-24, 5.0 cm per pixel, published on OpenAerialMap.

Dates of Images: Post-Event: January 12, 2025 Pre-Event: December 18, 2024 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 Short Wave Infrared (SWIR) RGB is a product that is created using the SWIR, NIR, and Red channels of the respective instrument. The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted by the fires. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. The Normalized Burn Ratio identifies burned areas using this calculation. Darker areas are potentially burned areas. 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. The Short Wave Infrared (SWIR) RGB is a product that can provides value in flood detection. Areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta, snow will appear as a bright blue/cyan, and bare soils being multicolor dependent on their makeup. Compare pre-event imagery to post-event imagery to identify potential affected areas. A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: True Color and Color Infrared: 10 meters Shortwave Infrared: 20 meters Credits: NASA/MSFC, USGS, ESA Copernicus Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=cc620f9a325a4c8a9e10fa90f1ce7e11 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_sentinel2/ImageServer/WMSServer

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

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

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

Date of Image(s): 1/11/2025 Date of Next Image: 1/16/2025 Summary: This product visualizes three dominant principal components derived from the surface reflectance estimates from airborne imaging spectroscopy measurements taken by NASA JPL’s AVIRIS-3. The image visualizes the relative strength of three dominant eigenvectors, estimated from data across the full scene. Eigenvectors from this high (284) dimensional dataset do not specifically identify physical features but are correlated to physical phenomena; similar to how ‘red’, ‘green’, or ‘blue’ in a traditional image are correlated to, but not directly indicative of, specific physical processes. Instead of just “red,” “green,” and “blue,” this analysis considers 284 unique “colors” in the visible to shortwave infrared spectrum, providing a much more robust depiction of spectral differences. Thus, contrast between different colored regions in this image can be used to infer strong differences in surface type. For example, the fire boundary at the time of image collection is readily apparent. Explained another way, within a given scene, burned structures will share a unique spectral reflectance signature in “284-color-space” compared to other surfaces and display as the same color in the PCA image. The product ground spatial resolution is approximately 2.8 m, and flights occurred between 19:40 and 21:00 UTC on Jan 11th 2025. Raw data are available for download here: https://popo.jpl.nasa.gov/pub/LA_Fires/dist/eaton_pca_20250111.tif, https://popo.jpl.nasa.gov/pub/LA_Fires/dist/palisades_pca_20250111.tif. In the absence of a supervised image classification, the PCA image can demonstrate areas with similar spectral reflectance properties. On visual inspection of the PCA image, we can intuitively differentiate burned areas from non-burned areas, even for different types of burned surfaces (e.g. burned vegetation vs. burned structures). Note: PCA images are produced on a scene-specific basis. This means unique PCA color representation in one scene does not translate to the same colors in another scene. For example, colors in the Eaton Fire scene do not relate to the same colors in the Palisades fire scene. Suggested Use: As an unsupervised method to reduce dimensionality and improve feature extraction in high-spectral resolution datasets, the PCA image can serve as an intermediate visual guide to reveal the information content and diversity of surfaces in the AVIRIS-3 datasets. Analysts may use this image to visually understand the spectral diversity of fire impacted areas, especially in conjunction with ancillary data that characterizes the unique underlying physical phenomena represented by unique colors in the PCA image. POC: Philip G. Brodrick (JPL) and David R. Thompson (JPL) Citation: AVIRIS-3 Radiance Data: Eckert, R., D.R. Thompson, A.M. Chlus, J.W. Chapman, M. Eastwood, M. Bernas, S. Geier, M. Helmlinger, D. Keymeulen, E. Liggett, S. Nadgauda, L.M. Rios, L.A. Shaw, W. Olson-Duvall, P.G. Brodrick, and R.O. Green. 2024. AVIRIS-3 L1B Calibrated Radiance, Facility Instrument Collection. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2356 AVIRIS-3 Reflectance Data: Brodrick, P.G., A.M. Chlus, U.N. Bohn, E. Greenberg, J. Montgomery, J.W. Chapman, M. Eastwood, S.R. Lundeen, R. Eckert, W. Olson-Duvall, D.R. Thompson, and R.O. Green. 2025. AVIRIS-3 L2A Orthocorrected Surface Reflectance, Facility Instrument Collection. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2357 Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=6898f96fcb1d4b12ba0ba02fb7df28e3 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_aviris_pca/ImageServer/WMSServer

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

Dates of Images Post-Event: 5/21/2026 Summary Skysat is a high-resolution satellite constellation operated by Planet and allows for multiple viewing opportunities of any location on Earth daily. Skysat collects 4-band imagery which allows for multiple derived products to be produced. The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. The Color Infrared composite is created using the near-infrared, red, and green channels. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Resolution ~1 m Credits NASA Disasters Program, Processing from MSFC, Planet. For derived products: “Includes copyrighted material of Skysat. All rights reserved.” This work utilized data made available through the NASA Commercial Satellite Data Acquisition (CSDA) program.

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

Dates of Images: May 18 Summary 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, 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. 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 2012 Legal and Statistical Entities Web Map Service; January 1, 2012 vintage