United States of America imagery and land cover data
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Feature class data showing the adopted 2023 Growth Centers from the City's 2023 Comprehensive Plan. The growth centers are derived from the Future Land Use map, which guides the locations of appropriate places to live, work. and play. The growth centers are to help guide development in key strategic areas within the City and ETJ. For more information regarding Growth Centers, please see the Land Use chapter of the 2023 Comprehensive Plan.
This tiled imagery service represents a snapshot of the land surface within Napa County and is made available for public use. The tile cache scheme follows the ArcGIS Online Model and can viewed down to ~1:283 scale. A hard copy of this data is also available directly from the GIS Team. For more information, contact gisstaff@countyofnapa.org. Date(s) of photography: April 22-23, 2002Scale of photography: 1:24,000 (1" = 2,000')Number of flight lines: 19Number of photos: 471Overlap: 60%Sidelap: 30%Camera type: Wild RC30Camera/lens serial number: 13302Calibrated focal length: 153.670 mmFilm type: Color For questions regarding use and access, please contact gisstaff@countyofnapa.org.
Dates of Images: Post-Event: 9/18/2018 Pre-Event: 8/29/2018 Date of Next Image: Unknown Summary: Normalized Differential Vegetation Index (NDVI) maps are produced by converting pixel values to NDVI = (Red – NIR)/(Red + NIR), NIR indicates the near infrared band. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. NDVI change was created by subtracting the cloud-masked post-event NDVI from the cloud-masked pre-event NDVI to show areas of potentially impacted vegetation as negative values (darker shades) and healthy/unimpacted vegetation in positive values (lighter shades). NDVI change can give insights to impacts from an event (like the scouring of vegetation from the riverbeds) but can also show changes in unrelated areas like the clearing of agriculture fields. This layer is a binary NDVI change polygon (threshold of areas where NDVI decreased by -0.25 or more) for May 6, 2024 compared to April 21, 2024. Suggested Use: NDVI change: Commonly used to distinguish healthy/active vegetation (positive values) from potentially unhealthy/impacted vegetation (negative values). This map shows a binary flag where NDVI decreased. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page
Date of Image: May 19, 2026 Date of Next Image: None Expected Summary: The polygon was derived from a combination of a normalized burn ratio (NBR) thresholding derived via Sentinel-2 near-infrared (NIR) and shortwave-infrared bands and a simple single-band NIR threshold from the same image. The NBR threshold for the binary burned classification was set to values Credits: USGS, Landsat, Clayton Elder (ARC) Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202605_FIRE_KS/202608_landsat_nbrburnperimeter/MapServer/WMSServer
Dates of Images: Post-Event: 6/29/2026, 7/01/2026, 7/02/2026, 7/04/2026, 7/6/2026, 7/9/2026, 7/11/2026, 7/14/2026 Pre-Event: 6/16/2026, 6/19/2026 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. The Color Infrared composite is created using the near-infrared, red, and green channels. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input images were generated from the Sentinel2- dataset at 10m resolution. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. dNBR is computed by the difference between the pre-fire NBR and the post-fire NBR. More information on dNBR can be found here: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio. Check fire containment and image dates for further context on image timing. Federal permitted grazing land maps from the US Forest Service and Department of Interior - Bureau of Land Management (BLM) are shown in the web map. For more information: https://data-usfs.hub.arcgis.com/datasets/usfs::range-unit-allotment-3/about & https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-colorado-grazing-allotments/about Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. Since the dNBR considers the condition of the scene before the fire occurred, the resulting value has been used as a proxy for burn severity. Higher dNBR values represent a proxy for greater burn severity. Negative dNBR values may represent a re-greening of or growth of vegetation in between pre and post imagery. The use of this dNBR product as a quantitative metric of burn severity at the time of posting this dataset should be strongly caveated. This is due to several dNBR limitations: The spectral band selections used for dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure). This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with representative conditions for each scene; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: True Color: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See layers below. WMS Endpoint: See individual layer pages.
This tiled imagery service represents a snapshot of the land surface within Napa County and is made available for public use. The tile cache scheme follows the ArcGIS Online Model and can viewed down to ~1:283 scale. A hard copy of this data is also available directly from the GIS Team. For more information, contact gisstaff@countyofnapa.org. Extent and Resolution:Orthoimagery and Oblique imagery within Napa County, California. Projection:State Plane Coordinate System1983 California Zone 2North American Datum 1983 (2011) NAVD88 US Survey Feet Capture Window:Imagery was obtained between the dates of March 16 - March 21. Bands:3 (RGB)4th band IR is available in the raw imagery mosaic. Sensor: Accuracy of Orthophotography: For questions regarding use and access, please contact gisstaff@countyofnapa.org.
Dates of Images: Post-Event: May 24, 2026 Pre-Event: May 7, 2026 Date of Next Image: Unknown Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input images were generated from the Harmonized Landsat Sentinel-2 (HLS) dataset at 30m resolution. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. dNBR is computed by the difference between the pre-fire NBR and the post-fire NBR. More information on dNBR can be found here: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio. dNBR data may be computed while the fire is in progress. This is intentionally done to prioritize rapid data availability for proactive disaster response but means data can change over the course of the fire. Ground slopes were calculated using the slope algorithm in QGIS. This algorithm determined ground slopes in degrees by analyzing USGS 1/3 arcsecond (i.e. roughly 10m) digital elevation rasters in the NAD27 / UTM zone 13N projection. To produce the dNBR and ground slope exceedance map, dNBR data was filtered for values greater than 0.4 and ground slope data was filtered for areas greater than or equal to 23 degrees. These thresholds were defined by NASA subject matter expertise to indicate critical levels for enhanced debris flow risk where both are in exceedance. The locations where both dNBR and slope thresholds were exceeded were initially identified in a binary raster and then vectorized to polygons using 8-neighbor feature selection (data represented here). Suggested Use: dNBR is commonly used as a proxy to identify fire-affected areas. Higher dNBR value are statistically correlated with greater burn severity, where negative dNBR values may represent a re-greening of or growth of vegetation in between pre and post-vegetation. The use of this dNBR product as a quantitative metric of burn severity at the time of posting this dataset should be strongly caveated. This is due to several dNBR limitations: The spectral band selections used for dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR will not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure), and the interpretation of dNBR values may vary widely based on the type of vegetation burned. This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with representative conditions for each scene; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Satellite/Sensor: NASA Disasters/MSFC, USGS, ESA Copernicus Resolution: 10 meters Credits: NASA Disasters Program, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://maps.disasters.nasa.gov/ags03/services/NM_FIre_202506/dnbr_hls/MapServer/WMSServer
NOTE: this is exercise data and not representative of real events. The original data is from wildfires in New Mexico in June 2025. Dates of Images: May 24, 2026 Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at the Jet Propulsion Laboratory, California Institute of Technology produced flood mapping products using the OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 (DSWx-HLS) dataset. These products provide a rapid assessment of surface water extent to support emergency response and situational awareness during flood events. The dataset provides: Surface Water Extent maps on individual dates Dates and Input Data: Flood images: May 24, 2026 Products are generated from OPERA DSWx-HLS satellite imagery and mosaicked across tiles to provide continuous coverage. Product Description: Surface water is identified from optical satellite imagery and mapped at 30 m spatial resolution. Maximum Surface Water Extent maps show all locations where water was detected at any time during the analysis period. Caveats and Limitations: We note the HLS snow/ice mask often misclassifies sediment rich water as snow/ice. Detecting inundation under vegetation, in the urban perimeter, in arid environments, and along water body edges is known to be challenging. The local accuracy of the flood maps in such environments may be impacted. These results are intended to support rapid emergency response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk. Suggested Use: The 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. Water (WTR) Layer Values: File names: OPERA_L3_DSWX-HLS_V1_BWTR_20260524T1609_mosaic.tif, Values: 0: Not Water - an area with valid reflectance data that is not open water (class 1), partial surface water (class 2), snow/ice (class 252), cloud/cloud shadow (class 253), or ocean masked (class 254). Masking can result in a 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) 2: Partial Surface Water - an inundated area that is at least 20% and less than 100% open water. This may be referred to as “subpixel inundation” when referring to a pixel’s area. Examples include wetlands, water bodies with emergent vegetation, and pixels bisected by coastlines. (suggested color: #b4d5f4) 252: Snow/Ice/Sediment Rich Water - an area identified as snow/ice according to input HLS Fmask quality assurance (QA) data (suggested color: #00ffff) 253: Cloud/Cloud Shadow - an area identified as cloud or cloud shadow or adjacent to cloud/cloud shadow according to input HLS Fmask quality assurance (QA) data (suggested color: #afafaf) 254: Ocean Masked - an area identified as ocean using a shoreline database with an added margin (suggested color: #00007f) 255: No data (suggested color: transparent) Satellite/Sensor: Harmonized Landsat Sentinel-2 (HLS) derived from MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B/2C satellites and Operational Land Imager 2 (OLI-2) on NASA/USGS Landsat 8/9 satellites. Spatial Resolution: 30 meters Temporal Resolution: Sub-weekly Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2604_202605_FIRE_CA/ex2605_opera_dswx_hls/MapServer/WMSServer
Dates of Images: August 13, 2026 Date of Next Image: Varies by region, typically 12 days since previous pass. Summary: Watermap Extent products generated by ASF from Sentinel-1 SAR imagery using the HydroSAR Hydro30 workflow. Pixel values of 1 indicate surface water, 0 is used for non-water pixels. These products have been validated in the Hindu Kush Himalayan region, but not elsewhere. Use caution when viewing these products in unvalidated areas. Refer to https://ntrs.nasa.gov/citations/20210020271 for more information. Additional SAR Information Suggested Use:In this image, water appears in red. 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-1C/D satellite Resolution:30 meters Credits: Sentinel data used in this derived product, contains modified Copernicus Sentinel data (2019-2022), processed by ESA, Alaska Satellite Facility. Esri REST Endpoint: Surface Water Extent Product:https://gis.earthdata.nasa.gov/portal/rest/services/DISASTERS_202608_FLOOD_OH/202620_sentinel1_hydrosar/ImageServer
2016 aerial photos from DRCOG. The frontrange is 6 inch and the foothills is 1 foot.
Aerial photographs of Boulder from 2012, provided by DRCOG and published by BoulderCO.
Date of Images Pre-event: 2025-10-08 Co and Post-event: 2025-10-13 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 NASA Harmonized Landsat Sentinel-2 product (DSWx-HLS). 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-HLS The OPERA DSWx-HLS was used to identify surface water for the flooding event. The images provided are a mosaic of OPERA DSWx-HLS 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 partial surface water), snow/ice, cloud/cloud shadow and adjacent to cloud/cloud shadow, ocean masked, or no data classes as a result of processing the diagnostic (DIAG) layer through all intermediate steps/layers (WTR-1, and WTR-2). Water (WTR) Layer Values: 0: Not Water - an area with valid reflectance data that is not open water (class 1), partial surface water (class 2), snow/ice (class 252), cloud/cloud shadow (class 253), or ocean masked (class 254). 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) 2: Partial Surface Water - 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: #b4d5f4) 252: Snow/Ice - an area identified as snow/ice according to input HLS Fmask quality assurance (QA) data (suggested color: #00ffff) 253: Cloud/Cloud Shadow - an area identified as cloud or cloud shadow or adjacent to cloud/cloud shadow according to input HLS Fmask quality assurance (QA) data (suggested color: #afafaf) 254: Ocean Masked - an area identified as ocean using a shoreline database with an added margin (suggested color: #00007f) 255: No data (suggested color: transparent) Satellite/Sensor: Multi Spectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/B/C satellites and Operational Land Imager 2 (OLI-2) on NASA's Landsat 8 satellite. Resolution: 30 meters OPERA DSWx-HLS data availability The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20251012_Alaska_Floods/DSWx-HLS/. The OPERA DSWx-HLS products have been in production since April 2023, are freely distributed to the public via NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC), and can be downloaded through NASA's Earthdata search. For more information about the 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 the URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_FLOOD_AK/202510_opera_dswx_hls/MapServer/WMSServer Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (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: HLS data was accessed through NASA EarthData Search. The product contains modified Copernicus Sentinel data (2025), processed by the European Space Agency, NASA/USGS Landsat 8/9 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 Jet Propulsion Laboratory. NASA JPL-Caltech ARIA/OPERA Team ================== Last Update 20 Oct 2025
This tiled imagery service represents a snapshot of the land surface within Napa County and is made available for public use. The tile cache scheme follows the ArcGIS Online Model and can viewed down to ~1:283 scale. A hard copy of this data is also available directly from the GIS Team. For more information, contact gisstaff@countyofnapa.org. Extent and Resolution:Ortho-imagery and Oblique imagery within Napa County, California. Projection: Capture Window: Bands: Sensor: Accuracy of Orthophotography: For questions regarding use and access, please contact gisstaff@countyofnapa.org.
Dates of Images: Post-Event: 9/28/2026 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. The 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. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is is blue. 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 flooding. 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: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL to the right.
Dates of Images: 20180914T1609, 20180917T1553, 20180918T1549, 20180919T1540, 20180920T1539 (GMT) (YYYYMMDD: HHMM) Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at the Jet Propulsion Laboratory, California Institute of Technology produced flood mapping products using the OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 (DSWx-HLS) dataset. These products provide a rapid assessment of surface water extent to support emergency response and situational awareness during flood events. The dataset provided includes two products: Surface Water Extent maps on individual dates Maximum Surface Water Extent maps, showing the full extent of water observed over a defined time period Dates and Input Data: Flood images: September 14, 2018, September 17, 2018, September 18, 2018, September 19, 2018, September 20, 2018 Products are generated from OPERA DSWx-HLS satellite imagery and mosaicked across tiles to provide continuous coverage. Product Description: Surface water is identified from optical satellite imagery and mapped at 30 m spatial resolution. Maximum Surface Water Extent maps show all locations where water was detected at any time during the analysis period. Caveats and Limitations: We note the HLS snow/ice mask often misclassifies sediment rich water as snow/ice. Detecting inundation under vegetation, in the urban perimeter, in arid environments, and along water body edges is known to be challenging. The local accuracy of the flood maps in such environments may be impacted. These results are intended to support rapid emergency response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk. Suggested Use: The 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. Water (WTR) Layer Values: File names: OPERA_L3_DSWX-HLS_V1_WTR_20180914T1609_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20180917T1553_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20180918T1549_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20180919T1540_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20180920T1539_mosaic.tif Values:0: Not Water - an area with valid reflectance data that is not open water (class 1), partial surface water (class 2), snow/ice (class 252), cloud/cloud shadow (class 253), or ocean masked (class 254). Masking can result in a 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) 2: Partial Surface Water - an inundated area that is at least 20% and less than 100% open water. This may be referred to as “subpixel inundation” when referring to a pixel’s area. Examples include wetlands, water bodies with emergent vegetation, and pixels bisected by coastlines. (suggested color: #b4d5f4) 252: Snow/Ice/Sediment Rich Water - an area identified as snow/ice according to input HLS Fmask quality assurance (QA) data (suggested color: #00ffff) 253: Cloud/Cloud Shadow - an area identified as cloud or cloud shadow or adjacent to cloud/cloud shadow according to input HLS Fmask quality assurance (QA) data (suggested color: #afafaf) 254: Ocean Masked - an area identified as ocean using a shoreline database with an added margin (suggested color: #00007f) 255: No data (suggested color: transparent) Maximum Water Extent Layer Values: File names: ARIA-OPERA_L3_DSWX-HLS_V1_WTR_20180830T1604_20180920T1539_max_extent.tif 0: Not Water. No water was detected within the time period of the images. Masked pixels are set to No data. (suggested color: #ffffff) 1: Water - an area where open water or partial surface water was detected during the time period of the images. (suggested color: #ff15e4) Satellite/Sensor/Resolution: Satellite/Sensor: Harmonized Landsat Sentinel-2 (HLS) derived from MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B/2C satellites and Operational Land Imager 2 (OLI-2) on NASA/USGS Landsat 8/9 satellites. Spatial Resolution: 30 meters Temporal Resolution: Sub-weekly Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_opera_dswx_hls/MapServer/WMSServer
satellite imagery by North Metro Fire Rescue District, acquired 2026-07-23 to 2026-07-23, 3.3 cm per pixel, published on OpenAerialMap.
uav imagery by North Metro Fire Rescue District, acquired 2026-03-28 to 2026-03-28, 1.7 cm per pixel, published on OpenAerialMap.
Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Hyperspectral Reflectance data product (GLREFL) is to provide high-resolution reflectance data, ranging in wavelength from 418 to 920 nanometers across 114 spectral ranges. Reflectance data is computed as the ratio between observed upwelling radiance and downwelling hemispheric irradiance and corrected for differences in cross-track illumination and Bidirectional Reflectance Distribution Function (BRDF) using an empirically derived multiplier. At a nominal flying height of 335 m above ground level (AGL), the at-sensor reflectance is a close approximation of surface reflectance. GLREFL data are processed as a zipped raster data product (GeoTIFF) with associated header file (.hdr) at 1 meter spatial resolution over locally defined areas. A low-resolution browse is also provided with a color map applied in PNG format.
Date of Image: Pre-Event: September 2024 Post-Event: October 10, 2024 - October 18, 2024 Summary: These Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30, and show the impact of Hurricane Milton on electric grids in the Southeast US. The baseline image is from September 2024, a cloud-free, moon-free composite, and the “after" image from October 10, 2024. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts from Francine to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Grey represents cloud cover. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 NOAA-20: September 2024 SNPP: October 10, 2024 - October 17, 2024 Resolution: 500 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=add2acb8ac46404e82fb9932a170f430 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202410_HURRICANE_MILTON/2428_BlackMarble_brdf_cloud/ImageServer/WMSServer
Dates of Images: Post-Event: 1/14/2025 Pre-Event: 1/6/2025 Date of Next Image: Unknown Summary: Natural Color: The Natural Color RGB provides a false composite look at the surface. This RGB uses a shortwave, the near-infrared, and red channels from the instrument. Color Infrared: The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted from the fires. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. 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: Natural Color: areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta. Color Infrared: depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. 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_CA_WILDFIRES_202501/202501_landsat_gl/ImageServer/WMSServer
Date of Images: Post-Event: 6/22/2025, 6/19/2025, 6/18/2025, 6/28/2025 Pre-Event: 6/16/2025 Date of Next Image: Unknown Summary: The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted by the event. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. 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: A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. The True Color RGB provides a product of how the surface would look to the naked eye from space and may show damage caused by severe weather. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Satellite/Sensor: PlanetScope Resolution: 3 Meters Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=67fbd8bf81ec48bd9530609a9430b8e3 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202506_FIRE_NM/2506_planet/ImageServer/WMSServer
Dates of Images: Post-Event: October 12, October 10, October 7, October 5, October 2, September 27 Pre-Event: September 20, September 22 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. The 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. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. 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 flooding. 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: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202409_HURRICANE_HELENE/SENTINEL2/ImageServer/WMSServer Data Download: https://maps.disasters.nasa.gov/download/gis_products/event_specific/2024/hurricane_helene_202409/sentinel2/
Dates of Images: Post-Event: 4/14/2025; 4/15/2025 Pre-Event: 4/8/2025 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. Modified Normalized Difference Water Index (MNDWI) provides a different view of the Earth's surface combining different bands to make water bodies more obvious. This index can be used to identify flooded areas as well as heavily saturated areas. 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. NDVI: dark green colors are areas with a lot of green leaf growth which indicates the presence of chlorophyll. Chlorophyll reflects more infrared light and less visible light. Areas with some green leaf growth are in light greens, and areas with little to no vegetation growth are even lighter greens. mNDWI: areas of existing water, such as a rivers or lakes appear in the darkest green with high values near 1, while dry land appears in the darkest brown with low values near -1. This product can be used to identify potentially flooded areas that can be seen in varying shades of green and light brown depending on the amount of moisture picked up by the satellite. Note: Areas of cloud cover will show up in varying shades of green/blue similar to water. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI), Landsat 8 Resolution: 30 meters Credits: NASA/MSFC, USGS Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=3b491d2372d94c4cbc8bac96407e68e8 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202504_SEVEREWX_SOUTHEASTUS/2505_landsat/ImageServer/WMSServer
Date of Images: Post-Event: April 26, 2024-May 22, 2024 (Daily) Pre-Event: August 2028 (Composite) Summary: The Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images are corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30. The baseline image is from August 2026, generated from all daily atmospheric and lunar-corrected data from that month. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. The Black Marble Day-Night Band (BRDF-Corrected) cloud masks were created by the NASA Black Marble Science team. This is the layer to displaying where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. For the cloud masks, grey represents cloud cover. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 15 arc-second (~500 meters) Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Service URL: See REST Endpoint on the right.
Dates of Images: Post-Event: 1/18/2025, 1/19/2026 Pre-Event: 11/27/2025, 12/19/2025, 12/20/2025, 1/18/2026, 1/19/2026 Date of Next Image: Unknown Summary: Natural Color: The Natural Color RGB provides a false composite look at the surface. This RGB uses a shortwave, the near-infrared, and red channels from the instrument. 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: Natural Color: areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta. 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_202601_WINTERWX_US/202601_landsat/ImageServer/WMSServer
Date(s) of Images: Post-Event: 01/26/2026 Summary: BlackSky’s nighttime product utilizes the panchromatic band aboard the BlackSky Gen-2 constellation to collect imagery during the nighttime hours. These nighttime images typically show features that radiate artificial illumination such as bright lights from cities and roadway streetlights. The average sensitivity is 0.2 W/m2/str/µm meaning features brighter than this limit would be detectable. This nighttime product is more sensitive to noise in forms of lines and/or pixels because the panchromatic band is detecting features that are dimmer than daylight imagery. Blacksky will works to remove as much noise as possible, but noise may continue through to the final products. Nighttime imagery by Blacksky is not georeferenced or orthorectified due to current collection methodology. Suggested Use: The image shows nighttime lights in brighter colors. Darker colors represent less luminosity. Resolution: ~1 m (may vary be between 0.8 m and 1.1 m dependent on viewing angels) Credits: BlackSky
Date of Images: 05/18/2024 Summary: The visible-wavelength true color RGB digital camera images were taken by astronauts on board the International Space Station showing flooding in southeastern Texas. These images were manually georeferenced by members of the Earth Science and Remote Sensing Unit at NASA Johnson Space Center. Suggested Use: This image provides regional context, and may be useful for visualization of the ongoing event. Higher spatial resolution images may be suitable for spatial analysis to support decision making or research applications. Sensor: Nikon Z9 Electronic Still Camera. Additional Information about the astronaut photography from the ISS. Credits: International Space Station Program, NASA Johnson Space Center Earth Science and Remote Sensing Unit, ARES Division, Exploration Architecture, Integration & Science Directorate Data Download: Full datasets are available for download at the Gateway to Astronaut Photography of Earth. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=c9bb2094473649b2a3802305f4fc8eb1 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_TX/ISS/ImageServer/WMSServer
Dates of Images: Post-Event: August 14, 2026 Date of Next Image: None Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space. 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: Airbus Pléiades Neo 4 Resolution: ~1.3 meters Credits: NASA, Airbus. For derived products: “Includes copyrighted material of Airbus. All rights reserved.” This work utilized data made available through the NASA Commercial Satellite Data Acquisition (CSDA) program. Esri REST Endpoint: See layers below.
Orthorectified imagery data for Arlington County Virginia captured in 2021. This imagery was captured in the late winter, early spring timeframe. The resolution for the imagery is 3 inches.
2019 National Land Cover Dataset (NLCD) for CONUS.