Imagery and land cover data
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uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-06-01 to 2026-06-02, 3.5 cm per pixel, published on OpenAerialMap.
satellite imagery by Vantor, acquired 2026-09-06 to 2026-09-06, 38.9 cm per pixel, published on OpenAerialMap.
Dates of Images: Post-Event: 6/28/2026 Pre-Event: 5/3/2026 Date of Next Image: Unknown Summary: True Color: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. Color Infrared: The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted from the fires. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Suggested Use: True Color: provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Color Infrared: depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. Satellite/Sensor: Landsat 8 Operational Land Imager (OLI), Landsat 9 Operational Land Imager (OLI-2) 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_202606_FLOOD_LA/202610_landsat/ImageServer/WMSServer
Dates of Images: Post-Event: February 20, 2025, February 23, 2025, February 25, 2025 Pre-Event: February 10, 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. Snow cover appears as white. The Short Wave Infrared (SWIR) RGB is a product that is created using the SWIR, NIR, and Red channels of the respective instrument. Snow cover appears as various shades of teal. 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. Snow cover appears as white. 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/GSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202502_FLOOD_OHVALLEY/2502_sentinel2/ImageServer/WMSServer
satellite imagery by BF, acquired 2026-06-11 to 2026-06-11, 5.4 cm per pixel, published on OpenAerialMap.
__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer 9-class v2 dataset._ Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. This map uses an updated model from the 10-class model and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification. All years are available under a Creative Commons BY-4.0.
ArcGIS Image ServiceTime Interval: Seasonal Climatology Version: 1 Spatial Resolution: 3 km Time Extent: 2015 to 2025 (Dec-Feb, Mar-May, Jun-Aug, Sep-Nov) Projection: GCS WGS84 Extent: Regional | Australia Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains Australian IR detection climatological statistics averaged over: Dec-Feb, Mar-May, Jun-Aug, Sep-Nov 2015-2025 Satellite Mapping and Analysis of Severe Hailstorms (SMASH) ProjectThis Hailstorm research project seeks to address knowledge gaps in the severe hail climatology using regional to global scale satellite observations and provides mechanisms to explore related datasets. For questions/issues please contact: kristopher.m.bedka@nasa.gov
uav imagery by Leigh Lunas, acquired 2026-08-17 to 2026-08-17, 4.0 cm per pixel, published on OpenAerialMap.
ArcGIS Image ServiceTime Interval: Seasonal Climatology With Hourly Timesteps Version: 1 Spatial Resolution: 3 km Time Extent: 2015 to 2025 (Dec-Feb, Mar-May, Jun-Aug, Sep-Nov) (With 24 Hours) Projection: GCS WGS84 Extent: Regional | Australia Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains Australian CTH detection climatological diurnal statistics averaged over: Dec-Feb, Mar-May, Jun-Aug, Sep-Nov 2015-2025 Satellite Mapping and Analysis of Severe Hailstorms (SMASH) ProjectThis Hailstorm research project seeks to address knowledge gaps in the severe hail climatology using regional to global scale satellite observations and provides mechanisms to explore related datasets. For questions/issues please contact: kristopher.m.bedka@nasa.gov
Dates of Images: Post-Event: January 14, 2025 Pre-Event: January 6, 2025 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. The Normalized Burn Ratio identifies burned areas using this calculation. Darker areas are potentially burned areas. 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. Darker Areas are potentially burned in the NBR product. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=a95eee54bd9e45548ad2b7e49ac0fd93 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_landsat/ImageServer/WMSServer
OPERA Level-2 Radiometric Terrain Corrected (RTC) products in VV polarization generated from Sentinel-1 SAR imagery, processed by OPERA (JPL). Values for the VV polarization are commonly driven by surface roughness and/or soil moisture, with rougher surfaces and higher soil moisture returning higher backscatter values. Surface water appears very dark under calm conditions, as the signal bounces off the surface away from the sensor. OPERA RTC-S1 data is available over all landmasses (excluding Antarctica) for Sentinel-1 acquisitions starting April 14, 2016, but only products from October 4, 2023, onward are included in this image service. The source rasters referenced in this service are hosted by the Alaska Satellite Facility in NASA's Earthdata Cloud, and the service is updated daily to include new products. More information about the dataset is available at https://asf.alaska.edu/datasets/daac/opera/. OPERA includes partners from NASA’s Goddard Space Flight Center, the U.S. Geological Survey (USGS), the University of Maryland College Park, the University of Alaska Fairbanks, Southern Methodist University, and EarthScope. Refer to https://www.jpl.nasa.gov/go/opera for more details. This service is still under development. Please contact hjkristenson@alaska.edu if you encounter any problems.
uav imagery by North Metro Fire Rescue District, acquired 2026-05-28 to 2026-05-28, 1.7 cm per pixel, published on OpenAerialMap.
ArcGIS Image ServiceTime Interval: Seasonal Climatology With Hourly Timesteps Version: 1 Spatial Resolution: 3 km Time Extent: 2004 to 2024 (Dec-Feb, Mar-May, Jun-Aug, Sep-Nov) (With 24 Hours) Projection: GCS WGS84 Extent: Regional | Europe Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains European OT detection climatological diurnal statistics averaged over: Dec-Feb, Mar-May, Jun-Aug, Sep-Nov 2004-2024 Satellite Mapping and Analysis of Severe Hailstorms (SMASH) ProjectThis Hailstorm research project seeks to address knowledge gaps in the severe hail climatology using regional to global scale satellite observations and provides mechanisms to explore related datasets. For questions/issues please contact: kristopher.m.bedka@nasa.gov
ArcGIS Image ServiceTime Interval: Seasonal Climatology Version: 1 Spatial Resolution: 3 km Time Extent: 2015 to 2025 (Dec-Feb, Mar-May, Jun-Aug, Sep-Nov) Projection: GCS WGS84 Extent: Regional | Australia Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains Australian Anvil Cloud Days per Year detection climatological statistics averaged over: Dec-Feb, Mar-May, Jun-Aug, Sep-Nov 2015-2025 Satellite Mapping and Analysis of Severe Hailstorms (SMASH) ProjectThis Hailstorm research project seeks to address knowledge gaps in the severe hail climatology using regional to global scale satellite observations and provides mechanisms to explore related datasets. For questions/issues please contact: kristopher.m.bedka@nasa.gov
satellite imagery by Vantor, acquired 2026-08-28 to 2026-08-28, 58.1 cm per pixel, published on OpenAerialMap.
uav imagery by Rodolfo Vargas Chile. GEÓGRAFO, acquired 2026-07-23 to 2026-07-23, 5.0 cm per pixel, published on OpenAerialMap.
uav imagery by Dronecoria, acquired 2026-07-25 to 2026-07-26, 2.0 cm per pixel, published on OpenAerialMap.
uav imagery by Rodolfo Vargas Chile, Ing. GEÓGRAFO - MTO, acquired 2026-10-06 to 2026-10-06, 6.0 cm per pixel, published on OpenAerialMap.
uav imagery by private, acquired 2026-10-05 to 2026-10-06, 5.0 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 1.7 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 3.0 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 2.8 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 2.7 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-13 to 2026-09-13, 5.0 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-13 to 2026-09-13, 2.3 cm per pixel, published on OpenAerialMap.
uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-09-06 to 2026-09-07, 3.5 cm per pixel, published on OpenAerialMap.
uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-09-06 to 2026-09-07, 3.5 cm per pixel, published on OpenAerialMap.
uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-09-06 to 2026-09-07, 3.5 cm per pixel, published on OpenAerialMap.
uav imagery by North Metro Fire, acquired 2026-09-07 to 2026-09-07, 3.2 cm per pixel, published on OpenAerialMap.
uav imagery by Anna Ko, acquired 2026-09-06 to 2026-09-07, 5.0 cm per pixel, published on OpenAerialMap.
aircraft imagery by IGAC Emergencia Terremoto 2026, acquired 2026-09-01 to 2026-09-01, 10.0 cm per pixel, published on OpenAerialMap.
uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-08-31 to 2026-09-01, 3.5 cm per pixel, published on OpenAerialMap.
uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-08-30 to 2026-08-31, 3.5 cm per pixel, published on OpenAerialMap.
satellite imagery by Vantor, acquired 2026-08-31 to 2026-08-31, 60.1 cm per pixel, published on OpenAerialMap.
uav imagery by Ebbe Valbak, acquired 2026-07-30 to 2026-08-04, 5.0 cm per pixel, published on OpenAerialMap.