Canada imagery and land cover data
＋ Follow new data223 datasets about imagery and land cover in Canada.
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: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input images were generated from the Sentinel-2 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: 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.
Dates of Images: 20220427T1719, 20220505T1728, 20220507T1719, 20220510T1709, 20220515T1727, 20220522T1718, 20220524T1721, 20220527T1719 (GMT) (YYYYMMDDTHHMM) 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 three 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: April 27, 2022, May 05, 2022, May 07, 2022, May 10, 2022, May 15, 2022, May 22, 2022, May 24, 2022, May 27, 2022 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. Water Gain maps highlight newly inundated areas by comparing pre- and flood-event conditions. 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. The OPERA DSWx-HLS product from 27 April 2022 was likely impacted by unmitigated aerosol effects on the western edge of the image, potentially causing false detections limited to approximately 10 km measured from the western edge into the image. The granule is provided but caution should be taken when interpreting the affected area. 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_20220427T1719_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220505T1728_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220507T1719_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220510T1709_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220515T1727_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220522T1718_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220524T1721_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220527T1719_mosaic.tif 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 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) Maximum Water Extent Layer Values: The maximum water extent observed during the event was created by aggregating OPERA DSWx-HLS water products.. File names: ARIA-OPERA_L3_DSWX-HLS_V1_WTR_20220505T1728_20220527T1719_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: 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 Credits: The product contains modified Copernicus Sentinel data, processed by the European Space Agency and analyzed by the NASA-JPL/Caltech ARIA/OPERA team. The DSWx products are produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group, and managed by NASA's Jet Propulsion Laboratory. Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (bato@jpl.nasa.gov) Emre Havazli (emre.havazli@jpl.nasa.gov) Renato Frasson (renato.prata.de.moraes.frasson@jpl.nasa.gov) Alexander Handwerger (alexander.handwerger@jpl.nasa.gov ) Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2603_202205_FLOOD_MN/ex2603_opera_dswx_hls/MapServer/WMSServer
Dates of Images: 20220427T1719, 20220505T1728, 20220507T1719, 20220510T1709, 20220515T1727, 20220522T1718, 20220524T1721, 20220527T1719 (GMT) (YYYYMMDDTHHMM) 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 three 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: April 27, 2022, May 05, 2022, May 07, 2022, May 10, 2022, May 15, 2022, May 22, 2022, May 24, 2022, May 27, 2022 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. Water Gain maps highlight newly inundated areas by comparing pre- and flood-event conditions. 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. The OPERA DSWx-HLS product from 27 April 2022 was likely impacted by unmitigated aerosol effects on the western edge of the image, potentially causing false detections limited to approximately 10 km measured from the western edge into the image. The granule is provided but caution should be taken when interpreting the affected area. 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_20220427T1719_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220505T1728_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220507T1719_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220510T1709_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220515T1727_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220522T1718_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220524T1721_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220527T1719_mosaic.tif 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 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) Maximum Water Extent Layer Values: The maximum water extent observed during the event was created by aggregating OPERA DSWx-HLS water products.. File names: ARIA-OPERA_L3_DSWX-HLS_V1_WTR_20220505T1728_20220527T1719_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: 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 Credits: The product contains modified Copernicus Sentinel data, processed by the European Space Agency and analyzed by the NASA-JPL/Caltech ARIA/OPERA team. The DSWx products are produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group, and managed by NASA's Jet Propulsion Laboratory. Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (bato@jpl.nasa.gov) Emre Havazli (emre.havazli@jpl.nasa.gov) Renato Frasson (renato.prata.de.moraes.frasson@jpl.nasa.gov) Alexander Handwerger (alexander.handwerger@jpl.nasa.gov ) Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2603_202205_FLOOD_MN/ex2603_opera_dswx_hls/MapServer/WMSServer
Dates of Images Post-Event: 8/3/2026 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. Resolution ~1 m Credits NASA Disasters Program, Processing from MSFC, Planet. For derived products: “Includes copyrighted material of Planet Labs PBC. All rights reserved.” This work utilized data made available through the NASA Commercial Satellite Data Acquisition (CSDA) program.
Retirement Notice: This item is in mature support as of March 2019 and will retire in December 2029. A replacement item has not been identified at this time. See Sunsetting Legacy Basemaps blog for more information. This map presents land cover and detailed topographic maps for the United States. The map includes the National Park Service (NPS) Natural Earth physical map at 1.24km per pixel for the world at small scales, i-cubed eTOPO 1:250,000-scale maps for the contiguous United States at medium scales, and National Geographic TOPO! 1:100,000 and 1:24,000-scale maps (1:250,000 and 1:63,000 in Alaska) for the United States at large scales. The TOPO! maps are seamless, scanned images of United States Geological Survey (USGS) paper topographic maps. Known Issues: There is a known issue with minor positional shifts (~300 meters) of topo map sheets in the Alaska region. Please be aware if using the topo map for orthorectification or similar purposes.
uav imagery by veritas terrae, acquired 2026-05-02 to 2026-05-02, 5.0 cm per pixel, published on OpenAerialMap.
Tacoma 1931 - 3 foot Aerial Photography Contact Info: GIS Team Email: GISteam@cityoftacoma.org Company: Fairchild Aerial Surveys, Inc. Flight Time: May, 1931 Scale: 1” = 800’ Project: C-1540
Dates of Images: 20260724T1849, 20260725T1843, 20260727T1901 (GMT) (YYYYMMDDTHHMM) 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 maximum vegetation anomaly loss and disturbance status, using the OPERA Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (DIST-ALERT-HLS) dataset. The dataset provided includes two product types: Vegetation Disturbance Status maps on individual dates Maximum Vegetation Anomaly loss maps on individual dates Dates and Input Data: Images: June 24, July 25, and July 27, 2026 Products are generated from OPERA DIST-ALERT-HLS satellite imagery and mosaicked across tiles to provide continuous coverage. Product Description: Surface Disturbance, specifically vegetation loss, is identified from optical satellite imagery and mapped at 30 m spatial resolution. The Vegetation Disturbance Status layer indicates the confidence that vegetation loss has occurred, and whether the disturbance is still ongoing or has ended. The Max Vegetation Anomaly loss shows how much vegetation has been lost at each location, based on the largest change observed during the time period. As the number of post-event images increase, the confidence in the detections increases. Note we applied a temporal filter to these products. The temporal filter was used to remove any surface disturbance that occurred prior to the onset of the wildfires. Caveats and Limitations: This product detects vegetation loss due to any reason. Importantly, not all detected changes are due to wildfire on July 23, 2026. 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 DIST-ALERT-HLS maps per pixel vegetation disturbance (specifically, vegetation cover loss) from the Harmonized Landsat Sentinel-2 (HLS) scenes. We provide the (1) maximum vegetation anomaly value (VEG-ANOM-MAX) layer and (2) vegetation disturbance status (VEG-DIST-STATUS). All provided layers have been filtered to include only disturbance detected following the start of the fires. VEG-ANOM-MAX: Difference between 3 year historical baseline and current year observed vegetation cover at the date of maximum decrease (vegetation loss of 0-100%). This layer can be used to threshold vegetation disturbance per a given sensitivity (e.g. disturbance of >=20% vegetation cover loss). Layer Values: 0-100: Maximum loss of percent vegetation (suggested color: white to red gradient) 255: No data (suggested color: transparent) File names: OPERA_L3_DIST-ALERT-HLS_V1_VEG-ANOM-MAX_20260724T1849_mosaic.tif, OPERA_L3_DIST-ALERT-HLS_V1_VEG-ANOM-MAX_20260725T1843_mosaic.tif, OPERA_L3_DIST-ALERT-HLS_V1_VEG-ANOM-MAX_20260727T1901_mosaic.tif VEG-DIST-STATUS: Indication of vegetation cover loss (vegetation disturbance). The status label is based on the maximum anomaly value, confidence level, and whether it is ongoing or finished. ''First'' means the pixel has had an anomaly detection but no subsequent observations whether anomalous or not. ''Provisional'' means there have been two consecutive disturbance detections but not yet with high confidence. ''Confirmed'' means that vegetation disturbance is detected with high confidence. The label ''finished'' is applied to confirmed disturbances that have had two consecutive no-anomaly observations or one 15 days or more after the last anomaly detection. If a new disturbance is detected, it will overwrite those in a ''finished'' state. These labels are reported for both above and below the 50% disturbance threshold based on the maximum anomaly value. Layer Values: 0: No disturbance (suggested color: #121212) 1: first 2: provisional 3: confirmed 4: first >=50% (suggested color: #008888) 5: provisional >=50% (suggested color: #e48727) 6: confirmed >=50% (suggested color: #e01b07) 7: confirmed 8: confirmed >=50%, finished (suggested color: #dddddd) 255: No data (suggested color: transparent) File names: OPERA_L3_DIST-ALERT-HLS_V1_VEG-DIST-STATUS_20260724T1849_mosaic.tif, OPERA_L3_DIST-ALERT-HLS_V1_VEG-DIST-STATUS_20260725T1843_mosaic.tif, OPERA_L3_DIST-ALERT-HLS_V1_VEG-DIST-STATUS_20260727T1901_mosaic.tif 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 Credits: The product contains modified Copernicus Sentinel-2 and NASA Landsat 8/9 data, processed by the European Space Agency and NASA. Data were analyzed by the NASA-JPL/Caltech ARIA/OPERA team. The DIST products are produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group, and managed by NASA's Jet Propulsion Laboratory. The DIST-ALERT-HLS product was developed and produced by the OPERA Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland. NASA JPL-Caltech-UMD ARIA/OPERA Team Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (bato@jpl.nasa.gov) Alexander Handwerger (alexander.handwerger@jpl.nasa.gov ) Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202607_FIRE_OR/202618_opera_dist_vegDistStatus/ImageServer/WMSServer Data Download: https://aria-share.jpl.nasa.gov/20260723_Oregon_Fires/
uav imagery by Sean Frey, acquired 2025-08-08 to 2025-08-08, 3.6 cm per pixel, published on OpenAerialMap.
This dataset is a tile mosaic of 1-meter aerials of the City of Tacoma from 1990.Contact Info: Contact Info: GIS Team Email: GISteam@cityoftacoma.org Company: U.S. Geological Survey Flight Time: July, 1990 Filename: Digital Orthophoto Quarter Quadrangles (HTM) Earth Explorer Full Display Record 1 (HTM)
uav imagery by gber62, acquired 2026-01-01 to 2026-01-01, 5.0 cm per pixel, published on OpenAerialMap.
uav imagery by Maxwell Ward, acquired 2025-07-03 to 2025-07-03, 7.2 cm per pixel, published on OpenAerialMap.
uav imagery by SF Farms, acquired 2025-08-08 to 2025-08-08, 5.9 cm per pixel, published on OpenAerialMap.
uav imagery by DroneTM, Alex and Ivan, acquired 2025-09-09 to 2025-09-09, 4.9 cm per pixel, published on OpenAerialMap.
