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  • ABoVE: Riverbank Erosion and Vegetation Changes, Yukon River Basin, Alaska, 1984-2017

    https://cmr.earthdata.nasa.gov/search/concepts/C2162145546-ORNL_CLOUD.xml
    Description:

    This dataset provides a time series of riverbank erosion and vegetation colonization along reaches of the Yukon River (3 study areas), Tanana and Nenana Rivers (1 area), and Chandalar River (1 area) in interior Alaska over the period 1984-2017. The change data were derived from selected 30-m images from Landsat TM, Landsat ETM+, and Landsat Operational Land Imager (OLI) surface reflectance products. Image classification used the Normalized Differenced Vegetation Index (NDVI) with an NDVI threshold of 0.2 to differentiate vegetated from non-vegetated pixels. Images were assigned to one of seven or eight multiyear intervals, within the 1984-2017 overall range, for each study area. Time intervals vary by study site. Change detection identified shifts from one time interval to the next: changes from vegetated to non-vegetated classes were considered riverbank erosion and changes from non-vegetated to vegetated classes were considered vegetation colonization.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 61.9076 -161.459 68.1463 -143.305

    ORNL_CLOUD Short Name: Erosion_Vegetation_Yukon_1616 Version ID: 1 Unique ID: C2162145546-ORNL_CLOUD

  • ABoVE: Tree Canopy Cover and Stand Age from Landsat, Boreal Forest Biome, 1984-2020

    https://cmr.earthdata.nasa.gov/search/concepts/C2539841646-ORNL_CLOUD.xml
    Description:

    This dataset contains Landsat-derived locally-calibrated estimates of tree canopy cover (TCC) and forest stand age across global boreal forests from 1984-2020 in Cloud-Optimized GeoTIFF (*.tif) format. These raster data span the circum-hemispheric boreal forest biome between 47 to 73 degrees north at 30 m resolution. Machine learning models calibrated with data from the World Reference System 2 were used to predict TCC from Landsat data at 30-m spatial resolution at annual temporal resolution. Through analysis of TCC time series, forest change estimates of stand age from 1984-2020 were developed. The broad spatial and temporal coverage of these data provide insight into forest and carbon dynamics of the global boreal forest system. Boreal forests store a large proportion of global soil and biomass carbon and have experienced disproportionately high levels of warming over the past century.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 45 -180 73 180

    ORNL_CLOUD Short Name: Boreal_CanopyCover_StandAge_2012 Version ID: 1 Unique ID: C2539841646-ORNL_CLOUD

  • ABoVE: Tundra Plant Functional Type Continuous-Cover, North Slope, Alaska, 2010-2015

    https://cmr.earthdata.nasa.gov/search/concepts/C2143401689-ORNL_CLOUD.xml
    Description:

    This dataset provides predicted continuous-field cover for tundra plant functional types (PFTs), across ~125,000 km2 of Alaska's North Slope at 30-m resolution. The data cover the period 2010-07-01 to 2015-08-31. The data were derived using a random forest data-mining algorithm, predictors derived from Landsat satellite observations (surface reflectance composites for ~15-day periods from May-August), and field vegetation cover and site characterization data spanning bioclimatic and geomorphic gradients. The field vegetation cover was stratified by nine PFTs, plus open water, bare ground and litter, and using the cover metrics total cover (areal cover including the understory) and top cover (uppermost canopy or ground cover), resulting in a total of 19 field cover types. The field data and predictor values at the field sites are also included.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 65.5858 -167.476 73.8004 -143.978

    ORNL_CLOUD Short Name: AK_Tundra_PFT_FractionalCover_1830 Version ID: 1 Unique ID: C2143401689-ORNL_CLOUD

  • ABoVE: Wetland Type, Slave River and Peace-Athabasca Deltas, Canada, 2007 and 2017

    https://cmr.earthdata.nasa.gov/search/concepts/C2240727799-ORNL_CLOUD.xml
    Description:

    This dataset provides ecosystem-types for the Slave River Delta (SRD) and Peace-Athabasca Delta (PAD), Canada, for the time periods circa 2007 and circa 2017. The image resolution is 12.5 m with 0.2-hectare minimum mapping unit. Included are an 18-class modified Enhanced Wetland Classification (EWC) scheme for wetland, peatland, and upland areas. Classes were derived from a Random Forest classification trained on multi-seasonal moderate-resolution images and synthetic aperture radar (SAR) imagery sourced from aerial and satellite sensors, field data, and calculated indices. Indices included Height Above Nearest Drainage (HAND) and Topographic Position Index (TPI), both derived from a digital elevation model, to differentiate between land cover types. The c. 2007 remote sensing data were comprised of early and late growing season Landsat-5, ERS2, L-Band PALSAR from 2006 to 2010 and growing season Landsat thermal composites. The c. 2017 remote sensing data were comprised of early and late growing season Landsat-8 and L-Band PALSAR-2 from 2017 to 2019, Sentinel-1 June VV and VH mean and standard deviations, and growing season Landsat thermal composites. Elevation indices from multi-resolution TPI and HAND were created from the Japan Aerospace Exploration Agency Advanced Land Observing Satellite 30 m Global Spatial Data Model. Also included are the images used for classification and the classification error matrices for each map and time period. Data are provided in GeoTIFF and GeoPackage file formats.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 57.7746 -115.291 61.7919 -109.643

    ORNL_CLOUD Short Name: Ecosystem_Map_SRD_PAD_1947 Version ID: 1 Unique ID: C2240727799-ORNL_CLOUD

  • Automated Greenland Glacier Termini Position Time Series, Version 1

    https://cmr.earthdata.nasa.gov/search/concepts/C2849438379-NSIDCV0.xml
    Description:

    This data set contains shapefiles of termini traces from 294 Greenland glaciers, derived using a deep learning algorithm (AutoTerm) applied to satellite imagery. The model functions as a pipeline, imputing publicly availably satellite imagery from Google Earth Engine (GEE) and outputting shapefiles of glacial termini positions for each image. Also available are supplementary data, including temporal coverage of termini traces, time series data of termini variations, and updated land, ocean, and ice masks derived from the <a href="https://nsidc.org/data/nsidc-0714/versions/1">Greenland Ice Sheet Mapping Project (GrIMP) ice masks</a>.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 57 -75 85 -8

    NSIDCV0 Short Name: NSIDC-0788 Version ID: 1 Unique ID: C2849438379-NSIDCV0

    This collection does not contain any granules

  • CALFIN Subseasonal Greenland Glacial Terminus Positions V001

    https://cmr.earthdata.nasa.gov/search/concepts/C3298062391-NSIDC_CPRD.xml
    Description:

    This data set contains shapefiles of Greenland’s glacial termini and basins for the years 1972 to 2019. These vector data were created from Landsat 1-8 satellite imagery using the Calving Front Machine (CALFIN) an automated processing workflow utilizing neural networks for extracting calving fronts from satellite images of marine-terminating glaciers.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 60 -75 80 -15

    NSIDC_CPRD Short Name: NSIDC-0764 Version ID: 1 Unique ID: C3298062391-NSIDC_CPRD

  • Carbon Pools across CONUS using the MaxEnt Model, 2005, 2010, 2015, 2016, and 2017

    https://cmr.earthdata.nasa.gov/search/concepts/C2389289428-ORNL_CLOUD.xml
    Description:

    This dataset provides annual estimates of six carbon pools, including forest aboveground live biomass, belowground biomass, aboveground dead biomass, belowground dead biomass, litter, and soil organic matter, across the conterminous United States (CONUS) for 2005, 2010, 2015, 2016, and 2017. Carbon stocks were estimated using a modified MaxEnt model. Measurements of pixel-specific site conditions from remote sensing data were combined with field inventory data from the U.S. Forest Service Forest Inventory and Analysis (FIA). Remote sensing data inputs included Thematic Mapper on Landsat 5, Operational Land Imager on Landsat 8, Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua, microwave radar measurements from Phased Array type L-band Synthetic Aperture Radar (PALSAR) on Advanced Land Observation Satellite (ALOS) and PALSAR-2 ALOS-2, airborne imagery from National Agriculture Imagery Program (NAIP), and the digital elevation model from the Shuttle Radar Topography Mission (SRTM). Data from satellite and airborne sources were co-registered on a common 100 m (1 ha) grid.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 21.5875 -130.233 52.8561 -64.1285

    ORNL_CLOUD Short Name: CMS_CONUS_Biomass_1752 Version ID: 1 Unique ID: C2389289428-ORNL_CLOUD

  • Chesapeake Land Cover

    https://cmr.earthdata.nasa.gov/search/concepts/C2781412641-MLHUB.xml
    Description:

    This dataset contains high-resolution aerial imagery from the USDA NAIP program, high-resolution land cover labels from the Chesapeake Conservancy, low-resolution land cover labels from the USGS NLCD 2011 dataset, low-resolution multi-spectral imagery from Landsat 8, and high-resolution building footprint masks from Microsoft Bing, formatted to accelerate machine learning research into land cover mapping. The Chesapeake Conservancy spent over 10 months and $1.3 million creating a consistent six-class land cover dataset covering the Chesapeake Bay watershed. While the purpose of the mapping effort by the Chesapeake Conservancy was to create land cover data to be used in conservation efforts, the same data can be used to train machine learning models that can be applied over even wider areas.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 36.5643108 -80.8092703 43.9973515 -74.2529408

    MLHUB Short Name: Chesapeake Land Cover Version ID: 1 Unique ID: C2781412641-MLHUB

    This collection does not contain any granules

  • CMS: Landsat-derived Annual Land Cover Maps for the Colombian Amazon, 2001-2016

    https://cmr.earthdata.nasa.gov/search/concepts/C2389083233-ORNL_CLOUD.xml
    Description:

    This dataset provides annual maps of land cover classes for the Colombian Amazon from 2001 through 2016 that were created by classifying time segments detected by the Continuous Change Detection and Classification (CCDC) algorithm. The CCDC algorithm detected changes in Landsat pixel surface reflectance across the time series, and the time segments were classified into land cover types using a Random Forest classifier and manually collected training data. Annual maps of land cover were created for each Landsat scene and then post-processed and mosaicked. Land cover types include unclassified, forest, natural grasslands, urban, pastures, secondary forest, water, or highly reflective surfaces. The training data are not included with this dataset.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -3.88273 -78.0306 5.37713 -65.954

    ORNL_CLOUD Short Name: Landcover_Colombian_Amazon_1783 Version ID: 1 Unique ID: C2389083233-ORNL_CLOUD

  • CMS: Mangrove Forest Cover Extent and Change across Major River Deltas, 2000-2016

    https://cmr.earthdata.nasa.gov/search/concepts/C2389022166-ORNL_CLOUD.xml
    Description:

    This dataset provides estimates of mangrove extent for 2016, and mangrove change (gain or loss) from 2000 to 2016, in major river delta regions of eight countries: Bangladesh, Gabon, Jamaica, Mozambique, Peru, Senegal, Tanzania, and Vietnam. For mangrove extent, a combination of Landsat 8 OLI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) elevation data were used to create country-wide maps of mangrove landcover extent at a 30-m resolution. For mangrove change, the global mangrove map for 2000 (Giri et al., 2010) was used as the baseline. Normalized Difference Vegetation Indices (NDVI) were calculated for every cloud- and shadow-free pixel in the Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI collection and used to create an NDVI anomaly from 2000 to 2016. Areas of change (loss or gain) occurred at the extremes of the cumulative anomalies.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -28.0001 -82.0002 22.5041 107.027

    ORNL_CLOUD Short Name: CMS_Mangrove_Cover_1670 Version ID: 1.1 Unique ID: C2389022166-ORNL_CLOUD