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.

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

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.

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NSIDC_ECS Short Name: NSIDC-0764 Version ID: 1 Unique ID: C2116307977-NSIDC_ECS

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.

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

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.

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

Collaborative With: McPhee 0732804, Holland 0732869, Truffer 0732730, Stanton 0732926, Anandakrishnan 0732844 Title: Collaborative Research: IPY: Ocean-Ice Interaction in the Amundsen Sea Sector of West Antarctica The Office of Polar Programs, Antarctic Integrated and System Science Program has made this award to support an interdisciplinary study of the effects of the ocean on the stability of glacial ice in the most dynamic region the West Antarctic Ice Sheet, namely the Pine Island Glacier in the Amundsen Sea Embayment. The collaborative project builds on the knowledge gained by the highly successful West Antarctic Ice Sheet program and is being jointly sponsored with NASA. Recent observations indicate a significant ice loss, equivalent to 10% of the ongoing increase in sea-level rise, in this region. These changes are largest along the coast and propagate rapidly inland, indicating the critical impact of the ocean on ice sheet stability in the region. While a broad range of remote sensing and ground-based instrumentation is available to characterize changes of the ice surface and internal structure (deformation, ice motion, melt) and the shape of the underlying sediment and rock bed, instrumentation has yet to be successfully deployed for observing boundary layer processes of the ocean cavity which underlies the floating ice shelf and where rapid melting is apparently occurring. Innovative, mini ocean sensors that can be lowered through boreholes in the ice shelf (about 500 m thick) will be developed and deployed to automatically provide ocean profiling information over at least three years. Their data will be transmitted through a conducting cable frozen in the borehole to the surface where it will be further transmitted via satellite to a laboratory in the US. Geophysical and remote sensing methods (seismic, GPS, altimetry, stereo imaging, radar profiling) will be applied to map the geometry of the ice shelf, the shape of the sub ice-shelf cavity, the ice surface geometry and deformations within the glacial ice. To integrate the seismic, glaciological and oceanographic observations, a new 3-dimensional coupled ice-ocean model is being developed which will be the first of its kind. NASA is supporting satellite based research and the deployment of a robotic-camera system to explore the environment in the ocean cavity underlying the ice shelf and NSF is supporting all other aspects of this study.

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SCIOPS Short Name: Bindschadler_0732906 Version ID: Not provided Unique ID: C1214604853-SCIOPS