This is a catalog record. The data is held outside UD Dataverse. Original dataset: https://doi.org/10.5281/zenodo.16783114. Please cite the original dataset rather than this record.
"Coastal regions of the eastern United States are increasingly vulnerable to saltwater intrusion and rising soil salinity, which can significantly alter land cover patterns. Monitoring these changes over time requires consistent, high-quality datasets. This work presents a multi-temporal, classified land cover dataset for the Delmarva Peninsula (14 coastal counties in Delaware, Maryland, and Virginia, USA) for five target years: 2000, 2002, 2005, 2009, and 2016. The datasets were produced using a Random Forest classifier trained on Continuous Change Detection and Classification (CCDC)-derived synthetic Landsat surface reflectance, and target variables dervied from a high-resolution (1m) NAIP–Landsat derived dataset. The classification distinguishes seven land cover classes: Bare Soil, Built, Farmland, Forest, Marsh, Other Vegetation, and Water. Overall testing accuracy reached 90.6% with a five-fold cross-validation accuracy of 90.2% ± 0.13%, demonstrating robust model performance. These datasets can support further studies on land use change, coastal resilience, and salinity-linked landscape dynamics. This dataset contains five integer-coded raster files, one for each study year. Each raster is a single-band GeoTIFF with 30m spatial resolution."
Time period: 2000, 2002, 2005, 2009, 2016
Location: 14 counties of the Delmarva peninsula
Formats: GeoTiff
Version: 1
Access (per the DSI catalog): Public (Dataset is or could be made publicly available to all without restrictions)
Project: Multi-temporal land cover dataset for the Delmarva Peninsula (2000, 2002, 2005, 2009, 2016) at 30m Resolution. "*Subject to change* Coastal regions of the eastern United States are increasingly vulnerable to saltwater intrusion and rising soil salinity, which can significantly alter land cover patterns. Monitoring these changes over time requires consistent, high-quality datasets. This work presents a multi-temporal, classified land cover dataset for the Delmarva Peninsula (14 coastal counties in Delaware, Maryland, and Virginia, USA) for five target years: 2000, 2002, 2005, 2009, and 2016. The datasets were produced using a Random Forest classifier trained on Continuous Change Detection and Classification (CCDC)-derived synthetic Landsat surface reflectance, and target variables dervied from a high-resolution (1m) NAIP–Landsat derived dataset. The classification distinguishes seven land cover classes: Bare Soil, Built, Farmland, Forest, Marsh, Other Vegetation, and Water. Overall testing accuracy reached 90.6% with a five-fold cross-validation accuracy of 90.2% ± 0.13%, demonstrating robust model performance. These datasets can support further studies on land use change, coastal resilience, and salinity-linked landscape dynamics. This dataset contains five integer-coded raster files, one for each study year. Each raster is a single-band GeoTIFF with 30m spatial resolution." Funding: NASA EPSCoR Grant DE-80NSSC20M0220, NSF EPsCOR grants: 2418394, 2418395, 2418396. Additionally: State of Maryland, Harry R. Hughes Center for Agro-Ecology..
Source: UD Dataset Catalog, UD Data Science Institute: https://research.dsi.udel.edu/dataset/metadata.html#Multi-Temporal-Land-Cover-Dataset-for-the-Delmarva-Peninsula-2000-2002-2005-2009-2016-at-30m-Resolution
