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Data

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Radiocarbon dates, charcoal, and polycyclic aromatic hydrocarbon (PAH) data from Great Dismal Swamp Sites GDS-519 and GDS-520

Sediment cores were collected in Great Dismal Swamp National Wildlife Refuge in November, 2017 to advance understanding of climate- and land-management driven changes in vegetation, hydrology, and fire regimes. Radiocarbon dates were obtained from samples in two cores (GDS-519-3-21-2017 and GDS-520-3-21-2017) to generate age models for the cores. Bulk sediment samples, charcoal, plant macrofossils

Enhanced Terrain Imagery of the Scranton 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution

This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Scranton 30 x 60 minute quadrangle in Pennsylvania. It also covers part of the Delaware River Basin. The source data used to construct this imagery consists of 1-meter resolution lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acquisitions published between 2017 and 2

Data release for depth to bedrock derived from Hydrogeology of Southeastern Connecticut by Melvin (1974)

This data release consists of a single ESRI shapefile, Hydrogeo_SECTpts, with geologic information from the previously published Hydrogeology of Southeastern Connecticut (Melvin, 1974). Test boring location points digitized from georeferenced area maps (1:24,000 scale) are attributed with associated well log information: town, identification numbers, altitude, depth to bottom, and remarks regardin

Enhanced Terrain Imagery of the Little Lehigh Watershed from Lidar-Derived Elevation Models at 1-Meter Resolution

This imagery dataset consists of 1-meter resolution, Lidar-derived imagery of the Little Lehigh Basin area in Pennsylvania and covers part of the Delaware River Basin. The footprint of this dataset covers USGS Hydrologic Unit Code (HUC) areas HUC 12-020401060701, HUC 12-020401060702 and part of HUC 12-020401060703. The source data used to construct this imagery consists of 1-meter resolution Lidar

Enhanced Terrain Imagery of the White Clay Creek Watershed from Lidar-Derived Elevation Models at 1-Meter Resolution

This imagery dataset consists of 1-meter resolution, lidar-derived imagery of the White Creek Clay watershed in Pennsylvania and Delaware, which includes part of the Delaware River Basin. The footprint of this dataset covers USGS Hydrologic Unit Code (HUC) areas HUC 12-020402050301, HUC 12-02040205030102, HUC 12-02040205030103, HUC 12-02040205030106, and part of HUC 12-02040205030108. The source d

Enhanced Terrain Imagery of the Neversink River Watershed from Lidar-Derived Elevation Models at 1-Meter Resolution

This imagery dataset consists of 1-meter resolution, lidar-derived imagery of the Neversink Basin area in New York and covers part of the Delaware River Basin. The footprint of this dataset covers USGS Hydrologic Unit Code (HUC) areas HUC 12-020401040301, HUC 12-020401040302, and part of HUC 12-020401040303. The source data used to construct this imagery consists of 1-meter and 2-meter resolution

Enhanced Terrain Imagery of the Allentown 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution

This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Allentown 30 x 60 minute quadrangle in Pennsylvania and part of New Jersey. It also covers part of the Delaware River Basin. The source data used to construct this imagery consists of 1-meter and 2-meter resolution Lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acqui

Lidar-derived closed depression vector data and density raster in karst areas of Monroe County, West Virginia

Monroe County in southeastern West Virginia hosts world-class karst within carbonate units of Mississippian and Ordovician age. Lidar-derived elevation data acquired in late December of 2016 were used to create a 3-meter resolution working digital elevation model (DEM), from which surface depressions were identified using a semi-automated workflow in ArcGIS. Depressions in the automated inventory

Hydrologic, slope movement, and soil property data from the coastal bluffs of the Atlantic Highlands, New Jersey, 2016-2018

Seasonal variations in vegetation, rainfall, and soil moisture conditions have the potential to impact the slope stability of locally forested coastal bluffs in the Atlantic Highlands of New Jersey. Both the seasonality and rainfall amounts of the two types of storms that induce shallow landslides in the area vary considerably. Most of the documented historical landslides are the result of heavy r

Monitoring data from the Aleppo rockslide, Allegheny County, Pennsylvania, November 2013 - December 2018

Upon reactivation, movement of deep-seated landslides in the Greater Pittsburgh region may persist for long periods of time. Monitoring equipment was located at two sites on a deep-seated rockslide in Aleppo Township, Pennsylvania to establish relationships between precipitation and changes in the state of activity and velocity. Precipitation, snow depth, and air temperature are monitored at a wea

Enhanced Terrain Imagery of the Pepacton Reservoir 30 x 60 Minute Quadrangle from Lidar-derived Elevation Models at 3 Meter Resolution

This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Pepacton Reservoir 30 x 60 minute quadrangle in New York and covers part of the Delaware River Basin. The source data used to construct this imagery consist of 1 meter and 2 meter resolution lidar-derived digital elevation models (DEM). The lidar source data were compiled from different acquisitions published between

Enhanced Terrain Imagery of the Monticello 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution

This imagery dataset consists of 3-meter resolution, Lidar-derived imagery of the Monticello 30 x 60 minute quadrangle in New York and covers part of the Delaware River Basin. The source data used to construct this imagery consists of 1-meter and 2-meter resolution Lidar-derived digital elevation models (DEM). The Lidar source data were compiled from different acquisitions published between 2005 a