Geosciences and Environmental Change Science Center

Data and Tools

GECSC staff are responsible for the development of data and tools that support global environmental research, landscape change investigations, geologic studies and emergency response activities. 

Filter Total Items: 86
Date published: April 10, 2020

Modeling bird species richness for the southeastern United States (2001, 2006, and 2011)

These datasets are the inputs, script, and output of bird species richness modeling for the southeastern United States in the years 2001, 2006, and 2011 using general joint attribute modeling (GJAM). Inputs include environmental predictor variables for Breeding Bird Survey routes, bird counts from Breeding Bird Survey observations, and environmental predictor variables on a half-

Date published: April 10, 2020

Supply of and demand for wild pollinator habitat in the Southeast United States

Wild insect pollination has significant positive effects on pollinator-dependent crop production. To assess the spatial distribution of potential wild insect pollination, we mapped the supply of potential wild pollinator habitat (forest, grassland, wetland, and shrubland land cover types) and the demand for pollination (directly pollinator-dependent crops). A foraging travel

Date published: April 10, 2020

Supply of and demand for water purification of nonpoint source pollutants in the Southeast United States

Natural land cover can remove pollutants from runoff water by slowing water flow and physically trapping suspended particles. We identified natural land cover in the Southeast US potentially contributing to water purification due to its location in the flowpath between sources of nonpoint-source pollution and waterways.

Date published: April 10, 2020

Recreational birding in the Southeast United States

Total recreational birding activity (by state and year) estimated by the National Survey for Fishing, Hunting, and WIldlife-Associated Recreation was spatially distributed using birding observations reported through the eBird citizen science database and summarized by land cover type for each analysis year (2001, 2006, and 2011).

Date published: April 10, 2020

Total ecosystem carbon storage in the Southeast United States

Carbon storage by ecosystem type and protection status was derived from total ecosystem carbon estimates provided by Sleeter et al. 2018 and used to estimate terrestrial carbon storage in developed, forested, shrub/scrub, grassland/herbaceous, and agricultural land in the Southeast United States. It does not include estimates for wetland carbon storage.

Date published: March 23, 2020

Data release for Geologic Map of the Homestake Reservoir 7.5' quadrangle, Lake, Pitkin, and Eagle Counties, Colorado

The Homestake Reservoir 7.5' quadrangle lies at the northwestern end of the Upper Arkansas Valley, and headwaters of the Arkansas River, and the Roaring Fork, Fryingpan, and Eagle Rivers of the Colorado River system. The quadrangle lies within tectonic provinces of the 1.4 Ga Picuris Orogeny, the late Paleozoic Ancestral Rockies, Late Cretaceous-Paleocene Laramide orogeny, Oligo

Date published: March 19, 2020

Uranium-, thorium-, strontium-, carbon- and oxygen-isotope data used to evaluate a 300,000-year history of water-table fluctuations at Wind Cave, South Dakota, USA — scale, timing, and groundwater mixing in the Madison Aquifer

Tables of U- and Th-isotopic data used to calculate uranium-series age estimates and initial 234U/238U activity ratios as well as 87Sr/86Sr, δ13C, and δ18O for samples of phreatic speleothems from Wind Cave National Park and U- and Sr-isotopic compositions of waters from the southern Black Hills of South Dakota, USA

Date published: March 11, 2020

Data Release for Geologic Map of Petroglyph National Monument and Vicinity, Bernalillo County, New Mexico

This geologic map depicts and briefly describes geologic units underlying Petroglyph National Monument and immediately adjacent areas in Bernalillo County, New Mexico. The Monument is underlain dominantly by Quaternary basalts of the Albuquerque Volcanoes volcanic field, a series of basin-filling volcanic flows and associated vents from a monogenetic volcanic highland along the eastern margin...

Date published: March 4, 2020

Pre-fire biomass, burn severity, biomass consumption, and fire perimeter data for the 1987 Black Dragon Fire in China

Geospatial data were developed to characterize pre-fire biomass, burn severity, and biomass consumed for the Black Dragon Fire that burned in northern China in 1987. Pre-fire aboveground tree biomass (Mh/ha) raster data were derived by relating plot-level forest inventory data with pre-fire Landsat imagery from 1986 and 1987. Biomass data were generated for individual species: Dahuri

Date published: February 28, 2020

A national dataset of rasterized building footprints for the U.S.

The Bing Maps team at Microsoft released a U.S.-wide vector building dataset in 2018, which includes over 125 million building footprints for all 50 states in GeoJSON format. This dataset is extracted from aerial images using deep learning object classification methods. Large-extent modelling (e.g., urban morphological analysis or ecosystem assessment models) or accuracy assessment with v

Date published: February 28, 2020

Data for Dust deposited on snow cover in the San Juan Mountains, Colorado, 2011-2016: Compositional variability bearing on snow-melt effects

Light-absorbing particles in atmospheric dust deposited on snow cover (dust-on-snow, DOS) diminish albedo and accelerate the timing and rate of snow melt. Identification of these particles and their effects are relevant to snow-radiation modeling and water-resource management. Laboratory-measured reflectance of DOS samples from the San Juan Mountains (USA) were compared with DOS mass lo

Date published: February 28, 2020

A national dataset of rasterized building footprints for the U.S.

The Bing Maps team at Microsoft released a U.S.-wide vector building dataset in 2018, which includes over 125 million building footprints for all 50 states in GeoJSON format. This dataset is extracted from aerial images using deep learning object classification methods. Large-extent modelling (e.g., urban morphological analysis or ecosystem assessment models) or accuracy assessment with v