Skip to main content
Advanced Search

Filters: Tags: land use and land cover (X) > Extensions: ArcGIS Service Definition (X)

6 results (8ms)   

View Results as: JSON ATOM CSV
thumbnail
Version 10.0 of these data are part of a larger U.S. Geological Survey (USGS) project to develop an updated geospatial database of mines, mineral deposits, and mineral regions in the United States. Mine and prospect-related symbols, such as those used to represent prospect pits, mines, adits, dumps, tailings, etc., hereafter referred to as “mine” symbols or features, have been digitized from the 7.5-minute (1:24,000, 1:25,000-scale; and 1:10,000, 1:20,000 and 1:30,000-scale in Puerto Rico only) and the 15-minute (1:48,000 and 1:62,500-scale; 1:63,360-scale in Alaska only) archive of the USGS Historical Topographic Map Collection (HTMC), or acquired from available databases (California and Nevada, 1:24,000-scale...
Categories: Data, Data Release - Revised; Types: ArcGIS REST Map Service, ArcGIS Service Definition, Downloadable, Map Service; Tags: Alabama (AL), Alaska (AK), Arizona (AZ), Arkansas (AR), California (CA), All tags...
thumbnail
Multispectral remote sensing data acquired by Landsat 8 Operational Land Imager (OLI) sensor were analyzed using an automated technique to generate surficial mineralogy and vegetation maps of the conterminous western United States. Six spectral indices (e.g. band-ratios), highlighting distinct spectral absorptions, were developed to aid in the identification of mineral groups in exposed rocks, soils, mine waste rock, and mill tailings across the landscape. The data are centered on the western U.S. and cover portions of Texas, Oklahoma, Kansas, the Canada-U.S. border, and the Mexico-U.S. border during the summers of 2013 – 2014. Methods used to process the images and algorithms used to infer mineralogical composition...
Categories: Data; Types: ArcGIS REST Map Service, ArcGIS Service Definition, Downloadable, Map Service; Tags: Arizona, California, Canada, Colorado, Idaho, All tags...
This data release contains land cover-derived statistics regarding estuarine vegetated wetland area change within estuary drainage areas along the conterminous U.S. This dataset includes net change in estuarine vegetated wetland area based on National Oceanic and Atmospheric Administration's (NOAA) Coastal Change Assessment Program (C-CAP) 1996 and 2016 land cover data. Net change was assessed between estuarine vegetated wetlands (i.e., estuarine marshes, mangroves, non-mangrove estuarine woody wetlands, and salt pannes, depending on vegetation coverage and type) and the following other landcover classes: 1) water; 2) unconsolidated shore; 3) freshwater woody wetlands; 4) freshwater marsh; 5) upland; and 6) agriculture....
thumbnail
Multispectral remote sensing data acquired by the Landsat 8 Operational Land Imager (OLI) sensor were analyzed using a new, automated technique to generate a map of exposed mineral and vegetation groups in the western San Juan Mountains, Colorado and the Four Corners Region of the United States (Rockwell and others, 2021). Spectral index (e.g. band-ratio) results were combined into displayed mineral and vegetation groups using Boolean algebra. New analysis logic has been implemented to exploit the coastal aerosol band in Landsat 8 OLI data and identify concentrations of iron sulfate minerals. These results may indicate the presence of near-surface pyrite, which can be a potential non-point source of acid rock drainage....
Vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area’s exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of digital elevation models (DEMs) developed using light detection and ranging data acquisition on a per-project basis to gauge whether the datasets meet quality and accuracy standards. To better understand the vertical accuracy of DEMs along the Gulf of Mexico and Atlantic coast, we collated over 5200 contractor points for this region that were collected for per-project-level analyses produced for assessing DEMs acquired for the U.S. Geological Survey’s 3D Elevation...
thumbnail
Landscape intactness has been defined as a quantifiable estimate of naturalness measured on a gradient of anthropogenic influence and based on available spatial data. We developed a multiscale index of landscape intactness for use as a broad-scale indicator of resource condition for the Bureau of Land Management’s (BLM) Landscape Approach, which requires multiple scales of information to quantify the cumulative effects of land use. The multiscale index of landscape intactness represents a gradient of anthropogenic influence as represented by development levels at two analysis scales. To calculate landscape intactness we combined the terrestrial development index (TDI) summarized at two scales (using a 2.5- and a...


    map background search result map search result map Landscape Intactness Index for the Western United States Prospect- and Mine-Related Features from U.S. Geological Survey 7.5- and 15-Minute Topographic Quadrangle Maps of the United States (ver. 10.0, May 2023) Digital map of iron sulfate minerals, other mineral groups, and vegetation of the San Juan Mountains, Colorado, and Four Corners Region derived from automated analysis of Landsat 8 satellite data Digital map of iron sulfate minerals, other mineral groups, and vegetation of the western United States derived from automated analysis of Landsat 8 satellite data Digital map of iron sulfate minerals, other mineral groups, and vegetation of the San Juan Mountains, Colorado, and Four Corners Region derived from automated analysis of Landsat 8 satellite data Digital map of iron sulfate minerals, other mineral groups, and vegetation of the western United States derived from automated analysis of Landsat 8 satellite data Landscape Intactness Index for the Western United States Prospect- and Mine-Related Features from U.S. Geological Survey 7.5- and 15-Minute Topographic Quadrangle Maps of the United States (ver. 10.0, May 2023)