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Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create...
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Arsenic concentrations from 20,450 domestic wells in the U.S. were used to develop a logistic regression model of the probability of having arsenic > 10 µg/L (“high arsenic”), which is presented at the county, state, and national scales. Variables representing geologic sources, geochemical, hydrologic, and physical features were among the significant predictors of high arsenic. For U.S. Census blocks, the mean probability of arsenic > 10 µg/L was multiplied by the population using domestic wells to estimate the potential high-arsenic domestic-well population. Approximately 44.1 M people in the U.S. use water from domestic wells. The population in the conterminous U.S. using water from domestic wells with predicted...
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
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Maintaining the native prairie lands of the Northern Great Plains (NGP), which provide an important habitat for declining grassland species, requires anticipating the effects of increasing atmospheric carbon dioxide (CO2) concentrations and climate change on the region’s vegetation. Specifically, climate change threatens NGP grasslands by increasing the potential encroachment of native woody species into areas where they were previously only present in minor numbers. This project used a dynamic vegetation model to simulate vegetation type (grassland, shrubland, woodland, and forest) for the NGP for a range of projected future climates and relevant management scenarios. Comparing results of these simulations illustrates...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Benthic diatom assemblages are known to be indicative of water quality but have yet to be widely adopted in biological assessments in the United States due to several limitations. Our goal was to address some of these limitations by developing regional multi-metric indices (MMIs) that are robust to inter-laboratory taxonomic inconsistency, adjusted for natural covariates, and sensitive to a wide range of anthropogenic stressors. We aggregated bioassessment data from two national-scale federal programs and used a data-driven analysis in which all-possible combinations of 2-7 metrics were compared for three measures of performance. The datasets in this release support the Carlisle, et al. 2022 report cited herein....
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
These datasets were created from high-resolution (1-m) datasets representing median conditions during a 2014-2019 time period. These datasets used National Agricultural Inventory Program (NAIP) imagery, as well as Sentinel-2 satellite imagery, to estimate the fractional composition of unvegetated, vegetated, and water in each pixel. Random samples from these high resolution datasets were used to inform calibration and validation of the moderate resolution (30-m) Landsat datasets. To facilitate comparability with the Landsat datasets, these data were aggregated up to 30-m resolution.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
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The USGS’s FORE-SCE model was used to produce land-use and land-cover (LULC) projections for the conterminous United States. The projections were originally created as part of the "LandCarbon" project, an effort to understand biological carbon sequestration potential in the United States. However, the projections are being used for a wide variety of purposes, including analyses of the effects of landscape change on biodiversity, water quality, and regional weather and climate. The year 1992 served as the baseline for the landscape modeling. The 1992 to 2005 period was considered the historical baseline, with datasets such as the National Land Cover Database (NLCD), USGS Land Cover Trends, and US Department of Agriculture's...
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Approximately 44.1 million people (about 14 percent of the U.S. population) rely on domestic wells as their source of drinking water. Unlike community water systems, which are regulated by the Safe Drinking Water Act, there is no comprehensive national program for testing domestic well water to ensure that is it safe to drink. There are many activities, e.g., resource extraction, climate change-induced drought, and changes in land use patterns that could potentially affect the quality of the ground water source for domestic wells. The Health Studies Branch (HSB) of the National Center for Environmental Health, Centers for Disease Control and Prevention, created a Clean Water for Health Program to help address domestic...
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This shapefile contains summaries of habitat condition indices (HCI scores) from the National Fish Habitat Action Plan (NFHAP) 2010 National Assessments for 12 digit Hydrological Unit Codes (HUC12s) of the United States. Initial HCI scores were developed in three separate assessments (Conterminous U.S., Hawaii, and Alaska) due to differences in data availability across these regions. In the NFHAP 2010 Alaska assesment HCI values were already attributed to HUC12s. For this reason values for Alaska in this shapefile are identical to those represented in the Alaska assessment. To summarize data into HUC12s for the Conterminous United States and Hawaii a length-weighted average was used (i.e. the cumulative HCI score...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: 2010 National Assessment, 2010 National Assessment, Alabama, Alaska, Anthropogenic factors, All tags...
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.


map background search result map search result map Projecting the Future Encroachment of Woody Vegetation into Grasslands of the Northern Great Plains by Simulating Climate Conditions and Possible Management Actions National Fish Habitat Partnership (NFHP) 2010 HCI Scores - by HUC12 Modeled conterminous United States Crop Cover datasets for 2012 Variables used as input to a logistic regression model to estimate high-arsenic domestic-well population in the conterminous United States, 1970 through 2013 Probability of arsenic concentrations greater than 10 micrograms per liter in groundwater used by domestic wells in the United States Conterminous United States Land Cover Projections - 1992 to 2100 A NAIP and Sentinel-2 based quantification of fractional composition of unvegetated, vegetated, and water in the Gulf of Mexico Coast, 2014-2019 used for calibration and validation of Landsat based datasets L5_1989_GOM_Fractional_Land_FAV_SAV_Water L5_1992_GOM_Fractional_Land_FAV_SAV_Water L5_1995_GOM_Fractional_Land_FAV_SAV_Water L5_1999_GOM_Fractional_Land_FAV_SAV_Water L5_2000_GOM_Fractional_Land_FAV_SAV_Water L5_2001_GOM_Fractional_Land_FAV_SAV_Water L5_2008_GOM_Fractional_Land_FAV_SAV_Water_post_Hurricanes_Gustav_Ike L8_2013_GOM_Fractional_Land_FAV_SAV_Water L8_2016_GOM_Fractional_Land_FAV_SAV_Water Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2011 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2012 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2018 Data Release for: A Web-Based Tool for Assessing the Condition of Benthic Diatom Assemblages in Streams and Rivers of the Conterminous United States Projecting the Future Encroachment of Woody Vegetation into Grasslands of the Northern Great Plains by Simulating Climate Conditions and Possible Management Actions L5_1989_GOM_Fractional_Land_FAV_SAV_Water L5_1992_GOM_Fractional_Land_FAV_SAV_Water L5_1995_GOM_Fractional_Land_FAV_SAV_Water L5_1999_GOM_Fractional_Land_FAV_SAV_Water L5_2000_GOM_Fractional_Land_FAV_SAV_Water L5_2001_GOM_Fractional_Land_FAV_SAV_Water L5_2008_GOM_Fractional_Land_FAV_SAV_Water_post_Hurricanes_Gustav_Ike L8_2013_GOM_Fractional_Land_FAV_SAV_Water L8_2016_GOM_Fractional_Land_FAV_SAV_Water A NAIP and Sentinel-2 based quantification of fractional composition of unvegetated, vegetated, and water in the Gulf of Mexico Coast, 2014-2019 used for calibration and validation of Landsat based datasets Variables used as input to a logistic regression model to estimate high-arsenic domestic-well population in the conterminous United States, 1970 through 2013 Probability of arsenic concentrations greater than 10 micrograms per liter in groundwater used by domestic wells in the United States Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2011 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2012 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2018 Conterminous United States Land Cover Projections - 1992 to 2100 Data Release for: A Web-Based Tool for Assessing the Condition of Benthic Diatom Assemblages in Streams and Rivers of the Conterminous United States Modeled conterminous United States Crop Cover datasets for 2012 National Fish Habitat Partnership (NFHP) 2010 HCI Scores - by HUC12