AndreaBozzo/ceres-open-data-index huggingface dataset:
LANDFIRE 2016 Remap Elevation (Elev) Marshall Islands

IA DEM was produced from 1/3rd arc-second downloaded from viewer.nationalmap.gov/basic. The 3DEP 1/3rd arc-second DEMs, previously referred to as the National Elevation Dataset (NED), are derived from diverse source data, processed to a common coordinate system and unit of vertical measure.  All 3DEP data are distributed in geographic coordinates in units of decimal degrees, and in conformance with the North American Datum of 1983 (NAD 83).  All elevation values are provided in units of meters, and are referenced to the North American Vertical Datum of 1988 (NAVD 88) over the conterminous United States. For the LANDFIRE Elevation product, the 1/3 arc-second 3DEP DEM data were projected from geographic to Albers Equal Area, resampled to 30m spatial resolution, and clipped out to the IA 0k boundary.

Tags: biota, elevation, fires, geographic-information-systems, geospatial-datasets, hazard-preparedness, ia, image-collections, insular-areas, lf-2016-remap, marshall-islands, oconus, raster-digital-data, remote-sensing, topo, topography, u-s-geological-survey-usgs, united-states, us, usgs-57a6fcfa-829f-44ba-879f-10e588629237
Organization: Department of the Interior
San Pedro River Aquifer Data Release - Transboundary Aquifer Assessment Program (TAAP)

This is a release of data presented in the report "Binational Study of the Transboundary San Pedro Aquifer", Callegary and others, 2016:
Callegary, J.B., Minjárez Sosa, I., Tapia Villaseñor, E.M., dos Santos, P., Monreal Saavedra, R., Grijalva Noriega, F.J., Huth, A.K., Gray, F., Scott, C.A., Megdal, S.B., Oroz Ramos, L.A., Rangel Medina, M., Leenhouts, J.M., 2016, Binational Study of the Transboundary San Pedro Aquifer: International Boundary and Water Commission.
The United States and Mexico share waters in a number of hydrological basins and aquifers that cross the International Boundary. Both the United States and Mexico recognize that, in a region of scarce water and expanding populations, better scientific understanding of water quantity and quality in these aquifer systems would benefit decision makers planning and managing water resources on both sides of the border. A focus on the sustainability of the aquifer and the San Pedro River which it supports would benefit both Mexico and the United States. These include the fact that the aquifer is transboundary in nature, the river has an elevated ecological value because of the riparian ecosystem it sustains, and that water resources are needed to permit continued development. Recognizing these goals, an agreement was made to integrate hydrologic and other pertinent data from both countries and to proceed with this joint binational study of the San Pedro River transboundary aquifer.

Tags: air-temperature, aquifers, arizona, atmospheric-and-climatic-processes, bedrock-geologic-units, bouguer-anomaly, drainage, geochemistry, geology, geoscientificinformation, groundwater, groundwater-level, groundwater-quality, inlandwaters, koppen-geiger-climate-classification, land, land-cover, land-surface-characteristics, landownership, major-ions, national-elevation-dataset, natural-resource-management, ph, physiographic-provinces, population, precipitation-atmospheric, riparian, san-pedro-river, san-pedro-river-basin, slope-processes, soil-science, sonora, total-dissolved-solids, usgs-57a506b2e4b0ebae89b6d853, water-chemistry, water-conductivity, water-level-measurements, water-temperature, wells
Organization: Department of the Interior
Geochemical analysis of authigenic carbonates and chemosynthetic mussels at Atlantic Margin seeps (ver. 2.0, March 2019)

Isotopic analyses of authigenic carbonates and methanotrophic deep-sea mussels, Bathymodiolus sp., was performed on samples collected from seep fields in the Baltimore and Norfolk Canyons on the north Atlantic margin. Samples were collected using remotely operated underwater vehicles (ROVs) during three different research cruises in 2012, 2013, and 2015. Analyses were performed by several different laboratories, and the results are presented in spreadsheet format.

Tags: atlantic-ocean, baltimore-canyon, biota, carbon-14-analysis, chemical-analysis, chemosynthesis, cmhrp, coastal-and-marine-hazards-and-resources-program, cold-seep, ecological-functions-and-services, geochemistry, geoscientificinformation, grab-sampling, habitat, light-stable-isotope-analysis, marine-chemistry, mollusks, natural-gas-resources, norfolk-canyon, oceans, pacific-coastal-and-marine-science-center, paleoceanography, pcmsc, physical-chemical-features, physical-habitats-and-geomorphology, pleistocene, radiometric-dating, sea-level-change, sedimentation, specimen-collecting, u-s-geological-survey, uranium-thorium-analysis, usgs, usgs-57a3c016e4b0d7d7d7af97c1
Organization: Department of the Interior
Modeled conterminous United States Crop Cover datasets for 2013

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 crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Tags: conterminous-united-states, crop-cover, cropland-data-layers, decision-tree-classifier, nass-cdl, usgs-578e4d53e4b0f1bea0e147aa
Organization: Department of the Interior
Spatial data: Projected shifts in fish species dominance in Wisconsin lakes under climate change

Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides).  Recent declines in walleye and increases in largemouth bass populations have raised questions regarding the future trajectories and appropriate management  actions for these important species. We developed a thermodynamic model of water temperatures driven by downscaled climate data and lake specific  characteristics to estimate daily water temperature profiles for 2148 lakes in Wisconsin, USA under contemporary (1989-2014) and future (2040-2064 and 2065-2089) conditions.  We correlated contemporary walleye recruitment success and largemouth bass relative abundance to modeled water temperature, lake morphometry, and lake productivity,  and projected lake specific changes in each species under future climate conditions. Walleye recruitment success was negatively related and largemouth bass abundance  was positively related to water temperature degree days. Both species exhibited a threshold response at the same degree day value, albeit in opposite directions.  Degree days were predicted to increase in the future, although the magnitude of increase varied among lakes, time periods, and global circulation models (GCMs).  Under future conditions, we predicted a loss of walleye recruitment in 30-70% of lakes, and an increase to high largemouth bass relative abundance in 17-55% of additional lakes.  The percentage of lakes with abundant largemouth bass and failed walleye recruitment was predicted to increase from 59% in contemporary conditions to 86% of  lakes by mid-century and to 91% of lakes by late century, based on median projections across GCMs. Conversely, the number of lakes with successful walleye recruitment and low  largemouth bass abundance was predicted to decline from 8.5% of lakes in contemporary conditions to only 38 1% of lakes in both future periods.  Importantly, we identify nearly 100 resilient lakes predicted to continue to support walleye recruitment. Management resources could target preserving these resilient walleye populations. This data set contains the following parameters: SP_ID, WBDY_NAME, WBDY_WBIC, SECCHI_AVG, ZMAX, CANOPY_HT, AREA, BASS_PRB1, BASS_PRB2, BASS_PRB3, WALL_PRB1, WALL_PRB2, WALL_PRB3. calculated for 2148 lakes

Tags: 007, 012, bass, climate-change, environment, inlandwaters, lake-depth, largemouth-bass, modeling, reservoirs, secchi-depth, temperate-lakes, united-states, us, usgs-57a37cf8e4b006cb4556923c, walleye, water, water-clarity, wi, wind-sheltering, wisconsin
Organization: Department of the Interior
Terrestrial Development Index for the Western United States: 20-kilometer Moving Window

The terrestrial development index (TDI) quantifies levels of development (urban, agriculture, energy and mineral extraction and transmission, and transportation). TDI scores represent the total percentage of the development footprint within a 20-kilometer radius circular moving window. The Surface Disturbance Footprint from Development for the Western United States is used in the moving window analysis. The TDI scores range from 0-100%.

Tags: agriculture, arizona, california, colorado, development, ecological-processes, energy-resources, environment, environmental-assessment, idaho, kansas, land-use-and-land-cover, mineral-resources, montana, natural-resource-management, nebraska, nevada, new-mexico, north-dakota, oklahoma, oregon, south-dakota, surface-disturbance-footprint, terrestrial-development-index-tdi, texas, transportation, urban-development, usgs-57f2bfdfe4b0bc0bec007a12, utah, washington, western-united-states, wyoming
Organization: Department of the Interior
Modeled conterminous United States Crop Cover datasets for 2011

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 crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Tags: conterminous-united-states, crop-cover, cropland-data-layers, decision-tree-classifier, nass-cdl, usgs-578e4bcde4b0f1bea0e147a4
Organization: Department of the Interior
Modeled conterminous United States Crop Cover datasets for 2010

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 crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Tags: conterminous-united-states, crop-cover, cropland-data-layers, decision-tree-classifier, nass-cdl, usgs-578e4ae9e4b0f1bea0e147a1
Organization: Department of the Interior
Modeled conterminous United States Crop Cover datasets for 2009

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 crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Tags: conterminous-united-states, crop-cover, cropland-data-layers, decision-tree-classifier, nass-cdl, usgs-578e49eae4b0f1bea0e1479f
Organization: Department of the Interior
Modeled conterminous United States Crop Cover datasets for 2008

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 crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Tags: conterminous-united-states, crop-cover, cropland-data-layers, decision-tree-classifier, nass-cdl, usgs-578e4908e4b0f1bea0e1479d
Organization: Department of the Interior
Modeled conterminous United States Crop Cover datasets for 2007

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 crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Tags: conterminous-united-states, crop-cover, cropland-data-layers, decision-tree-classifier, nass-cdl, usgs-578e4813e4b0f1bea0e1479b
Organization: Department of the Interior
Modeled conterminous United States Crop Cover datasets for 2005

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 crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Tags: conterminous-united-states, crop-cover, cropland-data-layers, decision-tree-classifier, nass-cdl, usgs-578e4544e4b0f1bea0e14791
Organization: Department of the Interior
Modeled conterminous United States Crop Cover datasets for 2004

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 crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Tags: conterminous-united-states, crop-cover, cropland-data-layers, decision-tree-classifier, nass-cdl, usgs-578e43f0e4b0f1bea0e14789
Organization: Department of the Interior
Modeled conterminous United States Crop Cover datasets for 2003

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 crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Tags: conterminous-united-states, crop-cover, cropland-data-layers, decision-tree-classifier, nass-cdl, usgs-578e4312e4b0f1bea0e14783
Organization: Department of the Interior
Liquefaction observations from ten earthquakes in the US, Japan, China, and Taiwan

These data include observations of liquefaction from ten earthquakes. The data are provided as a feature collection in a GeoJSON file format. Individual features are either points or polygons. Each feature has a single attribute called "earthquake" which gives the name and year of the earthquake associated with the liquefaction feature.

Tags: earthquake, earthquake-hazards, liquefaction, usgs-57ebd3fae4b09082500ef44a
Organization: Department of the Interior