ESRI GRID raster datasets were created to display and quantify nahcolite resources for eight oil shale zones in the Piceance Basin, Colorado as part of a 2009 National Oil Shale and Nahcolite Assessment. The zones in descending order are: L-5, R-5, L-4, R-4, L-3, R-3, L-2, and R-2. Each raster cell represents a one-acre square of the land surface and contains values for nahcolite tonnage. The gridnames follow the naming convention _n, where "" can be replaced by the name of the oil shale zone.

An ESRI GRID raster data model of the Mahogany bed structure was needed to perform overburden calculations in the Uinta Basin, Utah and Colorado as part of a 2009 National Oil Shale Assessment.

MODIS Water Lake Powell Raster Dataset

  Dataset Summary

Raster dataset comprised of MODIS surface reflectance bands along with calculated indices and a label (water/not-water)

  Dataset Structure





  Data Fields

water: Label, water or not-water (binary) sur_refl_b01_1: MODIS surface reflection band 1 (-100, 16000) sur_refl_b02_1: MODIS surface reflection band 2 (-100, 16000) sur_refl_b03_1: MODIS surface reflection band 3 (-100, 16000) sur_refl_b04_1:… See the full description on the dataset page: https://huggingface.co/datasets/nasa-cisto-data-science-group/modis-lake-powell-raster-dataset.

A series of maps at 1:10 000 scale showing base mapping for Northern Ireland. These raster maps can be used with other maps or information to enhance the mapping. Midscale Raster for Northern Ireland can be used as a general background to give context at local and regional level and as a base to overlay data. Includes water bodies, rivers, main roads, town names and townlands.Please Note for Open Data NI Users: Esri Rest API is not Broken, it will not open on its own in a Web Browser but can be copied and used in Desktop and Webmaps

ESRI GRID raster datasets were created to display and quantify oil shale resources for eighteen zones in the Uinta Basin, Utah and Colorado as part of a 2010 National Oil Shale Assessment. The oil shale zones in descending order are: Bed 76, Bed 44, A Groove, Mahogany Zone, B Groove, R-6, L-5, R-5, L-4, R-4, L-3, R-3, L-2, R-2, L-1, R-1, L-0, and R-0. Each raster cell represents a one-acre square of the land surface and contains values for either oil yield in barrels per acre, gallons per ton, or isopach thickness, in feet, as defined by the grid name: _b (barrels per acre), _g (gallons per ton), and _i (isopach thickness) where "" can be replaced by the name of the oil shale zone.

ESRI GRID raster datasets were created to display and quantify oil shale resources for seventeen zones in the Piceance Basin, Colorado as part of a 2009 National Oil Shale Assessment. The oil shale zones in descending order are: Bed 44, A Groove, Mahogany Zone, B Groove, R-6, L-5, R-5, L-4, R-4, L-3, R-3, L-2, R-2, L-1, R-1, L-0, and R-0. Each raster cell represents a one-acre square of the land surface and contains values for either oil yield in barrels per acre, gallons per ton, or isopach thickness, in feet, as defined by the grid name: _b (barrels per acre), _g (gallons per ton), and _i (isopach thickness) where "" can be replaced by the name of the oil shale zone.

This dataset is one of eight datasets produced by this study. Four of the datasets predict the probability of detecting atrazine and(or) desethyl-atrazine (a breakdown product of atrazine) in ground water in Colorado; the other four predict the probability of detecting elevated concentrations of nitrate in ground water in Colorado. The four datasets that predict the probability of atrazine and (or) desethyl-atrazine (atrazine/DEA) are differentiated by whether or not they incorporated atrazine use and whether or not they incorporated hydrogeomorphic regions. The four datasets that predict the probability of elevated concentrations of nitrate are differentiated by whether or not they incorporated fertilizer use and whether or not they incorporated hydrogeomorphic regions. Each of the eight datasets has its own unique strengths and weaknesses. The user is cautioned to read Rupert (2003, Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate in ground water in Colorado: U.S. Geological Survey Water-Resources Investigations Report 02-4269, 35 p., https://water.usgs.gov/pubs/wri/wri02-4269/) to determine if he(she) is using the most appropriate dataset for his(her) particular needs. This dataset specifically predicts the probability of detecting elevated concentrations of nitrate in ground water in Colorado with hydrogeomorphic regions and fertilizer use not included. The following text was extracted from Rupert (2003). Draft Federal regulations may require that each State develop a State Pesticide Management Plan for the herbicides atrazine, alachlor, metolachlor, and simazine. Maps were developed that the State of Colorado could use to predict the probability of detecting atrazine/DEA in ground water in Colorado. These maps can be incorporated into the State Pesticide Management Plan and can help provide a sound hydrogeologic basis for atrazine management in Colorado. Maps showing the probability of detecting elevated nitrite plus nitrate as nitrogen (nitrate) concentrations in ground water in Colorado also were developed because nitrate is a contaminant of concern in many areas of Colorado. Maps showing the probability of detecting atrazine/DEA at or greater than concentrations of 0.1 microgram per liter and nitrate concentrations in ground water greater than 5 milligrams per liter were developed as follows: (1) Ground-water quality data were overlaid with anthropogenic and hydrogeologic data by using a geographic information system (GIS) to produce a dataset in which each well had corresponding data on atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well construction. These data then were downloaded to a statistical software package for analysis by logistic regression. (2) Relations were observed between ground-water quality and the percentage of land-cover categories within circular regions (buffers) around wells. Several buffer sizes were evaluated; the buffer size that provided the strongest relation was selected for use in the logistic regression models. (3) Relations between concentrations of atrazine/DEA and nitrate in ground water and atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well-construction data were evaluated, and several preliminary multivariate models with various combinations of independent variables were constructed. (4) The multivariate models that best predicted the presence of atrazine/DEA and elevated concentrations of nitrate in ground water were selected. (5) The accuracy of the multivariate models was confirmed by validating the models with an independent set of ground-water quality data. (6) The multivariate models were entered into a geographic information system and the probability GRIDS were constructed.

ESRI GRID raster datasets were created to display and quantify oil shale resources for three zones in the Green River and Washakie Basins, southwestern Wyoming as part of a National Oil Shale Assessment. The oil shale zones in descending order are: LaClede Bed of the Laney Member of the Eocene Green River Formation, Wilkins Peak Member of the Eocene Green River Formation , and the Tipton Member of the Eocene Green River Formation. Each raster cell represents a one-acre square of the land surface and contains values for either oil yield in barrels per acre, gallons per ton, or isopach thickness, in feet, as defined by the grid name: _b (barrels per acre), _g (gallons per ton), and _i (isopach thickness) where "" can be replaced by the name of the oil shale zone.

An ESRI GRID raster data model of the overburden material above the Mahogany bed was needed to perform calculations in the Uinta Basin, Utah and Colorado as part of a 2009 National Oil Shale Assessment.

The High Plains aquifer extends from approximately 32 to 44 degrees north latitude and 96 degrees 30 minutes to 106 degrees west longitude. The aquifer underlies about 175,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This digital dataset consists of a raster of water-level changes for the High Plains aquifer, predevelopment (about 1950) to 2019. It was created using water-level measurements from 2,741 wells measured in both the predevelopment period (about 1950) and in 2019, the latest available static water level measured in 2015 to 2018 from 71 wells in New Mexico and using other published information on water-level change in areas with few water-level measurements. The map was reviewed for consistency with the relevant data at a scale of 1:1,000,000. Negative raster-cell values correspond to decline in water level and positive raster-cell values correspond to water-level rise.

Values in Megabytes.

This submission includes maps of the spatial distribution of basaltic, and felsic rocks in the Oregon Cascades. It also includes a final Play Fairway Analysis (PFA) model, with the heat and permeability composite risk segments (CRS) supplied separately. Metadata for each raster dataset can be found within the zip files, in the TIF images

Processed results from of surface grain size analysis of the sediment grab samples recovered as part of the Long Island Sound mapping project Phase II.Sediment grab samples have been taken in summer of 2017 and 2018 using a modified van Veen grab sampler. A sub-sample of the top two centimeter was taken and stored in a jar. Dried sub-samples samples were analyzed for grain size. First the samples were treated with hydroperoxide to remove organic components. Then the sample was passed through a series of standard sieves representing Phi sizes with the smallest being 64 µm. The content of each sieve was dried and weight. If there was sufficient fine material (< 64 µm), then this fine fraction was further analyzed using a Sedigraph system. The results of sieving and sedigraph analysis have been combined and the percentages for gravel, sand, silt and clay are determined following the Wentworth scale. In addition, other statistics including mean, median, skewness and standard deviation are calculated using the USGS GSSTAT program. The results of the LDEO/Queens College grain size analysis have been combined with data collected by the LISMARC group and analyzed by USGS. ArcGIS Pro empirical kriging has been used to interpolate values for gravel, sand, silt, clay, and mud percentages as well as for mean grain size onto a 50 m raster. The interpolated raster has been clipped to fit the extent of the phase 2 survey area. The final raster data are in GeoTiff format with UTM 18 N projection.Time period of content: 2017-08-01 to 2022-11-16Attribute accuracy: The attribute accuracy has not been determined. This raster dataset shown mainly the major trends and patterns of the value distribution in the Phase 2 study area.Completeness: The dataset is complete.Positional accuracy: The raster resolution is 50 m.Attributes:clay pct raster: Interpolated clay percent of the sample mass

The SESMAR dataset offers readily available maps and raster images tailored for scientists and decision-makers. It is derived from a wealth of remote sensing data covering the period from 2001 to 2023. Operating at a spatial resolution of 500m, this dataset evaluates soil loss susceptibility in the North African region. The application of the Revised Universal Soil Loss Equation (RUSLE) model, originally formulated by Wischmeier and Smith in 1978, was used to enhance the credibility of the dataset's computational methodology.

The dataset lies on the integration of diverse open-source datasets, namely MOD13A2.061 Terra Vegetation Indices for calculating the Cover Management factor, MCD12Q1.006 MODIS Land Cover Type Yearly Global, CHIRPS dataset for precipitation, Shuttle Radar Topography Mission (SRTM) dataset for topography, and Open Land Map Soil Texture Class (USDA System). This multi-source integration enhances the dataset's reliability and applicability for various environmental and agricultural studies.

The SESMAR dataset provides consistent susceptibility maps for major North African basins and offers readily classified raster images, enhancing its usability for researchers and practitioners. The basins are extracted from HydroSHEDS/v1/Basins/hybas dataset based on HYBAS_ID, also provided to ensure the identification of the specific basin for further analysis. The reliance on HydroSHEDS, a robust mapping product by Lehner and Grill (2013), ensures comprehensive hydrographic information across different scales, ranging from coarse to detailed. For the convenience of prospective users, it's noteworthy that the resultant raster datasets cover extensive basins, which can be further partitioned into smaller or medium-sized sub-basins as necessary.

The dataset is splitted into 22 rasters in a compressed format, consisting of a single band each one. It characterizes soil loss susceptibility, categorizing each raster cell into six distinct classes. The classification is based on the estimated annual soil loss rates per hectare, with associated values as follows:

- 0: No Data
This category designates areas where soil loss susceptibility information is unavailable, serving as a placeholder for missing or inaccessible data.

- 1: Very Low (< 5 t/ha/year)
Raster cells in this class represent areas with very low susceptibility to soil loss, indicating an annual rate of less than 5 tons per hectare.

- 2: Low (5 to 15 t/ha/year)
This class characterizes areas with low susceptibility, where the annual soil loss rate falls within the range of 5 to 15 tons per hectare.

- 3: Medium (15 to 50 t/ha/year)
Raster cells categorized as medium susceptibility denote moderate levels of soil loss, with an annual rate ranging from 15 to 50 tons per hectare.

- 4: High (50 to 80 t/ha/year)
This class identifies areas with high susceptibility to soil loss, where the annual rate ranges from 50 to 80 tons per hectare.

- 5: Very High (> 80 t/ha/year)
Raster cells in this category indicate the highest susceptibility to soil loss, with an annual rate exceeding 80 tons per hectare.

This comprehensive classification system is integral to the raster dataset, facilitating a nuanced understanding of soil loss susceptibility across different geographical locations. The dataset serves for environmental and agricultural planning, enabling stakeholders to identify and prioritize areas for targeted soil conservation measures. Continuous efforts to maintain data accuracy through updates and validation processes will ensure the dataset's reliability and relevance over time.

ESRI GRID raster datasets were created to display and quantify nahcolite resources for eight oil shale zones in the Piceance Basin, Colorado as part of a 2009 National Oil Shale and Nahcolite Assessment. The zones in descending order are: L-5, R-5, L-4, R-4, L-3, R-3, L-2, and R-2. Each raster cell represents a one-acre square of the land surface and contains values for nahcolite tonnage. The gridnames follow the naming convention _n, where "" can be replaced by the name of the oil shale zone.

The High Plains aquifer extends from approximately 32 to 44 degrees north latitude and 96 degrees 30 minutes to 106 degrees west longitude. The aquifer underlies about 175,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This dataset consists of a raster of estimated water-level changes for the High Plains aquifer from pre-irrigation season 2017 to pre-irrigation season 2019. This digital dataset was created using water-level measurements from 7,195 wells measured in both 2017 and 2019. The map was reviewed for consistency with the relevant data at a scale of 1:1,000,000. Negative raster-cell values correspond to decline in water level and positive raster-cell values correspond to water-level rise.

An ESRI GRID raster data model of the Mahogany Zone structure was needed to perform overburden calculations in the Piceance Basin, Colorado as part of a 2009 National Oil Shale Assessment.

This is a VRT file format of Logan DEM raster dataset

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This dataset is one of eight datasets produced by this study. Four of the datasets predict the probability of detecting atrazine and(or) desethyl-atrazine (a breakdown product of atrazine) in ground water in Colorado; the other four predict the probability of detecting elevated concentrations of nitrate in ground water in Colorado. The four datasets that predict the probability of atrazine and(or) desethyl-atrazine (atrazine/DEA) are differentiated by whether or not they incorporated atrazine use and whether or not they incorporated hydrogeomorphic regions. The four datasets that predict the probability of elevated concentrations of nitrate are differentiated by whether or not they incorporated fertilizer use and whether or not they incorporated hydrogeomorphic regions. Each of the eight datasets has its own unique strengths and weaknesses. The user is cautioned to read Rupert (2003, Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate in ground water in Colorado: U.S. Geological Survey Water-Resources Investigations Report 02-4269, 35 p., http://water.usgs.gov/pubs/wri/wri02-4269/) to determine if he(she) is using the most appropriate dataset for his(her) particular needs. This dataset specifically predicts the probability of detecting elevated concentrations of nitrate in ground water in Colorado with hydrogeomorphic regions included and fertilizer use not included. The following text was extracted from Rupert (2003).

Draft Federal regulations may require that each State develop a State Pesticide Management Plan for the herbicides atrazine, alachlor, metolachlor, and simazine. Maps were developed that the State of Colorado could use to predict the probability of detecting atrazine/DEA in ground water in Colorado. These maps can be incorporated into the State Pesticide Management Plan and can help provide a sound hydrogeologic basis for atrazine management in Colorado. Maps showing the probability of detecting elevated nitrite plus nitrate as nitrogen (nitrate) concentrations in ground water in Colorado also were developed because nitrate is a contaminant of concern in many areas of Colorado.

Maps showing the probability of detecting atrazine/DEA at or greater than concentrations of 0.1 microgram per liter and nitrate concentrations in ground water greater than 5 milligrams per liter were developed as follows: (1) Ground-water quality data were overlaid with anthropogenic and hydrogeologic data by using a geographic information system (GIS) to produce a dataset in which each well had corresponding data on atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well construction. These data then were downloaded to a statistical software package for analysis by logistic regression. (2) Relations were observed between ground-water quality and the percentage of land-cover categories within circular regions (buffers) around wells. Several buffer sizes were evaluated; the buffer size that provided the strongest relation was selected for use in the logistic regression models. (3) Relations between concentrations of atrazine/DEA and nitrate in ground water and atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well-construction data were evaluated, and several preliminary multivariate models with various combinations of independent variables were constructed. (4) The multivariate models that best predicted the presence of atrazine/DEA and elevated concentrations of nitrate in ground water were selected. (5) The accuracy of the multivariate models was confirmed by validating the models with an independent set of ground-water quality data. (6) The multivariate models were entered into a geographic information system and the probability GRIDS were constructed.