This data release is part of a larger data release including data collected in the pursuit of identifying pre- and post-colonial riparian ecosystems found throughout Anne Arundel County, Maryland, USA. A single raster file is included, and represents a topological classification of the entire county according to a hydrologically conditioned Digital Elevation Model (DEM). These data were generated through the use of r.geomorphon, a GRASS GIS toolkit, to classify local terrain conditions into one of ten distinct landforms called geomorphons.

This digital GIS dataset and accompanying nonspatial files synthesize model outputs from a regional-scale volumetric 3-D geologic model that portrays the generalized subsurface geology of the Powder River Basin and Williston Basin regions from a wide variety of input data sources. The study area includes the Hartville Uplift, Laramie Range, Bighorn Mountains, Powder River Basin, and Williston Basin. The model data released here consist of the stratigraphic contact elevation of major Phanerozoic sedimentary units that broadly define the geometry of the subsurface, the elevation of Tertiary intrusive and Precambrian basement rocks, and point data that illustrate an estimation of the three-dimensional geometry of fault surfaces. The presence of folds and unconformities are implied by the 3D geometry of the stratigraphic units, but these are not included as discrete features in this data release. The 3D geologic model was constructed from a wide variety of publicly available surface and subsurface geologic data; none of these input data are part of this Data Release, but data sources are thoroughly documented such that a user could obtain these data from other sources if desired. The PowderRiverWilliston3D geodatabase contains 40 subsurface horizons in raster format that represent the tops of modeled subsurface units, and a feature dataset “GeologicModel”. The GeologicModel feature dataset contains a feature class of 30 estimated faults served in elevation grid format (FaultPoints), a feature class illustrating the spatial extent of 22 fault blocks (FaultBlockFootprints), and a feature class containing a polygon delineating the study areas (ModelBoundary). Nonspatial tables define the data sources used (DataSources), define terms used in the dataset (Glossary), and provide a description of the modeled surfaces (DescriptionOfModelUnits). Separate file folders contain the vector data in shapefile format, the raster data in ASCII format, and the tables as comma-separated values. In addition, a tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables (EntityAndAttributes). An included READ_ME file documents the process of manipulating and interpreting publicly available surface and subsurface geologic data to create the model. It additionally contains critical information about model units, and uncertainty regarding their ability to predict true ground conditions. Accompanying this data release is the “PowderRiverWillistonInputSummaryTable.csv”, which tabulates the global settings for each fault block, the stratigraphic horizons modeled in each fault block, the types and quantity of data inputs for each stratigraphic horizon, and then the settings associated with each data input.

This map depicts the land-use and land-cover of Arkansas as it occurred in the year 2006. The data are derived from Landsat TM 5 scenes and extensive ground-truth information. The map focuses primarily on agricultural land-use: crop and pasture lands. The maps consist of a broad based inventory of land-use and land-cover. Map categories fall with 6 broad "Level 1" categories: water, forest, barren, herbaceous, agricultural lands, and urban. Specific Level 1 land-use categories: agriculture and urban are broken into more discrete, "Level 2" land-use subcategories: e.g. crop type, pasture type. Catagory LULC_NAME 10 Urban 11 Urban: Intensity 1 12 Urban: Intensity 2 13 Urban: Intensity 3 14 Urban: Other 30 Barren Land 31 Barren Land 40 Water 41 Water: Perennial 42 Water: Flooded 50 Herbaceous/Woody/Transitional 51 Herbaceous/Woody/Transitional 100 Forest Unclassified 101 Forest Unclassified / Transitional 200 Cropland 201 Soybeans 202 Rice 203 Cotton 204 Wheat/Oats 205 Sorghum/Corn 206 Other Cropland 208 Bare Soil/Seedbed 209 Warm Season Grasses 210 Cool Season Grasses

Pocatello, Idaho historical orthomosaic for 1963 was created by collecting, scanning, merging and georectifying historic photography of Pocatello. The total spatial error is less than 1 meter. These historical orthomosaic images were derived using SfM (Structure-from-motion photogrammetry). SfM uses a series of overlapping images aligned to form a 3D representation. Classification resulted in raster and vector data with discrete classes grouped into objects located in the urban corridor of Pocatello. High-resolution aerial photography of the Pocatello area was provided by Valley Air Photos and the Idaho State Historical Society for 1963. All images were transferred from a traditional 9x9 photograph and scanned at a 1210 dpi resolution. (Date: 09/04/1963, Scale: 1:12,000, Total GSD [GSD = photo scale x scanning resolution]: 52, Scanned resolution: 11432x11241 1210 dpi). The general workflow for processing was as follows: Image collection, image pre-processing combined with gps positioning and differential correction. Photo alignment, point cloud generation, point cloud meshing, orthomosaic and DSM (Digital Surface Models) output. Photos were aligned using Agisoft Photoscan. Focal lengths for data sets were 152mm. GPS points were collected for ground truthing. Photo alignment, dense cloud, and mesh generation using ground control points, resulted in orthomosaics and DSMs (Digital Surface Models) for time periods. Orthomosaics were produced at a fine scale spatial resolution: .25m resolution in all cases except the final year at .5m due to differences in scale of the original imagery. Each orthomosaic and DEM was outputted at .5 m and 1 m resolution respectively, in order to maintain continuity between data sets. See Brock Lipple Thesis, 2015, for more in-depth discussion of the scanning and merging process.[http://geology.isu.edu/thesis/Lipple.Brock.2015.pdf].

Remotely sensed imagery is increasingly used by emergency managers to monitor and map the impact of flood events to support preparedness, response, and critical decision making throughout the flood event lifecycle. To reduce latency in delivery of imagery-derived information, ensure consistent and reliably derived map products, and facilitate processing of an increasing volume of remote sensing data-streams, automated flood mapping workflows are needed. The U.S. Geological Survey is facilitating the development and integration of machine-learning algorithms in collaboration with NASA, National Geospatial Intelligence Agency (NGA), University of Alabama, and University of Illinois to create a workflow for rapidly generating improved flood-map products. A major bottleneck to the training of robust, generalizable machine learning algorithms for pattern recognition is a lack of training data that is representative across the landscape. To overcome this limitation for the training of algorithms capable of detection of surface inundation in diverse contexts, this publication includes the data developed from MAXAR Worldview sensors that is input as training data for machine learning. This data release consists of 100 thematic rasters, in GeoTiff format, with image labels representing five discrete categories: water, not water, maybe water, clouds and background/no data. Specifically, these training data were created by labeling 8-band, multispectral scenes from the MAXAR-Digital Globe, Worldview-2 and 3 satellite-based sensors. Scenes were selected to be spatially and spectrally diverse and geographically representative of different water features within the continental U.S. The labeling procedures used a hybrid approach of unsupervised classification for the initial spectral clustering, followed by expert-level manual interpretation and QA/QC peer review to finalize each labeled image. Updated versions of the data may be issued along with version update documentation. The 100 raster files that make up the training data are available to download here (https://doi.org/10.5066/P9C7HYRV).

This dataset consists of several measures of landscape characteristics, each of which is summarized from raster data within spatial polygons. These spatial polygons represent the land area upstream of sampled stream reaches. These stream reaches were sampled by the Maryland Department of Natural Resources for the Maryland Biological Stream Survey program during survey rounds one, two, and four. Landscape characteristics summarized here are either represented by continuous or discrete raster layers which are summarized as the average value of a given characteristic (continuous data) or the area occupied by each class (discrete data).
The continuous datasets summarized included percentage of area occupied by tree canopy (for the years 2011 and 2016) and urban land cover (for the years 2001, 2006, 2011, and 2016); the percentage of the surficial geology made up of various chemical constituents (including aluminum oxide, calcium oxide, ferric oxide, potassium oxide, magnesium oxide, ...

1991-2020 average total annual precipitation for the Salish Sea Bioregion. Created for the Salish Sea Atlas (wp.wwu.edu/SalishSeaAtlas).

Average annual total precipitation for the Salish Sea Bioregion.

1991-2020 average climate variables were statistically downscaled to 90 meter resolution using the standalone ClimateNA software based on elevations from the Salish Sea Atlas's Digital Elevation Model. The data were converted to raster format for analysis and clipped to the Salish Sea Atlas's bioregional boundary dataset. All processing and analysis was completed using the NAD 83 UTM Zone 10 N coordinate system.

For visualization purposes, raster climate datasets were reclassified into discrete ranges of values, then converted to vector polygons. Simplified attributes were calculated for each polygon.

Gridded raster versions of the data can be downloaded from the Salish Sea Atlas data portal.

description: This map depicts the land-use and land-cover of Arkansas as it occurred in the year 2004, and is one of three statewide map layers designed to show land-use changes throughout the year. Maps were also produced for spring, summer, and fall. The data are derived from Landsat TM 5 scenes and extensive ground-truth information. The maps focus primarily on agricultural land-use: crop and pasture lands. The maps consist of a broad based inventory of land-use and land-cover. Map categories fall with 6 broad "Level 1" categories: water, forest, barren, herbaceous, agricultural lands, and urban. Specific Level 1 land-use categories: agriculture and urban are broken into more discrete, "Level 2" land-use subcategories: e.g. crop type, pasture type. Catagory LULC_NAME 10 Urban 11 Urban: Intensity 1 12 Urban: Intensity 2 13 Urban: Intensity 3 14 Urban: Other 30 Barren Land 31 Barren Land 40 Water 41 Water: Perennial 42 Water: Flooded 50 Herbaceous/Woody/Transitional 51 Herbaceous/Woody/Transitional 100 Forest Unclassified 101 Forest Unclassified / Transitional 200 Cropland 201 Soybeans 202 Rice 203 Cotton 204 Wheat/Oats 205 Sorghum/Corn 206 Other Cropland 208 Bare Soil/Seedbed 209 Warm Season Grasses 210 Cool Season Grasses; abstract: This map depicts the land-use and land-cover of Arkansas as it occurred in the year 2004, and is one of three statewide map layers designed to show land-use changes throughout the year. Maps were also produced for spring, summer, and fall. The data are derived from Landsat TM 5 scenes and extensive ground-truth information. The maps focus primarily on agricultural land-use: crop and pasture lands. The maps consist of a broad based inventory of land-use and land-cover. Map categories fall with 6 broad "Level 1" categories: water, forest, barren, herbaceous, agricultural lands, and urban. Specific Level 1 land-use categories: agriculture and urban are broken into more discrete, "Level 2" land-use subcategories: e.g. crop type, pasture type. Catagory LULC_NAME 10 Urban 11 Urban: Intensity 1 12 Urban: Intensity 2 13 Urban: Intensity 3 14 Urban: Other 30 Barren Land 31 Barren Land 40 Water 41 Water: Perennial 42 Water: Flooded 50 Herbaceous/Woody/Transitional 51 Herbaceous/Woody/Transitional 100 Forest Unclassified 101 Forest Unclassified / Transitional 200 Cropland 201 Soybeans 202 Rice 203 Cotton 204 Wheat/Oats 205 Sorghum/Corn 206 Other Cropland 208 Bare Soil/Seedbed 209 Warm Season Grasses 210 Cool Season Grasses

Average summer (June-August) maximum temperature for the Salish Sea Bioregion. 1991-2020 average climate variables were statistically downscaled to 90 meter resolution using the standalone ClimateNA software based on elevations from the Salish Sea Atlas's Digital Elevation Model. The data were converted to raster format for analysis and clipped to the Salish Sea Atlas's bioregional boundary dataset. All processing and analysis was completed using the NAD 83 UTM Zone 10 N coordinate system.For visualization purposes, raster climate datasets were reclassified into discrete ranges of values, then converted to vector polygons. Simplified attributes were calculated for each polygon.Gridded raster versions of the data can be downloaded from the Salish Sea Atlas data portal.Data sources:Original climate records were downscaled using ClimateNA: Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. doi:10.1371/journal.pone.0156720ClimateNA downscaled data are derived from gridded climate records from PRISM Climate Group, Oregon State University: Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., ... & Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology: a Journal of the Royal Meteorological Society, 28(15), 2031-2064.Elevation data from the Salish Sea Atlas were used in downscaling temperature data.