A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks) For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub. To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

This dataset consists of a 25m resolution raster version of the Land Cover Map 2000 for Great Britain. Each 25m pixel represents a 25m area of land cover target class, broadly representing Broad Habitats (see below). The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2000. LCM2000 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990. Like the earlier 1990 products, LCM2000 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2000 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2000 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions.

The downloadable ZIP file contains a georeferenced TIF. This data set is a mosaic of 69 individual DRGs georeferenced to the IDTM83 grid. The original Digital Raster Graphic (DRG) is a raster image of a scanned USGS topographic map including the collar information, georeferenced to the UTM grid. DRGs are useful as a source or background layer in a GIS and as a means to perform quality assurance on other digital products.These data were contributed to INSIDE Idaho at the University of Idaho Library in 2004.

OS Open Raster stack of GB for use as base mapping from national scale through to street level data. The currency of the data is: GB Overview Maps - 12/2014 MiniScale - 01/2024 OS 250K Raster - 06/2024Vector Map District Raster - 05/2024Open Map Local Raster - 10/2024 The coverage of the map service is GB. The map projection is British National Grid. The service is appropriate for viewing down to a scale of approximately 1:2,500. For more information on OS Open Services see: https://osdatahub.os.uk/downloads/open Updated: 29/10/2024

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI, created using ArcGIS 10.2.2) values for Nevada during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Summer included telemetry locations (n = 14,058) from mid-March to June. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated using R Software (v 3.13) for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

Land cover maps are the basic data layer required for understanding and modeling ecological patterns and processes. The Circumpolar Arctic Vegetation Map (CAVM), produced in 2003, has been widely used as a base map for studies in the arctic tundra biome. However, the relatively coarse resolution and vector format of the map were not compatible with many other data sets. We present a new version of the CAVM, building on the strengths of the original map, while providing a finer spatial resolution, raster format, and improved mapping. The Raster CAVM uses the legend, extent and projection of the original CAVM. The legend has 16 vegetation types, glacier, saline water, freshwater, and non-arctic land. The Raster CAVM divides the original rock-water-vegetation complex map unit that mapped the Canadian Shield into two map units, distinguishing between areas with lichen- and shrub-dominated vegetation. In contrast to the original hand-drawn CAVM, the new map is based on unsupervised classifications of seventeen geographic/floristic sub-sections of the Arctic, using the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data (reflectance and Normalized Difference Vegetation Index (NDVI)) and elevation data. The units resulting from the classification were modeled to the CAVM types using a wide variety of ancillary data. The map was reviewed by experts familiar with their particular region, including many of the original authors of the CAVM from Canada, Greenland (Denmark), Iceland, Norway (including Svalbard), Russia, and the United States (U.S.). The analysis presented here summarizes the area, geographical distribution, elevation, summer temperatures, and NDVI of the map units. The greater spatial resolution of the Raster CAVM allowed more detailed mapping of water-bodies and mountainous areas. It portrays coastal-inland gradients, and better reflects the heterogeneity of vegetation type distribution than the original CAVM. Accuracy assessment of random 1-kilometer (km) pixels interpreted from 6 Landsat scenes showed an average of 70 percent (%) accuracy, up from 39 % for the original CAVM. The distribution of shrub-dominated types changed the most, with more prostrate shrub tundra mapped in mountainous areas, and less low shrub tundra in lowland areas. This improved mapping is important for quantifying existing and potential changes to land cover, a key environmental indicator for modeling and monitoring ecosystems. The Raster CAVM was released in 2019. Raster map data are available for download from Menedeley Data (DOI: 10.17632/c4xj5rv6kv.2). This data record contains PDFs a 36x36-inch print version of the map at at 1:7,000,000. The print map is illustrated with photographs of representative plant communities and species for each of the 16 map units, data on the area of each unit, and information on the making of raster CAVM. The press-quality version includes a 1/8-inch bleed on all sides to allow the map to printed at 36.25 square inches and trimmed to produce a "full bleed" map with color extending to the edge on all sides.

The Cooperative Land Cover Map is a project to develop an improved statewide land cover map from existing sources and expert review of aerial photography. The project is directly tied to a goal of Florida's State Wildlife Action Plan (SWAP) to represent Florida's diverse habitats in a spatially-explicit manner. The Cooperative Land Cover Map integrates 3 primary data types: 1) 6 million acres are derived from local or site-specific data sources, primarily on existing conservation lands. Most of these sources have a ground-truth or local knowledge component. We collected land cover and vegetation data from 37 existing sources. Each dataset was evaluated for consistency and quality and assigned a confidence category that determined how it was integrated into the final land cover map. 2) 1.4 million acres are derived from areas that FNAI ecologists reviewed with high resolution aerial photography. These areas were reviewed because other data indicated some potential for the presence of a focal community: scrub, scrubby flatwoods, sandhill, dry prairie, pine rockland, rockland hammock, upland pine or mesic flatwoods. 3) 3.2 million acres are represented by Florida Land Use Land Cover data from the FL Department of Environmental Protection and Water Management Districts (FLUCCS). The Cooperative Land Cover Map integrates data from the following years: NWFWMD: 2006 - 07 SRWMD: 2005 - 08 SJRWMD: 2004 SFWMD: 2004 SWFWMD: 2008 All data were crosswalked into the Florida Land Cover Classification System. This project was funded by a grant from FWC/Florida's Wildlife Legacy Initiative (Project 08009) to Florida Natural Areas Inventory. The current dataset is provided in 10m raster grid format.Changes from Version 1.1 to Version 2.3:CLC v2.3 includes updated Florida Land Use Land Cover for four water management districts as described above: NWFWMD, SJRWMD, SFWMD, SWFWMDCLC v2.3 incorporates major revisions to natural coastal land cover and natural communities potentially affected by sea level rise. These revisions were undertaken by FNAI as part of two projects: Re-evaluating Florida's Ecological Conservation Priorities in the Face of Sea Level Rise (funded by the Yale Mapping Framework for Biodiversity Conservation and Climate Adaptation) and Predicting and Mitigating the Effects of Sea-Level Rise and Land Use Changes on Imperiled Species and Natural communities in Florida (funded by an FWC State Wildlife Grant and The Kresge Foundation). FNAI also opportunistically revised natural communities as needed in the course of species habitat mapping work funded by the Florida Department of Environmental Protection. CLC v2.3 also includes several new site specific data sources: New or revised FNAI natural community maps for 13 conservation lands and 9 Florida Forever proposals; new Florida Park Service maps for 10 parks; Sarasota County Preserves Habitat Maps (with FNAI review); Sarasota County HCP Florida Scrub-Jay Habitat (with FNAI Review); Southwest Florida Scrub Working Group scrub polygons. Several corrections to the crosswalk of FLUCCS to FLCS were made, including review and reclassification of interior sand beaches that were originally crosswalked to beach dune, and reclassification of upland hardwood forest south of Lake Okeechobee to mesic hammock. Representation of state waters was expanded to include the NOAA Submerged Lands Act data for Florida.Changes from Version 2.3 to 3.0: All land classes underwent revisions to correct boundaries, mislabeled classes, and hard edges between classes. Vector data was compared against high resolution Digital Ortho Quarter Quads (DOQQ) and Google Earth imagery. Individual land cover classes were converted to .KML format for use in Google Earth. Errors identified through visual review were manually corrected. Statewide medium resolution (spatial resolution of 10 m) SPOT 5 images were available for remote sensing classification with the following spectral bands: near infrared, red, green and short wave infrared. The acquisition dates of SPOT images ranged between October, 2005 and October, 2010. Remote sensing classification was performed in Idrisi Taiga and ERDAS Imagine. Supervised and unsupervised classifications of each SPOT image were performed with the corrected polygon data as a guide. Further visual inspections of classified areas were conducted for consistency, errors, and edge matching between image footprints. CLC v3.0 now includes state wide Florida NAVTEQ transportation data. CLC v3.0 incorporates extensive revisions to scrub, scrubby flatwoods, mesic flatwoods, and upland pine classes. An additional class, scrub mangrove – 5252, was added to the crosswalk. Mangrove swamp was reviewed and reclassified to include areas of scrub mangrove. CLC v3.0 also includes additional revisions to sand beach, riverine sand bar, and beach dune previously misclassified as high intensity urban or extractive. CLC v3.0 excludes the Dry Tortugas and does not include some of the small keys between Key West and Marquesas.Changes from Version 3.0 to Version 3.1: CLC v3.1 includes several new site specific data sources: Revised FNAI natural community maps for 31 WMAs, and 6 Florida Forever areas or proposals. This data was either extracted from v2.3, or from more recent mapping efforts. Domains have been removed from the attribute table, and a class name field has been added for SITE and STATE level classes. The Dry Tortugas have been reincorporated. The geographic extent has been revised for the Coastal Upland and Dry Prairie classes. Rural Open and the Extractive classes underwent a more thorough reviewChanges from Version 3.1 to Version 3.2:CLC v3.2 includes several new site specific data sources: Revised FNAI natural community maps for 43 Florida Park Service lands, and 9 Florida Forever areas or proposals. This data is from 2014 - 2016 mapping efforts. SITE level class review: Wet Coniferous plantation (2450) from v2.3 has been included in v3.2. Non-Vegetated Wetland (2300), Urban Open Land (18211), Cropland/Pasture (18331), and High Pine and Scrub (1200) have undergone thorough review and reclassification where appropriate. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.2.5 to Version 3.3: The CLC v3.3 includes several new site specific data sources: Revised FNAI natural community maps for 14 FWC managed or co-managed lands, including 7 WMA and 7 WEA, 1 State Forest, 3 Hillsboro County managed areas, and 1 Florida Forever proposal. This data is from the 2017 – 2018 mapping efforts. Select sites and classes were included from the 2016 – 2017 NWFWMD (FLUCCS) dataset. M.C. Davis Conservation areas, 18331x agricultural classes underwent a thorough review and reclassification where appropriate. Prairie Mesic Hammock (1122) was reclassified to Prairie Hydric Hammock (22322) in the Everglades. All SITE level Tree Plantations (18333) were reclassified to Coniferous Plantations (183332). The addition of FWC Oyster Bar (5230) features. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com, including classification corrections to sites in T.M. Goodwin and Ocala National Forest. CLC v3.3 utilizes the updated The Florida Land Cover Classification System (2018), altering the following class names and numbers: Irrigated Row Crops (1833111), Wet Coniferous Plantations (1833321) (formerly 2450), Major Springs (4131) (formerly 3118). Mixed Hardwood-Coniferous Swamps (2240) (formerly Other Wetland Forested Mixed).Changes from Version 3.4 to Version 3.5: The CLC v3.5 includes several new site specific data sources: Revised FNAI natural community maps for 16 managed areas, and 10 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2019 – 2020 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. This version of the CLC is also the first to include land identified as Salt Flats (5241).Changes from Version 3.5 to 3.6: The CLC v3.6 includes several new site specific data sources: Revised FNAI natural community maps for 11 managed areas, and 24 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2018 – 2022 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.6 to 3.7: The CLC 3.7 includes several new site specific data sources: Revised FNAI natural community maps for 5 managed areas (2022-2023). Revised Palm Beach County Natural Areas data for Pine Glades Natural Area (2023). Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. In this version a few SITE level classifications are reclassified for the STATE level classification system. Mesic Flatwoods and Scrubby Flatwoods are classified as Dry Flatwoods at the STATE level. Upland Glade is classified as Barren, Sinkhole, and Outcrop Communities at the STATE level. Lastly Upland Pine is classified as High Pine and Scrub at the STATE level.

We summarized annual remote sensing land cover classifications from the U.S. Geological Survey Land Cover Monitoring, Assessment, and Projection (LCMAP) annual time series to characterize forest change across the conterminous United States (CONUS) for the years 1985-2020. The raster output includes a map where each pixel is given an integer value based on the number of years in which it was classified as forest across the annual LCMAP time series. Values of 36 indicate the pixel was classified as forest across all years while a value of 0 indicates forests (tree cover) was never detected during the time series.

The Frank Miller vegetation and disturbance history map, which was drawn in 1938, was scanned using a Vidar color scanner at 600 DPI. The map was originally separated into two segments. A digital image of each segment was brought into ArcMap. Each piece was Georeferenced based on the coordinates given on the map and was then Clipped based on the same coordinates to remove any white space. The Mosaic tool was then used to join the map pieces into one large image. The image was then projected using UTM17N as the basis for the projection.

The product represents a new design of the State Map at a scale of 1:5,000 in raster form, whose advantages are recency and colour processing. The map contains planimetry based on cadastral map, altimetry adopted from the altimetry part of ZABAGED and map lettering based on database of geographic names Geonames and abbreviations of feature type signification coming up from attributes of selected ZABAGED features. The cartographic visualisation is solved automatically without manual works of a cartographer. This new design of the SM 5 is repeatedly generated once a year on the part of the Czech territory where the vector form of cadastral map is available. Therefore, part of export units (map sheets of SM 5) has not a full coverage (price of such export unit is then proportionally reduced).

The product represents a new design of the State Map at a scale of 1:5,000 in raster form, whose advantages are recency and colour processing. The map contains planimetry based on cadastral map, altimetry adopted from the altimetry part of ZABAGED and map lettering based on database of geographic names Geonames and abbreviations of feature type signification coming up from attributes of selected ZABAGED features. The cartographic visualisation is solved automatically without manual works of a cartographer. This new design of the SM 5 is repeatedly generated once a year on the part of the Czech territory where the vector form of cadastral map is available. Therefore, part of export units (map sheets of SM 5) has not a full coverage (price of such export unit is then proportionally reduced).

This dataset consists of a 25m resolution raster version of the Land Cover Map 2007 for Great Britain. Each 25m pixel represents a 25m area of land cover target class, broadly representing Broad Habitats (see below). The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2007. LCM2007 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2007 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2007 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions.

The Digital Raster Graphic (DRG) is a raster image of a scanned USGS topographic map including the collar information, georeferenced to the UTM grid. This version of the Digital Raster Graphic (DRG) has been clipped to remove the collar (white border of the map) and has been reprojected to geographic coordinates.

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 30 meter cell size. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

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.

Offline-kart over Svalbard tilsvarende zoomnivået for 1:40 000 i cachetjenesten NP_Basiskart_Svalbard_WMTS_25833 / Offline map of Svalbard equivalent to the zoom level for 1:40 000 in the cache service NP_Basiskart_Svalbard_WMTS_25833

Kartet er i JPEG 2000 (JP2)-filformatet, og egnet for bruk i GIS og kart-apper for Android og iOS, f.eks. GeoViewer. Filstørrelse ca. 600MB. / The map is in JPEG 2000 (JP2) file format, and suited for use in GIS and map apps for Android and iOS, e.g. GeoViewer. File size approx. 600MB.

Quality

Kartet genereres med produksjonen av nye kartdata og oppdatering av karttjenesten over. / The map is generated with the production of new map data and updates in the map service above.

This dataset contains a raster map of the areas of the bathymetric map of Morris Lake that were interpolated.

This layer is a georeferenced raster image of an historic paper map of Africa originally created between 1750 and 1800. The features illustrated on this map include: rivers, lakes, and coastal islands. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, or other information associated with the principal map. This map was georeferenced by the Stanford University Geospatial Center. This map is part of a selection of digitally scanned and georeferenced historic maps of Africa held at Stanford University Libraries.

URL: https://geoscience.data.qld.gov.au/dataset/ds000095

This dataset includes a zip file of a DVD product release containing georeferenced raster images of 1:250,000 geology map sheets.

In 1992 the latest editions of published geological 1: 250 000 standard map sheets were scanned, and made available as a set of raster images covering most of the State. Generally each sheet map area was scanned in four parts with a separate scan for the map reference. The geo-referenced data were originally provided on compact disc as a set of files in TIFF format and later the data was release on DVD where raster data file sizes were minimised by saving the TIFF data in JPEG format with geo-referencing in geographical co-ordinates Lat/Long based on the Geocentric Datum of Australia, GDA94 geodetic (lat/long) cordinates. MapInfo .tab files of the 'map face' images are also supplied.

This dataset consists of raster geotiff outputs of 30-year average annual land use and land cover transition probabilities for the California Central Valley modeled for the period 2011-2101 across 5 future scenarios. The full methods and results of this research are described in detail in “Integrated modeling of climate, land use, and water availability scenarios and their impacts on managed wetland habitat: A case study from California’s Central Valley” (2021). Land-use and land-cover change for California's Central Valley were modeled using the LUCAS model and five different scenarios were simulated from 2011 to 2101 across the entirety of the valley. The five future scenario projections originated from the four scenarios developed as part of the Central Valley Landscape Conservation Project (http://climate.calcommons.org/cvlcp ). The 4 original scenarios include a Bad-Business-As-Usual (BBAU; high water availability, poor management), California Dreamin’ (DREAM; high water availability, good management), Central Valley Dustbowl (DUST; low water availability, poor management), and Everyone Equally Miserable (EEM; low water availability, good management). These scenarios represent alternative plausible futures, capturing a range of climate variability, land management activities, and habitat restoration goals. We parameterized our models based on close interpretation of these four scenario narratives to best reflect stakeholder interests, adding a baseline Historical Business-As-Usual scenario (HBAU) for comparison. The TGAP raster maps represent the average annual transition probability of a cell over a specified time period for a specified land use transition group and type. Each filename has the associated scenario ID (scn418 = DUST, scn419 = DREAM, scn420 = HBAU, scn421 = BBAU, and scn426 = EEM), transition group (e.g. FALLOW, URBANIZATION), transition type, model iteration (= it0 in all cases as only 1 Monte Carlo simulation was modeled and no iteration data used in the calculation of the probability value), timestep of the 30-year transition summary end date (ts2041 = average annual 30-year transition probability from modeled timesteps 2012 to 2041, ts2071 = average annual 30-year transition probability from modeled timesteps 2042 to 2071, and ts101 = average annual 30-year transition probability from modeled timesteps 2072 to 2101). For example, the following filename “scn418.tgap_URBANIZATION_ Grass_Shrub to Developed [Type].it0.ts2041.tif” represents 30-year cumulative URBANIZATION transition group, for the Grass/Shrub to Developed transition type, for the 2011 to 2041 model period. More information about the LUCAS model can be found here: https://geography.wr.usgs.gov/LUCC/the_lucas_model.php. For more information on the specific parameter settings used in the model contact Tamara S. Wilson (tswilson@usgs.gov)