This dataset holds the process chain to produce a orthomosaic from 220 aerial photographs acquired by IGN in 1963 under the mission AOF 566P5000. The dataset covers the Lamto reserve. We orthorectified the scanned images using ground control points from Google Maps and a structure from motion software (SfM). This dataset contains the scanned aerial photographes, the ground control points and the finale orthomosaic with a 17 cm ground resolution.

This dataset holds the unpublished map “Carte physionomique des faciès savaniens de Lamto" drawn by de la Souchère; P. and Badarello, L. in 1969. We georeferenced the scanned paper map using ground control points derived from Google Maps. The dataset contains the scanned map, the ground control points and the raster layer of the georeferenced map.

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.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This dataset holds the map “Carte du recouvrement ligneux de la réserve de Lamto" published by Gautier, L. in 1990. We georeferenced the scanned paper map using ground control points derived from Google Maps. The dataset contains the scanned map, the ground control points and the raster layer of the georeferenced map.

DOWNLOAD RASTER IMAGERYRS-FRIS Version 5.2 is a remote-sensing based forest inventory for WA DNR State Trust Lands.Predictions are derived from three-dimensional photogrammetric point cloud data (DAP), field measurements, and statistical methods. RS-FRIS 5.2 was constructed using remote sensing data collected in 2021 and 2022, and incorporates additional depletions for selected harvests completed after the source imagery was acquired. RS-FRIS combined origin year rasters report age and origin year at 0.1 acre resolution using a hierarchy of data sources.

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.

US Geologic Service (USGS) Digital Raster Graphics (1:24000 scale) covering the State of Georgia. A DRG is an image of a USGS standard series topographic map scanned at a minimum resolution of 250 dots per inch, and georeferenced to the Universal Transverse Mercator (UTM) projection. Each 7.5-minute DRG provides coverage for an area of land measuring 7.5-minutes of latitude by 7.5-minutes longitude. The horizontal positional accuracy and datum of the DRG matches that of the source map. Although these data have been processed successfully on a computer system at the Georgia GIS Data Clearinghouse, no warranty expressed or implied is made by Georgia GIS Data Clearinghouse regarding the utility of the data on any other system, nor shall the act of distribution constitute any such warranty.

The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat. Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

This dataset holds the process chain to produce a orthomosaic from oblique aerial photographs acquired by Gautier using a cessna airplane and a handheld 35 mm camera on April 11, 1988 . We digitized the original colour diapos and created a orthomosaic using ground control points from Google Maps and a structure from motion software (SfM). The datasets contains the scanned diapos, the ground control points and the finale orthomosaic with a 20 cm ground resolution.

Spatial raster datasets derived from 1 m LiDAR digital elevation model describe topographic control on hydrological processes for the Catalina - Jemez CZO research areas. These data are intended for the visualization and support topographic and geo-spatial analysis.

Raster functions are operations that apply processing directly to raster dataset pixels. The raster functions supplied here are the same operations applied to the 3 ft. DEM-related web services from NC OneMap (Aspect, Hillshade, Shaded Elevation, Shaded Relief, Slope, and raster contours for 1 foot, 2 feet, 4 feet, 20 feet, and 100 feet). The downloaded functions can be used in ArcGIS products.

These could be helpful if there is a need to use an NC OneMap DEM-derivative elevation product in a disconnected environment, an instance where web service use is not practical. The county-based DEMs can be downloaded and the raster functions applied in ArcGIS Pro, for use in an offline environment.

In the downloaded raster functions ZIP file are XML files for:

 Aspect
 Hillshade
 Shaded Elevation
 Shaded Relief
 Slope
 Raster Contours for intervals: 1 ft., 2 ft., 4 ft., 20 ft., 100 ft.

Information on using raster functions in ArcGIS Pro can be found here.

6 inch resolution raster image of New York City, classified by landcover type. High resolution land cover data set for New York City. This is the 6 inch version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

Eight Landsat TM and three SPOT satellite scenes were used to determine earth cover categories for this 9,300,000 hectare (23,000,000 acre) study area. Seven major classes and seventeen minor classes were classified. Datafiles (GIS Raster) are provided separately for download.

The Digital Raster Graphic (DRG) is a raster image of a scanned USGS topographic map. A DRG-Lam is useful as a source or background layer in a GIS, as a means to perform quality assurance on other digital products, and as a source for the collection and revision of DLG data. DRG-Lam's can also be merged with other digital data, e.g. DEM's or DOQ's, to produce a hybrid digital file. These DRGs were produced through an Innovative Partnership agreement between The Land Information Technology Company, Ltd., of Aurora, CO and the USGS.The full Kansas geospatial catalog is administered by the Kansas Data Access & Support Center (DASC) and can be found at the following URL: https://hub.kansasgis.org/

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

Data were derived from 2014 6" resolution Island County lidar data using ArcGIS and Spatial Analyst Tools. The resulting raster was then converted to polygons. Polygons spanning elevation differences <10' were deleted.

Aerial Imagery/Photography, Scanned Historical Maps, CCT DRAFT Ground Level Map (GLM), Infrared Imagery, Digital Elevation Models (DEM), etc. All Raster Image Services (WMS):Use URL below to add WMS Server Connection in ArcGIS Desktop, ArcPro, QGIS, AutoCAD, etc.https://cityimg.capetown.gov.za/erdas-iws/ogc/wms/GeoSpatial Datasets?service=WMS&request=getcapabilities&All Raster Image Services (REST):https://cityimg.capetown.gov.za/erdas-iws/esri/GeoSpatial%20Datasets/rest/services/Popular Image Services: 2021 Aerial Imagery , 2020 Aerial Imagery , 2019 Aerial Imagery , DRAFT CCT Ground Level Map (GLM) 2019_5m_ DEM

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

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).