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.

Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

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 files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

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

High resolution land cover data set for New York City. This is the 3ft 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.

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.

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).

Summary of Methods for Developing Ecological Units in Southern California

Allan Hollander and Emma Underwood, University of California Davis.

1) Compiling GIS layers. These data were compiled from a variety of sources and resolutions (Table 1) for the southern California study area (see Methods_figure_1.png for the study area). The original resolution of these raster layers ran from 10 meters to 270 meters, and resampling was conducted so all analyses were performed at a 30 meter raster resolution. We decided not to include vegetation in the data stack as the aim was to capture biophysical characteristics and vegetation will reflect current landscape history and land use patterns (e.g. fire history, type conversion from shrubland, or agricultural use). Lakes and reservoirs were omitted from the subsequent analysis. Data compiled:

a) Soil suborders. This was a discretely-classified raster layer with 22 soil suborder classes included in the southern California region. This was derived ...

This dynamic image service provides access to the USGS 7.5 minute quadrangles for the Commonwealth of Kentucky. The source data is the Kentucky Raster Graphics (KRG) dataset created by the Commonwealth. The KRGs are a higher-resolution derivative of the USGS 7.5 minute topographic quadrangle data commonly known as DRGs, or Digital Raster Graphics.

DOWNLOAD RASTER IMAGERYRS-FRIS Version 5.4 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 models. RS-FRIS 5.4 was constructed using remote sensing data collected in 2021 and 2022, and incorporates depletions for selected completed harvest types through 2025-08-31.

High resolution land cover dataset for City of Boston, MA. 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 primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) 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. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

High resolution land cover dataset for City of Boston, MA. 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 primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) 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. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

Credits: University of Vermont Spatial Analysis Laboratory in collaboration with the City of Boston, Trust for Public Lands, and City of Cambridge.

Sentinel-2 Level-1C imagery with on-the-fly renderings for visualization. This imagery layer pulls directly from the Sentinel-2 on AWS collection and is updated daily with new imagery.Sentinel-2 imagery can be applied across a number of industries, scientific disciplines, and management practices. Some applications include, but are not limited to, land cover and environmental monitoring, climate change, deforestation, disaster and emergency management, national security, plant health and precision agriculture, forest monitoring, watershed analysis and runoff predictions, land-use planning, tracking urban expansion, highlighting burned areas and estimating fire severity.Geographic CoverageGlobalContinental land masses from 65.4° South to 72.1° North, with these special guidelines:All coastal waters up to 20 km from the shoreAll islands greater than 100 km2All EU islandsAll closed seas (e.g. Caspian Sea)The Mediterranean Sea Temporal CoverageThis layer includes a rolling collection of Sentinel-2 imagery acquired within the past 14 months.This layer is updated daily with new imagery.The revisit time for each point on Earth is every 5 days.The number of images available will vary depending on location. Product LevelThis service provides Level-1C Top of Atmosphere imagery.Alternatively, Sentinel-2 Level-2A is also available. Image Selection/FilteringThe most recent and cloud free images are displayed by default.Any image available within the past 14 months can be displayed via custom filtering.Filtering can be done based on attributes such as Acquisition Date, Estimated Cloud Cover, and Tile ID.Tile_ID is computed as [year][month][day]T[hours][minutes][seconds]_[UTMcode][latitudeband][square]_[sequence]. More… Visual RenderingDefault rendering is Natural Color (bands 4,3,2) with Dynamic Range Adjustment (DRA).The DRA version of each layer enables visualization of the full dynamic range of the images.Rendering (or display) of band combinations and calculated indices is done on-the-fly from the source images via Raster Functions.Various pre-defined Raster Functions can be selected or custom functions created.Available renderings include: Agriculture with DRA, Bathymetric with DRA, Color-Infrared with DRA, Natural Color with DRA, Short-wave Infrared with DRA, Geology with DRA, NDMI Colorized, Normalized Difference Built-Up Index (NDBI), NDWI Raw, NDWI - with VRE Raw, NDVI – with VRE Raw (NDRE), NDVI - VRE only Raw, NDVI Raw, Normalized Burn Ratio, NDVI Colormap. Multispectral BandsBandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020Additional NotesOverviews exist with a spatial resolution of 150m and are updated every quarter based on the best and latest imagery available at that time.To work with source images at all scales, the ‘Lock Raster’ functionality is available. NOTE: ‘Lock Raster’ should only be used on the layer for short periods of time, as the imagery and associated record Object IDs may change daily.This ArcGIS Server dynamic imagery layer can be used in Web Maps and ArcGIS Desktop as well as Web and Mobile applications using the REST based Image services API.Images can be exported up to a maximum of 4,000 columns x 4,000 rows per request. Data SourceSentinel-2 imagery is the result of close collaboration between the (European Space Agency) ESA, the European Commission and USGS. Data is hosted by the Amazon Web Services as part of their Registry of Open Data. Users can access the imagery from Sentinel-2 on AWS, or alternatively access EarthExplorer or the Copernicus Data Space Ecosystem to download the scenes.For information on Sentinel-2 imagery, see Sentinel-2.

Basic raster layers from the MCM-LTER spatial data holdings have been exported and symbolized. The dataset files offered here include: 30m DEM made from USGS Topo map SPOT Satellite Image 39-558 LANDSAT 7 Satellite Image Note - the SPOT and LANDSAT layers are not MCM-LTER data products.  These resources were updated last in 2007, for more up-to-date layers, and potentially, higher resolution layers, please visit the Polar Geospatial Center and other affine geospatial data clearinghouses.Â

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.

Rasters assocaited with elevation (from the National elevation dataset), slope (created from the elevation dataset using ArcGIS), a Shannon diversity index as a metric of landscape fragmentation (created from the forest/shrub layer using Fragstats), distance to all roads (created in ArcGIS using a road TIGER shapefile), distance to forest/shrubs (created using NLCD 2016 data), human population density (created using data from the US Census Bureau). All rasters are at a 90m resolution.

GeoTIFF raster data with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). This link provides access to the following layers: (1) Wind speed (WS): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file (2) Power Density (PD): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file. (3) Elevation (ELEV): at ground level (4) Air Density (RHO): at ground level (5) Ruggedness Index (RIX): at ground level All layers have 250m resolution.

A digital raster graphic (DRG) is a scanned image of an U.S. Geological Survey (USGS) standard series topographic map, including all map collar information. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Universal Transverse Mercator projection. The horizontal positional accuracy and datum of the DRG matches the accuracy and datum of the source map. The map is scanned at a minimum resolution of 250 dots per inch.

DRG's are created by scanning published paper maps on high-resolution scanners. The raster image is georeferenced and fit to the UTM projection. Colors are standardized to remove scanner limitations and artifacts. The average data set size is about 6 megabytes in Tagged Image File Format (TIFF) with PackBits compression. DRG's can be easily combined with other digital cartographic products such as digital elevation models (DEM) and digital orthophoto quadrangles (DOQ).

DRG's are stored as rectified TIFF files in geoTIFF format. GeoTIFF is a relatively new TIFF image storage format that incorporates georeferencing information in the header. This allows software, such as ArcView, ARC/INFO, or EPPL7 to reference the image without an additional header or world file.

Within the Minnesota Department of Natural Resources Core GIS data set the DRG's have been processed to be in compliance with departmental data standards (UTM Extended Zone 15, NAD83 datum) and the map collar information has been removed to facilitate the display of the DRG's in a seamless fashion.

These DRG's were clipped and transformed to UTM Zone 15 using EPPL7 Raster GIS.

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.Changes from Version 3.7 to 4.0: CLC 4.0 represents a major update to CLC performed cooperatively by FWC and FNAI via a State Wildlife Grant to address changes on the landscape such as conversion to development and to integrate other recent high quality land cover sources. CLC v4.0 includes FWC's comprehensive delineation of solar farms, FLUCCS updates based on aerial photos from 2017-2022, ground-truthed mapping from FNAI and Florida Park Service, a statewide update of Intensive Development from Google Dynamic World land cover as of 2024, and additional FNAI review and revisions of target classes including sand beach and beach dune. A complete description of updates is available in the Cooperative Land Cover version 4.0 report, available from FNAI

Overview:
The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.

The Copernicus DEM for Europe at 3 arcsec (0:00:03 = 0.00083333333 ~ 90 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).

Processing steps:
The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in VRT format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:

gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt

In order to reduce the spatial resolution to 3 arc seconds, weighted resampling was performed in GRASS GIS (using r.resamp.stats -w and the pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.

Projection + EPSG code:
Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:
north: 82:00:30N
south: 18N
west: 32:00:30W
east: 70E

Spatial resolution:
3 arc seconds (approx. 90 m)

Pixel values:
meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)

Original dataset license:
https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf

Processed by:
mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)