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.

Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

Large scale data, 1:10m

The most detailed. Suitable for making zoomed-in maps of countries and regions. Show the world on a large wall poster.

Medium scale data, 1:50m

Suitable for making zoomed-out maps of countries and regions. Show the world on a tabloid size page.

Small scale data, 1:110m

Suitable for schematic maps of the world on a postcard or as a small locator globe.

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.

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

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

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

Monroe County in southeastern West Virginia hosts world-class karst within carbonate units of Mississippian and Ordovician age. Lidar-derived elevation data acquired in late December of 2016 were used to create a 3-meter resolution working digital elevation model (DEM), from which surface depressions were identified using a semi-automated workflow in ArcGIS®. Depressions in the automated inventory were systematically checked by a geologist within a grid of 1.5 square kilometer tiles using aerial imagery, lidar-derived imagery, and 3D viewing of the lidar imagery. Distinguishing features such as modification by human activities or hydrological significance (stream sink, ephemerally ponded, etc.) were noted wherever relevant to a particular depression. Relative confidence in depression identification was provided and determined by whether the depression was visible in the lidar imagery, aerial imagery, or both. Statistics on the geometric morphometry of each depression were calculated in ...

The global geospatial data market is poised for significant expansion, projected to reach $3,788 million and grow at a Compound Annual Growth Rate (CAGR) of 6.1% during the forecast period of 2025-2033. This robust growth is propelled by an increasing demand for location-based intelligence across diverse industries. Key drivers include the proliferation of IoT devices generating vast amounts of location data, advancements in satellite imagery and remote sensing technologies, and the growing adoption of AI and machine learning for analyzing complex geospatial datasets. The enterprise sector is emerging as a dominant application segment, leveraging geospatial data for enhanced decision-making in areas such as logistics, urban planning, real estate, and natural resource management. Furthermore, government agencies are increasingly utilizing this data for public safety, infrastructure development, and environmental monitoring. The market is characterized by a bifurcated segmentation between vector data, representing discrete geographic features, and raster data, depicting continuous phenomena like elevation or temperature. Both segments are experiencing healthy growth, driven by specialized applications and analytical needs. Emerging trends include the rise of real-time geospatial data streams, the increasing importance of high-resolution imagery, and the integration of AI-powered analytics to extract deeper insights. However, challenges such as data privacy concerns, high infrastructure costs for data acquisition and processing, and the need for skilled professionals to interpret and utilize the data effectively may pose some restraints. Despite these hurdles, the overwhelming benefits of actionable location intelligence are expected to drive sustained market expansion, with North America and Europe currently leading in adoption, followed closely by the rapidly growing Asia Pacific region. This in-depth report delves into the dynamic and rapidly evolving Geospatial Data Provider market, offering a comprehensive analysis from the historical period of 2019-2024 through to a robust forecast extending to 2033. With the Base Year and Estimated Year set at 2025, the report provides an up-to-the-minute snapshot and a forward-looking perspective on this critical industry. The market size, valued in the millions, is meticulously dissected across various segments, companies, and industry developments.

Support dataset for the workshop: "Introduction to Geospatial Raster and Vector Data with Python", from the Carpentries Incubator. The focus will be the wildfires that affected Rhodes in July 2023.

The data were created by transformation of vector cadastral component of SM 5 to raster file. In territories, where vector SM 5 has not been created yet, the cadastral and altimetry components were created by scanning of individual printing masters of planimetry and altimetry from the last issue of the State Map 1:5,000 - derived. The cadastral component does not contain parcel numbers.

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).

This map service provides dynamic access to data from the Corine Land Cover 1990 inventory. CORINE Land Cover (CLC) is a geographic land cover/land use database encompassing most of the countries of Europe. In 1985 the Corine programme was initiated in the European Union. Corine means 'coordination of information on the environment' and it was a prototype project working on many different environmental issues. The Corine databases and several of its programme have been taken over by the EEA. One of these is an inventory of land cover in 44 classes organised hierarchically in three levels, and presented as a cartographic product, at a scale of 1:100 000. The first level (5 classes) corresponds to the main categories of the land cover/land use (artificial areas, agricultural land, forests and semi-natural areas, wetlands, water surfaces). The second level (15 classes) covers physical and physiognomic entities at a higher level of detail (urban zones, forests, lakes, etc), finally level 3 is composed of 44 classes. CLC was elaborated based on the visual interpretation of satellite images (SPOT, LANDSAT TM and MSS). Ancillary data (aerial photographs, topographic or vegetation maps, statistics, local knowledge) were used to refine interpretation and the assignment of the territory into the categories of the CORINE Land Cover nomenclature. The smallest surfaces mapped (minimum mapping units) correspond to 25 hectares. Linear features less than 100 m in width are not considered. The scale of the output product was fixed at 1:100.000. Thus, the location precision of the CLC database is 100 m. This database is operationally available for most areas of Europe. Original inventories, based on and interpreted from satellite imagery as well as ancillary information sources, are stored within national institutions. One of the major tasks undertaken in the framework of the Corine programme has been the establishment of a computerised inventory on the land cover. Data on land cover is necessary for the environment policy as well as for other policies such as regional development and agriculture. At the same time it provides one of the basic inputs for the production of more complex information on other themes (soil erosion, pollutant emission into the air by the vegetation, etc.). The objectives of the land cover project are: - to provide those responsible for and interested in the European policy on the environment with quantitative data on land cover, consistent and comparable across Europe. Geographical coverage: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Netherlands, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Turkey Corine Land Cover 1990 raster data - version 16 (04/2012) can be accessed here: http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-1990-raster-2

The data were created by transformation of vector cadastral component of SM 5 to raster file. In territories, where vector SM 5 has not been created yet, the cadastral and altimetry components were created by scanning of individual printing masters of planimetry and altimetry from the last issue of the State Map 1:5,000 - derived. The cadastral component does not contain parcel numbers.

This data set was gridded by BORIS staff from a vector data set received from Canadian Soil Information System (CanSIS). The original data came in two parts that covered Saskatchewan and Manitoba. The data were gridded and merged into one data set of 84 files covering the BOReal Ecosystem-Atmosphere Study (BOREAS) region. The data were gridded into the Albers Equal-Area Conic (AEAC) projection. Because the mapping of the two provinces was done separately in the original vector data, there may be discontinuities in some of the soil layers because of different interpretations of certain soil properties.

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

The data were created by transformation of vector cadastral component of SM 5 to raster file. In territories, where vector SM 5 has not been created yet, the cadastral and altimetry components were created by scanning of individual printing masters of planimetry and altimetry from the last issue of the State Map 1:5,000 - derived. The cadastral component does not contain parcel numbers.

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 GIS-usable format employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM) projection, Zone 16, using North American Datum of 1983 (NAD83). To produce a polygon vector layer for use in ArcGIS, we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format using ArcGIS (Version 9.2, © 2006 Environmental Systems Research Institute, Redlands, California). 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 of INDU and immediate environs. 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, link to NVC association and alliance codes), 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 sites, 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 data were created by transformation of vector cadastral component of SM 5 to raster file. In territories, where vector SM 5 has not been created yet, the cadastral and altimetry components were created by scanning of individual printing masters of planimetry and altimetry from the last issue of the State Map 1:5,000 - derived. The cadastral component does not contain parcel numbers.

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer depicting potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise (slr) and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: https://www.coast.noaa.gov/slr These data depict the potential inundation of coastal areas resulting from current Mean Higher High Water (MHHW) conditions. The process used to produce the data can be described as a modified bathtub approach that attempts to account for both local/regional tidal variability as well as hydrological connectivity. The process uses two source datasets to derive the final inundation rasters and polygons and accompanying low-lying polygons: the Digital Elevation Model (DEM) of the area and a tidal surface model that represents spatial tidal variability. The tidal model is created using the NOAA National Geodetic Survey's VDATUM datum transformation software (http://vdatum.noaa.gov) in conjunction with spatial interpolation/extrapolation methods and represents the MHHW tidal datum in orthometric values (North American Vertical Datum of 1988). The model used to produce these data does not account for erosion, subsidence, or any future changes in an area's hydrodynamics. It is simply a method to derive data in order to visualize the potential scale, not exact location, of inundation from sea level rise. Both raster and vector data are provided. The raster data represent both the horizontal extent of inundation and depth above ground, in meters. The vector data represent the horizontal extent of both hydrologically connected and unconnected inundation. The vector "slr" data represent inundation that is hydrologically connected to the ocean. The vector "low" data represent areas that are hydrologically unconnected to the ocean, but are below MHHW and may also flood. For more information, contact coastal.info@noaa.gov.

Data about the top and bottom altitude, depth from land surface and/or the thickness of three geologic units in Tennessee were converted into geospatial format for this USGS data release from previously published paper maps and converted into digital formats for use by the public. The three geologic units were the Chattanooga Shale of Mississippian-Devonian age (Moore and Horton, 1999), the Wells Creek Dolomite of middle Ordovician age (Smith, 1959), and the Knox Group of lower Ordovician age (Newcome, 1954). These geologic units represent important geologic horizons across Tennessee. Geologic structure maps provide important information and, in digital format, support investigative and modeling efforts pertaining to water and mineral resources. Prior to this work, the paper source maps used for this data release existed in limited quantities, mainly restricted to the Nashville, TN offices of the Tennessee Department of Environment and Conservation (TDEC) and United States Geological Survey (USGS). The work for this project included (1) scanning and georeferencing original paper maps to create georeferenced images (GRI), (2) digitizing well location points and contour lines, (3) populating well and contour attribute tables with data from maps and associated reports, and (4) when possible, interpolating raster surfaces for the three geologic units of top and bottom altitude, depth from land surface to the top and bottom surface, and thickness. All raster surfaces were aligned to a modified version of the National Hydrogeologic Grid (Clark and others, 2018) to support USGS Lower Mississippi Gulf Water Science Center efforts to create a statewide hydrogeologic framework. All horizontal coordinated data are projected to NAD 1983 USGS Contiguous USA Albers. The raster vertical coordinate information was referenced to the North American Vertical Datum of 1988 (NAVD 88). This data release includes GRIs, vector data of the wells and mapped contours of top, bottom, or thickness, raster data, and related metadata files for each three geologic units under the associated child item tab. Dataset types can be identified by the following naming convention: i_ = georeferenced map images (GRI) po_ = points c_ = contours and closed depressions f_= faults and other structural features p_ = extent polygon ra_ = altitude raster rd_ = depth from land surface raster rt_ = thickness raster The datasets included on this main landing page are as follows: project_metadata.xml – metadata file for general project information studyarea_ext.zip: p_chttshl_ext.shp - mapped extent of the Chattanooga Shale in Tennessee p_wllscr_ext.shp - mapped extent of the Wells Creek Dolomite in Tennessee p_knx_ext.shp - mapped extent of the Knox Group in Tennessee The datasets included on the child item pages are as follows: Chattanooga Shale: geospatial geologic structural datasets in Tennessee: chttshl_metadata.xml - metadata file chttshl_alldata.zip: GRI/ i_chttshl_btm.tif - structure contour map of the bottom of the Chattanooga Shale (Moore and Horton, 1999) i_chttshl_data.tif - map of data used to create structure and isopach maps (Moore and Horton, 1999) i_chttshl_thk.tif - thickness contour map for the Chattanooga Shale (Moore and Horton, 1999) polygons/ p_knx_ext.shp - study area extent for the Chattanooga Shale p_hohenwald.shp - polygon for extend of the Hohenwald Platform (Moore and Horton, 1999) - supplemental data rasters/ ra_chttshl_btm.tif - altitude raster for the bottom of the Chattanooga Shale ra_chttshl_tp.tif - altitude raster for the top of the Chattanooga Shale rd_chttshl_btm.tif - depth from land surface raster of the bottom of the Chattanooga Shale rd_chttshl_tp.tif - depth from land surface raster of the top of the Chattanooga Shale rt_chttshl.tif - thickness raster for the Chattanooga Shale vectors/ c_chttshl_btm.shp - structure contours for the bottom of the Chattanooga Shale c_chttshl_btm_modified.shp - modified structure contours for the bottom of the Chattanooga Shale (hachures removed from closed basins). This vector used to interpolated raster for the bottom of the Chattanooga Shale c_chttshl_thk.shp - thickness contours for the Chattanooga Shale c_chttshl_thk_modified.shp - modified thickness contours for the Chattanooga Shale (hachures removed from closed basins). This vector used to interpolated raster for the thickness of the Chattanooga Shale po_chttshl.shp - point data of altitude and thickness for the Chattanooga Shale Knox Group: geospatial geologic structural datasets in Middle Tennessee: knx_metadata.xml - metadata file knx_alldata.zip: GRI/ i_knx_tp.tif - structure contour map on the top of the Knox Group (Newcome, 1954) i_knx_outcrop.tif - map of the Wells Creek Disturbance (Wilson and Stearns, 1968) polygons/ p_chttshl_ext.shp - study area extent for the Knox Group p_hohenwald.shp - extent of the Hohenwald Platform - supplemental data rasters/ ra_knx_tp.tif - altitude raster for the top of the Knox Group rd_knx_tp.tif - depth from land surface raster of the top of Knox Group vectors/ c_knx_tp.shp - structure contours for the top of the Knox Group c_knx_tp_modified.shp - modified structure contours for the top of the Knox Group (hachures removed from closed basins). This vector used to interpolated raster for the top of the Knox Group po_knx_tp.shp - point data for the altitude of top of the Knox Group Wells Creek Dolomite: geospatial geologic structural datasets in Tennessee: wllscr_metadata.xml - metadata file wllscr_alldata.zip: GRI/ i_wllscr.tif - thickness contour map for the Wells Creek Dolomite (Smith, 1959) polygons/ p_wllscr_ext.shp - study area extent for the Wells Creek Dolomite rasters/ ra_wllscr_btm.tif - altitude raster for the bottom of the Wells Creek Dolomite (same dataset as ra_knx_tp.tif [Newcome, 1954; Smith, 1959]) ra_wllscr_tp.tif - altitude raster for the top of the Wells Creek Dolomite rd_wllscr_btm.tif - depth from land surface raster of the bottom of the Wells Creek Dolomite (same dataset as ra_knx_tp.tif [Newcome, 1954; Smith, 1959]) rd_wllscr_tp.tif - depth from land surface raster of the top of the Wells Creek Dolomite rt_wllscr.tif - thickness raster for the Wells Creek Dolomite vectors/ c_wllscr.shp - thickness contours for the Wells Creek Dolomite po_wllscr.shp - point data for the thickness of Wells Creek Dolomite References: Moore, J.L., and Horton, A.B., 1999, Structure and Isopach Maps of the Chattanooga Shale in Tennessee, Tennessee Dept. of Conservation, Division of Geology, Report of Investigations 48, 3 plates. Newcome, R. Jr., 1954, Structure contour map on top of the Knox Dolomite in Middle Tennessee, Tennessee Division of Geology, Ground-Water Investigations Preliminary Chart 5, 1 sheet. Smith, O. Jr., 1959, Isopach map of the Wells Creek Dolomite in Middle Tennessee: Tennessee Division of Water Resources, one sheet. Wilson, C.W. and Stearns, R.G., 1968 Geology of the Wells Creek Structure, Tennessee: Tennessee Division of Geology, Bulletin 68, 248 p.