The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

This dataset consists of a map depicting the landcover of the Natchez Trace Parkway. The mapping output was created using mosaiced color infrared aerial photography of the Parkway. The map shows the distribution of 18 landcover classes based on the National Vegetation Classification Standard. Ground-based vegetation classification was provided by the National Park Service (NPS). The mapping output delineates grasses, road-developed areas, scrub-shrub, shrubland, plantation, water bodies, areas of white oak, oak, pine-oak, pine-cedar, pine-sweetgum, sweetgum (including sweetgum-oak), scattered trees, swamp forest, irregular classes, aquatic vegetation, invasive species, canopy gaps, and clouds.

Total mapped area includes a 100 m buffer outside the park boundary. 235 digital orthophoto quarter quadrangles (DOQQs) were required to cover the entire 715 km long Parkway. For ease of use, the DOQQs were grouped into 11 mosaics, each covering a section of the Parkway. At the request of the NPS, each mosaic was divided into ten tiles to allow for efficient loading on less robust computers.

description: This map was produced by the Division of Realty to depict landownership at Upper Mississippi River National Wildlife and Fish Refuge. It was generated from rectified aerial photography, cadastral surveys and recorded documents.; abstract: This map was produced by the Division of Realty to depict landownership at Upper Mississippi River National Wildlife and Fish Refuge. It was generated from rectified aerial photography, cadastral surveys and recorded documents.

This work has been published in the Nature Scientific Data. Suggested citation: Rajib et al. The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset. Nature Scientific Data 8, 271 (2021). https://doi.org/10.1038/s41597-021-01048-w

Here, we present the first-available dataset that quantifies land use change along the floodplains of the Mississippi River Basin (MRB) covering 60 years (1941-2000) at 250-m resolution. The MRB is the fourth largest river basin in the world (3.3 million sq km) comprising 41% of the United States and draining into the Gulf of Mexico, an area with an annually expanding and contracting hypoxic zone resulting from basin-wide over-enrichment of nutrients. The basin represents one of the most engineered systems in the world, and includes complex web of dams, levees, floodplains, and dikes. This new dataset reveals the heterogenous spatial extent of land use transformations in MRB floodplains. The domination transition of floodplains has been from natural ecosystems (e.g. wetlands or forests) to agricultural use. A steady increase in developed land use within the MRB floodplains was also evident.

To maximize the reuse of this dataset, our contributions also include four unique products: (i) a Google Earth Engine interactive map visualization interface: https://gishub.org/mrb-floodplain (ii) a Google-based Python code that runs in any internet browser: https://colab.research.google.com/drive/1vmIaUCkL66CoTv4rNRIWpJXYXp4TlAKd?usp=sharing (iii) an online tutorial with visualizations facilitating classroom application of the code: https://serc.carleton.edu/hydromodules/steps/241489.html (iv) an instructional video showing how to run the code and partially reproduce the floodplain land use change dataset: https://youtu.be/wH0gif_y15A

Cette carte d'utilisation des sols fournit une représentation visuelle de la manière dont les terres sont utilisées. Chaque pixel de la carte est classé dans une catégorie d'utilisation des terres. Langues : Anglais et françaisCréé par Meghan Jolley, février 2022Résolution spatiale : 10mProjection cartographique : NAD 1983 UTM Zone 18NSources des données : Meghan Jolley (2021) à l’aide de dérivés des données de Copernicus Sentinel (2019-2020)—--------------------This land use map provides a visual representation of how land is used. Each pixel on the map is classified into a land use category. Languages: English and FrenchCreated by Meghan Jolley, February 2022Spatial Resolution: 10mMap Projection: NAD 1983 UTM Zone 18NData Sources: Meghan Jolley (2021) using derivatives of Copernicus Sentinel data (2019-2020)

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

description: In the late 1880's and early 1900's the Mississippi River Commission (MRC) conducted an extensive high-resolution survey of the Mississippi River from Cairo, Illinois to Minneapolis, Minnesota. These data were published as a series of 89 survey maps and index. In the 1990's, the Upper Midwest Environmental Sciences Center (UMESC) in conjunction with the US Army Corps of Engineers Upper Mississippi River Restoration- Environmental Management Program -- Long Term Resource Monitoring Program element (LTRMP) for the Upper Mississippi River automated the maps' land cover/use symbology to create a turn of the century/pre-impoundment land cover/use data set. Other data on the maps that were not automated include; elevation contours, water depth soundings, proposed water control structures (e.g., wing dams), levees, benchmarks, railroads, and city streets.; abstract: In the late 1880's and early 1900's the Mississippi River Commission (MRC) conducted an extensive high-resolution survey of the Mississippi River from Cairo, Illinois to Minneapolis, Minnesota. These data were published as a series of 89 survey maps and index. In the 1990's, the Upper Midwest Environmental Sciences Center (UMESC) in conjunction with the US Army Corps of Engineers Upper Mississippi River Restoration- Environmental Management Program -- Long Term Resource Monitoring Program element (LTRMP) for the Upper Mississippi River automated the maps' land cover/use symbology to create a turn of the century/pre-impoundment land cover/use data set. Other data on the maps that were not automated include; elevation contours, water depth soundings, proposed water control structures (e.g., wing dams), levees, benchmarks, railroads, and city streets.

This dataset provides a land cover map focused on peatland ecosystems in the upper peninsula of Michigan. The map was produced at 12.5-m resolution using a multi-sensor fusion (optical and L-band SAR) approach with imagery from Landsat-5 TM and ALOS PALSAR collected between 2007 and 2011. A random forest classifier trained with polygons delineated from field data and aerial photography was used to determine pixel classes. Accuracy assessment based on field-sampled sites show high overall map accuracy (92%).

This dataset provides the spatial distribution of vegetation types, soil carbon, and physiographic features in the Imnavait Creek area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology. Data are also provided on the research grids for georeferencing. The map data are from a variety of sources and encompass the period 1970-06-01 to 2015-08-31.

As part of BOREAS, the RSS-15 team conducted an investigation using SIR-C , X-SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape. The procedure involved image classification with SAR and Landsat TM data and development of simple mapping techniques using combinations of SAR channels. For the SSA, the SIR-C data used were acquired on 06-Oct-1994, and the Landsat TM data used were acquired on September 2, 1995. The maps of the NSA were developed from SIR-C data acquired on 13-Apr-1994.

The Filled Land Surface Albedo Product for Southern Africa, which is generated from MOD43B3 Product (the official Terra/MODIS-derived Land Surface Albedo - http://geography.bu.edu/brdf/userguide/albedo.html ), is a subset of the global data set of spatially complete albedo maps computed for both white-sky and black-sky at 10 wavelengths. The data spatial extent is from approximately 5 degrees N to -30 degrees S latitude and 5 minutes E to 60 degrees E longitude and covers 7 sixteen day periods starting on July 11 through October 15, 2000.Map Products, containing spatially complete land surface albedo data, are generated at 1-minute resolution on an equal-angle grid. The maps are stored in separate HDF files for each wavelength, each 16-day period and each albedo type (white- and black-sky). Data belonging to black sky and white sky albedo have been zipped separately. This format allows the user to have flexibility to download and store only the data absolutely needed.The One-Minute Land Ecosystem Classification Product is a global (static map) data set of the International Geosphere-Biosphere Programme (IGBP) classification scheme stored on an equal-angle rectangular grid at 1-minute resolution. The dataset is generated from the official MODIS land ecosystem classification dataset, MOD12Q1 for year 2000, day 289 data (October 15, 2000). This dataset is used in generating the spatially complete albedo maps, but is also a stand-alone product designed for use by the user community. The Land Ecosystem Classification Map File product file is stored in Hierarchical Data Format (HDF).

FOR non-AGOL ACCOUNT HOLDERS, DOWNLOAD THIS GEOSPATIAL DATA HERE: https://gis-fws.opendata.arcgis.com/search?tags=lmvjvPolygon shapefile of the various state game agency managed lands (typically known as WMAs or Wildlife Management Areas) of the West Gulf Coastal Plain & Ouachitas ecological region, as compiled by the Lower Mississippi Valley Joint Venture partnership. Data was pulled from PAD US database aggregated by USGS and selected by location for inclusion in the region and includes data from Mississippi, Arkansas, Oklahoma and Texas. Units are considered areas that are actively managed for game and wildlife species.The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground. These lands are commonly known as Wildlife Management Areas (WMAs) but that nomenclature varies by state.

This data set is the Former Soviet Union (FSU) portion of the Generalized World Forest Map (WCMC, 1998), a 1-kilometer resolution generalized forest cover map for the land area of the Former Soviet Union. There are five forest classes in the original global generalized map. Only two of those classes were distinguished in the geographical portion comprising the FSU.

This dataset defines the symbology for the landcover map of the Natchez Trace Parkway. The map shows the distribution of 18 landcover classes based on the National Vegetation Classification Standard. Ground-based vegetation classification was provided by National Park Service (NPS). The mapping output and layer delineate grasses, road-developed areas, scrub-shrub, shrubland, plantation, water bodies, areas of white oak, oak, pine-oak, pine-cedar, pine-sweetgum, sweetgum (including sweetgum-oak), scattered trees, swamp forest, irregular classes, aquatic vegetation, invasive species, canopy gaps, and clouds. Mapped classes that have been digitized are noted with an asterisk (*) in the legend.

description: The Great River Environmental Action Team (GREAT) was a federal/state multi-agency cooperative program established in the late 1970's to evaluate current resource management practices and develop management strategies for the Upper Mississippi River (UMR). One of the problems facing the GREAT project was the lack of available information on many of the river's components. One project implemented by GREAT was the creation of a land cover/land use (LCU) database derived from color infrared aerial photography collected in 1975. Mississippi River Pools 3 through 10 were photographed at a scale of 1:9,600, and Lock and Dam 10 to the Ohio River were photographed at a scale of 1:24,000. The program's photo interpreters delineated whatever features could be viewed on the photos, using a minimum mapping unit that was less than half an acre. A contractor was hired to transfer the photo overlays for Pools 3 through 14 onto 1:24,000-scale USGS quadrangles, then automate the data using the geographic information system (GIS) program PIOS. The data were also distributed as map books that contained 1:6,000-scale enlargements of the photos and their overlays. During the data transfer process; the contractor hired to automate the data generalized it to a 2.5 acre minimum mapping unit. Documentation archived by the GREAT project described this automation process as; some polygons smaller than 2.5 acres and linear features were incorporated into nearby polygons. Others were manually enlarged so that the data contained within would be preserved. All generalizations were made in accordance with the guidelines established for the GREAT projects. The digital data sets were then enhanced by the GREAT project. Unfortunately no record of the enhancements or an archive of the original digital dataset are known to exist. The enhanced digital data, copies of the aerial photo overlays, copies of most of the map books, and some of the photos themselves were archived and preserved by the various agencies that participated in the GREAT project. These data were then passed to the Upper Midwest Environmental Sciences Center (UMESC) in the late 1980's and 1990's when the center became the administrator for the Upper Mississippi River System's Long Term Resource Monitoring Program (LTRMP). Comparisons of the archived digital data set to the photos and their overlays displayed discrepancies that were difficult to document. The 1975 data set is viewed by many as an important baseline data set, so in 1999 UMESC decided to use the archived photo overlays to (1) address the data discrepancies by reautomating Pools 3 through 14, and (2) complete the data set by automating Lock and Dam 14 to the Ohio River.; abstract: The Great River Environmental Action Team (GREAT) was a federal/state multi-agency cooperative program established in the late 1970's to evaluate current resource management practices and develop management strategies for the Upper Mississippi River (UMR). One of the problems facing the GREAT project was the lack of available information on many of the river's components. One project implemented by GREAT was the creation of a land cover/land use (LCU) database derived from color infrared aerial photography collected in 1975. Mississippi River Pools 3 through 10 were photographed at a scale of 1:9,600, and Lock and Dam 10 to the Ohio River were photographed at a scale of 1:24,000. The program's photo interpreters delineated whatever features could be viewed on the photos, using a minimum mapping unit that was less than half an acre. A contractor was hired to transfer the photo overlays for Pools 3 through 14 onto 1:24,000-scale USGS quadrangles, then automate the data using the geographic information system (GIS) program PIOS. The data were also distributed as map books that contained 1:6,000-scale enlargements of the photos and their overlays. During the data transfer process; the contractor hired to automate the data generalized it to a 2.5 acre minimum mapping unit. Documentation archived by the GREAT project described this automation process as; some polygons smaller than 2.5 acres and linear features were incorporated into nearby polygons. Others were manually enlarged so that the data contained within would be preserved. All generalizations were made in accordance with the guidelines established for the GREAT projects. The digital data sets were then enhanced by the GREAT project. Unfortunately no record of the enhancements or an archive of the original digital dataset are known to exist. The enhanced digital data, copies of the aerial photo overlays, copies of most of the map books, and some of the photos themselves were archived and preserved by the various agencies that participated in the GREAT project. These data were then passed to the Upper Midwest Environmental Sciences Center (UMESC) in the late 1980's and 1990's when the center became the administrator for the Upper Mississippi River System's Long Term Resource Monitoring Program (LTRMP). Comparisons of the archived digital data set to the photos and their overlays displayed discrepancies that were difficult to document. The 1975 data set is viewed by many as an important baseline data set, so in 1999 UMESC decided to use the archived photo overlays to (1) address the data discrepancies by reautomating Pools 3 through 14, and (2) complete the data set by automating Lock and Dam 14 to the Ohio River.

description: This data represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular survey data. The rectangular survey data are a reference system for land tenure based upon meridian, township/range, section, section subdivision and government lots. The non-rectangular survey data represent surveys that were largely performed to protect and/or convey title on specific parcels of land such as mineral surveys and tracts. The data are largely complete in reference to the rectangular survey data at the level of first division. However, the data varies in terms of granularity of its spatial representation as well as its content below the first division. Therefore, depending upon the data source and steward, accurate subdivision of the rectangular data may not be available below the first division and the non-rectangular minerals surveys may not be present. At times, the complexity of surveys rendered the collection of data cost prohibitive such as in areas characterized by numerous, overlapping mineral surveys. In these situations, the data were often not abstracted or were only partially abstracted and incorporated into the data set. These PLSS data were compiled from a broad spectrum or sources including federal, county, and private survey records such as field notes and plats as well as map sources such as USGS 7 minute quadrangles. The metadata in each data set describes the production methods for the data content. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. A complete PLSS data set includes the following: PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non-rectangular components of the PLSS) Meandered Water, Corners, Metadata at a Glance (which identified last revised date and data steward) and Conflicted Areas (known areas of gaps or overlaps or inconsistencies). The Entity-Attribute section of this metadata describes these components in greater detail. The PLSS First Division is commonly the section. This is the first set of divisions for a PLSS Township.; abstract: This data represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular survey data. The rectangular survey data are a reference system for land tenure based upon meridian, township/range, section, section subdivision and government lots. The non-rectangular survey data represent surveys that were largely performed to protect and/or convey title on specific parcels of land such as mineral surveys and tracts. The data are largely complete in reference to the rectangular survey data at the level of first division. However, the data varies in terms of granularity of its spatial representation as well as its content below the first division. Therefore, depending upon the data source and steward, accurate subdivision of the rectangular data may not be available below the first division and the non-rectangular minerals surveys may not be present. At times, the complexity of surveys rendered the collection of data cost prohibitive such as in areas characterized by numerous, overlapping mineral surveys. In these situations, the data were often not abstracted or were only partially abstracted and incorporated into the data set. These PLSS data were compiled from a broad spectrum or sources including federal, county, and private survey records such as field notes and plats as well as map sources such as USGS 7 minute quadrangles. The metadata in each data set describes the production methods for the data content. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. A complete PLSS data set includes the following: PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non-rectangular components of the PLSS) Meandered Water, Corners, Metadata at a Glance (which identified last revised date and data steward) and Conflicted Areas (known areas of gaps or overlaps or inconsistencies). The Entity-Attribute section of this metadata describes these components in greater detail. The PLSS First Division is commonly the section. This is the first set of divisions for a PLSS Township.

This dataset contains annual land use/cover (LUC) maps at 30 m resolution across Mawas, Central Kalimantan, Indonesia. There are six files, each representing a five-year interval over the period 1994-2019. An additional file for 2015 was created for accuracy assessment. A high-quality and low-cloud coverage image from Landsat 5 or Landsat 8 over each 5-year period was selected or composited for the January-August timeframe. Investigators used their knowledge to manually identify training polygons in these images for five LUC classes: peat swamp forest, tall shrubs/ secondary forest, low shrubs/ferns/grass, urban/bare land/open flooded areas, and river. Pixel values of Landsat Tier 1 surface reflectance products and selected indices were extracted for each LUC and used to predict LUC classes across the Mawas study area using the Classification and Regression Trees (CART) method. These data can be used to evaluate the relationship between fire occurrence and land cover type in the study site.

This data set provides the spatial distributions of vegetation types, soil carbon, and physiographic features in the Toolik Lake area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology.

This data set provides classified land cover transition images (maps) derived from Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) imagery for Ariquemes, Luiza, and Ji-Parana¡ areas in Rondonia, Brazil, at 30-m resolution. Images depict the age relative to the year 2000, of cleared land from the date the land was cut, to the date when primary forests transitioned into nonforest class (for example, 25 = cut by 1975, or 25 years before the year 2000).

Temporal changes in three regions are represented by 31 TM scenes acquired between 1984 and 1999, and a pair of MSS scenes from 1975 and 1978.

Data are provided as three GeoTiff (*.tif) images, one for each of the three areas.

These DEMs were produced from digitized contours at a cell resolution of 100 meters. Vector contours of the area were used as input to a software package that interpolates between contours to create a DEM representing the terrain surface. The vector contours had a contour interval of 25 feet. The data cover the BOREAS MSAs of the SSA and NSA and are given in a UTM map projection.