A grouped feature layer that includes Water Mains, Water Services, Same Side Tap Only and No New Taps layers.Water Mains are large buried pipes that distribute water from a supply source ultimately to customer's service lines. Water Services are lines representing a water service delivered from a watermain to a property.Same Side Tap Only and No New Taps are water main restrictions which represent the availability or access to water main assets. Same Side Tap Only are lines representing where water services are only allowed to be tapped on one side of the water main. No New Taps are lines representing water mains where new water services are no longer permitted to tap into the water main.This data provides a limited view of Seattle's water infrastructure. For example, the data does not include transmission pipelines or feeder mains for reasons of water system network security. The data may show water mains that are not eligible for new water service connections (e.g., obsolete or "no-tap" water mains).

NEW WATER SERVICE

Constructing a new home or building or renovating an existing facility? Information for property owners who want new connections to Seattle’s water supply system or to upgrade existing water services. Learn about large and small water service, standard charges, street restoration, how to check for water availability, getting help installing water mains and installation requirements. Learn more about New Water Service.

Analysis of ‘SPU Retail Water Service Area’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/6882d644-72e8-4177-8343-abd2b1a805b7 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

NEW WATER SERVICE

Constructing a new home or building or renovating an existing facility? Information for property owners who want new connections to Seattle’s water supply system or to upgrade existing water services. Learn about large and small water service, standard charges, street restoration, how to check for water availability, getting help installing water mains and installation requirements. Learn more about New Water Service.

--- Original source retains full ownership of the source dataset ---

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. The files below contain a map of Canada showing the locations of all facilities that reported direct releases to land to the NPRI. The data are for the most recent reporting year, by reported total quantities of these releases. The map is available in both ESRI REST (to use with ARC GIS) and WMS (open source) formats. For more information about the individual reporting facilities, a dataset is available in a CSV format. Please consult the following resources to enhance your analysis: - Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html - Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Purpose: The purpose of this dataset is to provide a high resolution map of available water content for Burkina Faso derived from 30m soil properties maps using the iSDA Africa dataset and the USDA Rosetta3 model. The map can be used to notably support spatialized crop simulation models to determine potential crop yields across West Africa. Nature and extent of data: The dataset consists of a high resolution map of plant available water content derived from four soil property datasets from iSDA Africa. The soil properties include sand, clay, silt and fine-earth bulk density, and were predicted at a 30m resolution for 0-20 cm and 20-50cm depth intervals. The USDA Rosetta model was used to compute soil water retention properties from four iSDA layers (sand, clay, silt content, and fine-earth bulk density), including the van Genuchten parameters of residual water content, saturated water content, 'alpha' shape parameter, 'n' shape parameter, and saturated hydraulic conductivity. From these parameters, the volumetric water content at field capacity and permanent wilting point was computed using the van Genuchten-Mualem model. The output map has been filled with nearest neighbor interpolation where NaN values were present. Finally, available water content integrated over the soil profile down to 50cm and to the bedrock depth was calculated. Location and coverage: The dataset covers the country of Burkina Faso in West Africa at a 30m resolution. Temporal scope: The dataset was created using soil property data and models from iSDA Africa and USDA Rosetta3, respectively, and covers the temporal scope of the original datasets. Archive formats / unzipping: Some of the archive files are separated in multiple parts, as indicated by their numeral file extensions (i.e. *.001, *.002, etc). To unzip these files, please consider using an unzipping software such as 7zip, that will manage file concatenation and unzipping given that all parts are stored under the same folder in your file system.

While the Waimakariri District Council has taken all reasonable care in providing correct information, all information should be considered as being illustrative and indicative only. Your use of this information is entirely at your own risk. You should independently verify the accuracy of any information before taking any action in reliance upon it.The Council does not guarantee the existence of laterals (service lines) to vacant lots, regardless of whether a lateral (service line) is shown or not.If you are planning on undertaking any excavation work, please request service plans via beforeUdig to ensure you receive all the required information.Read full disclaimer here.A full description is available in the Metadata and 3 Waters Asset Information Metadata Standard.AbstractThis dataset displays water supply service line assets within the Waimakariri District (WDC) area. This data is collected to support the maintenance and management of WDC's water supply network. This layer includes fields that classify, e.g., material, length, diameter etc.For other water supply pipe assets, please refer to the other water pipe datasets: Network Mains & Facility Pipes and Water Pipes Other.A Pipe, in general terms, represents a group of asset types that can be categorised by the following definition – ‘A man-made, hollow tube used for the transmission of water’.Pipes can be used for a variety of purposes including:- Delivering potable water to network users- Conveying stormwater and wastewater to treatment facilities and outfalls- Ducting used to enclose pipes or other linear assetPipe asset types include conduit, ventilation pipe, culvert, facility pipe, manifold, network main, service line, subsoil drain and tunnel.Service Line: a reticulation pipe that forms the connection between a private property utility pipe and the Council reticulation network. The demarcation point is usually the service connection box for a potable water supply or the property boundary for a sewer or stormwater connection.Please refer to the 3 Waters Asset Information Metadata Standard Data Standard for further information.Update FrequencyDailyPoint of ContactWaimakariri District CouncilLineageData has been compiled from a number of sources and its accuracy may vary (e.g. Field Verification, Deposited Plans, AsBuilt plans and forms, sketches, aerial photo, Google Street View). Attribute information is stored in Waimakariri District Council's Asset Management System. This is joined to a spatial dataset containing the location of each asset and published as a GIS feature layer for use within WDC GIS applications, Before U Dig, and open data portal. There may be delays before data is updated to reflect changes in an area.

description: This coverage contains land-cover information for all of Ohio and portions of Indiana, Michigan, Kentucky, West Virginia, Pennsylvania, and New York. This dataset was derived from the U.S. Geological Survey's National Land Cover Dataset (NLCD). NLCD raster grids were downloaded from the USGS EROS Data Center web server at http://landcover.usgs.gov/natllandcover.html, by state. These grids were then reprojected, mosaiced and clipped against a polygon coverage representing the study area. Grid cell resolution is approximately 30 meters or 1 arc-second.; abstract: This coverage contains land-cover information for all of Ohio and portions of Indiana, Michigan, Kentucky, West Virginia, Pennsylvania, and New York. This dataset was derived from the U.S. Geological Survey's National Land Cover Dataset (NLCD). NLCD raster grids were downloaded from the USGS EROS Data Center web server at http://landcover.usgs.gov/natllandcover.html, by state. These grids were then reprojected, mosaiced and clipped against a polygon coverage representing the study area. Grid cell resolution is approximately 30 meters or 1 arc-second.

Analysis of ‘SPU Retail Water Service Area’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a790300f-8040-4bc6-9110-7126fa0a4b93 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

NEW WATER SERVICE

Constructing a new home or building or renovating an existing facility? Information for property owners who want new connections to Seattle’s water supply system or to upgrade existing water services. Learn about large and small water service, standard charges, street restoration, how to check for water availability, getting help installing water mains and installation requirements. Learn more about New Water Service.

--- Original source retains full ownership of the source dataset ---

This EnviroAtlas dataset describes the total counts and percentage of population, land area, and impervious surface in the 1% Annual Chance Flood Hazard area or 0.2% Annual Chance Flood Hazard area of each block group. The flood hazard area is defined by the National Flood Hazard Layer (NFHL) produced by the Federal Emergency Management Agency (FEMA, https://msc.fema.gov/portal/advanceSearch). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

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

This dataset (2017-2023) is a compilation of the Land Use/Land Cover datasets created by the 5 Water Management Districts in Florida based on imagery -- Northwest Florida Water Management District (NWFWMD) 2022.Bay (1/4/2022 – 3/24/2022), Calhoun (1/7/2022 – 1/18/2022),Escambia (11/13/2021 – 1/15/2021), Franklin (1/7/2022 – 1/18/2022), Gadsden (1/7/2022 – 1/16/2022), Gulf (1/7/2022 – 1/14/2022), Holmes (1/8/2022 – 1/18/2022), Jackson (1/7/2022 – 1/14/2022), Jefferson (1/7/2022 – 2/16/2022), Leon (February 2022), Liberty (1/7/2022 – 1/16/2022), Okaloosa (10/31/2021 – 2/13/2022), Santa Rosa (10/26/2021-1/17/2022), Wakulla (1/7/2022 – 1/14/2022), Walton (1/7/2022-1/14/2022), Washington (1/13/2022 – 1/19/2022).Suwannee River Water Management District (SRWMD) 2019-2023.(Alachua 20200102-20200106), (Baker 20200108-20200126), (Bradford 20181020-20190128), (Columbia 20181213-20190106), (Gilchrist 20181020-20190128), (Levy 20181020-20190128), (Suwannee 20181217-20190116), (Union 20181020-20190128).(Dixie 12/17/2021-01/29/2022), (Hamilton 12/17/2021-01/29/2022), (Jefferson 01/07/2022-02/16/2022), (Lafayette 12/17/2021-01/29/2022), (Madison 12/17/2021-01/29/2022), (Taylor 12/17/2021-01/29/2022.Southwest Florida Water Management District (SWFWMD) 2020. South Florida Water Management District (SFWMD) 2021-2023.St. John's River Water Management District (SJRWMD) 2020.Year Flight Season Counties:2020 (Dec. 2019 - Mar 2020) Alachua, Baker, Clay, Flagler, Lake, Marion, Osceola, Polk, Putnam.2021 (Dec. 2020 - Mar 2021) Brevard, Indian River, Nassau, Okeechobee, Orange, St. Johns, Seminole, Volusia. 2022 (Dec. 2021 - Mar 2022) Bradford, Union. Codes are derived from the Florida Land Use, Cover, and Forms Classification System (FLUCCS-DOT 1999) but may have been altered to accommodate region differences by each of the Water Management Districts.

These data are high-resolution datasets related to in-land water for limnology (study of in-land waters) and remote sensing applications. This includes: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates on a high-resolution (1/360x1/360 degree) grid, produced by the Department of Meteorology at the University of Reading. Data was derived using the ESA CCI Land Cover Map (see linked documentation). Datasets containing information to locate and identify water bodies have been generated from high-resolution (1/360x1/360 degree, about 300mx300m) data locating static-water-bodies recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The new datasets provide: distance to land, distance to water, water body identifiers and lake centre locations. The lake identifiers (IDs) are from the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. The LC CCI water bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR (Advanced Synthetic Aperture Radar) on Envisat between 2005 and 2010. Temporal change in water body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated. The paper associated with this dataset is: L.Carrea O. Embury C.J. Merchant "High-resolution datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates" Geoscience Data Journal, vol. 2 issue 2, pp. 83-97, November 2015. DOI: 10.1002/gdj3.32

This EnviroAtlas dataset describes the percentage of different land cover types within 15- and 50-meters of hydrologically connected streams, rivers, and other water bodies within the Atlas Area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

To evaluate land use and land cover (LULC) maps an independent and representative test dataset is required. Here, a test dataset was generated via stratified random sampling approach across all areas in Fiji not used to generate training data (i.e. all Tikinas which did not contain a training data point were valid for sampling to generate the test dataset). Following equation 13 in Olofsson et al. (2014), the sample size of the test dataset was 834. This was based on a desired standard error of the overall accuracy score of 0.01 and a user's accuracy of 0.75 for all classes. The strata for sampling test samples were the eight LULC classes: water, mangrove, bare soil, urban, agriculture, grassland, shrubland, and trees.

There are different strategies for allocating samples to strata for evaluating LULC maps, as discussed by Olofsson et al. (2014). Equal allocation of samples to strata ensures coverage of rarely occurring classes and minimise the standard error of estimators of user's accuracy. However, equal allocation does not optimise the standard error of the estimator of overall accuracy. Proportional allocation of samples to strata, based on the proportion of the strata in the overall dataset, can result in rarely occurring classes being underrepresented in the test dataset. Optimal allocation of samples to strata is challenging to implement when there are multiple evaluation objectives. Olofsson et al. (2014) recommend a "simple" allocation procedure where 50 to 100 samples are allocated to rare classes and proportional allocation is used to allocate samples to the remaining majority classes. The number of samples to allocate to rare classes can be determined by iterating over different allocations and computing estimated standard errors for performance metrics. Here, the 2021 all-Fiji LULC map, minus the Tikinas used for generating training samples, was used to estimate the proportional areal coverage of each LULC class. The LULC map from 2021 was used to permit comparison with other LULC products with a 2021 layer, notably the ESA WorldCover 10m v200 2021 product.

The 2021 LULC map was dominated by the tree class (74\% of the area classified) and the remaining classes had less than 10\% coverage each. Therefore, a "simple" allocation of 100 samples to the seven minority classes and an allocation of 133 samples to the tree class was used. This ensured all the minority classes had sufficient coverage in the test set while balancing the requirement to minimise standard errors for the estimate of overall accuracy. The allocated number of test dataset points were randomly sampled within each strata and were manually labelled using 2021 annual median RGB composites from Sentinel-2 and Planet NICFI and high-resolution Google Satellite Basemaps.

Data format

The Fiji LULC test data is available in GeoJSON format in the file fiji-lulc-test-data.geojson. Each point feature has two attributes: ref_class (the LULC class manually labelled and quality checked) and strata (the strata the sampled point belongs to derived from the 2021 all-Fiji LULC map). The following integers correspond to the ref_class and strata labels:

1. water
2. mangrove
3. bare earth / rock
4. urban / impervious
5. agriculture
6. grassland
7. shrubland
8. tree

Use

When evaluating LULC maps using test data derived from a stratified sample, the nature of the stratified sampling needs to be accounted for when estimating performance metrics such as overall accuracy, user's accuracy, and producer's accuracy. This is particulary so if the strata do not match the map classes (i.e. when comparing different LULC products). Stehman (2014) provide formulas for estimating performance metrics and their standard errors when using test data with a stratified sampling structure.

To support LULC accuracy assessment a Python package has been developed which provides implementations of Stehman's (2014) formulas. The package can be installed via:

pip install lulc-validation

with documentation and examples here.

In order to compute performance metrics accounting for the stratified nature of the sample the total number of points / pixels available to be sampled in each strata must be known. For this dataset that is:

1. 1779768,
2. 3549325,
3. 541204,
4. 687659,
5. 14279258,
6. 15115599,
7. 4972515,
8. 116131948

Acknowledgements

This dataset was generated with support from a Climate Change AI Innovation Grant.

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

Download this data or get more information. This data publication contains 2015 high-resolution land cover data for each of the 105 counties within Kansas. These data are a digital representation of land cover derived from 1-meter aerial imagery from the National Agriculture Imagery Program (NAIP). There is a separate file for each county. Data are intended for use in rural areas and therefore do not include land cover in cities and towns. Land cover classes (tree cover, other land cover, water, or city/town) were mapped using an object-based image analysis approach and supervised classification.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This EnviroAtlas dataset describes the percentage of forested, vegetated, and impervious land within 15- and 50-meters of hydrologically connected streams, rivers, and other water bodies within the EnviroAtlas community area. In this community, forest is defined as Trees & Forest and Woody Wetlands and vegetated cover is defined as Trees & Forest, Grass & Herbaceous, Woody Wetlands, and Emergent Wetlands. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset contains Land Cover data by Wetness Index for each Watershed Boundary Dataset (WBD) 12-Digit Hydrologic Unit Code (HUC-12) of the conterminous United States, based on the National Land Cover Database (NLCD) from 2011, the December 30, 2009 Soil Survey Geographic (SSURGO) Database, and the USDA's Cropland Data Layer (CDL) data from 2011. The dataset includes the percentages of each HUC-12 belonging to several land cover groups that are on land with a Wetness Index greater than 550 (WET550). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset categorizes land cover into structural elements (e.g. core, edge, connector, etc.). It depicts core areas of natural land cover, core fragmentation, and patterns of connectivity among core patches. Water is treated as background in this dataset; waterbodies are separated from the natural land cover classes and included in the analysis with the developed land cover classes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).