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eng; USA
utf8
dataset
EnviroAtlas Coordinator
U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas
Geospatial Data Owner
(919) 541-3832
109 T.W. Alexander Drive
Research Triangle Park
NC
27709
EnviroAtlas@epa.gov
https://www.epa.gov/enviroatlas
pointOfContact
2025-02-13
ISO 19115 Geographic Information - Metadata
ISO 19115:2003/Cor.1:2006(E)
EnviroAtlas -- Virginia Beach/Williamsburg, VA -- Meter-Scale Urban Land Cover (MULC)
2017-10-16
publication
U.S. EPA Office of Research & Development (ORD) - National Exposure Research Laboratory (NERL)
Research Triangle Park
NC
publisher
US Environmental Protection Agency (US EPA) (additional partners are listed in Supplemental)
originator
The Virginia Beach, VA EnviroAtlas Meter-scale Urban Land Cover (MULC) dataset comprises 3,724 km2 of southeast Virginia and includes Hampton, Newport News, Suffolk, Virginia Beach and Williamsburg. The area was selected based on the US Census Bureau's 2010 Urban Area for Virginia Beach, VA with a 1 km buffer added. These MULC data and maps are an adaptation of data developed in succession by three organizations. First, WorldView Solutions Inc., a contractor of the Virginia Geographic Information Network (VGIN), created an original dataset for the state of Virginia that used 6 inch to 1 foot-resolution, four-band (red, green, blue, and near-infrared), leaf-off imagery collected in 2013 for the Virginia Base Mapping Program, as well as 2015 VGIN LiDAR, and ancillary data inputs. These data did not fully capture tree canopy. Second, the Chesapeake Conservancy amended the original dataset using 2014, leaf-on United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) and 2010 and 2013 LiDAR data. The amendments added some of the tree canopy missing from the original data. Third, EPA amended the data further by collapsing multiple land cover classes into the seven EnviroAtlas Meter-scale Urban Land Cover (MULC) classes, and making corrections to the Wetlands classes, also using 2010 and 2013 LiDAR. Seven land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Tree/Forest, Grass/Herbaceous Non-Woody Vegetation, Agriculture (Cultivated Crops) and Wetlands (Woody and Emergent). An analysis of 712 photo-interpreted land cover reference points yielded an overall user's accuracy of 83.5% (MAX) and an overall fuzzy user's accuracy of 84.1% (RIGHT) (see confusion matrices below). This dataset was produced by Virginia Geographic Information Network and their contractor WorldView Solutions Inc. (WSI), in collaboration with the Chesapeake Bay High-Resolution Land Cover Project - a cooperative agreement between the Chesapeake Conservancy and the National Park Service. It was adapted by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://enviroatlas.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://enviroatlas.epa.gov/EnviroAtlas/DataFactSheets).
The purpose of the Meter-Scale Urban Land Cover (MULC) data is to provide a detailed snapshot of the urban environment and ecosystem for analysis of multiple human and ecosystem health metrics and services. This analysis is performed typically by U.S. Census block group. Greenspace and impervious surface land cover classes figure prominently. Land cover maps are integral to sound, scientific decision making and effective stewardship. A high resolution urban LC map quantitatively and qualitatively represents the physical environment and ecosystems of a city. Land cover varies at a fine scale in urban environments, and the existing NLCD (National Land Cover Database, www.mrlc.gov) land cover maps at 30 m spatial resolution are too coarse to measure many urban phenomena. There are many possible applications, for example: - Knowing the exact location and size of green and grey spaces; - Identifying highly impervious areas that would most benefit from additional trees and other vegetation to mitigate surface runoff and urban heat island effects; - Measuring the width of urban riparian buffers; - Locating potential wildlife corridors, gaps and linkages; - Planning park development for maximum benefits. The overall goal of EnviroAtlas is to employ and develop the best available science to map indicators of ecosystem services production, demand, and drivers for the nation.
completed
EnviroAtlas Coordinator
U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas
Geospatial Data Owner
(919) 541-3832
109 T.W. Alexander Drive
Research Triangle Park
NC
27709
EnviroAtlas@epa.gov
https://www.epa.gov/enviroatlas
pointOfContact
asNeeded
Ecology
Ecosystem
Environment
Health
Impact
Land
theme
EPA GIS Keyword Thesaurus
Agriculture
Biophysical
Communities
Ecosystem Services
EnviroAtlas
Environment
Environmental Atlas
Green Space
Land
Land Cover
Surface Water
Water
Wetlands
theme
EnviroAtlas
Virginia
Virginia Beach/Williamsburg, VA
place
None
https://edg.epa.gov/EPA_Data_License.html
otherRestrictions
Use Constraints: None. Please check sources, scale, accuracy, currency and other available information. Please confirm that you are using the most recent copy of both data and metadata. Acknowledgement of the EPA would be appreciated.
otherRestrictions
Access Constraints: None
PUBLIC
https://project-open-data.github.io/schema/#accessLevel
Standard Technical Controls
eng; USA
environment
geoscientificInformation
imageryBaseMapsEarthCover
-76.901980
-75.855670
36.530153
37.421087
2010-01-01
2013-01-01
2014-01-01
2015-01-01
Contributors to this dataset include: Virginia Geographic Information Network and their contractor WorldView Solutions Inc. (WSI), in collaboration with the Chesapeake Bay High-Resolution Land Cover Project - a cooperative agreement between the Chesapeake Conservancy and the National Park Service.
true
Entity and Attribute Information
Entity and Attribute Overview: For easier readability, this XML metadata can be opened in a text editor (e.g., Notepad). Processing Steps: These MULC data and maps are an adaptation of data developed in succession by three organizations. First, WorldView Solutions Inc. (WSI), a contractor of the Virginia Geographic Information Network (VGIN), created an original dataset for the state of Virginia that used 6 inch to 1 foot-resolution, four-band (red, green, blue, and near-infrared), leaf-off imagery collected in 2013 for the Virginia Base Mapping Program, as well as 2015 VGIN LiDAR, and ancillary data inputs (WorldView Solutions Inc.; Virginia Geographic Information Network). These data did not fully capture tree canopy. Second, the Chesapeake Conservancy amended the original dataset using 2014, leaf-on United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) and 2010 and 2013 LiDAR data. The amendments added some of the tree canopy missing from the original data. Third, EPA amended the data further, making corrections to the Wetlands classes, also using 2010 and 2013 LiDAR. WorldView Solutions Inc. Processing: Preparing Imagery The WorldView Solutions Inc. (WSI) land cover was created using 6 inch to 1 foot-resolution, four-band (red, green, blue, and near-infrared), leaf-off imagery collected in 2013 for the Virginia Base Mapping Program. The first step in organizing the imagery was to add the imagery tiles to a mosaic dataset. Before extraction could begin on the raster imagery data, all tiles had to have a consistent resolution. Due to the land cover classes having a desired 1-meter resolution, the decision was made to resample all imagery to 1 meter, reducing processing time and maintaining the output resolution. The resampling geoprocessing tool was executed within the mosaic dataset. Bilinear Interpolation resampling technique was selected to create a new image dataset. Feature Analyst & Final Cleanup Workflow Initial extraction was conducted through Feature Analyst, an extension to Esri's (Environmental Systems Research Institute) ArcGIS software suite. The tools within Feature Analyst streamlined the geoprocessing tasks related to extracting land cover features from high-resolution aerial imagery. The object-oriented software utilized the spatial context, as well as spectral and pattern data, through the creation of digitized training datasets and user-defined learning parameters. Upon initial extraction, a number of built-in tools were available for further data cleanup. Image extraction was carried out prior to the use of external data sources, and then subjected to a series of automated cleanup scripts and manual editing. These steps are detailed in the following sections. Creating Training Data A series of training data sets were created, using field and "screen" verified data samples, for each of the land cover classification schemes. Training data was used by Feature Analyst to "learn" the band composition associated with each classification and apply that knowledge to extract full coverage vector features over a defined raster image. Training data were created as polygon shape-files, and were represented as a compilation of vector features for each land cover type; most were heads-up digitized by Imagery Analysts, supplemented by external features from existing localities' datasets. The number of samples that could be directly input into the training sets for land cover extraction depended on the availability of existing data, and knowledge of the project area. Separate training data layers were created for each individual mosaic dataset. Typically, training sets work best with a discrete number of features that are representative of the overall frequency and size of features. Training sets were refined and revised on an as needed basis in response to the accuracy of results emerging from data interpretation tests of the evolving feature extraction learning parameters. However, a minimum of 20 samples were collected per class within each tiled boundary, with additional samples depending on the frequency of that particular class. A variety of factors were employed when generating training samples for feature extraction. In order to increase accuracy of initial land cover vector output, the following factors employed included but were not limited to: • Full spectral variety included in sample features • Varied orientation of polygon features representative of ground coverage • Samples digitized to encompass entire feature and distinguish edge of classification change The individual land cover layers were then represented in a multi-class input layer that combined the training sets into a single feature class. Attribution delineated subtypes and symbology for each of the desired land cover classes. This final multi-class input layer was the input to the Supervised Learning feature extraction. Set up of Learning Parameters The initial vector output from the Supervised Learning method was dependent on the Input Representation parameters set up for extraction. Changes to the pattern and width of raster image cells that Feature Analyst analyzed for each cell's classification led to diversity in outputs. The various outputs were evaluated to identify the ideal classification pattern in each working area. Other parameters that modified results included applying a histogram stretch to the input bands and adjusting band type. Additional options were applied to the dataset to aggregate small regions, remove large regions, and smooth features, based on user input to size and conversion factor. Results were optimized on a per region basis, while taking advantage of some of the functions in the Conduct Supervised Feature Extraction step below on a per classification basis. The best results were collected using the Bulls Eye 2 Input Representation with a Pattern Width of 15. Aggregation was applied to certain features, such as Ag fields having a minimum pixel size of 4047 square meters, and masking the locality impervious datasets. Conducting Supervised Feature Extraction Once training data was developed, learning parameters set, and extraction area defined, supervised feature extraction analysis was initiated, and the Feature Analyst software ran independently to analyze and extract features from the base imagery, based on the detailed training datasets. Once extraction was completed, Imagery Analysts reviewed individual outputs to assess the overall quality of the land cover extraction. Due to the size limitations of larger features, the multi-input extractions were developed as raster output, while the individual classes were extracted as vector. Complementing the output from the original training datasets, new learning parameters were subsequently designed and configured from discrepancies found in the first output dataset. The derived multi-class input layer was broken into individual land cover classes for further processing to each feature layer as necessary. This process was refined by Imagery Analysts and project stakeholders, through application of Feature Analyst post processing and hierarchical learning functions. The Feature Analyst Learner relied on a series of learning passes (feature extractions) to determine whether or not image pixels represented the target features identified in each training set. Feature Analyst functions included: Clutter Removal - Removed extraneous features from the feature extraction output, within the minimum mapping unit parameters. • Heads-up digitized/selected correct features • Heads-up digitized/selected incorrect features Adding Missed Features - Refined results that already had majority of clutter removed, to further improve learning. • Digitized new training samples to include in dataset After each function was completed, a final supervised learning pass (feature extraction) was executed. A combined Input layer from the most accurate of each class extraction was created and Imagery Analysts evaluated the accuracy within each layer to create a hierarchical structure to resolve overlapping regions. The learning parameter definition and refinement process was iterative and typically repeated numerous times during the project lifecycle. Once the Analyst was sufficiently satisfied with feature extraction results, the dataset was passed to the data clean-up stage. Conducted Data Clean-Up After feature extraction using Feature Analyst, a variety of techniques and geoprocessing functions were configured and automated to clean up artifacts and errors known to be made in the initial learning output by Feature Analyst. Initially, a series of tools were executed against the output to improve the dataset and classifications. In some cases, this involved splitting the multi-class polygon layer into separate or distinct subsets of classification upon which to perform clean-up activities. The Feature Analyst geoprocessing tools that were used to automate the clean-up of the full output dataset included, but were not limited to: • Aggregate features - Aggregation of features that met the same criteria but were improperly segmented by other features or classification types. • Smooth features - Within the resolution and minimum mapping units, smoothed features to produce better contrast between feature classifications. • Feature reclassification - Where applicable, reclassified features to meet refined feature classification types. The Feature Analyst geoprocessing tools that were performed on a per class basis included: • Reshape features - Filled holes/gaps in features, merged features with a newly digitized connection. • Square Up features - Best for buildings and other impervious features that had distinctly square geometry. • Feature Simplification - Exported extracted features to a new shapefile with additional specifications as needed. • Minimum vertex spacing • Area Attribution • Eliminate shapes by area • Eliminate shapes by attribute Following the use of cleanup tools within the software to improve upon initial results, a number of custom scripts were created and applied at various levels, paired with manual stages of cleanup, to meet accuracy standards on the various classifications. While it was possible to produce a defensible land cover product based purely on automated feature extraction and clean-up processes, it was anticipated that a significant amount of manual data clean-up would need to be performed as part of the project to differentiate some of the requested classifications that could not be identified based on spectral signature alone, such as Pastures vs TurfGrass, or yards separated from a Pasture by a fence line. This was accomplished through visual inspection against statewide imagery. Scripted methods for cleaning up the data included reclassification of certain land use types due to total area of independent vector features within a class, proximity of one class's features to another, and identification of typical class confusion. The methodology behind script automation refined further as new regions were developed that introduce unique features, such as forest shadows in the mountains. A distinct script was developed to automate cleanup against the subclassified regions (e.g., wetlands), as these areas with wetland characteristics typically reduce to only include forest, turf, and hydro from the initial feature extraction. Developed Final Classifications At this point in the extraction process, where Feature Analyst classification output had been cleaned up using automated processes and manual editing, the 12 deliverable classes were finalized. In the previous step, many classes that are not listed in the final set of land cover features were created to assist in image extraction, such as Forest Shadow. With the vector output layers defining quality boundaries between different feature types, Analysts engaged the local impervious, wetland, and other available datasets to erase and append into the final vector results, as well as amend and merge the broken out classes to develop the final delivery classifications. This is where the subclassification occurred within wetland features, and these features received automated cleanup. Additional post-processing steps taken to reach the final output included running Esri geoprocessing tools to eliminate outliers, checking for data gaps due to the processing of very large and complex features, as well as delineating impervious features from outside datasets into their own class. Upon delivery of a draft version of the land cover product, each classification's accuracy was tested by an independent QA/QC effort, where results were shared and final dataset revisions completed. The period of corrective actions allowed time for any necessary changes to be made to the final product and methodologies. The WSI land cover classes are included in Table 1. Chesapeake Conservancy Processing: The Chesapeake Conservancy amended the land cover dataset created by WSI, which had been created using leaf-off imagery, and, as a result, underrepresented tree canopy. Using 2014, 1 m resolution imagery from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) (leaf-on) and USGS LiDAR datasets from 2010 and 2013 (U.S. Geological Survey, 2014 and 2015), the Chesapeake Conservancy re-estimated tree canopy for the study area based on spectral signature and height. In addition to re-creating the tree canopy class, the Chesapeake Conservancy also identified land cover classes under the newly added tree canopy and added a "Roads" class based on NAVTEQ data (https://here.com/en/navteq). Those added classes are included in Table 1. WSI used the National Wetlands Inventory to include an undifferentiated Wetland class in its dataset. The Chesapeake Conservancy used NAIP, LiDAR, and the National Wetlands Inventory (NWI) to reclassify these wetland areas into the differentiated "Automated Forest extraction under wetlands" (Woody Wetlands), "Automated Turf and Hydro extraction under wetlands" (Emergent Wetlands) classes. EPA Processing: The dataset obtained from the Chesapeake Conservancy (CC) was adapted to match EnviroAtlas specifications for its Meter-scale Urban Land Cover (MULC) product. Using the Reclassify (Spatial Analyst) tool in ArcGIS, pixels were reclassified to the most appropriate MULC class (Table 1). Also, the data that EPA obtained required the addition of Water on the east side of the study area. The additional water was added by rasterizing the entire study area and assigning water (10) to each pixel, creating a constant raster. Mosaic to New Raster (Data Management) tool was then used to overlay the land cover on the water raster, giving preference to existing pixels and assigning the NoData pixels in the CC dataset to the Water class. Additionally, the CC dataset extended southward to the Virginia/North Carolina state line, whereas the EnviroAtlas study area boundary extended an additional 1 km into North Carolina. The original WSI land cover data was visually inspected and no problems were identified, and as a result, was mosaicked onto the CC land cover and reclassified to match EnviroAtlas class codes. (Tip: Tables can be copied into an Excel spreadsheet for easier interpretation.) Table 1 WSI/CC Class Name WSI/CC Code MULC Class Name MULC Class Code (CC) Roads 1 Impervious 20 Hydro 11 Water 10 Automated Hydro extraction under wetlands 12 Emergent Wetland 92 Extracted Impervious 21 Impervious 20 External Impervious 22 Impervious 20 Barren 31 Soil/Barren 30 Forest 41 Tree/Forest 40 Tree 42 Tree/Forest 40 Automated Forest extraction under wetlands 43 Woody Wetland 91 Scrub/Shrub 51 Grass/Herbaceous 70 Harvested/Disturbed 61 Grass/Herbaceous 70 Turf 71 Grass/Herbaceous 70 Automated Turf extraction under wetlands 72 Emergent Wetland 92 Pasture 81 Grass/Herbaceous 70 Cropland 82 Agriculture 80 (CC) Tree Canopy over Roads 101 Tree/Forest 40 (CC) Tree Canopy over Hydro 111 Tree/Forest 40 (CC) Tree Canopy over Automated Hydro 112 Tree/Forest 40 (CC) Tree Canopy over Extracted Impervious 121 Tree/Forest 40 (CC) Tree Canopy over External Impervious 122 Tree/Forest 40 (CC) Tree Canopy over Barren 131 Tree/Forest 40 (CC) Tree Canopy over Turf 171 Tree/Forest 40 (CC) Tree Canopy over Automated Turf 172 Tree/Forest 40 (CC) Tree Canopy over Pasture 181 Tree/Forest 40 (CC) Tree Canopy over Cropland 182 Tree/Forest 40 Notes: "(CC)" indicates a class added by the Chesapeake Conservancy. Inspection of the Wetland areas in the dataset found confusion between Woody Wetland and Emergent Wetland classes. These were Wetland areas classified as Woody Wetland but that did not have the woody, above ground characteristics consistent with EPA's Woody Wetland definition. LiDAR was used to differentiate Wetland classes. LiDAR processing: LAS LiDAR raw data were processed into a Digital Surface Model (DSM) and a Digital Elevation Model (DEM) using ArcMap version 10.3 (www.esri.com). The DSM consisted of all return points in the dataset, resulting in a raster representing the complete surface of the Earth including all objects on the ground such as trees and buildings. The DEM consisted of only ground points, resulting in a raster representing the ground surface of the Earth only. A height above ground (HAG) raster was created from the DEM and DSM by subtracting the DEM from the DSM. This raster represented the height of objects residing on the surface of the Earth. Using the HAG raster, a conditional statement was used to classify all Woody Wetlands with heights less than 2 m (based on EPA's height threshold for Tree) as Emergent Wetland. Statement: Con(("Land cover Raster" == "Woody Wetland")) & ("Land cover Raster" < 2), "Emergent Wetland", "Land cover Raster") This resulted in "speckle" in the Wetland Class. To correct this, a majority filter (5x5) was used to smooth the raster and create a more usable product for users. EPA Accuracy Assessment EPA conducted an accuracy assessment on the completed land cover classification. An analysis of 712 photo-interpreted land cover reference points yielded an overall user's accuracy of 84.0% (MAX) and an overall fuzzy user's accuracy of 84.1% (RIGHT) (see confusion matrices below) Accuracy Assessment - A second Analyst performed photointerpretation of the NAIP aerial photography used in the classification. Six hundred random reference points (100 per land cover class) and 112 additional stratified random points were interpreted and labeled using a fuzzy classification approach (Gopal and Woodcock, 1994). This permitted the Analyst to assign a confidence value to the photo interpreted label at each reference point. Wetlands were copied from existing data produced elsewhere and not included in the accuracy assessment. Confidence is expressed as an integer from 1 to 5: 1: Absolutely wrong: classification value was unacceptable (Very Wrong); 2: Understandable but Wrong: classification value was not good. There was something about the site that made the answer understandable, but there was clearly a better answer. Classification would pose a problem for users of the map. (Not Right); 3: Reasonable or Acceptable: Maybe not the best possible classification but it was acceptable; the classification did not pose a problem to users of the map. (Right); 4: Good Answer: Would be happy to find this classification given on the map (Very Right); 5: Absolutely Right: No doubt about the match. (Perfect) After the initial assessment, additional samples were generated for any class with less than 50 reference samples. Using stratified random sampling, sufficient sample points were generated such that all classes had a minimum of fifty reference samples (most classes had approximately 100 reference samples). These points were interpreted and included in the final accuracy assessment. The accuracy assessment produced two confusion (error) matrices. One was a more conservative ("MAX") view and one was a more liberal ("RIGHT") view. The MAX interpretation was correct if the classified land cover matched the interpreter's highest score, illustrating that the interpreter found this class to be the most appropriate for that location. The RIGHT interpretation was correct if the classified land cover matched any class the interpreter gave a value of 3 or greater, illustrating that the interpreter found the classification to be acceptable but another class may have been more appropriate. This analysis yielded an overall user's accuracy of 84.0% (MAX) and 84.1% (RIGHT). The following confusion matrices summarize the accuracy assessment MAX and RIGHT results. For easier readability, this XML metadata can be opened in a text editor (e.g., Notepad) and the confusion matrix may be copied from text editor to an Excel spreadsheet. Confusion matrices can also be viewed in original formatting by opening in ArcCatalog: MAX Agricult Grass_Herb Impervious SoilBarren TreeForest Water Row Total User's Accuracy Agricult 82 5 0 0 0 0 87 0.942529 Grass_Herb 0 103 7 8 7 0 125 0.824 Impervious 0 12 69 2 1 0 84 0.821429 SoilBarren 0 2 0 57 0 1 60 0.95 TreeForest 2 46 20 2 134 1 205 0.653659 Water 0 1 0 0 0 150 151 0.993377 Row Total 84 169 96 69 142 152 712 Producer's Accuracy 0.97619 0.609467 0.71875 0.826087 0.943662 0.986842 Overall Accuracy 0.835674 K_Hat 0.798902 K Variance 0.000289 RIGHT Agricult Grass_Herb Impervious SoilBarren TreeForest Water Row Total User's Accuracy Agricult 82 5 0 0 0 0 87 0.942529 Grass_Herb 0 106 7 8 4 0 125 0.848 Impervious 0 12 69 2 1 0 84 0.821429 SoilBarren 0 1 0 58 0 1 60 0.966667 TreeForest 2 46 20 2 134 1 205 0.653659 Water 0 1 0 0 0 150 151 0.993377 Row Total 84 171 96 70 139 152 712 Producer's Accuracy 0.97619 0.619883 0.71875 0.828571 0.964029 0.986842 Overall Accuracy 0.841292 K_Hat 0.80592 K Variance 0.00028 References: Gopal, S. and Woodcock, C. (1994). Theory and Methods for Accuracy Assessment of Thematic Maps Using Fuzzy Sets. Photogrammetric Engineering and Remote Sensing 60(2), 181-188. Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K. (2015). Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354, http://www.asprs.org/a/publications/pers/2015journals/PERS_May_2015/HTML/index.html#345/z. O'Neil-Dunne J, MacFaden S, Royar A. (2014). A Versatile, Production-Oriented Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes Using GEOBIA and Data Fusion. Remote Sens. 2014, 6, 12837-12865. http://www.mdpi.com/2072-4292/6/12/12837. U.S. Department of Agriculture. (2014). National Agriculture Imagery Program imagery. Farm Service Agency. Aerial Photography Field Office: U.S. Department of Agriculture Web page, http://www.fsa.usda.gov/FSA/apfoapp?area=home&subject=prog&topic=nai. U.S. Fish and Wildlife Service. Virginia National Wetlands Inventory digital data. Accessed April 2017. http://wetlands.fws.gov/. U.S. Geological Survey. (2015). USGS Lidar Point Cloud VA Norfolk 2013 (2013 collection). https://nationalmap.gov. U.S. Geological Survey. (2014). USGS Lidar Point Cloud (LPC) ARRA-VA_11County_2010 (2010 collection). https://nationalmap.gov. Virginia Geographic Information Network. (2016). Land Cover. https://www.vita.virginia.gov/isp/default.aspx?id=6442474319. Accessed spring 2017. WorldView Solutions Inc. (2016). Technical Plan of Operations: Virginia Statewide Land Cover Data Development. http://www.vita.virginia.gov/uploadedFiles/VITA_Main_Public/ISP/VGIN/Land_Cover/LandCover_TechnicalPlanOfOperations_v7_20160506.pdf. Accessed spring 2017. Entity and Attribute Detail Citation: https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets
EnviroAtlas Coordinator
U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas
Geospatial Data Owner
(919) 541-3832
109 T.W. Alexander Drive
Research Triangle Park
NC
27709
EnviroAtlas@epa.gov
https://www.epa.gov/enviroatlas
distributor
https://enviroatlas.epa.gov/download/VBWVA_MULC_tif.zip
download
Download
The URL providing direct access to the downloadable dataset.
download
https://enviroatlas.epa.gov/arcgis/rest/services/Supplemental/Landcover_AllCommunities/ImageServer
ESRI:ArcGIS
API
The endpoint of a web service to access the dataset (REST endpoint, WMS GetCapabilities URL, or a SOAP WSDL endpoint).
webMapService
https://www.epa.gov/enviroatlas/enviroatlas-data
information
Documentation
URL for accessing related documents such as data dictionary, technical information about a dataset, developer documentation, etc.
information
https://www.epa.gov/enviroatlas
information
Home Page
Alternative landing page used to redirect user to a contextual, Agency-hosted "homepage" for the Dataset or API
information
dataset
Horizontal Positional Accuracy Report
Data were collected using methods that are accurate to within 2-5 meters (EPA National Geospatial Data Policy [NGDP] Accuracy Tier 2). For more information, please see EPA's NGDP at https://www.epa.gov/geospatial/geospatial-policies-and-standards)
Features represented have not been tested for completeness
Features represented have not been tested for completeness
Tests for integrity have not been performed.
This lineage was generated by the FGDC CSDGM to 19139 transformation
See overview section for detailed processing workflow.
2017-10-16T00:00:00
unknown
2029-02-13
This metadata was automatically generated from the FGDC Content Standard for Digital Geospatial Metadata standard (version FGDC-STD-001-1998) using the 2024-02-09T11:11:00 version of the FGDC CSDGM to ISO 19139 transform.
EnviroAtlas Coordinator
U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas
Geospatial Data Owner
(919) 541-3832
109 T.W. Alexander Drive
Research Triangle Park
NC
27709
EnviroAtlas@epa.gov
https://www.epa.gov/enviroatlas
pointOfContact