{6bb22dd7-3087-40b4-8969-a12a8bebe625} 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 -- Washington DC -- Meter-Scale Urban Land Cover (MULC) Data (2013-2014) 2019-04-11 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 Washington DC EnviroAtlas Meter-scale Urban Land Cover (MULC) dataset comprises an area of 5423.43 km2 encompassing the entire area of Washington DC, and portions of the state of Maryland and state of Virginia. These MULC data and maps were derived from 1-m pixel, four-band (red, green, blue, and infrared light) leaf-on aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) as well as ancillary data (e.g., Lidar, National Wetlands Inventory [NWI], cropland, land parcels, power lines, building footprints). Eight land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Trees/Forest, Grass/Herbaceous, Agriculture, Woody Wetlands and Emergent Wetlands. Wetlands were delineated using the state wide wetlands data from National Wetlands Inventory (NWI) layer updated on October 15, 2018 (https://www.fws.gov/wetlands/Data/State-Downloads.html ). An analysis of 600 completely random and 111 stratified random photo-interpreted land cover reference points yielded a simple overall user's accuracy (MAX) of 85.4% and an overall fuzzy user's accuracy (RIGHT) of 91.5% within the census block group boundary (see confusion matrices below). These data were developed as part of the Chesapeake Bay High-Resolution Land Cover Project, a cooperative agreement between the Chesapeake Conservancy and the National Park Service, funded through an interagency agreement with the Environmental Protection Agency (EPA). The Chesapeake Conservancy, under the direction of Margaret Markham, created the initial statewide 1-meter land cover data. EPA added agriculture and wetlands taken from ancillary data sources. See detailed processing steps and workflow below. This dataset was produced by the Chesapeake Conservancy, the National Park Service, and 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). The EnviroAtlas MULC provides spatially detailed foundational data for approximately 100 derived EnviroAtlas Community information layers. For example, layers derived from MULC data include: Percent impervious area in 50m stream buffer; Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr); Percent of tree cover near busy roadways. Some of the many possible applications of MULC include: Knowing the exact location and size of vegetated and non-vegetated 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 and character of urban riparian and street 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. The EnviroAtlas National Component is based on NLCD (National Land Cover Database, www.mrlc.gov) land cover data at 30m spatial resolution. For some analysis, however, the density and heterogeneity of an urban landscape requires higher resolution land cover data. MULC data were developed to fulfill that need. To illustrate, there are 900 MULC pixels (1 x 1m2) for every 1 NLCD pixel (30m x 30m). This allows for the analysis of common urban landscape features in the 1-10m range, which are unresolvable using 30m NLCD pixels. 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 Exposure Health Impact Land Natural Resources Recreation Surface Water Water 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 Maryland Virginia Washington DC 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 -77.9714930 -76.645805 38.355362 39.430352 2013-01-01 2014-01-01 This MULC dataset currently represents the most detailed and accurate land cover for Washington DC. It was produced by the Chesapeake Bay High-Resolution Land Cover Project, a cooperative agreement between the Chesapeake Conservancy and the National Park Service, funded through an interagency agreement with the Environmental Protection Agency (EPA). 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). For information about the Chesapeake Conservancy (CC) and Chesapeake Bay High-Resolution Land Cover Project, go to https://chesapeakeconservancy.org. These data were developed as part of the Chesapeake Bay High-Resolution Land Cover Project, a cooperative agreement between the Chesapeake Conservancy and the National Park Service, funded through an interagency agreement with the Environmental Protection Agency (EPA). The Chesapeake Conservancy, under the direction of Margaret Markham, created the statewide 1-meter land cover data for the study area. The landcover data represents a 2013-2014 snapshot of on-the-ground conditions as captured in USDA National Agricultural Inventory Program imagery and the dataset represents common classes among different contractors who did classification across the watershed. The Bay-wide Land Cover Dataset has 6 classes. EPA added agriculture and wetlands taken from ancillary data sources and recoded land cover from 6 classes into 8 MULC classes. This Meter-Scale Urban Land Cover (MULC) dataset for the Washington DC area includes the District of Columbia; Loudon, Auquier, Prince William, Statford, Manassas, Manassas Park, Fairfax, Falls Church, Arlington, and Alexandria counties of Virginia; and Frederick, Carroll, Howard, Montgomery, Anne Arundel and Prince George's counties in Maryland. Eight land cover classes were mapped: Water (10), Impervious Surfaces (20), Soil/Barren (30), Trees (40), Grass/Herbaceous (70), Agriculture (80) Woody Wetlands [91] and Emergent Wetlands [92]). The primary sources used to derive this land cover layer were 2013 National Agriculture Imagery Program (NAIP) imagery (U.S. Department of Agriculture) for Maryland and the District of Columbia and 2014 NAIP imagery for Virginia; and Lidar data of Virginia for year 2004, 2008, 2011, 2012, 2015 and 2016. These were the best Lidar data available for the area. Chesapeake Conservancy (CC) Processing Steps: Land cover information was extracted using a four (4)-stage process. A semi-automated feature extraction tool was used to create 1-meter resolution data by dividing the image into segments of like pixels. Rules were then created to group the segments into classes, a rule-based classification. The data were exported into ArcDesktop and misclassified segments were manually corrected. To enhance classification accuracy, post-processing procedures were employed using supplementary county planimetric datasets (e.g. roads and structures) depicting man-made features on the earth's surface. Following data production, object-based assessments were performed to ensure accuracy and precision of the dataset. This dataset represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. EPA Processing Steps: EPA processing steps included: adding Agriculture, Wetlands (Woody and Emergent), systematic QA and conducting an accuracy assessment. Agriculture - To identify Agriculture (80), 2008 USDA Common Land Unit (CLU) vector data and 30m 2013/2014 USDA Cropland Data Layer (CDL) raster data were combined to determine the area most likely to be used for cultivated row crops. CLU data delineate agricultural land boundaries based on land use features such as roads and water bodies and, as a result, remain relatively constant over time. CLU data is not available for download; see https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-products/common-land-unit-clu/index for information. Due to restrictions, the CLU data solely contain parcel boundaries and lack attribute information. These boundaries contain land with common land cover, land management, and a common owner, agricultural or otherwise. CDL data delineate crop-specific land cover boundaries based on supervised landcover classification of satellite imagery. The CDL rasters were downloaded from the following URLs: https://nassgeodata.gmu.edu/nass_data_cache/byfips/CDL_2014_51.zip for VA; https://nassgeodata.gmu.edu/nass_data_cache/byfips/CDL_2013_24.zip for MD; https://nassgeodata.gmu.edu/nass_data_cache/byfips/CDL_2013_11.zip for DC. The 30m CDL was converted to 1m resolution and ran a zonal statistic to get the majority cropland within each parcel, which was then vectorized. Within the CDL attribute table, all non-agricultural attributes (Deciduous Forest, Open Water, etc.) were removed. All the other classes that are not cultivated agriculture according to EnviroAtlas standards were removed as well. Using the mask of all identified agriculture polygons, all the grass and soil class from the landcover raster was reclassified to agriculture class. Finally, it was mosaicked into the landcover dataset. Wetlands - Wetlands are defined as areas of low vegetation located along marine or estuarine regions with the look of saturated ground. Wetlands vector data were obtained from the US Fish and Wildlife Service National Wetlands Inventory (NWI) (U.S. Fish and Wildlife Service) for year 2013 for the state of Maryland and the District of Columbia and for the year 2014 for the state of Virginia to match with the year of NAIP used for analysis. These data are updated on an ongoing basis in collaboration with state and local agencies and organizations. The forested area in the landcover raster, within the freshwater forested/shrub Wetlands delineated in the NWI layer were recoded as Woody Wetland (91). Similarly, all the grass within the NWI wetland class were reclassified as Emergent Wetland (92). These were finally mosaicked on to the landcover data. ***-------------------------------------------*** Accuracy Assessment - An accuracy assessment was conducted on the completed land cover classification. Wetlands were not included in the accuracy assessment due to difficulties identifying those areas in the imagery per EnviroAtlas accuracy assessment methods. Detailed photointerpretation of the NAIP aerial photography used in the classification was done. Six hundred random reference points (100 per land cover class) were interpreted and a confidence value was applied to the photo interpreted label at each reference point. 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 references samples. Using stratified random sampling, enough sample points were generated such that all classes had a minimum of fifty reference samples. In total, 111 points were added, photo-interpreted and included in the final accuracy assessment. The final accuracy assessment resulted in confusion matrices for non-fuzzy method ("MAX") and fuzzy method ("RIGHT"), both presented below. MAX was a more conservative view and RIGHT was a more liberal 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 fuzzy method allows for uncertainty in the analyst's photo interpretation due to complex land cover characteristics. For example, for a point located within a pixel on land comprised of patchy grass and soil, the interpreter may have assigned a 4 for grass and a 3 for soil. This was accounted for by the RIGHT results, while MAX only accounted for the highest value recorded by the interpreter. 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. The following confusion matrices summarize the accuracy assessment MAX and RIGHT results. MAX results: Agricult Grass_Herb Impervious SoilBarren TreeForest Water Row Total User's Accuracy Agricult 44 3 2 0 0 0 49 0.897959 Grass_Herb 7 105 8 11 19 0 150 0.7 Impervious 0 6 109 5 3 0 123 0.886179 SoilBarren 2 4 1 40 2 0 49 0.816327 TreeForest 0 14 9 3 262 0 288 0.909722 Water 0 1 1 2 0 46 50 0.92 Row Total 53 133 130 61 286 46 709 Producer's Accuracy 0.830189 0.789474 0.838462 0.655738 0.916084 1 Overall Accuracy 0.854725 K_Hat 0.80603 K Variance 0.000308 RIGHT results: Agricult Grass_Herb Impervious SoilBarren TreeForest Water Row Total User's Accuracy Agricult 46 1 2 0 0 0 49 0.938776 Grass_Herb 2 132 8 5 3 0 150 0.88 Impervious 0 4 111 5 3 0 123 0.902439 SoilBarren 2 1 1 43 2 0 49 0.877551 TreeForest 0 8 8 2 270 0 288 0.9375 Water 0 0 1 2 0 47 50 0.94 Row Total 50 146 131 57 278 47 709 Producer's Accuracy 0.92 0.90411 0.847328 0.754386 0.971223 1 Overall Accuracy 0.915374 K_Hat 0.887165 K Variance 0.000192 Classification errors stemmed from multiple sources during this project. The landcover classification of Washington D.C. and Maryland state was done by Chesapeake Conservancy with 14 classes, Virginia was done by WorldView Solutions Inc. with 12 landcover classification scheme and University of Vermont Spatial Analysis Lab did classification of Pennsylvania and Delaware. While creating Chesapeake Bay watershed landcover class, different classes were merged to create a uniform Bay-wide landcover dataset with 6 classes. The landcover data had trees and shrubs combined in a single class, that was recoded to forest class and hence many shrubby areas were mapped as forest, which came as a major classification error. Part of the errors can be attributed to variation in the dates of lidar and orthophotos used for classification for different counties. This introduced many temporal lag issues especially in a dynamically changing landscape. The major landuse change included conversion from forested area to bare soil because of clear-cut, mainly for urban development. Conversion of soil to impervious surface in the form of houses, buildings and roads was another major landuse change. Since the original classified product had used orthophotos and lidar from variable dates, while comparing it to the NAIP base year of 2013 (for Maryland) and 2014 (for Virginia) clearly shows the temporal lag during which major landuse change has occurred. Some random sampling points for accuracy assessment landed on mixed pixels, shadows and edges between classes, causing photo-interpretation difficulty. Some of the classification errors are due simply to mixing land cover and land use in the analysis. For example, Agriculture-Grass or Agriculture-Soil confusion are mostly artifacts. Agriculture land use is also Soil land cover while barren, and Grass-Herbaceous land cover while growing row crops. Grass-Soil is the most common confusion due to intermixing and the presence of brown, senescent grass or sparse grass. An underlying assumption is that most non-arid region soil (not barren rock) is capable of supporting some Grass-Herbaceous vegetation at some point during the year. Grass-Tree confusion and Soil-Impervious confusion are also relatively common. Numerous small water bodies were missed in the original classification, which had to be fixed. For more information about EnviroAtlas data, go to https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets. 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. U.S. Department of Agriculture. 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. Maryland National Wetlands Inventory digital data. Accessed 11 November, 2018. https://www.fws.gov/wetlands/Data/State-Downloads.html U.S. Geological Survey's (USGS) National Geospatial Program. The National Map Web page, https://viewer.nationalmap.gov/basic/ 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/WDC_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. 2019-04-11T00:00:00 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