{464dfac7-34e1-4d0d-9caa-6b190ef827d4} 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 - Baltimore, MD - Meter-Scale Urban Land Cover (MULC) Data 2017-11-30 publication U.S. EPA Office of Research and Development (ORD) - National Exposure Research Laboratory (NERL) Research Triangle Park NC publisher U.S. Environmental Protection Agency (US EPA)(additional partners are listed in Supplemental) originator The Baltimore, MD EnviroAtlas Meter-scale Urban Land Cover (MULC) dataset comprises 4,545 km2 around the city of Baltimore and portions of surrounding counties. These MULC data and maps were derived from LiDAR and 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 vector data (e.g., roads, building footprints). Seven land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Trees, Grass/Herbaceous, Agriculture, and Wetlands (Woody and Emergent). Wetlands were delineated using the best available existing wetlands data, which was a National Wetlands Inventory (NWI) layer from the years 1981, 1999, and 2013 (http://wetlands.fws.gov). An analysis of about 600 completely random and 90 stratified random photo-interpreted land cover reference points yielded a simple overall user's accuracy (MAX) of 90% and an overall fuzzy user's accuracy (RIGHT) of 93% 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). This MULC dataset currently represents the most detailed and accurate land cover for the Baltimore Region. The EnviroAtlas MULC provide 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 1 m2) for every one 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 Ecosystem Environment theme EPA GIS Keyword Thesaurus Agriculture Ecology Ecosystem Ecosystem Services EnviroAtlas Environment Environmental Atlas Exposure Health Impact Land Land Cover Natural Resources Recreation Surface Water Water Wetlands theme User Maryland Baltimore, MD 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 -77.19886 -76.27793 38.71327 39.71371 2010-01-01 2012-01-01 2013-01-01 This dataset 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. EPA added agriculture and wetlands taken from ancillary data sources and collapsed land cover from 12 classes into seven MULC classes. This Meter-Scale Urban Land Cover (MULC) dataset for the Baltimore Region includes Anne Arundel County, Baltimore County, Baltimore City, and Howard County, Maryland and York County, Pennsylvania. Seven land cover classes were mapped: Water (10), Impervious Surfaces (20), Soil/Barren (30), Trees (40), Grass/Herbaceous (70), Agriculture (80) and Wetlands (Woody [91] and Emergent [92]). The primary sources used to derive this land cover layer were 2004, 2005, 2011, and 2015 LiDAR data (Maryland), 2013 National Agriculture Imagery Program (NAIP) imagery (U.S. Department of Agriculture) and 2014 Ortho imagery. 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), and conducting an accuracy assessment. Agriculture - To identify Agriculture (80), 30m 2008 USDA Common Land Unit (CLU) vector data and 1m 2013 USDA Cropland Data Layer (CDL) raster data were combined to determine the areas 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. 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 30m CDL was converted to 1m resolution and joined to the 1m CLU parcel boundaries, to identify those land boundaries containing cropland. Within the CDL attribute table, all non-agricultural attributes (Deciduous Forest, Open Water, etc.) were removed. Trees located within CLU parcel boundaries, visually observed from the land cover data, were extracted from parcels. All identified agricultural areas were converted to raster and mosaicked into the landcover dataset. Wetlands - Wetlands vector data were obtained from the US Fish and Wildlife Service National Wetlands Inventory (NWI) (U.S. Fish and Wildlife Service). These data are updated on an ongoing basis in collaboration with state and local agencies and organizations. The Baltimore, MD data were updated in the years 1981, 1999, and 2013. The Freshwater Forested/Shrub Wetlands located in the NWI attribute table were exported and assigned as Woody Wetland (91). The Freshwater Emergent Wetlands located in the NWI attribute table were exported and assigned as Emergent Wetland (92). In addition, the Wetland land cover class created by the Chesapeake Conservancy was added as Emergent Wetland (92). Wetlands were defined as areas of low vegetation located along marine or estuarine regions with the look of saturated ground. These vector data were converted to raster and overlaid on the land cover. ***-------------------------------------------*** Accuracy Assessment - An accuracy assessment was conducted on the completed land cover classification that included the area classified by CC within the block groups of the study area plus the Agriculture class added by EPA. Wetlands were not included in the accuracy assessment due to difficulties identifying those areas in the imagery per EnviroAtlas accuracy assessment methods. An Analyst performed photointerpretation of the NAIP aerial photography used in the classification. About six hundred random reference points (100 per land cover class) 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. 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, sufficient sample points were generated such that all classes had a minimum of fifty reference samples (most classes had approximately 100 reference samples). In total, 90 points were added 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 allowed 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. 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 48 1 0 1 0 0 50 0.96 Grass_Herb 3 103 11 1 8 0 126 0.81746 Impervious 0 6 82 2 1 0 91 0.901099 SoilBarren 0 4 0 46 0 1 51 0.901961 TreeForest 0 21 6 0 248 0 275 0.901818 Water 0 1 0 0 1 92 94 0.978723 Row Total 51 136 99 50 258 93 687 Producer's Accuracy 0.941176 0.757353 0.828283 0.92 0.96124 0.989247 Overall Accuracy 0.901019 K_Hat 0.870604 K Variance 0.000222 RIGHT Agricult Grass_Herb Impervious SoilBarren TreeForest Water Row Total User's Accuracy Agricult 49 0 0 1 0 0 50 0.98 Grass_Herb 2 107 10 1 6 0 126 0.849206 Impervious 0 1 89 0 1 0 91 0.978022 SoilBarren 0 4 0 46 0 1 51 0.901961 TreeForest 0 17 4 0 254 0 275 0.923636 Water 0 1 0 0 1 92 94 0.978723 Row Total 51 130 103 48 262 93 687 Producer's Accuracy 0.960784 0.823077 0.864078 0.958333 0.969466 0.989247 Overall Accuracy 0.92722 K_Hat 0.904696 K Variance 0.000169 Classification errors may stem from multiple sources. Some 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. This is perhaps a less problematic error than Grass-Impervious, or Tree-Impervious. Grass and Tree are both living vegetation, versus non-living Impervious surfaces. 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. Maryland.gov. MD iMAP Maryland's Mapping & GIS Data Portal Web page, http://imap.maryland.gov/Pages/lidar-metadata.aspx. 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 2016. http://wetlands.fws.gov/. 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/BMD_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. 2017-05-01T00:00:00 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