{c28bb96a-9a58-445b-a284-9ed2b9147704} 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 -- Philadelphia, PA -- Meter-Scale Urban Land Cover Data (MULC) Data (2013) 2018-07-13 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 Philadelphia, PA Meter-Scale Urban Land Cover (MULC) dataset comprises 7184 km2 around the city of Philadelphia and surrounding land in parts of fourteen counties within four states (PA, DE, NJ, MD): New Castle County in Delaware and Cecil County Maryland; Bucks, Chester, Lancaster, Montgomery, Philadelphia, and Delaware Counties in Pennsylvania; and Burlington, Mercer, Camden, Gloucester, Salmen and Atlantic Counties in New Jersey. These MULC data and maps were derived from several sources from multiple years: leaf-off LiDAR; 1-m pixel, four-band (red, green, blue, and near-infrared) leaf-on aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP); 1-ft pixel orthoimagery; additional leaf-on and leaf-off imagery as well as ancillary vector data (e.g., roads, building footprints.). Ten land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Tree/Forested, Shrub, Grass/Herbaceous NonWoody Vegetation, Agriculture, Orchard, and Wetlands (Woody and Emergent). Wetlands were delineated using the best available existing wetlands data, which was a National Wetlands Inventory (NWI) layer. An analysis of 600 completely random and 251 stratified random photo-interpreted land cover reference points yielded a simple overall user's accuracy (MAX) of 78% and an overall fuzzy user's accuracy (RIGHT) of 86% (see confusion matrices below). This dataset was produced by the University of Vermont Spatial Analysis Laboratory, the United States Forest Service Urban Tree Canopy (UTC) assessment program, and the US EPA to support research and online mapping activities related to the 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 purpose of the 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 ecosystem matrix 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 Delaware Maryland New Jersey Pennsylvania Philadelphia, PA 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 http://project-open-data.github.io/schema/#accessLevel Standard Technical Controls eng; USA environment geoscientificInformation imageryBaseMapsEarthCover -76.089481 -74.547769 39.182267 40.754351 2012-01-01 2015-01-01 2008-01-01 2013-01-01 These data were created by University of Vermont Spatial Analysis Laboratory and the United States Forest Service Urban Tree Canopy (UTC) assessment program as part of an interagency agreement between the US Environmental Protection Agency (EPA) and the US Forest Service. The Forest Service and the University of Vermont Spatial Analysis Laboratory (SAL), under the direction of Jarlath O'Neil-Dunne, created the majority of the land cover for the study area. EPA added agriculture and wetlands taken from ancillary data sources. 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 more information about EnviroAtlas data, go to https://www.epa.gov/enviroatlas/enviroatlas-data. For information about the University of Vermont (UVM) Spatial Analysis Lab (SAL), go to https://www.uvm.edu/rsenr/sal/. These data were created by two institutions and shared as part of an interagency agreement between the US Environmental Protection Agency (EPA) and the US Forest Service Urban Tree Canopy Assessment program. The University of Vermont Spatial Analysis Laboratory (SAL), under the direction of Jarlath O'Neil-Dunne, created the majority of the land cover for the study area. EPA added agriculture and wetlands taken from ancillary data sources. This Meter-Scale Urban Land Cover (MULC) dataset for the city of Philadelphia and surrounding land in parts of fourteen counties within four states (PA, DE, NJ, MD): New Castle County in Delaware and Cecil County Maryland; Bucks, Chester, Lancaster, Montgomery, Philadelphia, and Delaware Counties in Pennsylvania; and Burlington, Mercer, Camden, Gloucester, Salmen and Atlantic Counties in New Jersey. Ten land cover classes were mapped: Water (10), Impervious Surfaces (20), Soil/Barren (30), Trees (40), Shrub (52), Grass/Herbaceous (70), Agriculture (80), Orchard (82) and Wetlands (Woody [91] and Emergent [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 MD, NJ), 2012 leaf-off imagery and 2013 leaf-on imagery (DE), 2013-2014 Orthoimagery (MD), 2015 Orthoimagery (1ft resolution)(NJ),2005-2008 leaf-off orthoimagery and 2013 leaf-on orthoimagery (PA), and 2006-2008 (PA), 2011-2015 (MD), 2014 (DE), and 2015 (NJ) leaf-off LiDAR. UVM SAL Processing Steps: Ancillary data sources included GIS data provided by each county or created by the UVM SAL. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets (O'Neil-Dunne, MacFaden, and Royar 2014). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:5,000 and all observable errors were corrected. This dataset represents a 'top down' mapping perspective in which tree canopy overhanging other features is assigned to the tree canopy class. EPA Processing Steps: EPA processing steps included: adding Agriculture, Wetlands (Woody and Emergent), fixing misclassifications in the shrub class, and conducting an accuracy assessment. Agriculture - To identify Agriculture (80), 2008 USDA Common Land Unit (CLU) vector data and 2013 USDA Cropland Data Layer (CDL), and 2013 NAIP and Google imagery were used as guides to determine the areas most likely to be used for cultivated row crops. CLU data is not classified but delineate agricultural land boundaries based on land use features such as roads and water bodies and, as a result, remain relatively constant over time. CDL data has lower resolution (30m), but more up-to-date land cover classification based on supervised classification of satellite imagery. Majority defined classes were assigned to CLU vector data based on the 2013 CDL landcover resampled to 1m resolution. CLU vector data assigned with agriculture classes from the CDL were recoded into the MULC Agriculture (80) classification through reclassification of soil, grass and impervious surface MULC classes to agriculture masked by the CLU vectors, leaving trees, shrub, and water within fields as they were originally classified. Sub-classed road and building footprint raster classes were also excluded in the reclassification. CLU vector data assigned with orchard classes from the CDL were recoded into the MULC Orchard (82) classification through reclassification of tree and shrub MULC class to orchard masked by the CLU vectors, leaving grass, soil, impervious surface, and water in orchards as they were originally classified. Additionally, areas that appeared to be Agriculture upon visual inspection of imagery but that were not included in the CLU data were hand digitized. All identified Agriculture areas were mosaicked into the land cover 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 Pennsylvania, New Jersey, and Maryland data were updated in the mid-1980s and the Delaware data were updated in the mid-2000s. These vector data were converted to raster and overlaid on the land cover. Areas of the land cover previously classified as Trees that were located under the wetlands vector data were reclassified as Woody Wetlands (91). Areas of the land cover previously classified as Grass that were located under the wetlands vector data were reclassified as Emergent Wetland (92). Shrubs - the Shrub class contained some areas misclassifying orchards as shrubs. Similar to the method for defining agriculture, the shrub classification was converted to polygons and majority defined classes were assigned to the shrub vectors based on the 2013 CDL landcover resampled to 1m resolution. Shrub polygons greater than 1400m2 assigned to CDL orchard classes were recoded into MULC Orchard (82) classification through reclassification of shrub MULC class to orchard masked by the shrub vector data. ***-------------------------------------------*** Accuracy Assessment - An accuracy assessment was conducted on the completed land cover classification that included the area classified by SAL 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 2013 NAIP aerial photography was used in the classification. 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: Absolute 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). In total, 251 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 Shrub SoilBarren TreeForest Water Row Total User's Accuracy Agricult 2 53 0 0 4 0 0 59 0.033898 Grass_Herb 2 174 19 2 3 13 2 215 0.809302 Impervious 0 6 123 0 1 3 0 133 0.924812 Shrub 1 4 1 29 1 2 0 38 0.763158 SoilBarren 0 8 4 1 31 0 0 44 0.704545 TreeForest 0 31 14 11 2 254 0 312 0.814103 Water 0 0 0 0 2 1 47 50 0.94 Row Total 5 276 161 43 44 273 49 851 nan Producer's Accuracy 0.4 0.630435 0.763975 0.674419 0.704545 0.930403 0.959184 nan nan Overall Accuracy 0.775558 nan nan nan nan nan nan nan nan K_Hat 0.705519 nan nan nan nan nan nan nan nan K Variance 0.00034 nan nan nan nan nan nan nan nan RIGHT Agricult Grass_Herb Impervious Shrub SoilBarren TreeForest Water Row Total User's Accuracy Agricult 54 3 0 0 2 0 0 59 0.915254 Grass_Herb 0 180 17 1 3 12 2 215 0.837209 Impervious 0 5 124 0 1 3 0 133 0.932331 Shrub 0 2 1 33 1 1 0 38 0.868421 SoilBarren 0 7 4 1 32 0 0 44 0.727273 TreeForest 0 24 14 8 2 264 0 312 0.846154 Water 0 0 0 0 2 0 48 50 0.96 Row Total 54 221 160 43 43 280 50 851 nan Producer's Accuracy 1 0.81448 0.775 0.767442 0.744186 0.942857 0.96 nan nan Overall Accuracy 0.86369 nan nan nan nan nan nan nan nan K_Hat 0.823354 nan nan nan nan nan nan nan nan K Variance 0.000234 nan nan nan nan nan nan nan nan 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. Shrub intermixing with Grass and Soil is likely introduced from prioritizing height above ground data derived from LIDAR during classification. 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. 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. 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. Pennsylvania, Delaware, New Jersey, and 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/PhiPA_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. 2018-07-13T00:00:00 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