{2474DC34-035D-495B-99A7-70346BA9CAD5} 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 -- Pittsburgh, PA -- Meter-Scale Urban Land Cover (MULC) Data (2010) 2017-09-05 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) originator The EnviroAtlas Pittsburgh, PA Meter-Scale Urban Land Cover (MULC) data was generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution. Imagery was collected on multiple dates in June 2010. Five land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, and grass and herbaceous non-woody vegetation. An accuracy assessment of 500 completely random and 81 stratified random points yielded an overall user's accuracy (MAX) of 86.57% and an overall fuzzy user's accuracy (RIGHT) of 89.33%. The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Pittsburgh, PA. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). 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 land cover (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 gray 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 Pennsylvania Pittsburgh, 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 https://project-open-data.github.io/schema/#accessLevel Standard Technical Controls eng; USA environment geoscientificInformation imageryBaseMapsEarthCover -80.29781 -79.68825 40.24197 40.67122 2010-06-01 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). The Pittsburgh, PA Meter-Scale Urban Land Cover (MULC) data set was developed using 69 United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) Digital Orthophoto Quarter Quadrangles (DOQQ) ("quarter quads") Each DOQQ is approximately 7.7 km N-S and 6.1 km E-W, comprising approximately 47,000,000 pixels. Each NAIP DOQQ consists of three visible bands and one near infrared (NIR) band with 1 meter pixel resolution. All DOQQs were projected in UTM 17N, NAD83. PAMAP Program LAS Files (LiDAR Data of Pennsylvania) and PAMAP Program 3.2 ft Digital Elevation Model of Pennsylvania (http://www.dcnr.state.pa.us/topogeo/pamap/lidar/index.htm) were used in the classification. PAMAP LiDAR data are distributed in 10000x10000 ft blocks. 310 LAS and Digital Elevation Model (DEM) blocks collected in 2006 were acquired for use in our study area. LiDAR data was provided in the Pennsylvania State Plane South projection, units of feet, NAVD88 vertical datum, and Geoid03. Supervised classifications were performed using Genie Pro 2.4 feature extraction software (www.observera.com). Genie Pro uses genetic algorithms to generate solution algorithms that condition the data as input to the classifier. Synthetic bands such as texture, band ratios and edges are created to increase the dimensionality of the data for better classification results. Training pixels were collected from multiple image DOQQs to capture and mitigate brightness variations and artifacts resulting from between-DOQQ differences in sun angle and radiometric preprocessing of the flight lines. Three supervised classifications were completed (Genie Pro 2.4) for the study area, each limited to one or two classes against a background. The classifications and corresponding pixel values were: 1. (Background - 0, Water - 1); 2. (Background - 0, Vegetation - 1, Soil & Barren - 2); 3. (Background - 0, Tree & Forest - 1). Classifications 1 and 2 used the 4 band NAIP imagery as inputs. In addition to the 4 band imagery, classification 3 included ancillary bands derived from the PAMAP LiDAR and DEM data. A LAS dataset was created from the LAS blocks and a digital surface model (DSM) was produced using the LAS Dataset to Raster tool (ArcGIS 10.1). Average elevation values for all returns in each pixel were calculated and a linear void fill method was used to calculate pixel values with no returns. DSM was output in floating point with 3.28084 ft (~1 meter) pixel size. A height above ground (HAG) model was created as the difference between the DSM and DEM. A LiDAR intensity raster was created using the same options as the DSM. The HAG model and intensity were scaled to 8 bit unsigned integer (0 - 255) and layer stacked as bands 5 and 6 to the 4 band imagery. The three classification algorithms were applied to each DOQQ. Obvious errors in each classification were manually corrected on screen using Genie Pro 2.4. Time varied depending on the DOQQs heterogeneity. Typically, manual correction was limited to 30 minutes to 1 hour per DOQQ. Soil versus impervious surface confusion and salt-and-pepper grass in forest canopy were the most common errors corrected. A mosaic of each classification was created (ENVI 4.8) and a hierarchical raster calculator expression (ArcGIS 10.1) was used to determine each pixel's final class. Map Algebra Expression: Con("Water"==1,1, Con("VS"==1,("Tree"(-1)+5), Con(("VS"==1)|("VS"==2),("VS"/2+2),0))). The land cover mosaic was reclassified using a modified Anderson Level 1 classification similar to that used for the North American Land Cover Data 20016 (NLCD) with the following classes: 0 - Unclassified; 10 - Water; 20 - Impervious Surface; 30 - Soil & Barren; 40 - Trees & Forest; 70 - Grass & Herbaceous. **12/2016 UPDATE The prior published Pittsburgh, PA MULC data included areas beyond the study area boundary. In 2016, this land cover product was clipped to match the 2010 US Census Urban Area boundary plus 1 km buffer. (https://www.epa.gov/enviroatlas/enviroatlas-spatial-extents). Please make sure to use the most up to date version of this land cover product. A sample of five hundred completely random points was generated to assess the accuracy of the classified land cover map. The random points were provided to a second analyst who had not been involved with generating the land cover map. The second analyst performed photo interpretation of the validation points on the original NAIP imagery used to develop the land cover map, assigning a land cover class to each point. Ancillary image data such as Google Satellite and Street Views, and Bing Aerial and Birdseye views, were used as needed to substantiate the interpretation. Uninterpretable points (e.g., dark shadow) were noted and discarded. This analysis used a fuzzy classification approach (Gopal and Woodcock, 1994). The second analyst recorded her confidence in her interpreted land cover at each point assigning the reference point a value between 1 and 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 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). If any LC classes ended up with less than fifty reference points, a stratified (by LC class) random sample of additional points was generated and interpreted. (This step typically applies only to Soil-Barren, and less frequently Water). Thus in the end, all classes had a minimum of fifty reference samples, and most classes (all but Soil and Water) had approximately 100 reference samples. The final accuracy assessment resulted in confusion matrices for a non-fuzzy method (MAX) and a fuzzy method (RIGHT), both presented below. The 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 error matrix (confusion matrix) below summarizes the results of the MAX accuracy assessment. Producer's and User's accuracies are reported, and the inter-class confusions are shown in the off-diagonal cells 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. RASTER_TXT Grass_Herb Impervious SoilBarren TreeForest Water N(n) Users Accuracy Errors of Commission Grass_Herb 96 12 5 13 0 126 76.19 23.81 Impervious 7 82 3 1 0 93 88.17 11.83 SoilBarren 4 3 43 0 0 50 86.0 14.0 TreeForest 24 5 0 233 0 262 88.93 11.07 Water 1 0 0 0 49 50 98.0 2.0 N(n) 132 102 51 247 49 581 Producers Accuracy 72.73 80.39 84.31 94.33 100.0 Errors of Omission 27.27 19.61 15.69 5.67 0.0 OVERALL USER'S ACCURACY 86.57 The error matrix (confusion matrix) below summarizes the results of the RIGHT fuzzy accuracy assessment. Producer's and User's accuracies are reported, and the inter-class confusions are shown in the off-diagonal cells 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.. RASTER_TXT Grass_Herb Impervious SoilBarren TreeForest Water N(n) Users Accuracy Errors of Commission Grass_Herb 101 12 3 10 0 126 80.16 19.84 Impervious 7 82 3 1 0 93 88.17 11.83 SoilBarren 0 2 48 0 0 50 96.0 4.0 TreeForest 19 4 0 239 0 262 91.22 8.78 Water 1 0 0 0 49 50 98.0 2.0 N(n) 128 100 54 250 49 581 Producers Accuracy 78.91 82.0 88.89 95.6 100.0 Errors of Omission 21.09 18.0 11.11 4.4 0.0 OVERALL FUZZY USER'S ACCURACY 89.33 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. 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/PitPA_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 Acquired 2010 4 band NAIP imagery of Pittsburgh, PA study area from. Publisher: USDA-FSA-APFO Aerial Photography Field Office. Publication_Date: 20101221. Publication_Place: Salt Lake City, Utah. Link: http://www.fsa.usda.gov/FSA/apfoapp?area=home&subject=prog&topic=nai 2013-02-04T00:00:00 Acquired 2006 PAMAP Program LAS Files (LiDAR Data of Pennsylvania). Publisher: PAMAP Program, PA Department of Conservation and Natural Resources, Bureau of Topographic and Geologic Survey. Publication_Date: 2006 - 2008. Publication_Place: Middletown, PA. Link: http://www.pasda.psu.edu/uci/MetadataDisplay.aspx?entry=PASDA&file=pamap_lidar_LAS.xml&dataset=1244 2013-02-15T00:00:00 Acquired 2006 PAMAP Program 3.2 ft Digital Elevation Model of Pennsylvania. Publisher: PAMAP Program, PA Department of Conservation and Natural Resources, Bureau of Topographic and Geologic Survey. Publication_Date: 20061213. Publication_Place: Middletown, PA. Link: http://www.pasda.psu.edu/uci/MetadataDisplay.aspx?entry=PASDA&file=PAMAP_DEM.xml&dataset=1247 2013-02-15T00:00:00 Developed classification algorithms to identify the following classes: Background (non-vegetation), vegetation, and soil. Applied to all DOQQs. (Genie Pro 2.4) 2013-02-11T00:00:00 Developed classification algorithms to identify the following classes: Background, Water. Applied to all DOQQs. (Genie Pro 2.4) 2013-02-18T00:00:00 Performed manual corrections to fix obvious errors in the classifications. (Genie Pro 2.4) 2013-03-05T00:00:00 Mosaicked background + vegetation + soil classified tiles (ENVI 4.8) 2013-03-06T00:00:00 Mosaicked background + water classified tiles (ENVI 4.8) 2013-03-07T00:00:00 Mosaicked 3.2 ft DEM tiles (ENVI 4.8) 2013-05-16T00:00:00 Produced Digital Surface Model (DSM) from LAS Files (ArcGIS 10.1) 2013-05-16T00:00:00 Produced Height Above Ground Model by subtracting DEM from DSM (ArcGIS 10.1) 2013-05-17T00:00:00 Scaled Height Above Ground to 8 bit integer (0-255) (ArcGIS 10.1) 2013-05-17T00:00:00 Produced Intensity Return raster from LAS Files (ArcGIS 10.1) 2013-05-20T00:00:00 Scaled Intensity raster to 8 bit integer (0-255) (ArcGIS 10.1) 2013-05-20T00:00:00 Layer stack: 4 band NAIP Imagery + HAG(8 bit) + Intensity(8 bit) (ArcGIS 10.1) 2013-05-20T00:00:00 Developed classification algorithms to identify the following classes: Background, Tree/Forest. Applied to all DOQQs (Genie Pro 2.4) 2013-07-11T00:00:00 Mosaicked background + tree classified tiles (ENVI 4.8) 2013-07-17T00:00:00 Raster calculator to determine final class for each pixel from three classifications (ArcGIS 10.1) 2013-07-17T00:00:00 Accuracy Assessment 2013-09-09T00:00:00 Reclassified all values to match our classification numbering scheme (ArcGIS 10.1) 2013-10-29T00:00:00 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