{A7C3F637-67AB-4E6E-90C0-04A9EBD6A1C9} 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 -- Portland, ME -- 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 Portland, ME Meter-Scale Urban Land Cover (MULC) data was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2010 at 1 m spatial resolution. Eight land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, agriculture, and wetlands (woody and emergent). An accuracy assessment using a stratified random sampling of 600 samples yielded an overall user's accuracy of 87.5 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Portland. 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 Maine Portland, ME 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 -70.493457 -70.078404 43.446672 43.880517 2010-01-01 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 Portland, ME land cover dataset was developed using 38 United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) Digital Orthophoto Quarter Quadrangles (DOQQ) ("quarter quads"). Each quarter quad is 4 bands including visible RGB and a Near Infrared (NIR) band with 1 meter pixel resolution. Ancillary data used in the classification was the National Wetlands Inventory (NWI) Maine geodatabase (http://www.fws.gov/wetlands/Downloads/ME_wetlands.zip) completed in 2006. Metadata for the NWI layer can be found at www.fws.gov/wetlands/Data/SupMapInf/R05Y05P02.pdf A supervised classification was performed using Genie Pro 2.3 feature extraction software (www.observera.com) to map the following land cover classes: Impervious Surface, Tree and Forest, Grass and Herbaceous, Soil and Barren. 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 quarter quads to capture and mitigate brightness variations and artifacts resulting from differences in sun angle and radiometric preprocessing of the flight lines. Five Genie Pro solution algorithms were developed using multiple representative quarter quad base images from across the study area. Most of the tiles were classified using two algorithms based on urban areas (quarter quads 4307029SE and 4307029SW) Two solution algorithms were developed for areas containing mostly water along the coast and a final solution for a predominately rural area. Attempts to classify water with the other land cover classes resulted in confusion with shadowed areas and very dark impervious surfaces. Through visual inspection, it was determined that the NWI and NAIP imagery were almost exactly coregistered. One GeniePro solution algorithm was developed from QQ 4307022SE with a binary classification of water and background and applied to all quarter quads. Water classification was limited to areas within the NWI boundary plus a 5 meter buffer. Obvious errors in the classification were hand edited on all results files. Time varied depending on how heterogeneous the tiles were. Typically hand editing was limited to 30 minutes to 1 hour per tile. Soil/Impervious Surface confusion and salt-and-pepper grass in forest canopy were the most common errors corrected. Raster calculator (ArcGIS 10.0) was used with a conditional to combine the water classification with the other classes. Agriculture was developed using only surfaces that were classified as either soil & barren or grass & herbaceous. A final genie pro solution algorithm was created with a binary classification of agriculture and background and applied to all tiles with obvious agriculture. Results were mosaicked, converted to polygons, and dissolved. Polygons less than 2 acres were eliminated, leaving approximately 800 polygons. Each polygon was inspected for accuracy. Errors of commission were deleted and detected areas of omission were heads up digitized. 441 agriculture polygons remained. These polygons were burned into the classification mosaic. Both woody and emergent wetlands were included in the final classification, again using the NWI dataset. Trees and Forest within NWI wetland polygons were reclassified to woody wetlands. Grass and herbaceous within NWI wetland polygons were reclassified to emergent wetlands. Wetland boundaries were accepted as accurate and not photo interpreted as wetlands, but were photo interpreted to assess the accuracy of the underlying Grass/Herbaceous or Tree/Forest classes. The land cover mosaic was reclassified using a modified Anderson Level 1 classification similar to that used for the North American Land Cover Data 2001 (NLCD) with the following classes: 0 - Unclassified 10 - Water 20 - Impervious Surface 30 - Soil & Barren 40 - Trees & Forest 70 - Grass & Herbaceous 80 - Agriculture 91 - Woody Wetlands 92 - Emergent Wetlands *12/2016 UPDATE *A 2016 quality assurance review of EnviroAtlas Communities MULC data revealed some minor issues that required update in certain communities. These updates consisted of correction of areas of misclassified pixels, and less frequently, correction of areas with artifacts that caused an apparent reduction in spatial resolution over limited areas. In Portland, ME, such areas constituted <1 percent of the total study area and did not measurably change the overall accuracy of the classification. These areas were marked and reclassified to the correct classes using ArcMap and Genie Pro. Also, the prior published Portland, ME MULC data included areas beyond the study area boundary. 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 stratified random sample of approximately 100 points per land cover class was generated to assess the accuracy of the classified land cover map. To reduce the number of sample points on edges of landscape features (ambiguous or impossible to interpret), only points contained in homogeneous patches at least 3x3 pixels (3x3 meters) were used in the sample. A shapefile of sampling points was provided to two analysts who had not been involved with generating the land cover map. They performed photo interpretation of the six hundred validation points on the original NAIP imagery used to develop the land cover map, assigning a land cover class to each point. Ancillary data such as Google Satellite and Street Views, and Bing Aerial and Birdseye views, were used as needed to substantiate the interpretation. 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. Imp. Soil Forest Grass Water Ag Row User's Accuracy Impervious 90 3 0 5 2 0 100 90.00% Soil 13 72 1 2 4 6 98 73.47% Forest 0 0 89 11 0 1 101 88.12% Grass 3 3 5 86 1 13 101 85.15% Water 0 0 0 1 99 0 100 99.00% Agriculture 0 2 1 8 0 89 100 89.00% Column Total 106 80 96 113 106 99 600 Producer's Accuracy 84.91% 90.00% 92.71% 76.11% 93.40% 89.90% Overall Accuracy 87.50% 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/PME_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 2011 4 band NAIP imagery of Portland, ME study area from USDA 2012-02-01T00:00:00 Acquired 2006 National Wetland Inventory (NWI) dataset from Fish and Wildlife Service 2012-03-01T00:00:00 Subset all NAIP tiles with NWI polygons plus 5 meter buffer 2012-03-07T00:00:00 Developed classification algorithm for NWI subset tiles to identify water 2012-03-08T00:00:00 Developed classification algorithms to identify the following classes: Impervious surface, Soil & Barren, Trees & Forest, Grass & Herbaceous 2012-04-01T00:00:00 Developed classification algorithm to identify agriculture from within areas already classified as either soil & barren or grass & herbaceous 2012-05-23T00:00:00 Performed hand editing to fix obvious errors in the classification 2012-05-25T00:00:00 Burned in water and agriculture classification 2012-06-18T00:00:00 Mosaic of all classified NAIP quarter quads 2012-06-19T00:00:00 Reclassified Trees & Forest and Grass & Herbaceous within NWI polygons to Woody Wetlands and Emergent Wetlands 2012-06-20T00:00:00 Reclassified all values to match our classification numbering scheme 2012-06-20T00:00:00 Accuracy Assessment 2012-06-25T00:00:00 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