{0C356577-EACE-442D-B723-317B518F1885} 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 -- Paterson, New Jersey -- 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 Paterson, New Jersey EnviroAtlas Meter-Scale Urban Land Cover (MULC) data comprises approximately 66 km2 around the city of Paterson. The land cover data were 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 pixel size. Imagery was collected in July 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 64 stratified random reference points yielded an overall user's accuracy (MAX) of 86.9% and an overall fuzzy user's accuracy (RIGHT) of 92.5%. For more information on our accuracy assessment see the overview section. 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 MULC data is to provide a detailed snapshot of the urban environment and ecosystem for the analysis of multiple human and ecosystem health metrics and services. This analysis is typically performed 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 and ecosystem environment of a city. Land cover varies at a fine scale in urban areas, 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 of interest, and may not be sufficiently current in rapidly changing urban environments. Some possible applications include: - Knowing the exact location and size of existing 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 New Jersey Paterson, NJ 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 -74.218601 -74.116523 40.879437 40.951010 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 Paterson, NJ Meter-Scale Urban Land Cover (MULC) dataset was developed using five 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. All quarter quads were projected in NAD83/UTM 18N. 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. Representative 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 balancing. Three 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. (Impervious - 0, Vegetation - 1, Soil & Barren - 2); 3. (Background - 0, Tree & Forest - 1). The three classification algorithms were applied to each quarter quad. Obvious errors in each classification were manually corrected. Time varied depending on the quarter quads heterogeneity. Typically, manual correction was limited to 30 minutes to 1 hour per quarter quad. Soil/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 10.2) was used to determine each pixel's final class. Vegetated areas from "classification 2" that were identified as trees/forest in "classification 3" were classified as tree/forest while remaining vegetated areas were classified as grass/herbaceous. Soil/barren and impervious surfaces remained unchanged. Finally areas identified as water in "classification 1" took precedence and were burned into the final land cover mosaic. The land cover mosaic was reclassified using a modified Anderson Level 1 classification similar to that used for the National Land Cover Database 2001 (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 Paterson, NJ 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 500 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 accuracy assessment included the following classes: Water, Impervious Surface, Soil-Barren, Tree, and Grass-Herbaceous. 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 reference pixel. Ancillary image data such as Google Satellite and Street Views, and Bing Aerial and Birdseye views, were used as appropriate to substantiate the interpretation based on the NAIP imagery. 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 following error matrix summarizes the MAX accuracy assessment data 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 51 6 0 14 0 71 71.83 28.17 Impervious 16 241 2 13 2 274 87.96 12.04 SoilBarren 1 2 46 1 0 50 92.0 8.0 TreeForest 10 5 0 104 0 119 87.39 12.61 Water 0 1 1 0 48 50 96.0 4.0 N(n) 78 255 49 132 50 564 Producers Accuracy 65.38 94.51 93.88 78.79 96.0 Errors of Omission 34.62 5.49 6.12 21.21 4.0 OVERALL USER'S ACCURACY 86.88 The following error matrix summarizes the RIGHT fuzzy accuracy assessment data 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 58 5 0 8 0 71 81.69 18.31 Impervious 7 257 1 7 2 274 93.8 6.2 SoilBarren 1 0 48 1 0 50 96.0 4.0 TreeForest 3 5 0 111 0 119 93.28 6.72 Water 0 1 1 0 48 50 96.0 4.0 N(n) 69 268 50 127 50 564 Producers Accuracy 84.06 95.9 96.0 87.4 96.0 Errors of Omission 15.94 4.1 4.0 12.6 4.0 OVERALL FUZZY USER'S ACCURACY 92.55 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/PNJ_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 Paterson, NJ study area. Publisher: USDA-FSA-APFO Aerial Photography Field Office. Publication_Date: 20120130. Publication_Place: Salt Lake City, Utah. Link: http://www.fsa.usda.gov/FSA/apfoapp?area=home&subject=prog&topic=nai 2014-04-01T00:00:00 See overview section for detailed processing workflow 2014-04-01T00:00:00 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