{D556044C-FCCC-4E8B-B157-5E7B8587AEE7} 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 -- Phoenix, AZ -- 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 Environmental Remote Sensing and Geoinformatics Lab (ERSG) at Arizona State University originator The EnviroAtlas Phoenix, AZ Meter-Scale Urban Land Cover (MULC) data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near-infrared) aerial photography taken from June through September, 2010 at 1 m spatial resolution. Seven land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, shrubs, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment using a completely random sampling of 598 land cover reference points yielded an overall user's accuracy (MAX) of 69.2% and an overall fuzzy user's accuracy of 75.4%. The area mapped includes the entirety of the Central Arizona-Phoenix Long-Term Ecological Research (CAP-LTER) area, which was classified by the Environmental Remote Sensing and Geoinformatics Lab (ERSG) at Arizona State University. The land cover dataset also includes an area of approximately 625 square kilometers which is located north of Phoenix. This section was classified by the EPA land cover classification team. This dataset was produced by the Environmental Remote Sensing and Geoinformatics Lab (ERSG) at Arizona State University 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). The purpose of this EnviroAtlas Meter-Scale Urban Land Cover (MULC) dataset 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 Arizona Phoenix, AZ 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 -112.828955 -111.503980 33.148357 34.057945 2010-06-01 2010-09-01 The Environmental Remote Sensing and Geoinformatics Lab (ERSG) at Arizona State University contributed to this dataset. 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 Phoenix, AZ Meter-Scale Urban Land Cover (MULC) dataset includes data for the cities of Phoenix, Mesa, Tempe and Scottsdale as well as surrounding towns and rural areas. The total area classified is approximately 8910 square kilometers. The vast majority (93%, or 8285 square kilometers) of the land cover data derives from a land cover classification of the Central Arizona-Phoenix Long-Term Ecological Research (CAP-LTER) area, which was developed by the Environmental Remote Sensing and Geoinformatics Lab (ERSG) at Arizona State University (Xiaoxiao Li, analyst). The remaining area, encompassing approximately 625 square kilometers or 7% of the total area in the north central part of the study area, was classified by the EPA EnviroAtlas land cover team. Both classifications that were incorporated into the Phoenix, AZ dataset were developed using Digital Orthophoto Quarter Quadrangle ("quarter quads") that were acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP). Each quarter quad includes four bands (visible RGB plus a near-infrared band) with one-meter spatial resolution. The imagery was collected by USDA between June and September 2010. The CAP-LTER land cover map was developed using 2010 cadastral parcel data as an ancillary dataset. The CAP-LTER land cover map was developed using eCognition, an Object Based Image Analysis software program. The land cover mapping methods were based on an expert knowledge decision rule set. The classification rules were established on a hierarchical image object network and on the land cover type according to the properties of parcels in the GIS layer. Image segmentations were initially used to identify parcel sized objects, and were then further used to identify land cover types within the parcels. Contextual and geometrical aspects of features were used in the decision rule set to reduce the spectral limitation of the four-band aerial photography. The CAP-LTER classification procedure produced the following 13 land use / land cover (LULC) classes: 1- Building 2- Road 3- Soil 4- Tree 5- Grass 6- Shrub 7- Active Cropland 8- Inactive Cropland 9- Orchard 10- Lake 11- Canal 12- Swimming Pool 13- Seasonal River The 13 ASU LULC classes were collapsed into 7 EPA LULC classes according to EPA EnviroAtlas protocols. The lake and canal classes were reclassified as "water" (class number 10). The building, road, and swimming pool classes were reclassified as "impervious" (class number 20). The active cropland, inactive cropland, and orchard classes were reclassified as "agriculture" (class number 80). The soil and seasonal river classes were reclassified as "soil and barren" (class number 20). The tree class was assigned to the "tree and forest" class (class number 40), and the grass class was assigned to the "grass and herbaceous vegetation" class (class number 70). Finally, shrubs remained in the 'shrub' class (class number 52). In addition to the CAP-LTER dataset, the EPA land cover classification team classified approximately 625 square kilometers of NAIP imagery. This area is located to the north of Phoenix and is mostly rural. A visual inspection of the imagery revealed only five predominant LULC classes in this area: water, impervious, trees, shrubs and soil. These five classes were classified using GeniePro 2.4, a machine-learning classification software program. Representative training pixels were selected for water, tree, and impervious surface classes in order to identify those classes over the 625 square kilometers. Already classified pixels were masked and a Normalized Difference Vegetation Index (NDVI) was added to the 4-band imagery. Additional representative training pixels were selected for shrub and background. Pixels not identified as water, tree, impervious surface, or shrub were classified as bare soil. The resulting land cover dataset was then merged with the reclassified CAP-LTER dataset. **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 Phoenix, AZ, 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. Please make sure to use the most up to date version of this land cover product. Also, the prior published Phoenix, AZ 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 sample of 600 completely random points (100 per class) was generated and provided to an independent analyst who had not been involved with generating the land cover map to assess the accuracy of the classified land cover map (combined ASU plus EPA). The accuracy assessment included the following classes: Water, Impervious Surface, Soil-Barren, Tree, Grass-Herbaceous and Agriculture. The independent 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 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 Agricult Grass_Herb Impervious Shrubland SoilBarren TreeForest Water N(n) Users Accuracy Errors of Commission Agricult 16 1 0 0 3 0 0 20 80.0 20.0 Grass_Herb 0 19 2 1 5 6 0 33 57.58 42.42 Impervious 0 3 98 1 14 3 0 119 82.35 17.65 Shrubland 3 2 20 209 35 5 0 274 76.28 23.72 SoilBarren 6 6 39 9 60 7 1 128 46.88 53.12 TreeForest 0 4 4 1 3 7 0 19 36.84 63.16 Water 0 0 0 0 0 0 5 5 100.0 0.0 N(n) 25 35 163 221 120 28 6 598 Producers Accuracy 64.0 54.29 60.12 94.57 50.0 25.0 83.33 Errors of Omission 36.0 45.71 39.88 5.43 50.0 75.0 16.67 OVERALL ACCURACY 69.23 The error matrix (confusion matrix) below summarizes the results of the RIGHT 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 Agricult Grass_Herb Impervious Shrubland SoilBarren TreeForest Water N(n) Users Accuracy Errors of Commission Agricult 16 1 0 0 3 0 0 20 80.0 20.0 Grass_Herb 0 20 2 1 5 5 0 33 60.61 39.39 Impervious 0 3 99 1 14 2 0 119 83.19 16.81 Shrubland 3 2 19 226 20 4 0 274 82.48 17.52 SoilBarren 5 4 32 4 77 5 1 128 60.16 39.84 TreeForest 0 3 4 1 3 8 0 19 42.11 57.89 Water 0 0 0 0 0 0 5 5 100.0 0.0 N(n) 24 33 156 233 122 24 6 598 Producers Accuracy 66.67 60.61 63.46 97.0 63.11 33.33 83.33 Errors of Omission 33.33 39.39 36.54 3.0 36.89 66.67 16.67 OVERALL ACCURACY 75.42 In the Phoenix, AZ land cover map, the primary source of confusion between classes was between soil and impervious. This is largely due the fact that pavement and desert soil have similar spectral signatures. Prior to processing at EPA, the accuracy of the original CAP-LTER land cover map was assessed by Arizona State University. For the ASU 13-class map, the observed overall accuracy was above 90 %. 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/PAZ_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 the Overview section for the complete processing workflow. 2013-11-05T00:00:00 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