{87041CF3-05BC-43C3-82DA-F066267C9871} 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 -- Fresno, California -- 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 Fresno, CA EnviroAtlas Meter-Scale Urban Land Cover (MULC) Data were generated via supervised classification of combined aerial photography and LiDAR data. The air photos were 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. Aerial photography ('imagery') was collected on multiple dates in summer 2010. Seven land cover classes were mapped: Water, impervious surfaces (Impervious), soil and barren (Soil), trees and forest (Tree), and grass and herbaceous non-woody vegetation (Grass), agriculture (Ag), and Orchards. An accuracy assessment of 500 completely random and 103 stratified random points yielded an overall user's accuracy (MAX) of 81.1 percent and a fuzzy user's accuracy (RIGHT) of 86.9 (see below). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Fresno, CA plus a 1-km buffer. 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 land cover (LC) data is to provide a detailed snapshot of the urban environment and ecosystem for analysis of multiple human and ecosystem health metrics and services. In EnviroAtlas, this analysis is typically performed by U.S. Census block group. 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 spatial scale in urban environments, and the existing NLCD (National Land Cover Database, www.mrlc.gov) LC maps at 30 m spatial resolution are too coarse to measure many urban phenomena. There are many possible applications of these high-resolution urban LC data, for example: - Knowing the exact location and size of vegetated and non-vegetated spaces; - Identifying highly impervious areas that would most benefit from trees and other vegetation to mitigate surface runoff and urban heat island effects; - Measuring the width and character of urban riparian buffers; - Locating potential wildlife corridors, gaps and linkages; - Planning park development and street trees 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 California Fresno, CA 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 -119.987249 -119.507299 36.616154 36.968864 2010-01-01 2012-01-01 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). For more information about EnviroAtlas data, go to https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets. The 2010 Fresno, CA land cover data set was developed using 32 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 11N, NAD83. LiDAR collected in May, 2012 was available for approximately 75% of the study area (SAIC LiDAR). LAS point cloud was not available; however, post-processed bare earth, reflective surfaces, and intensity returns were used to aid in classification. LiDAR raster products were provided in UTM 11N, NAD83, NAVD88, meters at 1-meter spatial resolution. Height above ground (HAG) was calculated as the difference of reflective surfaces and bare earth. 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 NAIP imagery. Four sequential supervised classifications were completed for the study area, each classification 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); 4. (Background - 0, Agriculture - 1, Orchard - 2). Classifications 1, 2, and 4 used only four band NAIP imagery as input. Classification 3 included LiDAR intensity and HAG bands in addition to the four band NAIP imagery. Supervised classifications 1, 2, and 3 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 NAIP four band intensity data for better classification results. Training pixels were collected from multiple image DOQQs to capture and mitigate brightness variations and artifacts resulting from differences in sun angle and scene brightness between image acquisition dates. The three classification algorithms were applied to each DOQQ. Obvious errors in each classification were manually corrected on screen using Genie Pro 2.4. Typically, manual correction of misclassified pixels was limited to 30-60 minutes per DOQQ. Confusion between Soil and Impervious was the most common error encountered. Agriculture in Fresno was present in multiple forms: fallow fields, orchards, crops at different phenological stages. Classification 4 (Agriculture, Orchard) was completed using a combination of object based image analysis (ENVI Feature Extraction Module 4.8), supervised classifications (Genie Pro 2.4) and on-screen manual digitizing (ArcGIS 10.1). Areas classified as Agriculture fields that were dominated by trees were manually labeled as Orchards. Note: Orchards were only identified within the census urban statistical area and 1-km boundary; outside of this boundary, Orchards were lumped into the Agriculture class. Because an important consideration of EnviroAtlas is tree canopy, Orchard is defined as only the tree canopy within an identified orchard and the background (Soil, Grass-Herbaceous) remain as Agriculture. A normalized difference vegetation index (NDVI) was calculated and used to identify tree canopy within Orchards via thresholding. Pixels with an NDVI greater than or equal to 0.15 were classified as Orchard and all other background pixels remained as Agriculture. A mosaic of each classification was created (using 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("Classification 1" == 1,10, Con("Classification 4" == 1,80, Con(("Classification 4" == 2)&("NDVI" < .15) ,80, Con(("Classification 4" == 2)&("NDVI" >= .15),82, Con("Classification 2" == 1,(70-(" Classification 3"*30)), Con("Classification 2" <= 2, (20+(5*"Classification 2")),0)))))). In short, the hierarchy followed: 1. Water took precedence; 2. Agriculture and Orchards; 3. Vegetation was divided into Tree/Forest and Grass/Herbaceous; 4. Soil and Impervious Surfaces. The map algebra expression also set the final land covers to the appropriate class number. The following LC classes were used in the Fresno classification: 0 - Unclassified; 10 - Water; 20 - Impervious Surface; 30 - Soil & Barren; 40 - Trees & Forest; 70 - Grass & Herbaceous; 80 - Agriculture; 82 - Orchard. The definitions of these classes are close to Anderson Level 1 classification used for the National Land Cover Database 2006 (NLCD). **12/2016 UPDATE The prior published Fresno, CA 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. Accuracy Assessment: A random sample of reference pixels was generated to compare with the classified LC data. We created a shapefile of 600 pixels (n=100 samples for each of six MULC classes). These sample data were provided to a second, independent analyst who had not been involved with developing the LC data. 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 for example, for a point located within a pixel on land comprised of patchy grass and soil, an interpreter may 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 SoilBarren TreeForest Water N(n) Users Accuracy Errors of Commission Agricult 124 3 0 17 0 0 144 86.11 13.89 Grass_Herb 3 30 6 5 4 0 48 62.5 37.5 Impervious 0 3 139 18 1 1 162 85.8 14.2 SoilBarren 12 16 11 106 2 0 147 72.11 27.89 TreeForest 0 8 0 0 44 0 52 84.62 15.38 Water 0 3 0 1 0 46 50 92.0 8.0 N(n) 139 63 156 147 51 47 603 Producers Accuracy 89.21 47.62 89.1 72.11 86.27 97.87 Errors of Omission 10.79 52.38 10.9 27.89 13.73 2.13 OVERALL USER'S ACCURACY 81.09 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 SoilBarren TreeForest Water N(n) Users Accuracy Errors of Commission Agricult 126 3 0 15 0 0 144 87.5 12.5 Grass_Herb 3 34 5 2 4 0 48 70.83 29.17 Impervious 0 3 148 10 0 1 162 91.36 8.64 SoilBarren 2 11 9 124 1 0 147 84.35 15.65 TreeForest 0 8 0 0 44 0 52 84.62 15.38 Water 0 1 0 1 0 48 50 96 4 N(n) 131 60 162 152 49 49 603 Producers Accuracy 96.18 56.67 91.36 81.58 89.8 97.96 Errors of Omission 3.82 43.33 8.64 18.42 10.2 2.04 OVERALL FUZZY USER'S ACCURACY 86.9 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/FCA_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 Fresno, CA 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 2014-01-08T00:00:00 Acquired 2012 post-processed LiDAR elevation, reflective surfaces, and intensity. Publisher: SAIC LiDAR. Publication Date: 20120731 2014-01-08T00:00:00 See overview section for detailed processing workflow 2014-01-08T00:00:00 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