{F5ED8E1B-8772-4082-A38B-89810D7C7BA3} 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 -- Woodbine, IA -- Meter-Scale Urban Land Cover (MULC) Data (2011) 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 Woodbine, IA 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 from Late Summer 2011 at 1 m spatial resolution. Six land cover classes were mapped: water, impervious surfaces (dark and light), soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment using a completely random sampling of 600 samples yielded an overall user's accuracy (MAX) of 87.03% and an overall fuzzy user's accuracy (RIGHT) of 90.23% 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 Woodbine. 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. 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) 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 vegetated and non-vegetated 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 Iowa Woodbine, IA 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 -88.473227 -84.888246 30.144425 35.008028 2011-01-01 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 Woodbine, IA land cover dataset was developed using 5 United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) Digital Orthophoto Quarter Quadrangle (DOQQ) ("quarter quads"). Each quarter quad image contains four bands: three visible bands Red Green Blue (RGB) and a Near Infrared (NIR) band with 1-meter pixel resolution, 8-bit pixel depth. The five quarter quads were classified to land cover using primarily the NAIP imagery and LiDAR data. LiDAR data for the study area was obtained from the University of Iowa's GeoTree website (http://www.geotree.uni.edu/lidar/) and used to derive Height Above Ground (HAG) and Intensity rasters. To highlight agriculture, we used 2008 USDA Common Land Unit (CLU) shapefiles delineating permanent boundaries for a common land cover and land management practice. Derivative products such as Normalized Difference Vegetation Index (NDVI), and a Variance raster (standard deviation for each cell using a 3x3 moving window) were created from the NAIP tiles. The Metadata for the LiDAR can be provided upon request. A supervised classification was performed using Genie Pro 2.4 feature extraction software (www.observera.com) to map the following land cover classes and codes modified after the North American Land Cover Data 2001 (NLCD): 0 - Unclassified, 10 - Water, 20 - Impervious Surface, 30 - Soil/Barren, 40 - Trees/Forest, 70 - Grass/Herbaceous, 80 - Agriculture. 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. Cover classes were delineated in a five step procedure whereby each class was removed from the imagery after delineation. Step one: All NAIP imagery were mosaicked into a single image for classification with Genie Pro. Step two: Training pixels were collected for WATER using just the NAIP bands. The solution file was applied to the imagery and obvious errors were hand edited on the result files. The resulting Water/Not Water raster was used to create two new rasters, Water only and Non-Water/Background. The areas of background or NoData were used as a mask for the next step. Step three: Training pixels were collected for VEGETATED areas using band stacked imagery consisting of NAIP, NDVI, and VARIANCE with WATER masked out. The solution file was applied to the imagery and obvious errors were hand edited on the result files. The resulting Vegetation/Non-vegetation raster was used to create two new rasters, Vegetation only and Non-vegetation only. These served as mask layers for Tree/Grass delineation as well as Soil/Impervious delineation. Step four: Training pixels were collected for TREE and GRASS features using band stacked imagery consisting of NAIP, NDVI, HAG, and Intensity with WATER and NON-VEGETATED areas masked out. The solution file was applied to the imagery and obvious errors were hand edited on the result files. The TREE and GRASS features were then reclassified into separate raster layers for later processing. Step five: Training pixels were collected for SOIL and IMPERVIOUS features using band stacked imagery consisting of NAIP and NDVI with WATER and VEGETATED areas masked out. The solution file was applied to the imagery and obvious errors were hand edited on the result files. The SOIL and IMPERVIOUS features were then reclassified into separate raster layers for later processing. Step six: Common Land Unit (CLU) polygons created by the US Department of Agriculture were acquired through a publicly available GIS source (Iowa Department of Natural Resources) and used as an overlay to determine agricultural fields. The CLU polygons serve as the boundaries for locating agricultural fields. Those polygons demarcating agricultural fields were selected and converted to an agriculture raster layer. Step seven: Each individual cover class raster was combined into a single raster image using the MOSAIC TO NEW RASTER TOOL, whereby the order of individual layers is as follows: WATER, IMPERVIOUS, TREE, AGRICULTURE, SOIL, and GRASS. **12/2016 UPDATE The prior published Woodbine, IA 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 completely random sample of 600 points (100 points for each class) was created for the study area. A shapefile of sampling points was provided to a second analyst who had not been involved with generating the land cover map. The second analyst 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 if the NAIP data alone were insufficient. 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 Agricult Grass_Herb Impervious SoilBarren TreeForest Water N(n) Users Accuracy Errors of Commission Agricult 374 24 0 3 1 0 402 93.03 6.97 Grass_Herb 7 60 0 1 3 0 71 84.51 15.49 Impervious 0 6 36 3 1 1 47 76.6 23.4 SoilBarren 10 20 0 16 0 0 46 34.78 65.22 TreeForest 0 6 0 0 65 0 71 91.55 8.45 Water 0 0 1 2 0 46 49 93.88 6.12 N(n) 391 116 37 25 70 47 686 Producers Accuracy 95.65 51.72 97.3 64.0 92.86 97.87 Errors of Omission 4.35 48.28 2.7 36.0 7.14 2.13 OVERALL USER'S ACCURACY 87.03 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 Agricult Grass_Herb Impervious SoilBarren TreeForest Water N(n) Users Accuracy Errors of Commission Agricult 381 18 0 2 1 0 402 94.78 5.22 Grass_Herb 0 69 0 1 1 0 71 97.18 2.82 Impervious 0 5 37 3 1 1 47 78.72 21.28 SoilBarren 10 15 0 21 0 0 46 45.65 54.35 TreeForest 0 6 0 0 65 0 71 91.55 8.45 Water 0 0 1 2 0 46 49 93.88 6.12 N(n) 391 113 38 29 68 47 686 Producers Accuracy 97.44 61.06 97.37 72.41 95.59 97.87 Errors of Omission 2.56 38.94 2.63 27.59 4.41 2.13 OVERALL FUZZY USER'S ACCURACY 90.23 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/WIA_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