{A4152198-978D-4C0B-959F-42EABA9C4E1B} 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 -- Des Moines, IA -- 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) (additional partners are listed in Supplemental) originator The Des Moines, IA EnviroAtlas Meter-Scale Urban Land Cover (MULC) Data were generated from the High Resolution Land Cover (HRLC) product created by the Iowa Department of Natural Resources (IDNR), consisting of data for Dallas, Polk, and Warren counties. The 14 class IDNR land cover product was modified to fit the land cover classification structure of the EPA EnviroAtlas resulting in six land cover classes mapped: water, impervious surfaces, soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment of 600 completely random and 90 stratified random points yielded an overall user's accuracy of 78% and an overall user's fuzzy accuracy of 85%. The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Des Moines, IA plus a 1 km buffer. This dataset was produced by the Iowa Department of Natural Resources 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 EnviroAtlas MULC provides spatially detailed foundational data for approximately 100 derived EnviroAtlas Community information layers. Some layers are developed at the native one-meter MULC resolution and others by U.S. Census block group. While the EnviroAtlas National component is based on NLCD (National Land Cover Database, www.mrlc.gov) land cover data at 30 m spatial resolution (30x30 m pixel dimensions from the Landsat sensor), the EnviroAtlas Community component is based on MULC data at 900 times the spatial resolution of the national data. This permits analysis of common landscape features in the 1-10 m range which are unresolvable using 30 m NLCD pixels. EnviroAtlas includes close to 100 data layers derived from MULC data. Three such layers are: Percent impervious area in 50m stream buffer; Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr); and Percent of busy roadway bordered by < 25 percent tree buffer. Some of many possible applications of MULC include: * 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 and character of urban riparian and street 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 Des Moines, 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 -93.911162 -93.332772 41.433485 41.787472 2010-01-01 The Iowa Department of Natural Resources 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). For more information about EnviroAtlas data, go to https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets. The Des Moines, IA land cover dataset was developed by modifying existing High Resolution Land Cover (HRLC) product created by the Iowa Department of Natural Resources (IDNR), consisting of data for Dallas, Polk, and Warren counties at 1-meter pixel resolution, 8-bit pixel depth. The data were created from 3 sets of imagery including 1 meter NAIP 3-band color imagery for 2008 and 2009 plus 0.61 meter 4-band spring imagery from different regions of the state for 2007, 2009 and 2010. Additionally, 1 meter LiDAR data from 2009 were included in the data stack. An independent component analysis (ICA) was performed on the data layers to create statistically independent input signals for the classification process. An ISODATA classification produced 250 clusters that were then grouped into the final 14 cover classes (http://ir.uiowa.edu/etd/1169/). The 14 class IDNR data product for the study area was downloaded from the Iowa Department of Natural Resources GIS Library (https://programs.iowadnr.gov/nrgislibx/). To highlight agriculture, we used 2008 USDA Common Land Unit (CLU) shapefiles delineating permanent boundaries for a common land cover and land management practice. Modification processing steps: Step one: High Resolution Land Cover products for Dallas, Polk, and Warren counties were downloaded from the Iowa Department of Natural Resources GIS Library, and mosaiced into a single image. Cover classes include: Water, Wetland, Coniferous Trees, Deciduous Tree, Grass 1, Grass 2, Hay, Corn, Beans, Other Rowcrop, Structure, Roads, Barren, and Shadow/NoData. The raster data were split up using a grid system of ~7.7 sq KM each. Each raster was then converted to polygons, preserving all geometry. Each step below was performed on all tiles recursively. Step two: Areas classified as Shadow/NoData were folded into surrounding classified polygons using the Eliminate tool (ArcGIS 10.2) , whereby the Shadow/NoData polygons were merged with the largest neighboring polygon. Step three: Areas classified as wetland did not match the National Wetland Inventory (NWI) data or appear to more effectively map wetlands than the NWI and it was determined necessary to fold these polygons into surrounding classified polygons using the Eliminate tool, merging them with the largest neighboring polygon. This modification was necessary for this study area but does not indicate poor classification throughout the Iowa HRLC. Step four: The agriculture classification (Hay, Corn, Beans, Other Row crop) contained some over-classifications resulting in small objects unnecessarily classified as agriculture that were truly grass/herbaceous. Common Land Unit data were used to identify locations of agricultural land use and serve as extent boundaries for agricultural lands. Agricultural polygons found within the CLU polygons remained and the small objects outside were folded into surrounding classified polygons using the Eliminate tool, merging them with the largest neighboring polygon. Step five: The resulting polygon tiles were merged together into a single layer and rasterized to GEOTIFF. This raster layer was then reclassified to match the EPA classification schema. Water (1) to Water (10), Coniferous Forest (3) to Tree/Forest (40), Deciduous Short (4) to Tree/Forest (40), Deciduous Medium (5) to Tree/Forest (40), Deciduous Tall (6) to Tree/Forest (40), Grass 1 (7) to Grass/Herbaceous (70), Grass 2 (8) to Grass/Herbaceous (70); Cut Hay (9) to Agriculture (80); Corn (10) to Agriculture (80); Soy Beans (11) to Agriculture (80); Barren (12) to Soil/Barren (30); Structures (13) to Impervious (20); and Roads/Impervious (14) to Impervious (20). Step six: Wetland data were burned into the classification using the National Wetland Inventory (NWI) data for Iowa. NWI classified as Freshwater Forested/Shrub Wetland or Freshwater Emergent Wetland were converted to a raster layer. Using a Con function (ArcGIS 10.2), areas of Tree/Forest that intersected with the NWI raster were converted to Woody Wetlands (90) and areas of Grass/Herbaceous intersecting the NWI raster were converted to Emergent Wetlands (95). *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 Des Moines, IA, 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 Des Moines, IA 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) and the two wetlands classes were added. 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. A fuzzy classification scheme was used as defined by Gopal and Woodcock (1994), whereby each point is given a value between 1 and 5 for a given land cover class: 1: Absolutely Wrong: classification value is unacceptable (Very Wrong); 2: Understandable but Wrong: classification value is not good. There is something about the site that makes the answer understandable, but there is 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 it is acceptable; the classification does 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) In situations where a particular class does not result in at least 50 points falling within that class, additional random points were added to reach the minimum 50 points (stratified random). 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 147 11 1 3 1 0 163 90.18 9.82 Grass_Herb 21 123 5 3 9 1 162 75.93 24.07 Impervious 2 19 94 2 2 0 119 78.99 21.01 SoilBarren 13 22 10 6 0 1 52 11.54 88.46 TreeForest 0 13 0 1 92 2 108 85.19 14.81 Water 0 2 0 1 2 46 51 90.2 9.8 N(n) 183 190 110 16 106 50 655 Producers Accuracy 80.33 64.74 85.45 37.5 86.79 92.0 Errors of Omission 19.67 35.26 14.55 62.5 13.21 8.0 OVERALL ACCURACY 77.56 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 150 8 1 3 1 0 163 92.02 7.98 Grass_Herb 2 146 4 3 6 1 162 90.12 9.88 Impervious 2 19 94 2 2 0 119 78.99 21.01 SoilBarren 5 16 8 22 0 1 52 42.31 57.69 TreeForest 0 9 0 1 96 2 108 88.89 11.11 Water 0 2 0 1 2 46 51 90.2 9.8 N(n) 159 200 107 32 107 50 655 Producers Accuracy 94.34 73.0 87.85 68.75 89.72 92.0 Errors of Omission 5.66 27.0 12.15 31.25 10.28 8.0 OVERALL ACCURACY 84.58 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/DMIA_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