{82ab1edf-8fc8-4667-9c52-5a5acffffa34} 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 -- Cleveland, OH -- Meter-Scale Urban Land Cover (MULC) Data (2011 and 2013) 2016-06-08 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 Cleveland, OH EnviroAtlas Meter-scale Urban Land Cover (MULC) dataset comprises 2,737 km2 around the city of Cleveland and portions of surrounding counties. The area classified is based on the US Census Bureau's 2010 Urban Area for Cleveland, OH with a 1 km buffer added. This area includes the majority of Cuyahoga and Lake Counties, and portions of Ashtabula, Geauga, Medina, Lorain, Portage and Summit Counties. These MULC data and maps were derived from LiDAR and 1-m pixel, four-band (red, green, blue, and near-infrared) leaf-on aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) in 2011 and 2013. The NAIP imagery was collected on several dates: August 12, 2011, September 03, 2011 and August 24, 2013. The data was comprised of 85% 2011 NAIP and 15% 2013 NAIP imagery. LiDAR data collected between March 18, 2006 and May 07, 2006 covered the entire study area. Six land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Trees/Forest, Grass/Herbaceous Non-Woody Vegetation, and Wetlands (Woody and Emergent). Wetlands were copied from the best available existing wetlands data, which was a National Wetlands Inventory (NWI) layer from 2006. An analysis of 500 completely random and 81 stratified random photo-interpreted land cover reference points yielded an overall user's accuracy of 86.2% (see confusion matrix below). This dataset was produced by the US EPA, the University of Vermont, and the US Forest Service 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. This permits analysis of common landscape features in the 1-10 m range which are unresolvable using 30 m NLCD pixels. Three such layers derived from MULC data are: Percent impervious area in 50m stream buffer; Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr); 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 Exposure Health Impact Land Natural Resources Recreation Water 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 Ohio United States 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 -82.089515 -80.989975 41.188787 41.863870 2011-01-01 2013-01-01 These data were created by two institutions and shared as part of an interagency agreement. The EPA created 57% of the classification while 43% was created by the University of Vermont Spatial Analysis Laboratory (SAL) under the direction of Jarlath O'Neil-Dunne as part of the United States Forest Service Urban Tree Canopy (UTC) assessment program. 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. EPA Processing Steps: The Cleveland, OH MULC dataset was developed using 104 United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) (U.S. Department of Agriculture, 2011 and 2013) Digital Orthophoto Quarter Quadrangle (DOQQ) ("quarter quads") collected on August 12, 2011, September 03, 2011 and August 24, 2013. The data was comprised of 85% 2011 NAIP and 15% 2013 NAIP imagery. A DOQQ is approximately 7.7 km N-S and 6.1 km E-W, comprising approximately 47,000,000 pixels. Some DOQQs used were only portions that intersected the study area. 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. All DOQQs remained in their original UTM 17N, NAD83 projection for classification. The 104 quarter quads were classified to land cover using NAIP imagery and LiDAR data. LiDAR data for the study area were obtained from the Ohio Statewide Imagery Program (OSIP, http://ogrip.oit.ohio.gov/ProjectsInitiatives/StatewideImagery.aspx). The LiDAR data were collected from March 18, 2006 to May 07, 2006, covered the entire study area, and were used to create a Height Above Ground (HAG) raster. The LiDAR dataset average point spacing was approximately 2 meters. Intensity values were also used in the classification. A supervised classification was performed using Genie Pro 2.4 feature extraction software (www.observera.com) to map the following land cover classes with codes similar to the North American Land Cover Data 2001 (NLCD): 10 - Water, 20 - Impervious Surface, 30 - Soil/Barren, 40 - Trees/Forest, 70 - Grass/Herbaceous. Genie Pro uses machine learning and genetic algorithms to generate solution algorithms that condition the data as input to a classifier. Land cover classes were delineated in a multi-step procedure. Step one: Individual NAIP tiles mosaicked into single image. Step two: LAS LiDAR raw data were processed into a Digital Surface Model (DSM) and a Digital Elevation Model (DEM) using ArcMap version 10.3 (www.esri.com). The DSM consisted of all return points in the dataset, resulting in a raster representing the complete surface of the Earth including all objects on the ground such as trees and buildings. The DEM consisted of only ground points, resulting in a raster representing the ground surface of the Earth only. A height above ground (HAG) raster was created from the DEM and DSM by subtracting the DEM from the DSM. This raster represented the height of objects residing on the surface of the Earth. Step three: LAS LiDAR raw data were also used to process an intensity surface raster. Intensity values represent the strength of return for each pulse. Intensity values vary depending on the reflectivity of the surface being measured. Intensity values are relatively consistent across different types of land cover and this added information assists the classification process. Step four: A Normalized Difference Vegetation Index (NDVI) raster was created from the original NAIP imagery. NDVI normalizes illumination differences across the mosaic, highlights vegetation vigor, and helps separate vegetation and non-vegetation. The calculation is as follows: (NIR-VIR)/(NIR+VIR) (dimensionless number ranging from -1 to 1). Step five: The NAIP, NDVI, HAG, and Intensity rasters were stacked together into a single seven band image, then cut into tiles along the NAIP Quarter Quad boundaries. Step six: Water bodies were identified using voids in LiDAR data within the study area (Environmental Systems Research Institute, 2013). LiDAR pulses over water do not generally return to the sensor, which results in areas with no data, or voids. These LiDAR voids can be used to identify water areas. In ArcMap, a surface raster was created from the raw LiDAR points. All cells with LiDAR values were assigned a constant value and cells with no values were assigned no data values. Using the Expand and Shrink tools, small and irrelevant cells were removed. The raster was converted into vector format and further refined using the Eliminate tool, which removed polygons with areas ≤ 100 m2. Remaining areas were compared to the imagery and hand edited as needed. Step seven: Using Genie Pro, training pixels were collected for Trees/Forests, Grass/Herbaceous and all non-vegetation. These training pixels were used to develop a solution algorithm for each tile. When possible, a single solution algorithm was used for multiple tiles. The capacity to use a single algorithm on multiple tiles is limited by radiometric (brightness) variability between tiles. After masking out the completed Trees/Forests and Grass/Herbaceous classification, the remaining non-vegetation was classified into Impervious Surfaces and Soil/Barren. Throughout the process, hand editing was performed on each tile, paying special attention to Soil and Impervious classes because of their spectral and textural similarity and the high confusion rate. Step eight: After completing the classification for all tiles, the GeoTiff's were mosaicked into a single image using the Mosaic to New Raster tool in ArcMap. Taking the completed tiles, each individual land cover class was reclassified into separate rasters and then combined into a single land cover raster image. Step nine: The National Wetland Inventory (NWI) for Ohio (U.S. Fish and Wildlife Service, 2006) was used to delineate water bodies within the study area. These data were released in 2011 and updated to match imagery from 2006. While not perfect, this dataset did an excellent job of classifying large bodies of water including lakes, streams and ponds. The polygon data were converted to raster and combined with the other classes. Areas that coincided with Trees/Forest were classified as Woody Wetland (91) and areas that coincided with Grass/Herbaceous were classified as Emergent Wetland (92). UVM SAL Processing Steps: The section of the land cover classification created by the University of Vermont (UVM) Spatial Analysis Laboratory (SAL) under the direction of Jarlath O'Neil-Dunne as part of the United States Forest Service Urban Tree Canopy (UTC) assessment program, was created using object based image analysis (O'Neil-Dunne, MacFaden, and Royar, 2014). The UVM portion of the classification was created using the following methods: Seven land cover classes were mapped: (1) Tree Canopy, (2) Grass/Shrub, (3) Bare Earth, (4) Water, (5) Buildings, (6) Roads, and (7) Other Paved Surfaces. The minimum mapping unit for the delineation of features was set at 9 feet square. The primary sources used to derive this land cover layer were the 2011 National Agricultural Imagery Program (NAIP) color infrared imagery and the 2005 Center of Excellence for Geospatial Information Science (CEGIS) (U.S. Geological Survey, http://cegis.usgs.gov/) LiDAR data. Ancillary data sources included GIS data (buildings, roads, hydrography) provided by CEGIS. This land cover dataset is considered current as of summer 2011. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product was both accurate and cartographically pleasing. The dataset was subject to a thorough review in which errors were manually corrected. The classes created for the UVM portion of the study area were reclassified to match the land cover classes used in EnviroAtlas. For example, the UVM Buildings, Roads and Other Paved Surfaces classes were collapsed into the single EPA Impervious category. ***-------------------------------------------*** An accuracy assessment was conducted on the completed land cover classification that included the area classified by EPA and the area classified by UVM. Accuracy Assessment - A second Analyst performed photointerpretation of the NAIP aerial photography used in the classification. Five hundred random reference points (100 per land cover class) and 81 additional stratified random points were interpreted and labeled using a fuzzy classification approach (Gopal and Woodcock, 1994). This permitted the Analyst to assign a confidence value to the photo interpreted label at each reference point. Wetlands were copied from existing data produced elsewhere and not included in the accuracy assessment. Confidence is expressed as an integer from 1 to 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 it was 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) After the initial assessment, additional samples were generated for any class with less than 50 references samples. Using stratified random sampling, sufficient sample points were generated such that all classes had a minimum of fifty reference samples (most classes had approximately 100 reference samples). These points were interpreted and included in the final accuracy assessment. The accuracy assessment produced two confusion (error) matrices. One was a more conservative ("MAX") view and one was a more liberal ("RIGHT") view. The MAX interpretation was correct if the classified land cover matched the interpreter's highest score, illustrating that the interpreter found this class to be the most appropriate for that location. The RIGHT interpretation was correct if the classified land cover matched any class the interpreter gave a value of 3 or greater, illustrating that the interpreter found the classification to be acceptable but another class may have been more appropriate. This analysis of 500 completely random and 81 stratified random photo-interpreted land cover reference points yielded an overall user's accuracy of 86.2% (MAX) and 90.5% (RIGHT). The following confusion matrices summarize the accuracy assessment MAX and RIGHT results. 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: MAX Grass_Herb Impervious SoilBarren TreeForest Water Row Total User's Accuracy Grass_Herb 106 9 3 30 1 149 0.711409 Impervious 3 92 2 1 0 98 0.938776 SoilBarren 1 8 43 0 0 52 0.826923 TreeForest 16 2 0 180 0 198 0.909091 Water 0 0 0 0 55 55 1 Row Total 126 111 48 211 56 552 Producer's Accuracy 0.84127 0.828829 0.895833 0.853081 0.982143 Overall Accuracy 0.862319 K_Hat 0.815756 K Variance 0.000392 RIGHT Grass_Herb Impervious SoilBarren TreeForest Water Row Total User's Accuracy Grass_Herb 115 7 2 24 1 149 0.771812 Impervious 0 96 2 0 0 98 0.979592 SoilBarren 1 4 47 0 0 52 0.903846 TreeForest 11 2 0 185 0 198 0.934343 Water 0 0 0 0 55 55 1 Row Total 127 109 51 209 56 552 Producer's Accuracy 0.905512 0.880734 0.921569 0.885167 0.982143 Overall Accuracy 0.902174 K_Hat 0.869255 K Variance 0.00029 Classification errors may stem from multiple sources. Some are due simply to mixing land cover and land use in the analysis. For example, Agriculture-Grass or Agriculture-Soil confusion are mostly semantic artifacts. Agriculture land use is also Soil land cover while barren, and Grass-Herbaceous land cover while growing row crops. Grass-Soil is the most common confusion, but may be an artifact due to timing of Grass phenology and date of image acquisition. An underlying assumption is that most non-arid region soil (not barren rock) is capable of supporting some Grass-Herbaceous vegetation. Grass-Tree confusion and Soil-Impervious confusion are also relatively common. This is perhaps a less problematic error than Grass-Impervious, or Tree-Impervious. Grass and Tree are both living vegetation, versus abiotic Impervious surfaces. For more information about EnviroAtlas data, go to https://enviroatlas.epa.gov/EnviroAtlas/DataFactSheets. References: Environmental Systems Research Institute (ESRI). (2013). Data area delineation from lidar points. ArcGIS Desktop Help 10.1. Accessed July 30, 2015. http://resources.arcgis.com/en/help/main/10.1/index.html#//015w0000003q000000. 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. Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K., (2015), Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354, http://www.asprs.org/a/publications/pers/2015journals/PERS_May_2015/HTML/index.html#345/z. Ohio Statewide Imagery Program. (2006). 2 m LiDAR Dataset. http://ogrip.oit.ohio.gov/ProjectsInitiatives/StatewideImagery.aspx. Data hosted by U.S. Geological Survey Land Surface Imaging Explorer. http://lidar.cr.usgs.gov/. O'Neil-Dunne J, MacFaden S, Royar A. (2014). A Versatile, Production-Oriented Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes Using GEOBIA and Data Fusion. Remote Sens. 2014, 6, 12837-12865. http://www.mdpi.com/2072-4292/6/12/12837. U.S. Department of Agriculture. (2011 and 2013). National Agriculture Imagery Program imagery. Farm Service Agency. Aerial Photography Field Office: U.S. Department of Agriculture Web page, http://www.fsa.usda.gov/FSA/apfoapp?area=home&subject=prog&topic=nai. U.S. Fish and Wildlife Service. (2006 (Imagery date)). Ohio National Wetlands Inventory digital data. Accessed April 2016. http://wetlands.fws.gov/. U.S. Geological Survey. (2016). Center of Excellence for Geospatial Information Science. http://cegis.usgs.gov/. 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/CleOH_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 Overview section for detailed processing workflow. 2016-06-01T00:00:00 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