{9AD339AD-9243-4EFB-A70B-E2471D96A23E} 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 -- Tampa, FL -- 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 EnviroAtlas Tampa, FL Meter-Scale Urban Land Cover (MULC) data was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from April-May 2010 at 1 m spatial resolution. Eight land cover classes were mapped: impervious surface, soil and barren, grass and herbaceous, trees and forest, water, agriculture, woody wetland, and emergent wetland. The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Tampa, and includes the cities of Clearwater and St. Petersburg, as well as additional out-lying areas. An accuracy assessment using a stratified random sampling of 600 samples (100 per class) yielded an overall accuracy of 70.67 percent and an area weighted accuracy of 81.87 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). 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) 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 Florida Tampa Bay, FL 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 -82.851123 -82.056207 27.651249 28.418373 2010-04-01 2010-05-01 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). The Tampa, FL Meter-Scale Urban Land Cover (MULC) dataset was developed using 145 United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) Digital Orthorectified Quarter Quads (DOQQ). Each quarter quad includes 4 bands in the visible (RGB) and Near Infrared (NIR) portions of the electromagnetic spectrum with 1 meter pixel resolution. The National Hydrography Dataset (NHD) geodatabase for the state of Florida was used as ancillary data for the classification of water and wetland classes (ftp://nhdftp.usgs.gov/DataSets/Staged/States/FileGDB/HighResolution/). Impervious, Soil, Forest, Grass: Impervious, soil, forest and grass were classified using 4 band NAIP imagery with Genie Pro image analysis software. The full Tampa extent included 145 quarter-quads with imagery collected over six different days in April and May of the year 2010. Three Genie Pro algorithms were created, each of which was trained using one quarter-quad from separate image dates: 1) northwestern Tampa and the gulf coast, 2) southwest Tampa, and 3) north and east Tampa. Each algorithm classified bright and dark impervious surfaces separately, after which they were combined into a single impervious class. Each classified quarter-quad was visually inspected by the analyst and manually re-classified if significant misclassification occurred in large, contiguous groups of pixels. Agriculture: Cultivated fields (row crops only) in Tampa, FL were identified using a feature extraction and rule-based classification method. In ENVI EX, one agriculture-dominated quarter-quad was selected in eastern Tampa to use as a training and test image. The image was segmented using the ENVI EX Feature Extraction workflow with a scale value of 40 and a merge value of 95. Spatial, spectral, and textural attributes were calculated for each of the segmented features. Due to large variation in the size of fields, two rulesets were defined: one to identify large fields and one to identify small fields. Small Fields Ruleset: rect_fit > 0.800; maindir Not [5.000, 85.000]; maindir NOT [95.000, 175.000]; area > 700.000; elongation [1.500, 8.000]; avgband_4 > 80.000; numholes < 0.7059; avgband_3 < 200.000; roundness < 0.4500. Large Fields Ruleset: area > 6000.000; rect_fit > 0.5355; solidity > 0.8212; elongation < 5.8076; convexity < 1.4057; avgband_3 [100.000, 200.000]; formfactor > 0.300; maindir Not [30.000, 60.000]; maindir NOT [120.000, 150.000]. The ruleset developed for large fields tended to have a strict size requirement (selecting only very large fields) but relatively loose restrictions in the other attributes (since most objects meeting the size requirement were either cultivated fields or large forest stands). Conversely, the ruleset for small fields had a loose size requirement (in order to identify fields that did not meet the size requirement of the large field ruleset), but relatively strict requirements in other attributes (in order to distinguish fields from other small objects such as buildings and lawns). The 145 quarter-quads within the Tampa boundary were visually inspected for presence of cultivated fields. Those that contained some presence of agriculture were selected for further classification with the previously defined rulesets. An IDL script was developed to perform the classification in batch mode. Each quarter-quad was individually segmented using ENVI EX as described above. Individual features were selected using the defined rulesets, and these features were exported to an ArcGIS shapefile. After this process was repeated for each quarter-quad in the agriculture subset, the selected features were merged so that all potential cultivated fields in Tampa were within a single layer. This layer was visually compared to the NAIP imagery in ArcMap, and features identified by the ruleset that were not actually fields (i.e. "false positives") were removed from the layer, and this final layer was converted to a raster image with two classes: agriculture and non-agriculture. The visual inspection of the final layer ensures that there are very few false positives in the agriculture layer, though may be many false negatives (i.e. fields that were not identified by the feature extraction and classification process). Water: The USGS National Hydrography Dataset (NHD) was used to classify all water within Tampa. A vector layer of NHD-defined water polygons was overlaid on the Tampa NAIP imagery in ArcMap, and was visually inspected by the analyst. The analyst found that the NHD-defined water polygons were generally accurate both positionally and thematically, though there were some errors of both commission and omission. The NHD was also compared to the National Wetland Inventory (NWI), but a simple qualitative assessment indicated substantially better accuracy of the NHD than NWI in the Tampa region. A simple conditional statement was defined: anything identified as water in NHD (ocean, lakes, rivers, etc.) was classified as water in the Tampa land cover classification. Wetlands: Two wetland classes were delineated using a combination of the NHD and Genie Pro. NHD polygons defined as "swamp/marsh" within the Tampa boundary were selected and buffered by 10 meters. The buffered swamp/marsh polygons were then used to extract image subsets from each of the 4 band NAIP quarter-quads. A representative swamp/marsh NAIP subset was opened in Genie Pro, and the analyst defined three training classes: unclassified (in locations with "no data"), emergent wetland (any swamp/marsh pixels with no canopy cover), and woody wetland (any swamp/marsh pixels with canopy cover). The trained algorithm was then use to classify all quarter-quads that contained swamp/marsh pixels. Several of these quarter-quads (from different regions of Tampa and with different image dates) were visually inspected by the analyst to ensure that the trained algorithm was capable of producing accurate wetland classifications throughout Tampa. Since the swamp/marsh-segmented NAIP data was created with a 10 meter buffer, the original polygons were used to clip the final wetland classification to match the NHD-defined wetland areas. Post-classification: The four layers described above were combined into one classified image using a conditional statement in ArcGIS 10 Raster Calculator, where the latter three layers (water, wetland, and agriculture) took priority over the base classification produced by GENIE. Thus, any pixels in the initial GENIE classification that coincided with pixels from one of the other three layers were "overwritten" in the final classification. The wetland and water layers (both created from NHD) did not overlap each other, but occasionally overlapped the agriculture layer. In these cases, the agriculture layer was used in the final classification. *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 Tampa, FL, 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 Tampa, FL 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. Accuracy Assessment: A stratified random sample of approximately 100 points per land cover class was generated to assess the accuracy of the classified land cover map. To reduce the number of sample points on edges of landscape features (ambiguous or impossible to interpret), only points contained in homogeneous patches at least 3x3 pixels (3x3 meters) were used in the sample. A shapefile of sampling points was provided to two analysts who had not been involved with generating the land cover map. They 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. The following error matrix summarizes the accuracy assessment data. 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: Water Impervious Soil Forest Grass Ag Row Total User's Accuracy Water 95 2 0 1 2 0 100 95.00% Imp 2 87 8 0 3 0 100 87.00% Soil 25 8 31 3 31 2 100 31.00% Forest 2 1 0 72 22 0 100 72.00% Grass 2 1 3 4 90 0 100 90.00% Ag 0 0 16 5 30 49 100 49.00% Producer's Accuracy 75.40% 87.88% 53.45% 84.71% 50.56% 96.08% Overall Accuracy 70.67% The Soil/Barren and Agriculture classes had the lowest User's Accuracies (31% and 49%, respectively). The error comes from multiple sources, e.g: Soil "mis" classified as Grass (commonly Soil and Grass exist in a continuum and subject to interpretation); fallow Agricultural fields being "mis"classified as soil (and vice versa). Soil and Ag are also the rarest classes in the Tampa mapped region (1.9% and 0.75%, respectively). Thus, if each land cover class accuracy in the error matrix is expressed in proportion to its areal extent, the overall user's accuracy increases to 82%. 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/TFL_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. 2013-11-14T00:00:00 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