{02665f65-577e-46c1-926a-b83a9ae3fe6a} 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 - Brownsville, TX - Meter-Scale Urban Land Cover Data (2014) 2017-09-07 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 Brownsville, TX EnviroAtlas Meter-Scale Urban Land Cover (MULC) data were generated by the US EPA to support local disease vector mitigation efforts and to support ecosystem service analyses. The data were created from 1 m pixel aerial photography collected in 2014, combined with LiDAR data from 2011 and 2006. Ten land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, shrubs, grass and herbaceous non-woody vegetation, agriculture, orchards, woody wetland, and emergent wetland. An accuracy assessment of 704 completely random and 24 stratified random photo-interpreted reference points yielded an overall accuracy of 82 % (fuzzy) and 77% (non-fuzzy). The area mapped is the US Census Bureau's 2010 Urban Statistical Area for Brownsville, TX 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 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 examples of EnviroAtlas layers derived from MULC data are: Percent impervious area in 50 m stream buffer; Value of asthma exacerbation cases avoided due to sulfur dioxide removed by trees ($/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 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 Texas Brownsville, Texas 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 -97.683809 -97.131017 25.824946 26.190659 2006-01-01 2011-01-01 2014-01-01 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). The Brownsville MULC dataset was developed using 40 United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) Digital Orthophoto Quarter Quadrangles (DOQQ) ("quarter quads") collected in the 2014 leaf-on season. A DOQQ is approximately 7.7 km N-S and 6.1 km E-W, comprising approximately 47,000,000 pixels. Each quarter quad image contains four bands: three visible bands -- Red, Green, and Blue (RGB) -- and a Near Infrared (NIR) band with 1-meter pixel resolution and 8-bit pixel depth. All DOQQs remained in their original UTM 14N, NAD83 projection throughout all processing. The Brownsville, TX area was classified to land cover using NAIP imagery and LiDAR data. Two LiDAR datasets for the study area were obtained from the Texas Natural Resources Information System (www.tnris.org) and were used to derive a Height Above Ground (HAG) raster. One LiDAR dataset, which covered the majority of the study area, was collected in 2011. This dataset has an average point spacing of ≤ 0.5 m (https://tnris.org/data-catalog/entry/ibwc-2011-70cm/). The second dataset, which covered the northern region, was collected in 2006. This dataset has a nominal point spacing of 0.7 m (https://tnris.org/data-catalog/entry/ibwc-2006-70cm/) ). A supervised classification was performed using Genie Pro 2.4 feature extraction software (www.observera.com) to map the following land cover classes: Unclassified (0), Water (10), Impervious Surface (20), Soil/Barren (30), Trees/Forest (40), Grass/Herbaceous (70). The following land cover classes were added using ancillary data: Shrub (52), Agriculture (80), Orchard (82), Woody Wetland (91), and Emergent Wetland (92). Land cover classes were delineated in a multi-step procedure: Step one: Individual NAIP tiles were mosaicked into a single image. Step two: LiDAR raw data (.las files) were processed into a Digital Surface Model (DSM) and a Digital Elevation Model (DEM). The DSM consists of all 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 consists of only ground points, resulting in a raster representing the ground surface of the Earth only. A height above ground (HAG) raster was created by subtracting the DEM from the DSM. This raster represents the height of objects residing on the surface of the Earth. Step three: LiDAR raw data were used to produce a LiDAR 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, which helps to differentiate between land cover types. 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); NIR is near infrared and VIR is visible red. Step five: The NAIP, HAG, NDVI, and intensity rasters were stacked together into a single seven-band image, then cut into tiles along the NAIP Quarter Quad boundaries. Step six: Using Genie Pro, the classifier was given training pixels in the following categories: Light soil, dark soil, light impervious, dark impervious, light buildings, dark buildings, buildings with high red reflectance, light grass (lower NDVI than "dark grass"), dark grass, water, tree, and background (pixels outside of the image). These training pixels were used to develop a solution algorithm for each quarter quad. When possible, a single solution algorithm was used for multiple quarter quads. The capacity to use a single algorithm on multiple quarter quads was limited by radiometric (brightness) variability between quarter quads. Step seven: In ArcMap 10.4, the training classes were merged using the Reclassify tool to fit EnviroAtlas standards as follows: Background = NoData Water = Water (10) Light buildings, dark buildings, buildings with high reflectance, light impervious, and dark impervious = Impervious (20) Light soil, dark soil = Soil/ Barren (30) Tree = Tree (40) Light grass, dark grass = Grass/ Herbaceous (70) Priority Area Methods A "Priority Area" containing six quarter quads in the urban center of the City of Brownsville was edited first in order to fulfill separate research project needs. Steps used to edit the Priority Area are as follows: Step one: The six Priority Area quarter quads were mosaicked together in ArcMap. Step two: In the Genie Pro classification, many roofs were misclassified as Soil/ Barren or Grass/ Herbaceous. In order to correct the soil errors, a 2 m height above ground threshold was used to convert misclassified Soil/ Barren to Impervious. To correct Grass/ Herbaceous errors, the height above ground threshold was used in combination with an NDVI threshold to separate Grass/ Herbaceous from Impervious. Step three: In order to correct Tree and Grass/ Herbaceous misclassifications, a 2 m height above ground threshold was applied to separate vegetation pixels. Step four: Impervious speckle and Impervious misclassifications along water body edges were found throughout the image. In order to correct these errors, 2 m Shrink and Expand operations were applied to the impervious pixels in ArcMap. Step five: Improperly classified Water pixels were located on buildings and in shadows. Shrink and expand operations were applied in ArcMap in order to correct these misclassifications. Step six: Many impervious pixels were missed along streets and railroads in the Genie Pro classification. Rather, misclassified pixels were categorized as Grass/ Herbaceous, Soil/ Barren, or Water. We corrected these errors using NAVTEQ road and rail centerlines. The "SecHwys" centerline was buffered by 10 m on each side, the streets centerline was buffered by 6 m on each side, and the rail centerlines were buffered by 5 m on each side. Water, Soil/ Barren, and Grass/ Herbaceous pixels within those buffer zones were reclassified to Impervious. Step seven: Tree and Grass/ Herbaceous misclassifications caused by LiDAR streaking artifacts were corrected by converting Tree pixels to Grass/ Herbaceous if those pixels had a height above ground (HAG) value of less than 2 m. This HAG threshold was only applied to the affected areas rather than the whole image. Step eight: Finally, remaining errors were corrected manually in ArcMap. EnviroAtlas Area Methods: The EnviroAtlas Area is comprised of the US Census Bureau's 2010 Urban Statistical Area for Brownsville, TX plus a 1 km buffer. The area contains 40 quarter quads, including the six Priority Area quarter quads. The EnviroAtlas Area's classification from Genie Pro was edited as follows: Step one: Model Builder in ArcMap was used to correct street and railroad pixels that were not classified as Impervious in the remaining quarter quads outside the Priority Area. All classes except tree and grass were reclassified to impervious within a 6 m street buffer and a 5 m railroad buffer. Step two: All quarter quads within the EnviroAtlas study area were mosaicked together. Step three: To fix impervious/ water misclassifications, buffered NHD (National Hydrography Dataset) water shapefiles were used to reclassify impervious to water. Step four: 2014 USDA Cropland Data Layer (CDL) (https://nassgeodata.gmu.edu/CropScape/) attributes were assigned to USDA 2008 Common Land Unit (CLU)(www.fsa.usda.gov/Internet/FSA_File/clu__infosheet_2013.pdf) parcels using the Zonal Statistics tool with a Majority statistics type in ArcMap. The raster output from Zonal Statistics was converted to a polygon. The shapefile was categorically split into multiple shapefiles using the CDL class ID numbers as follows: 1-4, 44-48 = Crop 37 = Hay/ Pasture 62 = Fallow Crop 72 = Citrus 111 = Open Water 121-124 = Developed 131 = Barren 141-141 = Forest 152 = Shrubland 176 = Grass/ Pasture 190, 195 = Wetlands The resulting shapefiles were edited manually to correct for errors. Step five: In order to reduce Soil/ impervious confusion, Impervious was reclassified to Soil/ Barren within the following CLU/ CDL shapefiles: Crop, Hay/ Pasture, Fallow Crop, Citrus, Barren, Forest, Shrubland, Grass/ Pasture, and Wetlands. These changes were only applied to quarter quads outside the Priority Area. Step six: Impervious was reclassified to Water within water polygons retrieved from a City of Brownsville dataset. These changes were only applied to quarter quads outside the Priority Area to avoid introducing potential artifacts, such as accidentally reclassifying roads located within the shapefile. Step seven: A height threshold of 2 m was applied in order to better separate Grass/ Herbaceous and Tree. Step eight: The data were manually edited in order to add woody vegetation that was missed in the Genie Pro classification. Step nine: Tree pixels with a height of less than 2 m were reclassified to Shrub. A 9x9 Majority filter was applied to the new Shrub pixels in order to reduce artifacts caused by variation in LiDAR height, such as Shrub rings around Tree and Shrub speckle. Step ten: All classes except Tree were reclassified to Agriculture within the following CLU/ CDL shapefiles: Crop and Fallow Crop. Step eleven: All classes were reclassified to Orchard within the Citrus CLU/ CDL shapefile and within shapefiles hand drawn around orchards. Step twelve: The dataset was reviewed and remaining misclassifications were corrected manually. Step thirteen: Data from the U.S. Fish & Wildlife Service National Wetlands Inventory (NWI) (1990s and 2000s) were used to identify wetlands within the study area. Within the NWI shapefile, Tree and Shrub were reclassified as Woody Wetland (91) and Grass/Herbaceous was reclassified as Emergent Wetland (92). ***-------------------------------------------*** Accuracy Assessment - To reduce potential operator bias, a second analyst performed photointerpretation of the NAIP aerial photography used in the classification. Seven hundred random reference points representing approximately 100 points per land cover class were interpreted and labeled using a fuzzy classification approach (Gopal and Woodcock, 1994). This permits the analyst to assign a confidence value to the photo interpreted label at reach reference point. Confidence is expressed as an integer from 1 to 5: 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) 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. These points were interpreted and included in the final accuracy assessment. The accuracy assessment data are summarized in two confusion (error) matrices. One is more liberal ("RIGHT") view and one is more conservative ("MAX") view. The MAX interpretation is correct if the classified land cover matches the interpreter's highest maximum score, illustrating that the interpreter finds this class to be the most appropriate for that location. The RIGHT interpretation is correct if the classified land cover matches any class the interpreter has given a value of 3 or greater, illustrating that the interpreter finds the classification to be acceptable but another class may be more appropriate. 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 Agricult Grass_Herb Impervious Shrub SoilBarren TreeForest Water Row_Total User's_Accuracy Agricult 99 30 0 0 4 2 0 135 0.733333 Grass_Herb 10 196 1 7 11 7 3 235 0.834043 Impervious 0 8 51 0 4 0 0 63 0.809524 Shrub 0 12 0 31 0 7 0 50 0.62 SoilBarren 9 17 4 0 30 0 9 69 0.434783 TreeForest 0 9 1 4 1 48 0 63 0.761905 Water 0 1 0 0 2 0 81 84 0.964286 Row_Total 118 273 57 42 52 64 93 699 Producer's_Accuracy 0.838983 0.717949 0.894737 0.738095 0.576923 0.75 0.870968 Overall_Accuracy 0.76681 K_Hat 0.705888 K_Variance 0.000411 RIGHT Agricult Grass_Herb Impervious Shrub SoilBarren TreeForest Water Row_Total User's_Accuracy Agricult 99 30 0 0 4 2 0 135 0.733333 Grass_Herb 1 214 0 2 9 7 2 235 0.910638 Impervious 0 8 51 0 4 0 0 63 0.809524 Shrub 0 11 0 35 0 4 0 50 0.7 SoilBarren 0 16 4 0 44 0 5 69 0.637681 TreeForest 0 8 1 3 1 50 0 63 0.793651 Water 0 1 0 0 1 0 82 84 0.97619 Row_Total 100 288 56 40 63 63 89 699 Producer's_Accuracy 0.99 0.743056 0.910714 0.875 0.698413 0.793651 0.921348 Overall Accuracy 0.822604 K_Hat 0.77551 K_Variance 0.000337 Misclassifications between Grass/ Herbaceous, Tree, and Shrub are partly attributed to LiDAR quality and a discrepancy between LiDAR and NAIP collection date, especially in the 2006 LiDAR portion of Brownsville. The accuracy of the Shrub class was limited by our pixel-based classification method and LiDAR quality. Shrubs were classified based solely on a 0.5-2 m vegetation height threshold, which created artifacts in some instances, notably as speckle or rings around trees. Misclassifications between Water, Soil/ Barren, Impervious, and Grass/ Herbaceous may have been introduced because of Brownsville's extensive canal system. Pixels within close proximity of canals were mixed-class. Additionally, the presence of water in canals is transient, so some parts of canals were classified as Water whereas other parts were classified as Soil/ Barren, Impervious, or Grass/ Herbaceous. Some Soil/ Barren - Impervious - Grass/ Herbaceous confusion may be attributed to the existence of dirt and gravel roads throughout the study area since these road types have both Soil/ Barren and Impervious attributes. Soil/ Barren - Impervious errors are partly due to poor LiDAR quality (2006) in the northern region of the study area. Additionally, error may have been introduced in a Mexico quarter quad where LiDAR data were not available. Mexico water and infrastructure data were not available, which may have reduced classification accuracy. It should also be noted that Agriculture, Orchard, Woody Wetland, and Emergent Wetland classes were not mapped in the Mexico portion of the study area because related ancillary data were not available. Other 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 artifacts. Grass-Soil is a common type of confusion, which may be partly caused by the similar NDVI values found in soil and unhealthy vegetation. 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/BTX_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. 2017-07-01T00:00:00 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