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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 -- Birmingham, Alabama -- Meter-Scale Urban Land Cover (MULC) Data (2011)
2017-09-12
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 Birmingham, AL EnviroAtlas Meter-scale Urban Land Cover (MULC) dataset comprises 2,778 km2 around the city of Birmingham and portions of surrounding counties. The area classified is based on the US Census Bureau's 2010 Urban Area for Birmingham, AL with a 1 km buffer added. This area includes the majority of Jefferson County and a large percentage of Shelby County, as well as very small portions of Bibb, Blount, Chilton, and St. Clair counties. These MULC data and maps were derived from 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) that were collected in summer 2011. LiDAR datasets collected in 2010, 2011 and 2013 were also used to create this MULC. Six land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Tree/Forest, Grass/Herbaceous Non-Woody Vegetation, and Wetlands (Woody and Emergent). Wetlands were obtained from the best available existing wetlands data, which was a National Wetlands Inventory (NWI) layer. An analysis of 500 completely random and 95 stratified random photo-interpreted land cover reference points yielded an overall user's accuracy of 83.4% (MAX) and an overall fuzzy user's accuracy of 87.6% (RIGHT) (see confusion matrices below). 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 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
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
Alabama
Birmingham, AL
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
-87.083852
-86.457254
33.039730
33.818059
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. Processing Steps: The Birmingham, AL MULC dataset was developed using 101 United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) (U.S. Department of Agriculture, 2011) Digital Orthophoto Quarter Quadrangles (DOQQ) ("quarter quads") collected in summer 2011. 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 16N, NAD83 projection for classification. The Birmingham classification also used LiDAR data. Three LiDAR datasets were employed in order to obtain full coverage for the study area. The datasets were: 2013 USGS Lidar Point Cloud AL Jefferson County (collected in spring and summer 2013), 2011 USGS Lidar Point Cloud Tri-County (St. Clair County, Calhoun County, and Talladega County) (collected in winter 2010/2011) (https://nationalmap.gov/), and 2010 Shelby County LiDAR (collected in fall 2010). The LiDAR data were used to create a Height Above Ground (HAG) raster. 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), where NIR is the Near Infrared band and VIR is the visible red band (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 less than or equal to 100 m2. Remaining areas were compared to the imagery and hand edited as needed. These areas were also combined with a National Hydrography Dataset (NHD) vector layer to capture water bodies missed using the above method. 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 GeoTiffs 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: Quality assurance methods improved visual accuracy. Systematically panning through the study area at 1:5000 m scale, areas erroneously classified were hand digitized and reclassified in ArcGIS. Additionally, conditional statements were used to apply a focal majority to rectangular regions of interest of varying sizes across the study area, which removed speckle and polished the finished product. Step ten: The National Wetland Inventory (NWI) for Alabama (U.S. Fish and Wildlife Service) was used to delineate water bodies within the study area. These data were updated on an ongoing basis since the early 1980s. 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). ***-------------------------------------------*** An accuracy assessment was conducted on the completed land cover classification. 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 95 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 reference samples. Using stratified random sampling, which was based on MULC data, 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 yielded an overall user's accuracy of 83.3% (MAX) and 87.6% (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 83 10 4 15 0 112 0.741071 Impervious 3 48 1 2 0 54 0.888889 SoilBarren 8 16 25 1 0 50 0.5 TreeForest 22 8 0 297 3 330 0.9 Water 1 0 4 1 43 49 0.877551 Row Total 117 82 34 316 46 595 nan Producer's_Accuracy 0.709402 0.585366 0.735294 0.939873 0.934783 nan nan Overall_Accuracy 0.833613 nan nan nan nan nan nan K_Hat 0.741938 nan nan nan nan nan nan K Variance 0.000522 nan nan nan nan nan nan RIGHT Grass_Herb Impervious SoilBarren TreeForest Water Row_Total User's_Accuracy Grass_Herb 93 5 2 12 0 112 0.830357 Impervious 2 49 1 2 0 54 0.907407 SoilBarren 6 14 29 1 0 50 0.58 TreeForest 17 5 0 307 1 330 0.930303 Water 1 0 4 1 43 49 0.877551 Row Total 119 73 36 323 44 595 nan Producer's_Accuracy 0.781513 0.671233 0.805556 0.950464 0.977273 nan nan Overall_Accuracy 0.87563 nan nan nan nan nan nan K_Hat 0.805357 nan nan nan nan nan nan K Variance 0.000425 nan nan nan nan nan nan 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. Soil is usually the most difficult class to classify, and is commonly confused with bright Impervious surfaces and Grass. Grass-Soil confusion 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 perhaps a less problematic error than Grass-Impervious, or Tree-Impervious. Grass and Tree are both living vegetation, versus abiotic Impervious surfaces. In Birmingham, we encountered relatively high rates of confusion and errors of commission in the Impervious class. This was due in part to deep shadows and bright saturated areas in the NAIP data, and to Tree overhanging Impervious roads. For more information about EnviroAtlas data, go to https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets. References: Environmental Systems Research Institute (ESRI). (2013). Data area delineation from lidar points. ArcGIS Desktop Help 10.1. Accessed July 30, 2015. https://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, https://www.asprs.org/a/publications/pers/2015journals/PERS_May_2015/HTML/index.html#345/z. Shelby County LiDAR. (2010). https://maps.shelbyal.com/html5/. U.S. Department of Agriculture. (2011). National Agriculture Imagery Program imagery. Farm Service Agency. Aerial Photography Field Office: U.S. Department of Agriculture Web page, https://www.fsa.usda.gov/FSA/apfoapp?area=home&subject=prog&topic=nai. U.S. Fish and Wildlife Service. Alabama National Wetlands Inventory digital data. Accessed spring 2017. https://wetlands.fws.gov/. U.S. Geological Survey. (2007-2014). National Hydrography Dataset available on the World Wide Web. https://nhd.usgs.gov. Accessed spring 2015. U.S. Geological Survey. (2013). USGS Lidar Point Cloud AL Jefferson County. https://nationalmap.gov. U.S. Geological Survey. (2011). USGS Lidar Point Cloud Tri-County (St. Clair County, Calhoun County, and Talladega County). https://nationalmap.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/BirAL_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
2017-09-01T00:00:00
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