{66bb3d88-be8d-428e-b137-0a427d0ad41c} 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 - New Haven, CT - Meter-Scale Urban Land Cover Data (2014) 2017-03-21 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 New Haven, CT EnviroAtlas Meter-scale Urban Land Cover (MULC) Data were generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial scale acquired on September 25, 2014. Seven land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, and grass-herbaceous non-woody vegetation,as well as woody wetlands and emergent herbaceous wetlands. An accuracy assessment of 500 completely random and 50 stratified random photo-interpreted reference points yielded an overall MAX accuracy of 89 percent and an overall RIGHT accuracy of 92 percent. The area mapped is the US Census Bureau's 2010 Urban Statistical Area for New Haven, CT 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 scale 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 scale (30x30 m pixel dimensions from the Landsat sensor), the EnviroAtlas Community component is based on MULC data at 900 times the spatial scale of the NLCD. 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 50m 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 Connecticut New Haven, CT 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 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). For more information about EnviroAtlas data, go to https://enviroatlas.epa.gov/EnviroAtlas/DataFactSheets. The New Haven MULC dataset was developed using 63 United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) Digital Orthophoto Quarter Quadrangle (DOQQ) ("quarter quads") collected in September of 2014. 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, 8-bit pixel depth. All DOQQs remained in their original UTM 18N, NAD83 projection for classification. The New Haven, CT area was classified to land cover using NAIP imagery and LiDAR data. Two LiDAR datasets for the study area were obtained from the National Oceanic and Atmospheric Administration Data Access Viewer and used to derive a Height Above Ground (HAG) raster. One LiDAR dataset, which covered the majority of the study area, was collected from December, 2010 to May, 2011. This dataset has an average point spacing of 0.5 m. The second dataset, which covered the majority of the coastal region, was collected in December, 2006. This dataset has a nominal point spacing of 1.25 m. A supervised classification was performed using Genie Pro 2.4 feature extraction software (www.observera.com) to map the following land cover classes: 0 - Unclassified, 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 maximum likelihood classifier. Synthetic bands such as texture, band ratios, and edge enhancements are created and band stacked with the NAIP and LiDAR to increase the dimensionality of the data for better classification results. Land cover classes were delineated in a multi-step procedure: Step one: Individual NAIP tiles were mosaicked into a single image. Step two: LAS LiDAR raw data were processed into a Digital Surface Model (DSM) and a Digital Terrain Model (DTM). 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 DTM 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 DTM from the DSM. This raster represents the height of objects residing on the surface of the Earth. Step three: LAS 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), where NIR is near-infrared and VIR is visible red (dimensionless number ranging from -1 to 1). 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: Water bodies were identified using data from the USGS National Hydrography Dataset (NHD). Polygons from the NHD were hand edited to match the NAIP imagery. 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: Data from the U.S. Fish & Wildlife Service National Wetlands Inventory (2010) were used to identify wetlands within the study area. The polygon data were converted to raster to be 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). Step ten: Each individual cover class raster was combined into a single raster image using the Mosaic to New Raster tool (ArcGIS), whereby the order of individual layers is as follows (top to bottom): WATER, TREE and GRASS, SOIL and IMPERVIOUS. Step eleven: The dataset was reviewed and errors were manually corrected. ***-------------------------------------------*** Accuracy Assessment - A second Analyst performed photointerpretation of the NAIP aerial photography used in the classification. Five 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 produces 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 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 Grass Impervious SoilBarren Tree Water Row Total User's Accuracy Grass_Herb 60 4 5 9 2 80 0.75 Impervious 0 49 0 1 0 50 0.98 SoilBarren 5 10 28 2 3 48 0.58 TreeForest 14 1 1 257 0 273 0.94 Water 0 1 1 1 76 79 0.96 Row Total 79 65 35 270 81 530 Producer's Accuracy 0.76 0.75 0.80 0.95 0.94 Overall Accuracy 0.89 K_Hat 0.83 K Variance 3.97E-04 RIGHT Grass Impervious SoilBarren Tree Water Row Total User's Accuracy Grass_Herb 67 2 3 7 1 80 0.84 Impervious 0 49 0 1 0 50 0.98 SoilBarren 5 7 33 1 2 48 0.69 TreeForest 8 0 1 264 0 273 0.97 Water 0 1 1 0 77 79 0.97 Row Total 80 59 38 273 80 530 Producer's Accuracy 0.84 0.83 0.87 0.97 0.96 Overall Accuracy 0.92 K_Hat 0.89 K Variance 2.82E-04 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. 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 is 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. 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/NHCT_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. 2016-07-06T00:00:00 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