US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC) Data Characteristics

Meter-scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset, has been developed for 30 US communities for the United States Environmental Protection Agency (US EPA) EnviroAtlas. MULC data categorize the landscape into these land cover classes: impervious surface, tree, grass-herbaceous, shrub, soil-barren, water, wetland and agriculture. MULC data are used to calculate approximately 100 EnviroAtlas metrics that serve as indicators of nature’s benefits (ecosystem goods and services). MULC, a dataset for which development is ongoing, is produced by multiple classification methods using aerial photo and LiDAR datasets. The mean overall fuzzy accuracy across the EnviroAtlas communities is 88% and mean Kappa coefficient is 0.84. MULC is available in EnviroAtlas via web browser, web map service (WMS) in the user’s geographic information system (GIS), and as downloadable data at EPA Environmental Data Gateway. Fact Sheets and metadata for each MULC Community are available through EnviroAtlas. Some MULC applications include mapping green and grey infrastructure, connecting land cover with socioeconomic/demographic variables, street tree planting, urban heat island analysis, mosquito habitat risk mapping and bikeway planning. This article provides practical guidance for using MULC effectively and developing similar high resolution (HR) land cover data. This dataset is associated with the following publication: Pilant, D., K. Endres, D. Rosenbaum, and G. Gundersen. US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance. Remote Sensing. MDPI AG, Basel, SWITZERLAND, 12(12): 1909, (2020).

                                            [
  "020:00"
]
                                        

                                            {
  "fn": "Andrew Pilant",
  "hasEmail": "mailto:pilant.drew@epa.gov"
}
                                        

                                            [
  {
    "title": "pilant_mdpi_rs_dataTables_final.xlsx",
    "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
    "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1518915/pilant_mdpi_rs_dataTables_final.xlsx"
  }
]
                                        

                                            [
  "1 meter pixel",
  "EnviroAtlas",
  "GIS",
  "decision support",
  "ecosystem services",
  "high spatial resolution land cover data",
  "image classification",
  "machine learning",
  "object-based image classification",
  "pixel-based image classification",
  "remote sensing"
]
                                        

                                            [
  "020:097"
]
                                        

                                            {
  "name": "U.S. EPA Office of Research and Development (ORD)",
  "subOrganizationOf": {
    "name": "U.S. Environmental Protection Agency",
    "subOrganizationOf": {
      "name": "U.S. Government"
    }
  }
}
                                        

                                            [
  "https://doi.org/10.3390/rs12121909"
]
                                        

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