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Article

US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance

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MD243-05, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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Oak Ridge Institute for Science and Education, P.O. Box 117, Oak Ridge, TN 37831, USA
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Oak Ridge Associated Universities Inc., P.O. Box 117, Oak Ridge, TN 37831, USA
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 1909; https://doi.org/10.3390/rs12121909
Received: 6 May 2020 / Revised: 29 May 2020 / Accepted: 9 June 2020 / Published: 12 June 2020
This article defines the land cover classes used in Meter-Scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset 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. View Full-Text
Keywords: high spatial resolution land cover data; remote sensing; EnviroAtlas; ecosystem services; decision support; image classification; machine learning; object-based image classification; rule-based image classification; pixel-based image classification; GIS; 1 m pixel high spatial resolution land cover data; remote sensing; EnviroAtlas; ecosystem services; decision support; image classification; machine learning; object-based image classification; rule-based image classification; pixel-based image classification; GIS; 1 m pixel
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MDPI and ACS Style

Pilant, A.; Endres, K.; Rosenbaum, D.; Gundersen, G. US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance. Remote Sens. 2020, 12, 1909. https://doi.org/10.3390/rs12121909

AMA Style

Pilant A, Endres K, Rosenbaum D, Gundersen G. US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance. Remote Sensing. 2020; 12(12):1909. https://doi.org/10.3390/rs12121909

Chicago/Turabian Style

Pilant, Andrew, Keith Endres, Daniel Rosenbaum, and Gillian Gundersen. 2020. "US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance" Remote Sensing 12, no. 12: 1909. https://doi.org/10.3390/rs12121909

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