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Identifying Surface Mine Extent Across Central Appalachia Using Time Series Analysis, 1984-2015

Identifying Surface Mine Extent Across Central Appalachia Using Time Series Analysis, 1984-2015

Michael Lee Marston, Korine N. Kolivras
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 15
ISSN: 1947-9654|EISSN: 1947-9662|EISBN13: 9781799861089|DOI: 10.4018/IJAGR.2021010103
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MLA

Marston, Michael Lee, and Korine N. Kolivras. "Identifying Surface Mine Extent Across Central Appalachia Using Time Series Analysis, 1984-2015." IJAGR vol.12, no.1 2021: pp.1-15. http://doi.org/10.4018/IJAGR.2021010103

APA

Marston, M. L. & Kolivras, K. N. (2021). Identifying Surface Mine Extent Across Central Appalachia Using Time Series Analysis, 1984-2015. International Journal of Applied Geospatial Research (IJAGR), 12(1), 1-15. http://doi.org/10.4018/IJAGR.2021010103

Chicago

Marston, Michael Lee, and Korine N. Kolivras. "Identifying Surface Mine Extent Across Central Appalachia Using Time Series Analysis, 1984-2015," International Journal of Applied Geospatial Research (IJAGR) 12, no.1: 1-15. http://doi.org/10.4018/IJAGR.2021010103

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Abstract

The Appalachians, and Central Appalachia in particular, have a long history of resource extraction including coal mining. In the past half century, the region experienced a shift from underground to surface mining, which leaves highly visible changes on the landscape. This study presents an analysis of changes in surface mining extents between 1984 and 2015 using remote sensing techniques, and tests the methods of previous research over a broader study area. The authors found that 3070 km2 (7.1%) of land within the central Appalachian coalfield was classified as mined land through the study period, and that the rate of newly mined land, as well as total mined land has decreased in recent years. The overall classification accuracy was 0.888 and the kappa coefficient was 0.880. Study results indicate that previously developed methods for identifying surface mines in a sub-region of Central Appalachia can successfully be applied over the broader region. The resulting surface mining datasets will be applied to a future study examining the potential human health impacts of surface mining.