Reconstructing disturbance history for an intensively mined region by time-series analysis of Landsat imagery
- 444 Downloads
Surface mining disturbances have attracted attention globally due to extensive influence on topography, land use, ecosystems, and human populations in mineral-rich regions. We analyzed a time series of Landsat satellite imagery to produce a 28-year disturbance history for surface coal mining in a segment of eastern USA’s central Appalachian coalfield, southwestern Virginia. The method was developed and applied as a three-step sequence: vegetation index selection, persistent vegetation identification, and mined-land delineation by year of disturbance. The overall classification accuracy and kappa coefficient were 0.9350 and 0.9252, respectively. Most surface coal mines were identified correctly by location and by time of initial disturbance. More than 8 % of southwestern Virginia’s >4000-km2 coalfield area was disturbed by surface coal mining over the 28-year period. Approximately 19.5 % of the Appalachian coalfield surface within the most intensively mined county (Wise County) has been disturbed by mining. Mining disturbances expanded steadily and progressively over the study period. Information generated can be applied to gain further insight concerning mining influences on ecosystems and other essential environmental features.
KeywordsRemote sensing Appalachian coalfield Mining Change detection Trajectory analysis
We are grateful for support provided by China Scholarship Council. We appreciate Dr. Jie Ren’s help on post-classification process and Dr. Yang Shao’s recommendation on this paper. We also thank the US Geological Survey, USDA Farm Service Agency, and Virginia Department of Mines Minerals and Energy (DMME) for open access to the data. We offer sincere thanks to Daniel Kestner, Virginia DMME, for his advice and assistance to our study efforts.
Conflict of interest
The authors declare that they have no conflict of interest.
- Akram, J., & Imran, K. (2012). Land use/land cover change due to mining activities in Singrauli industrial belt, Madhya Pradesh using remote sensing and GIS. Journal of Environmental Research and Development, 6, 834–843.Google Scholar
- Areendran, G., Rao, P., Raj, K., Mazumdar, S., & Kanchan, P. (2013). Land use/land cover change dynamics analysis in mining areas of Singrauli district in Madhya Pradesh, India. Tropical Ecology, 54, 239–250.Google Scholar
- Bi, R., & Bai, Z. (2007). Land characteristic information and classification in opencast coal mine based on remote sensing images. Transactions of the Chinese Society of Agricultural Engineering, 23, 77–82 (in Chinese with English abstract).Google Scholar
- Brenner, F. J., Werner, M., & Pike, J. (1984). Ecosystem development and natural succession in surface coal mine reclamation. Environmental Geochemistry and Health, 6, 10–22.Google Scholar
- Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing (5th ed.). New York: The Guilford Press.Google Scholar
- Canters, F. (1997). Evaluating the uncertainty of area estimates derived from fuzzy land-cover classification. Photogrammetric Engineering & Remote Sensing, 63, 403–414.Google Scholar
- Card, D. H. (1982). Using known map category marginal frequencies to improve estimates of thematic map accuracy. Photogrammetric Engineering & Remote Sensing, 48, 431–439.Google Scholar
- Copeland, C. (2015). Mountaintop mining: Background on current controversies. U.S. Congressional Research Service Report RS21421.Google Scholar
- D’Appolonia Inc. (1980). Abandoned mine land inventory. Report for Virginia Department of Mined Land Reclamation. Project No. 78-411. March 1980.Google Scholar
- Fenneman, N. M. (1938). Physiography of Eastern United States. New York City: McGraw Hill.Google Scholar
- Hardisky, M. A., Klemas, V., & Smart, R. M. (1983). The influence of soil-salinity, growth form, and leaf moisture on the spectral radiance of spartina-alterniflora canopies. Photogrammetric Engineering & Remote Sensing, 49, 77–83.Google Scholar
- Hibbard, W. R. (1990). Virginia coal an abridged history. Blacksburg: Virginia Center for Coal and Energy Research, Virginia Tech.Google Scholar
- Kauth, R. J., & Thomas, G. S. (1976). The tasselled cap -- A graphic description of the spectral-temporal development of agricultural crops as seen by LANDSAT. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, 4B41-4B51.Google Scholar
- Kennedy, R. E., Serge, A., Cohen, W. B., Gómez, C., Griffiths, P., Hais, M., Healey, S. P., Helmer, E. H., Hostert, P., Lyons, M. B., Meigs, G. W., Pflugmacher, D., Phinn, S. R., Powell, S. L., Scarth, P., Sen, S., Schroeder, T. A., Schneider, A., Sonnenschein, R., Vogelmann, J. E., Wulder, M. A., & Zhu, Z. (2014). Bringing an ecological view of change to Landsat-based remote sensing. Frontiers of Ecology and Environment, 12, 339–346.CrossRefGoogle Scholar
- Key, C. H., & Benson, N. C. (1999). Measuring and remote sensing of burn severity: The CBI and NBR. Poster abstract. In L. F. Neuenschwander & K. C. Ryan (Eds.), Proceedings joint fire science conference and workshop (Vol. II, p. 284). Boise: University of Idaho and International Association of Wildland Fire.Google Scholar
- Milici, R. C. (2005). Appalachian coal assessment: Defining the coal systems of the Appalachian basin. In: P. D. Warwick (Ed.), Coal systems analysis: Geological Society of America Special Papers, 387, 9–30.Google Scholar
- Ricketts, T. H., Dinerstein, E., Olson, D. M., Loucks, C. J., Eichbaum, W., DellaSalla, D., Kavanagh, K., Hedao, P., Hurley, P., Carney, K., Abell, R., & Walters, S. (1999). Terrestrial ecoregions of North America: A conservation assessment. Washington, D.C: Island Press.Google Scholar
- Riitters, K., Wickham, J., O’Neill, R., Jones, B., & Smith, E. (2000). Globalscale patterns of forest fragmentation. Conservation Ecology, 4, 3. http://www.consecol.org/vol4/iss2/art3/.
- Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351, 309–317.Google Scholar
- Seaber, P. R., Brahana, J. V., & Hollyday, E. F. (1988). Appalachian plateaus and valley and ridge. In: W. Back, P. R. Seaber, J. S. Rosenshein (Eds.), Hydrogeology. Vol. 0-2, 189-200. The Geology of North America. Geological Society of America.Google Scholar
- U.S. Department of Agriculture (USDA). (2014). Imagery programs, NAIP imagery. http://www.fsa.usda.gov/FSA/apfoapp?area=home&subject=prog&topic=nai. Accessed 19 June 2015.
- U.S. Energy Information Administration (US EIA). (2015a). Annual coal report 2013. US Department of Energy (and data from this annual publication in prior years).Google Scholar
- U.S. Energy Information Administration (US EIA). (2015b). Annual energy outlook 2014 with projections to 2040. US Department of Energy, DOE/EIA-0383.Google Scholar
- U.S. Energy Information Administration (US EIA). (2015c). Quarterly coal report (abbreviated), October–December 2014. US Department of Energy.Google Scholar
- U.S. Environmental Protection Agency (US EPA). (2012). Mercury and air toxics standards. http://www.epa.gov/mats. Accessed 19 June 2015.
- US Environmental Protection Agency (US EPA). (2015). Surface coal mining activities under clean water act section 404. http://water.epa.gov/lawsregs/guidance/wetlands/mining.cfm. Accessed 28 Jan 2015.
- Virginia Department of Mines, Minerals and Energy (Virginia DMME). (2014). Mapping and resource center. http://www.dmme.virginia.gov/DMLR/MappingLandingPage.shtml.
- Wynne, R. H., Oderwald, R. G., Reams, G. A., & Scrivani, J. A. (2000). Optical remote sensing for forest area estimation. Journal of Forestry, 98, 31–36.Google Scholar