Reconstructing disturbance history for an intensively mined region by time-series analysis of Landsat imagery

  • Jing Li
  • Carl E. Zipper
  • Patricia F. Donovan
  • Randolph H. Wynne
  • Adam J. Oliphant


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.


Remote 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.


  1. 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
  2. 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
  3. Bell, F. G., Bullock, S. E. T., Halbich, T. F. J., & Lindsey, P. (2001). Environmental impacts associated with an abandoned mine in the Witbank Coalfield, South Africa. International Journal of Coal Geology, 45, 195–216.CrossRefGoogle Scholar
  4. 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
  5. 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
  6. Bridge, G. (2004). Contested terrain: mining and the environment. Annual Review of Environmental Resources, 29, 205–259.CrossRefGoogle Scholar
  7. Brom, J., Nedbal, V., Prochazka, J., & Pecharova, E. (2012). Changes in vegetation cover, moisture properties and surface temperature of a brown coal dump from 1984 to 2009 using satellite data analysis. Ecological Engineering, 43, 45–52.CrossRefGoogle Scholar
  8. Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing (5th ed.). New York: The Guilford Press.Google Scholar
  9. Canters, F. (1997). Evaluating the uncertainty of area estimates derived from fuzzy land-cover classification. Photogrammetric Engineering & Remote Sensing, 63, 403–414.Google Scholar
  10. 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
  11. Chatterjee, R. S., Roy, J., & Bhattacharya, A. K. (1996). Mapping geological features of the Jharia coalfield from Landsat-5 TM data. International Journal of Remote Sensing, 17, 3257–3270.CrossRefGoogle Scholar
  12. Copeland, C. (2015). Mountaintop mining: Background on current controversies. U.S. Congressional Research Service Report RS21421.Google Scholar
  13. D’Appolonia Inc. (1980). Abandoned mine land inventory. Report for Virginia Department of Mined Land Reclamation. Project No. 78-411. March 1980.Google Scholar
  14. Defries, R. S., & Townshend, J. R. G. (1994). NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing, 15, 3567–3586.CrossRefGoogle Scholar
  15. Drummond, M., & Loveland, T. (2010). Land-use pressure and a transition to forest-cover loss in the eastern United States. Bioscience, 60, 286–298.CrossRefGoogle Scholar
  16. Fenneman, N. M. (1938). Physiography of Eastern United States. New York City: McGraw Hill.Google Scholar
  17. 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
  18. Healey, S. P., Cohen, W. B., Zhiqiang, Y., & Krankina, O. N. (2005). Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment, 97, 301–310.CrossRefGoogle Scholar
  19. Hibbard, W. R. (1990). Virginia coal an abridged history. Blacksburg: Virginia Center for Coal and Energy Research, Virginia Tech.Google Scholar
  20. Huang, C., Goward, S. N., Schleeweis, K., Thomas, N., Masek, J. G., & Zhu, Z. (2009). Dynamics of national forests assessed using the Landsat record: case studies in eastern U.S. Remote Sensing of Environment, 113, 1430–1442.CrossRefGoogle Scholar
  21. Huang, C., Goward, S. N., Masek, J. G., Thomas, N., Zhu, Z., & Vogelmann, J. E. (2010). An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment, 114, 183–198.CrossRefGoogle Scholar
  22. Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., & Xian, G. (2013). A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132, 159–175.CrossRefGoogle Scholar
  23. 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
  24. Kemp, D., Bond, C. J., Franks, D. M., & Cote, C. (2010). Mining, water and human rights: making the connection. Journal of Cleaner Production, 18, 1553–1562.CrossRefGoogle Scholar
  25. Kennedy, R. E., Cohen, W. B., & Schroeder, T. A. (2007). Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110, 370–386.CrossRefGoogle Scholar
  26. 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
  27. 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
  28. Latifovic, R., Fytas, K., Chen, J., & Paraszczak, J. (2005). Assessing land cover change resulting from large surface mining development. International Journal of Applied Earth Observation and Geoinformation, 7, 29–48.CrossRefGoogle Scholar
  29. Lawrence, R. L., & Ripple, W. J. (1999). Calculating change curves for multitemporal satellite imagery: Mount St. Helens 1980-1995. Remote Sensing of Environment, 67, 309–319.CrossRefGoogle Scholar
  30. Li, J., Donovan, P. F., Zipper, C. E., Wynne, R. H., & Oliphant, A. O. (2015). Mining disturbances in Virginia’s southwestern coalfield, 1984-2011. Blacksburg: Virginia Tech. doi: 10.7294/W47P8W97.Google Scholar
  31. Malaviya, S., Munsi, M., Oinam, G., & Joshi, P. (2010). Landscape approach for quantifying land use land cover change (1972-2006) and habitat diversity in a mining area in Central India (Bokaro, Jharkhand). Environmental Monitoring and Assessment, 170, 215–229.CrossRefGoogle Scholar
  32. Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., Feng, G., Kutler, J., & Teng-Kui, L. (2006). A Landsat surface reflectance dataset for North America, 1990-2000. Geoscience and Remote Sensing Letters, 3, 68–72.CrossRefGoogle Scholar
  33. 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
  34. Milici, R. C., Flores, R. M., & Stricker, G. D. (2013). Coal resources, reserves and peak coal production in the United States. International Journal of Coal Geology, 113, 109–115.CrossRefGoogle Scholar
  35. Musy, F. R., Wynne, H. R., Blinn, E. C., Scrivani, A. J., & McRoberts, E. R. (2006). Automated forest area estimation using iterative guided spectral class rejection. Photogrammetric Engineering & Remote Sensing, 72, 949–960.CrossRefGoogle Scholar
  36. Omernik, J. M. (1987). Ecoregions of the conterminous United States. Annals of the Association of American Geographers, 77, 118–125.CrossRefGoogle Scholar
  37. Pickell, P. D., Hermosilla, T., Coops, N., Masek, J. G., Franks, S., & Huang, C. (2014). Monitoring anthropogenic disturbance trends in an industrialized boreal forest with Landsat time series. Remote Sensing Letters, 5, 783–792.CrossRefGoogle Scholar
  38. Prakash, A., & Gupta, R. P. (1998). Land-use mapping and change detection in a coal mining area—a case study in the Jharia coalfield, India. International Journal of Remote Sensing, 19, 391–410.CrossRefGoogle Scholar
  39. 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
  40. Riitters, K., Wickham, J., O’Neill, R., Jones, B., & Smith, E. (2000). Globalscale patterns of forest fragmentation. Conservation Ecology, 4, 3.
  41. 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
  42. Schmidt, H., & Glaesser, C. (1998). Multitemporal analysis of satellite data and their use in the monitoring of the environmental impacts of open cast lignite mining areas in Eastern Germany. International Journal of Remote Sensing, 19, 2245–2260.CrossRefGoogle Scholar
  43. Schroeder, T. A., Cohen, W. B., & Yang, Z. Q. (2007). Patterns of forest regrowth following clearcutting in western Oregon as determined from a Landsat time-series. Forest Ecology and Management, 243, 259–273.CrossRefGoogle Scholar
  44. 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
  45. Sen, S., Zipper, C. E., Wynne, R. H., & Donovan, P. (2012). Identifying revegetated mines as disturbance/recovery trajectories using an interannual Landsat chronosequence. Photogrammetric Engineering & Remote Sensing, 78, 223–235.CrossRefGoogle Scholar
  46. Simmons, J. A., Currie, W. S., Eshleman, K. N., Kuers, L., Monteleone, S., Negley, T. L., Pohlad, B. R., & Thomas, C. L. (2008). Forest to reclaimed land use change leads to altered ecosystem structure and function. Ecological Applications, 18, 104–118.CrossRefGoogle Scholar
  47. Song, C., Schroeder, T. A., & Cohen, W. B. (2007). Predicting temperate conifer forest successional stage distributions with multitemporal Landsat Thematic Mapper imagery. Remote Sensing of Environment, 106, 228–237.CrossRefGoogle Scholar
  48. Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., Vermote, E. F., & El Saleous, N. (2005). An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26, 4485–4498.CrossRefGoogle Scholar
  49. U.S. Department of Agriculture (USDA). (2014). Imagery programs, NAIP imagery. Accessed 19 June 2015.
  50. 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
  51. 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
  52. U.S. Energy Information Administration (US EIA). (2015c). Quarterly coal report (abbreviated), October–December 2014. US Department of Energy.Google Scholar
  53. U.S. Environmental Protection Agency (US EPA). (2012). Mercury and air toxics standards. Accessed 19 June 2015.
  54. US Environmental Protection Agency (US EPA). (2015). Surface coal mining activities under clean water act section 404. Accessed 28 Jan 2015.
  55. Virginia Department of Mines, Minerals and Energy (Virginia DMME). (2014). Mapping and resource center.
  56. Wickham, J. D., Riitters, K. H., Wade, T. G., Coan, M., & Homer, C. (2007). The effect of Appalachian mountaintop mining on interior forest. Landscape Ecology, 22, 179–187.CrossRefGoogle Scholar
  57. Wickham, J., Wood, P. B., Nicholson, M. C., Jenkins, W., Druckenbrod, D., Suter, G. W., Strager, M. P., Mazzarella, C., Galloway, W., & Amos, J. (2013). The overlooked terrestrial impacts of mountaintop mining. BioScience, 63, 335–348.CrossRefGoogle Scholar
  58. 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
  59. Xiao, H., & Wei, J. (2007). Relating landscape characteristics to nonpoint source pollution in mine waste-located watersheds using geospatial techniques. Journal of Environmental Management, 82, 111–119.CrossRefGoogle Scholar
  60. Zipper, C. E., Burger, J. A., McGrath, J. M., Rodrigue, J. A., & Holtzman, G. I. (2011a). Forest restoration potentials of coal mined lands in the eastern United States. Journal of Environmental Quality, 40, 1567–1577.CrossRefGoogle Scholar
  61. Zipper, C. E., Burger, J. A., Skousen, J. G., Angel, P. N., Barton, C. D., Davis, V., & Franklin, J. A. (2011b). Restoring forests and associated ecosystem services on Appalachian coal surface mines. Environmental Management, 47, 751–765.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jing Li
    • 1
  • Carl E. Zipper
    • 2
  • Patricia F. Donovan
    • 2
  • Randolph H. Wynne
    • 3
  • Adam J. Oliphant
    • 3
  1. 1.China University of Mining and TechnologyBeijingPeople’s Republic of China
  2. 2.Department of Crop and Soil and Environmental SciencesVirginia Polytechnic Institute and State UniversityBlacksburgUSA
  3. 3.Department of Forest Resources and Environmental ConservationVirginia Polytechnic Institute and State UniversityBlacksburgUSA

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