The Overlooked Terrestrial Impacts of Mountaintop Mining

Wickham, James D.; Wood, Petra Bohall; Nicholson, Matthew C.; Jenkins, W. Aaron; Druckenbrod, Daniel L.; Suter, Glenn W.; Strager, Michael P.; Mazzarella, Christine; Galloway, Walter; Amos, John

doi:10.1525/bio.2013.63.5.7

Cited by 96 publications

(88 citation statements)

References 51 publications

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“…A review of available terrain data for this region suggested that this is the most appropriate historic elevation data set for this analysis, as pre-mining DEM data are limited. Furthermore, visual inspection of a hillshade image produced from these data and an elevation change image produced by subtracting the historic and recent DEMs both suggest that the DLG data predate almost all large-scale MTR/VF activity in the study area, which began as early as the 1960s but was not widespread until the 1990s (Milici 2000;Wickham et al 2013). The contour data were gridded on a 9 m raster using the Topo to Raster tool in ArcMap 10.2 (ESRI 2012).…”

Section: Input Data and Preprocessingmentioning

confidence: 94%

“…Grasslands resulting from surface mine reclamation have been shown to be fundamentally different from other grasslands in terms of their impact on hydrology (Negley and Eshleman 2006;Ferrari et al 2009;McCormick et al 2009;Zégre, Maxwell, and Lamont 2013;Miller and Zégre 2014;Zégre et al 2014), terrestrial habitat (Weakland and Wood 2005;Wood, Bosworth, and Dettmers 2006;Simmons et al 2008;Wickham et al 2007Wickham et al , 2013, and aquatic ecosystems (Hartman et al 2005;Pond et al 2008;Fritz et al 2010;Pond 2010;Merriam et al 2011;Bernhardt et al 2012;Merriam et al 2013). Negley and Eshleman (2006) found that watersheds affected by mining and mine reclamation produce increased storm run-off and higher peak hourly run-off rates for storm events in comparison with watersheds not affected by mining.…”

Section: Introductionmentioning

confidence: 99%

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Differentiating mine-reclaimed grasslands from spectrally similar land cover using terrain variables and object-based machine learning classification

Maxwell

Warner

2015

International Journal of Remote Sensing

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(2015) Differentiating mine-reclaimed grasslands from spectrally similar land cover using terrain variables and object-based machine learning classification, International Journal of Remote Sensing, 36:17, Incorporating ancillary, non-spectral data may improve the separability of land use/ land cover classes. This study investigates the use of multi-temporal digital terrain data combined with aerial National Agriculture Imagery Program imagery for differentiating mine-reclaimed grasslands from non-mining grasslands across a broad region (6085 km 2 ). The terrain data were derived from historical digital hypsography and a recent light detection and ranging data set. A geographic objectbased image analysis (GEOBIA) approach, combined with two machine learning algorithms, Random Forests and Support Vector Machines, was used because these methods facilitate the use of ancillary data in classification. The results suggest that mine-reclaimed grasslands can be mapped accurately, with user's and producer's accuracies above 80%, due to a distinctive topographic signature in comparison with other spectrally similar grasslands within this landscape. The use of multitemporal digital elevation model data and pre-mining terrain data only generally provided statistically significant increased classification accuracy in comparison with post-mining terrain data. Elevation change data were of value, and terrain shape variables generally improved the classification. GEOBIA and machine learning algorithms were useful in exploiting these non-spectral data, as data gridded at variable cell sizes can be summarized at the scale of image objects, allowing complex interactions between predictor variables to be characterized.

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Section: Input Data and Preprocessingmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Differentiating mine-reclaimed grasslands from spectrally similar land cover using terrain variables and object-based machine learning classification

Maxwell

Warner

2015

International Journal of Remote Sensing

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“…The areas under high fragmentation, which were the areas at risk [5], are located close to coal mines. The high fragmentation areas increased in areas which were previously under low fragmentation, but the fragmentation impact on terrestrial environment have been largely overlooked in mining [28]. If eco-environment of grassland was destroyed, ecological restoration would be difficult, high-costed and time-consumed, as is the common issue in the development of coal resources; improper handling will affect the sustainable development of regional economy.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Temporal and spatial changes of land use and landscape in a coal mining area in Xilingol grassland

Guan

Zhang

et al. 2017

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. Coal mining, particularly surface mining, inevitably disturbs land. According to Landsat images acquired over Xilingol grassland in 2005Xilingol grassland in , 2009Xilingol grassland in and 2015, land uses were divided into seven classes, i. e., open stope, stripping area, waste-dump area, mine industrial area, farmland, urban area and the original landscape (grassland), using supervised classification and human-computer interactive interpretation. The overall classification accuracies were 97.72 %, 98.43 % and 96.73 %, respectively; the Kappa coefficients were 0.95, 0.97 and 0.95, respectively. Analysis on LUCC (Land Use and Cover Change) showed that surface coal mining disturbed grassland ecosystem: grassland decreased by 8661.15 hm2 in 2005-2015. The area and proportion of mining operation areas (open stope, stripping area, waste-dump area, mine industrial field) increased, but those of grassland decreased continuously. Transfer matrix of land use changes showed that waste-dump had the largest impacts in mining disturbance, and that effective reclamation of waste-dump areas would mitigate eco-environment destruction, as would be of great significance to protect fragile grassland eco-system. Six landscape index showed that landscape fragmentation increased, and the influences of human activity on landscape was mainly reflected in the expansion of mining area and urban area. Remote sensing monitoring of coal surface mining in grassland would accurately demonstrate the dynamics and trend of LUCC, providing scientific supports for ecological reconstruction in surface mining area.

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“…Mining on or near forest land can result in forest loss and fragmentation, soil degradation and loss, greenhouse gas emissions, biodiversity loss and water pollution, as well as increasing competition for land and water (OSISA 2015;Perch and Byrd 2015;Wickham et al 2013). The social and economic impacts of (commonly land-based) extractive industry operations include displacement of local communities, including indigenous people, increased threats of food security and loss of livelihoods in forest-dependent communities.…”

Section: Artisanal and Small-scale Miningmentioning

confidence: 99%

Global Gender and Environment Outlook 2016

Seager¹,

Bechtel²,

Bock³

et al. 2016

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The Overlooked Terrestrial Impacts of Mountaintop Mining

Cited by 96 publications

References 51 publications

Differentiating mine-reclaimed grasslands from spectrally similar land cover using terrain variables and object-based machine learning classification

Differentiating mine-reclaimed grasslands from spectrally similar land cover using terrain variables and object-based machine learning classification

Temporal and spatial changes of land use and landscape in a coal mining area in Xilingol grassland

Global Gender and Environment Outlook 2016

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