Evaluation of potential of multiple endmember spectral mixture analysis (MESMA) for surface coal mining affected area mapping in different world forest ecosystems Alfonso Fernández-Manso a , Carmen Quintano b, ⁎, Dar Roberts c a Agrarian Science and Engineering Department, University of León, 24400-Ponferrada, Spain b Electronic Technology Department, University of Valladolid, 47014-Valladolid, Spain c Department of Geography, University of California, Santa Barbara, CA 93106, United States abstract article info Article history: Received 17 February 2012 Received in revised form 17 August 2012 Accepted 19 August 2012 Available online xxxx Keywords: MESMA SMA Surface coal mining Landsat Surface coal mining (SCM) has undergone dramatic changes in the last 30 years. Large-scale SCM practices are at the center of an environmental and legal controversy that has spawned lawsuits and major environ- mental investigations. SCM techniques extract multiple coal seams by removing an area of many square ki- lometers and creating serious environmental problems. Information about mining activities location is essential for environmental applications, specifically the temporal and spatial patterns of land cover/land use change (LCLUC). Advancements in satellite imagery analysis provide possibilities to investigate new ap- proaches for LCLUC detection caused by SCM globally. However there is no study that analyzes the changes produced for SCM at a global scale. Our work examines three areas of coal extraction in the world: Spain, United States of America (USA), and Australia. We used Multiple Endmember Spectral Mixture Analysis (MESMA) applied to Landsat Thematic Mapper (TM) data to map SCM affected area. Endmember spectra of vegetation, soil, and impervious surfaces were collected from the Landsat TM image with the help of a fine resolution orthophotographs and the pixel purity index (PPI). Reference endmembers from an Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) spectral library were utilized as well. An unsupervised clas- sifier was applied to the fraction images to obtain an estimation of active SCM affected area. Classification ac- curacy was reported using error matrixes and κ statistic using active SCM affected area perimeters digitized from fine resolution orthophotographs as reference data. In addition, we compared the accuracy of the MESMA based estimation to estimates using Spectral Mixture Analysis (SMA), and a spectral index tradition- ally used as Normalized Difference Vegetation Index (NDVI) testing statistical significance using a Ζ-test of their κ statistics. Results showed a significant improvement in the accuracy of the SCM affected area using MESMA with an average increase of the κ statistic of 31%. We conclude that MESMA-based approach is effec- tive in mapping SCM active affected area. © 2012 Elsevier Inc. All rights reserved. 1. Introduction Mining, in general, and surface mining in particular may lead to severe environmental degradation. From an environmental point of view, surface coal mining (SCM) is a transforming activity with a high number of detrimental consequences, namely soil erosion, acid-mine drainage and increased sediment load as a result of aban- doned and un-reclaimed mined lands (Parks et al., 1987). Over 6185 million tonnes (Mt) of hard coal is currently produced world- wide and 1042 Mt of brown coal/lignite. The largest coal producing countries are not confined to one region — the top five hard coal pro- ducers are China, the United States of America (USA), India, Australia and South Africa (World Coal Association, 2005). For example, surface mining accounts for around 80% of production in Australia; while in the USA it accounts for about 67% of production (International Energy Agency, 2011). These data indicate the importance of surface mining in the global production of coal. SCM activity has important social, economic, political and envi- ronmental impacts on both local and global scale. At local scale many studies (e.g. García-Criado et al., 1999; Kennedy et al., 2003; Pond et al., 2008) have shown that coal mining activities negatively affect stream biota in nearly all parts of the globe. For example, Bernhardt and Palmer (2011) and Palmer et al. (2010) showed that the aquatic ecosystems of the Central Appalachians (USA) suffered water-quality degradation associated with acidic coal mine drainage as the sediments resulting from SCM (specifically mountain top re- moval), and chemical pollutants transmitted downstream through the river networks of the region. Similarly, Connor et al. (2004) showed a marked loss of biodiversity and water quality, as well as in- creased erosion, salinity, and siltation rates in large sections of the Remote Sensing of Environment 127 (2012) 181–193 ⁎ Corresponding author at: Electronic Technology Department, University of Valladolid, Industrial Engineering School (EII), C/ Francisco Mendizábal, s/n, 47014-Valladolid, Spain. Tel.: +34 983 186487; fax: +34 983 423490. E-mail addresses: alfonso.manso@unileon.es (A. Fernández-Manso), menchu@tele.uva.es (C. Quintano), dar@geog.ucsb.edu (D. Roberts). 0034-4257/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2012.08.028 Contents lists available at SciVerse ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse