Image and Signal Processing Methods

Detecting surface coal mining areas from remote sensing imagery: an approach based on object-oriented decision trees

[+] Author Affiliations
Xiaoji Zeng, Qun Ma

Beijing Normal University, Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing, China

Beijing Normal University, Institute of Disaster Reduction and Emergency Management, Faculty of Geographical Science, Beijing, China

Zhifeng Liu, Chunyang He

Beijing Normal University, Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing, China

Beijing Normal University, School of Natural Resources, Faculty of Geographical Science, Beijing, China

Jianguo Wu

Beijing Normal University, Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing, China

Arizona State University, School of Life Sciences and School of Sustainability, Tempe, Arizona, United States

J. Appl. Remote Sens. 11(1), 015025 (Mar 23, 2017). doi:10.1117/1.JRS.11.015025
History: Received December 14, 2016; Accepted March 13, 2017
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Abstract.  Detecting surface coal mining areas (SCMAs) using remote sensing data in a timely and an accurate manner is necessary for coal industry management and environmental assessment. We developed an approach to effectively extract SCMAs from remote sensing imagery based on object-oriented decision trees (OODT). This OODT approach involves three main steps: object-oriented segmentation, calculation of spectral characteristics, and extraction of SCMAs. The advantage of this approach lies in its effective integration of the spectral and spatial characteristics of SCMAs so as to distinguish the mining areas (i.e., the extracting areas, stripped areas, and dumping areas) from other areas that exhibit similar spectral features (e.g., bare soils and built-up areas). We implemented this method to extract SCMAs in the eastern part of Ordos City in Inner Mongolia, China. Our results had an overall accuracy of 97.07% and a kappa coefficient of 0.80. As compared with three other spectral information-based methods, our OODT approach is more accurate in quantifying the amount and spatial pattern of SCMAs in dryland regions.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Xiaoji Zeng ; Zhifeng Liu ; Chunyang He ; Qun Ma and Jianguo Wu
"Detecting surface coal mining areas from remote sensing imagery: an approach based on object-oriented decision trees", J. Appl. Remote Sens. 11(1), 015025 (Mar 23, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.015025


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