TY - JOUR
T1 - Detecting surface coal mining areas from remote sensing imagery
T2 - An approach based on object-oriented decision trees
AU - Zeng, Xiaoji
AU - Liu, Zhifeng
AU - He, Chunyang
AU - Ma, Qun
AU - Wu, Jianguo
N1 - Funding Information:
We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported in part by the National Basic Research Program of China (Grant Nos. 2014CB954303 and 2014CB954302) and the National Natural Science Foundation of China (Grant Nos. 41621061 and 41501195).
Publisher Copyright:
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Inner Mongolia
KW - Ordos
KW - object-oriented decision trees
KW - remote sensing
KW - surface coal mining area
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U2 - 10.1117/1.JRS.11.015025
DO - 10.1117/1.JRS.11.015025
M3 - Article
AN - SCOPUS:85016419766
SN - 1931-3195
VL - 11
JO - Journal of Applied Remote Sensing
JF - Journal of Applied Remote Sensing
IS - 1
M1 - 015025
ER -