Detecting surface coal mining areas from remote sensing imagery

An approach based on object-oriented decision trees

Xiaoji Zeng, Zhifeng Liu, Chunyang He, Qun Ma, Jianguo Wu

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish (US)
Article number015025
JournalJournal of Applied Remote Sensing
Volume11
Issue number1
DOIs
StatePublished - Jan 1 2017

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coal mining
imagery
remote sensing
coal industry
environmental assessment
bare soil
segmentation
decision

Keywords

  • Inner Mongolia
  • object-oriented decision trees
  • Ordos
  • remote sensing
  • surface coal mining area

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Cite this

Detecting surface coal mining areas from remote sensing imagery : An approach based on object-oriented decision trees. / Zeng, Xiaoji; Liu, Zhifeng; He, Chunyang; Ma, Qun; Wu, Jianguo.

In: Journal of Applied Remote Sensing, Vol. 11, No. 1, 015025, 01.01.2017.

Research output: Contribution to journalArticle

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