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Automatic detection of sinkhole collapses at finer resolutions using a multi-component remote sensing approach

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Listed:
  • Jie Dou
  • Xia Li
  • Ali Yunus
  • Uttam Paudel
  • Kuan-Tsung Chang
  • Zhongfan Zhu
  • Hamid Pourghasemi

Abstract

Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compared with other geological disasters, sinkholes are considerably smaller and scattered according to scale and spatial distribution. Nevertheless, detecting and investigating sinkholes have become increasingly challenging. This study proposes a novel method by applying case-based reasoning (CBR) combined with object-based image analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial images. This case study was performed in Paitan Town, Guangdong Province, China. The method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature selection, and (3) application of CBR techniques. The detected sinkholes were categorized into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated that the proposed method can obtain higher accuracy compared with the traditional supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A similar case library was also applied to another trial area for validation, the satisfactory results of which suggested that CBR is applicable for independently detecting sinkholes. The proposed method will be useful for preparing hazard maps that express the relative probability of a collapse in similar regions. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Jie Dou & Xia Li & Ali Yunus & Uttam Paudel & Kuan-Tsung Chang & Zhongfan Zhu & Hamid Pourghasemi, 2015. "Automatic detection of sinkhole collapses at finer resolutions using a multi-component remote sensing approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(2), pages 1021-1044, September.
    • Handle: RePEc:spr:nathaz:v:78:y:2015:i:2:p:1021-1044
    DOI: 10.1007/s11069-015-1756-0
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    References listed on IDEAS

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    1. Francisco Gutiérrez & Jesús Guerrero & Pedro Lucha, 2008. "Quantitative sinkhole hazard assessment. A case study from the Ebro Valley evaporite alluvial karst (NE Spain)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 45(2), pages 211-233, May.
    2. Lu, I.J. & Lin, Sue J. & Lewis, Charles, 2008. "Grey relation analysis of motor vehicular energy consumption in Taiwan," Energy Policy, Elsevier, vol. 36(7), pages 2556-2561, July.
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