Journal of Jilin University(Earth Science Edition) ›› 2016, Vol. 46 ›› Issue (1): 175-186.doi: 10.13278/j.cnki.jjuese.201601201

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Regional Landslide Risk Assessment Using Multi-Source Remote Sensing Data

Peng Ling1,2, Xu Suning2, Peng Junhuan1   

  1. 1. School of Land Science and Technology, China University of Geosciences, Beijing 100083, China;
    2. China Institute of Geo-Environment Monitoring, Beijing 100081, China
  • Received:2015-04-19 Online:2016-01-26 Published:2016-01-26
  • Supported by:

    Supported by the China Postdoctoral Science Foundation Funded Project (2014M560103), the National High-Tech R&D Program of China (2012AA121303), and the Special Project of Satellite and Application of Industry Development (2083[2012], General Office, NDRC)


Landslide risk management is an effective way for disaster prevention and mitigation, in which risk assessment is essential. The head area of the Three Gorges is selected as the study area. Using multi-source remote sensing data as the major data source, the authors extracted the environment information pregnant with landslides including geomorphological, land cover, geological, and hydrology by stereo image processing and spectral analysis, and analyzed the landslide hazard by using the random forest model. Furthermore, the rule of extraction for typical elements at risk is established through the approach of object-oriented image analysis, and then the building and road information is extracted from the high-resolution satellite images. Finally, a landslide risk map is developed by integrating the elements at risk and landslide hazard datasets. According to the map, 9% of the study area is identified as high-risk zones encompassing 41 km2, and these high-risk areas are located in county and towns covered by transportation networks and other urbanized areas with high density of populations and property. The results of the risk assessment are basically consistent with the field observation and survey.

Key words: landslides, hazard, elements at risk, risk assessment, remote sensing, Three Gorges

CLC Number: 

  • P642.22

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