Natural Hazards

, Volume 69, Issue 3, pp 1477–1495 | Cite as

Susceptibility evaluation and mapping of China’s landslides based on multi-source data

  • Chun Liu
  • Weiyue Li
  • Hangbin Wu
  • Ping Lu
  • Kai Sang
  • Weiwei Sun
  • Wen Chen
  • Yang Hong
  • Rongxing Li
Original Paper

Abstract

Landslides are occurring more frequently in China under the conditions of extreme rainfall and changing climate, according to News reports. Landslide hazard assessment remains an international focus on disaster prevention and mitigation, and it is an important step for compiling and quantitatively characterizing landslide damages. This paper collected and analyzed the historical landslide events data of the past 60 years in China. Validated by the frequencies and distributions of landslides, nine key factors (lithology, convexity, slope gradient, slope aspect, elevation, soil property, vegetation coverage, flow, and fracture) are selected to construct landslide susceptibility (LS) empirical models by back-propagation artificial neural network method. By integrating landslide empirical models with surface multi-source geospatial and remote sensing data, this paper further performs a large-scale LS assessment throughout China. The resulting landslide hazard assessment map of China clearly illustrates the hot spots of the high landslide potential areas, mostly concentrated in the southwest. The study implements a complete framework of multi-source data collecting, processing, modeling, and synthesizing that fulfills the assessment of LS and provides a theoretical basis and practical guide for predicting and mitigating landslide disasters potentially throughout China.

Keywords

Landslide susceptibility Empirical model Historical landslide events ANN Hot spots 

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Chun Liu
    • 1
    • 2
  • Weiyue Li
    • 1
    • 2
  • Hangbin Wu
    • 1
    • 2
  • Ping Lu
    • 1
    • 2
  • Kai Sang
    • 2
  • Weiwei Sun
    • 2
  • Wen Chen
    • 1
  • Yang Hong
    • 3
  • Rongxing Li
    • 1
    • 4
  1. 1.Center for Spatial Information Science and Sustainable Development ApplicationsTongji UniversityShanghaiChina
  2. 2.College of Surveying and Geo-InformaticsTongji UniversityShanghaiChina
  3. 3.School of Civil Engineering and Environmental Science, National Weather CenterUniversity of OklahomaNormanUSA
  4. 4.Mapping and GIS LabThe Ohio State UniversityColumbusUSA

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