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Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

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  • Zhi-hua Yang
  • Heng-xing Lan
  • Xing Gao
  • Lang-ping Li
  • Yun-shan Meng
  • Yu-ming Wu

Abstract

The Lushan earthquake with magnitude M s 7.0 (M w 6.6, USGS) in Sichuan Province, China, triggered a large number of landslides, which seriously aggravated the earthquake’s destructive consequences. This paper mainly focuses on the methodology of the urgent landslide susceptibility assessment right after the earthquake. The detailed landslide inventory (including 5,688 landslides) is prepared by means of urgent post-earthquake landslide field survey, landslide remote sensing interpretation of multi-source remote sensing images including high-resolution unmanned aerial vehicle images and historical landslide archives. Ten remarkable causative factors for landslide occurrence have been selected to conduct the landslide susceptibility assessment, including earthquake intensity, landslide density and slope gradient. An integration assessment approach is developed to facilitate the effective urgent post-earthquake landslide susceptibility assessment using three methods: factor sensitivity analysis, analytical hierarchy process and factor-weighted overlay. Such integration can effectively reduce the subjectivity and uncertainty resulting from using single method. The validation evaluation using the area under curve suggests the landslide susceptibility assessment results have satisfactory accuracy, and the suggested methodology is effective for the urgent post-earthquake landslide susceptibility assessment. The study results reveal that earthquake intensity and slope gradient are the two most important causative factors for post-earthquake landslide occurrence in the Lushan earthquake-impacted area. The dominant slope gradient and slope aspect with relatively higher landslide frequency are 45°–50° and south-east direction, respectively. The intense earthquake impact increased the dominant slope gradient of landslide spatial distribution, and the thrust campaign of seismogenic fault with strike NE–SW made south-east direction as the dominant slope aspect of the landslide spatial distribution. The locations with very high and high landslide susceptibility are mainly distributed in the regions with higher earthquake intensity and adverse terrain conditions, such as Shuangshi town and Longmen town of Lushan county and Muping town of Baoxing county. The study results are expected to provide a beneficial reference for the landslide prevention and infrastructure reconstruction after the Lushan earthquake. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Zhi-hua Yang & Heng-xing Lan & Xing Gao & Lang-ping Li & Yun-shan Meng & Yu-ming Wu, 2015. "Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China," 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. 75(3), pages 2467-2487, February.
  • Handle: RePEc:spr:nathaz:v:75:y:2015:i:3:p:2467-2487
    DOI: 10.1007/s11069-014-1441-8
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    1. Maria Kouli & Constantinos Loupasakis & Pantelis Soupios & Filippos Vallianatos, 2010. "Landslide hazard zonation in high risk areas of Rethymno Prefecture, Crete Island, Greece," 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. 52(3), pages 599-621, March.
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    3. Kai Ke & Yichen Zhang & Jiquan Zhang & Yanan Chen & Chenyang Wu & Zuoquan Nie & Junnan Wu, 2023. "Risk Assessment of Earthquake–Landslide Hazard Chain Based on CF-SVM and Newmark Model—Using Changbai Mountain as an Example," Land, MDPI, vol. 12(3), pages 1-20, March.
    4. Lina Han & Qing Ma & Feng Zhang & Yichen Zhang & Jiquan Zhang & Yongbin Bao & Jing Zhao, 2019. "Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models," IJERPH, MDPI, vol. 16(18), pages 1-17, September.
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