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A heuristic approach to global landslide susceptibility mapping

Author

Listed:
  • Thomas Stanley

    (Universities Space Research Association
    Goddard Earth Sciences Technology and Research
    NASA Goddard Space Flight Center)

  • Dalia B. Kirschbaum

    (NASA Goddard Space Flight Center)

Abstract

Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.

Suggested Citation

  • Thomas Stanley & Dalia B. Kirschbaum, 2017. "A heuristic approach to global landslide susceptibility mapping," 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. 87(1), pages 145-164, May.
  • Handle: RePEc:spr:nathaz:v:87:y:2017:i:1:d:10.1007_s11069-017-2757-y
    DOI: 10.1007/s11069-017-2757-y
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    References listed on IDEAS

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    2. Qigen Lin & Ying Wang & Thomas Glade & Jiahui Zhang & Yue Zhang, 2020. "Assessing the spatiotemporal impact of climate change on event rainfall characteristics influencing landslide occurrences based on multiple GCM projections in China," Climatic Change, Springer, vol. 162(2), pages 761-779, September.
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    4. Mustafa Kamal & Baolei Zhang & Jianfei Cao & Xin Zhang & Jun Chang, 2022. "Comparative Study of Artificial Neural Network and Random Forest Model for Susceptibility Assessment of Landslides Induced by Earthquake in the Western Sichuan Plateau, China," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
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    7. Chelsea Dandridge & Thomas A. Stanley & Dalia B. Kirschbaum & Venkataraman Lakshmi, 2023. "Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
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    10. Stephen R. Sobie, 2020. "Future changes in precipitation-caused landslide frequency in British Columbia," Climatic Change, Springer, vol. 162(2), pages 465-484, September.
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    14. Edris Alam & Fahim Sufi & Abu Reza Md. Towfiqul Islam, 2023. "A Scenario-Based Case Study: Using AI to Analyze Casualties from Landslides in Chittagong Metropolitan Area, Bangladesh," Sustainability, MDPI, vol. 15(5), pages 1-20, March.
    15. Derly Gómez & Edwin F. García & Edier Aristizábal, 2023. "Spatial and temporal landslide distributions using global and open landslide databases," 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. 117(1), pages 25-55, May.
    16. Jae-Hyeon Park & Seong-Gyun Park & Hyun Kim, 2022. "Applicability Evaluation of Landslide Vulnerability Criteria for Decision on Landcreep-Vulnerable Areas in South Korea," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    17. S. Modugno & S. C. M. Johnson & P. Borrelli & E. Alam & N. Bezak & H. Balzter, 2022. "Analysis of human exposure to landslides with a GIS multiscale 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. 112(1), pages 387-412, May.
    18. Yulin Chen & Enze Chen & Jun Zhang & Jingxuan Zhu & Yuanyuan Xiao & Qiang Dai, 2023. "Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions," Land, MDPI, vol. 12(9), pages 1-16, September.
    19. Huang, Xiaoxun & Hayashi, Kiichiro & Fujii, Minoru & Villa, Ferdinando & Yamazaki, Yuri & Okazawa, Hiromu, 2023. "Identification of potential locations for small hydropower plant based on resources time footprint: A case study in Dan River Basin, China," Renewable Energy, Elsevier, vol. 205(C), pages 293-304.
    20. Amit Bera & Bhabani Prasad Mukhopadhyay & Debasish Das, 2019. "Landslide hazard zonation mapping using multi-criteria analysis with the help of GIS techniques: a case study from Eastern Himalayas, Namchi, South Sikkim," 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. 96(2), pages 935-959, March.
    21. Lei Li & Chong Xu & Zhiqiang Yang & Zhongjian Zhang & Mingsheng Lv, 2022. "An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China," Data, MDPI, vol. 7(8), pages 1-9, August.

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