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Journal of Mountain Science

, Volume 15, Issue 6, pp 1299–1318 | Cite as

Statistical landslide susceptibility assessment in a dynamic environment: A case study for Lanzhou City, Gansu Province, NW China

  • Jewgenij Torizin
  • Li-chao Wang
  • Michael Fuchs
  • Bin Tong
  • Dirk Balzer
  • Li-qin Wan
  • Dirk Kuhn
  • Ang Li
  • Liang Chen
Article
  • 36 Downloads

Abstract

This study presents a statistical landslide susceptibility assessment (LSA) in a dynamic environment. The study area is located in the eastern part of Lanzhou, NW China. The Lanzhou area has exhibited rapid urbanization rates over the past decade associated with greening, continuous land use change, and geomorphic reshaping activities. To consider the dynamics of the environment in the LSA, multitemporal data for landslide inventories and the corresponding causal factors were collected. The weights of evidence (WofE) method was used to perform the LSA. Three time stamps, i.e., 2000, 2012, and 2016, were selected to assess the state of landslide susceptibility over time. The results show a clear evolution of the landslide susceptibility patterns that was mainly governed by anthropogenic activities directed toward generating safer building grounds for civil infrastructure. The low and very low susceptibility areas increased by approximately 10% between 2000 and 2016. At the same time, areas of medium, high and very high susceptibility zones decreased proportionally. Based on the results, an approach to design the statistical LSA under dynamic conditions is proposed, the issues and limitations of this approach are also discussed. The study shows that under dynamic conditions, the requirements for data quantity and quality increase significantly. A dynamic environment requires greater effort to estimate the causal relations between the landslides and controlling factors as well as for model validation.

Keywords

Landslide susceptibility assessment Dynamic environment Weights of evidence method Validation Urbanization Lanzhou City 

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Notes

Acknowledgments

This work was conducted in the framework of a scientific-technical cooperation project between the Federal Institute for Geosciences and Natural Resources (BGR) and the China Geological Survey (CGS) co-funded by the German Ministry of the Economic Affairs and Energy (BMWi) and Ministry of Land and Resources of the People’s Republik of China.

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Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Federal Institute for Geosciences and Natural Resources (BGR)HannoverGermany
  2. 2.China Institute of Geo-environment Monitoring (CIGEM)BeijingChina

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