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

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,...

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Bibliographic Details
Published in:Journal of mountain science 2018-06, Vol.15 (6), p.1299-1318
Main Authors: Torizin, Jewgenij, Wang, Li-chao, Fuchs, Michael, Tong, Bin, Balzer, Dirk, Wan, Li-qin, Kuhn, Dirk, Li, Ang, Chen, Liang
Format: Article
Language:English
Subjects:
Anthropogenic factors
Case studies
Dynamics
Earth and Environmental Science
Earth Sciences
Ecology
Environment
Geography
Geomorphology
Greening
Land use
Landslides
Landslides & mudslides
Statistics
Urbanization
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Summary: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.
ISSN:1672-6316
1993-0321
1008-2786
DOI:10.1007/s11629-017-4717-0