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The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China

Author

Listed:
  • Zhuo Chen

    (Sichuan University)

  • Fei Ye

    (Sichuan University)

  • Wenxi Fu

    (Sichuan University)

  • Yutian Ke

    (Univ. Paris Sud-CNRS-Université Paris-Saclay)

  • Haoyuan Hong

    (Nanjing Normal University
    State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)
    Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application)

Abstract

The selection of an appropriate map resolution is highly important for landslide susceptibility assessment. No consistent objective criteria, however, are currently applied to the choice of map resolution. This research, in conjunction with slope units, explores the effect of digital elevation model (DEM) resolution on susceptibility modelling using three statistical models (frequency ratio, index of entropy, and weight of evidence). Seven different spatial resolutions (30, 40, 50, 60, 70, 80, and 90 m) and three statistical models are investigated. For each resolution, we compare the performance of the three models using area under curve (AUC) analysis. The results show that, independent of the statistical models, the best performances are produced at 70 m DEM resolution. This highlights that finer resolutions do not necessarily lead to higher predictive accuracy in landslide susceptibility mapping. Rather, the frequency ratio model seems to be optimal for the coarser resolutions (i.e. 70, 80, and 90 m).

Suggested Citation

  • Zhuo Chen & Fei Ye & Wenxi Fu & Yutian Ke & Haoyuan Hong, 2020. "The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW 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. 101(3), pages 853-877, April.
    • Handle: RePEc:spr:nathaz:v:101:y:2020:i:3:d:10.1007_s11069-020-03899-9
    DOI: 10.1007/s11069-020-03899-9
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    References listed on IDEAS

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    Cited by:

    1. Ge Yan & Guoan Tang & Sijin Li & Dingyang Lu & Liyang Xiong & Shouyun Liang, 2023. "Uncertainty in regional scale assessment of landslide susceptibility using various resolutions," 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 399-423, May.
    2. Mohammad Mehrabi, 2022. "Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy," 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. 111(1), pages 901-937, March.
    3. Saeed Alqadhi & Hoang Thi Hang & Javed Mallick & Abdullah Faiz Saeed Al Asmari, 2024. "Evaluating landslide susceptibility and landscape changes due to road expansion using optimized machine learning," 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. 120(13), pages 11713-11741, October.

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