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Uncertainty in regional scale assessment of landslide susceptibility using various resolutions

Author

Listed:
  • Ge Yan

    (Ministry of Education
    Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Guoan Tang

    (Ministry of Education
    Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Sijin Li

    (Ministry of Education
    Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Dingyang Lu

    (Ministry of Education
    Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Liyang Xiong

    (Ministry of Education
    Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Shouyun Liang

    (The Ministry of Education of China)

Abstract

Resolution produces uncertainty in spatial analysis. The objective of this paper is to study the effects of resolution on landslide susceptibility mapping. First, a landslide survey map that contains 407 historical landslide location information is compiled. In this work, two sampling strategies were used to randomly regroup landslides into two parts for training and testing: one is 70% for training and 30% for testing, whereas the other is 75% for training and 25% for testing. Second, 11 conditioning factors, namely elevation, rainfall, distance to river, plan curvature, aspect, slope, lithology, profile curvature, distance to road, land use, and normalized difference vegetation index, were prepared in ArcGIS version 10.3 for 20 cell sizes from 30 to 600 m with an interval of 30 m. Third, 80 landslide susceptibility maps were generated by combining 20 cell sizes, two sampling strategies, and two models, namely, support vector machine (SVM) and logistic regression (LR). The resolution caused differences in the prediction rates, that is, 6.6–8.2% for SVM and 5.2–8.7% for LR. The best resolutions for the two aforementioned sampling strategies are 150 and 180 m, respectively. The optimal resolution should be related to the landslide size and close to the average area of the landslide when the landslide inventory map is presented by landslide points. This study provides a reference for the resolution comparison in landslide assessment and enhances a new understanding of the relationship between optimal resolution and landslide size.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:117:y:2023:i:1:d:10.1007_s11069-023-05865-7
    DOI: 10.1007/s11069-023-05865-7
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    References listed on IDEAS

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    1. 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.
    2. Silvana Moragues & María Gabriela Lenzano & Mario Lanfri & Stella Moreiras & Esteban Lannutti & Luis Lenzano, 2021. "Analytic hierarchy process applied to landslide susceptibility mapping of the North Branch of Argentino Lake, Argentina," 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. 105(1), pages 915-941, January.
    3. Roberta Plangg Riegel & Darlan Daniel Alves & Bruna Caroline Schmidt & Guilherme Garcia Oliveira & Claus Haetinger & Daniela Montanari Migliavacca Osório & Marco Antônio Siqueira Rodrigues & Daniela M, 2020. "Assessment of susceptibility to landslides through geographic information systems and the logistic regression model," 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. 103(1), pages 497-511, August.
    4. Anik Saha & Sunil Saha, 2021. "Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 4453-4483, March.
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