IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v117y2023i1d10.1007_s11069-023-05865-7.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-05865-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-023-05865-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Idris Bello Yamusa & Mohd Suhaili Ismail & Abdulwaheed Tella, 2022. "Highway Proneness Appraisal to Landslides along Taiping to Ipoh Segment Malaysia, Using MCDM and GIS Techniques," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    3. Rui-Xuan Tang & E-Chuan Yan & Tao Wen & Xiao-Meng Yin & Wei Tang, 2021. "Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 13(7), pages 1-25, March.
    4. Shaohan Zhang & Shucheng Tan & Hui Geng & Ronwei Li & Yongqi Sun & Jun Li, 2023. "Evaluation of Geological Hazard Risk in Yiliang County, Yunnan Province, Using Combined Assignment Method," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    5. Rajesh Kumar Dash & Philips Omowumi Falae & Debi Prasanna Kanungo, 2022. "Debris flow susceptibility zonation using statistical models in parts of Northwest Indian Himalayas—implementation, validation, and comparative evaluation," 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(2), pages 2011-2058, March.
    6. Jinming Zhang & Jianxi Qian & Yuefeng Lu & Xueyuan Li & Zhenqi Song, 2024. "Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    7. Shaohan Zhang & Shucheng Tan & Lifeng Liu & Duanyu Ding & Yongqi Sun & Jun Li, 2023. "Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, Ch," Sustainability, MDPI, vol. 15(13), pages 1-19, July.
    8. Yalu Han & Lizhi Du & Shiwei Shen, 2023. "Study on shear test and shear displacement of frozen joints with different opening degrees," 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. 115(1), pages 289-307, January.
    9. Michael Makonyo & Zahor Zahor, 2023. "GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania," 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. 118(2), pages 1085-1115, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:117:y:2023:i:1:d:10.1007_s11069-023-05865-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.