Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods

• Published in: Landslides Vol. 15; no. 3; pp. 465 - 474
• Main Authors: Luo, Wei, Liu, Cheng-Chien
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2018; Springer Nature B.V
• Subjects: Agriculture; Civil Engineering; Earth and Environmental Science; Earth Sciences; Geography; Geomorphology; Landforms; Landslides; Landslides & mudslides; Mapping; Methods; Mountain regions; Natural Hazards; Original Paper; Pattern recognition; Production methods; Property damage; Sensors; Spatial analysis; Statistical methods; Variance analysis; Geomorphon; Geographical detector; Weights; Slope units; Frequency ratio; Landslide susceptibility mapping
• ISSN: 1612-510X; 1612-5118
• DOI: 10.1007/s10346-017-0893-9

Landslides are among the most common and dangerous natural hazards in mountainous regions that can cause damage to properties and loss of lives. Landslide susceptibility mapping (LSM) is a critical tool for preventing or mitigating the negative impacts of landslides. Although many previous studies have employed various statistical methods to produce quantitative maps of the landslide susceptibility index ( LSI ) based on inventories of past landslides and contributing factors, they are mostly ad hoc to a specific area and their success has been hindered by the lack of a methodology that could produce the right mapping units at proper scale and by the lack of a general framework for objectively accounting for the differing contribution of various preparatory factors. This paper addresses these issues by integrating the geomorphon and geographical detector methods into LSM to improve its performance. The geomorphon method, an innovative pattern recognition approach for identifying landform elements based on the line of sight concept, is adapted to delineate ridge lines and valley lines to form slope units at self-adjusted spatial scale suitable for LSM. The geographical detector method, a spatial variance analysis method, is integrated to objectively assign the weights of contributing factors for LSM. Applying the new integrated approach to I-Lan, Taiwan produced very significant improvement in LSI mapping performance than a previous model, especially in highly susceptible areas. The new method offers a general framework for better mapping landslide susceptibility and mitigating its negative impacts.