Articles | Volume 15, issue 5
https://doi.org/10.5194/nhess-15-997-2015
https://doi.org/10.5194/nhess-15-997-2015
Research article
 | 
20 May 2015
Research article |  | 20 May 2015

A study on the use of planarity for quick identification of potential landslide hazard

M. H. Baek and T. H. Kim

Abstract. In this study we focused on identifying a geomorphological feature that controls the location of landslides. The representation of the feature is based on a high-resolution digital elevation model derived from the airborne laser altimetry (LiDAR) and evaluated by the statistical analysis of axial orientation data. The main principle of this analysis is generating eigenvalues from axial orientation data and comparing them. The planarity, a ratio of eigenvalues, would tell the degree of irregularity on the ground surface based on their ratios. Results are compared to the recent landslide case in Korea in order to evaluate the feasibility of the proposed methodology in identifying the potential landslide hazard. The preliminary landslide hazard assessment based on the planarity analysis discriminates features between stable and unstable domain in the study area well, especially in the landslide initiation zones. Results also show it is beneficial to build the landslide hazard inventory mapping, especially where no information on historical records of landslides exists. By combining other physical procedures such as geotechnical monitoring, the landslide hazard assessment using geomorphological features promises a better understanding of landslides and their mechanisms and provides an enhanced methodology to evaluate their hazards and appropriate actions.

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Short summary
This study focuses on identifying the geomorphological feature that controls the location of landslides. We propose generating eigenvalues from the axial orientation data that may tell ground characteristics. The preliminary landslide assessment using the proposed approach discriminates well the geomorphological feature between stable and unstable domains. Results are also useful in mapping the previous landslide inventory where the historical records of landslide incidents have vanished.
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