Abstract
Landslides cause serious damage to society, and some occur as reactivations of old landslides in response to earthquakes and/or rainfall. Landslide distributions are therefore useful when siting engineering projects such as road and tunnel constructions. Although several methods have been proposed to extract landslides from topographic data on the basis of their morphological features (crown, main scarp, and main body), such morphological features are gradually eroded by heavy precipitation or landslide recurrence. Therefore, conventional methods cannot always identify areas influenced by recurrent landslides. In this study, we investigated the relationship between ridgeline continuity and landslide distribution using AW3D30, which is a global digital surface model (DSM) dataset produced from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS) launched by the Japan Aerospace Exploration Agency (JAXA) in 2013. The relationship between the area of landslides and the number of ridge pixels was analyzed, and we propose a method for estimating the upper bound distribution of landslide topographies based on extracted ridgelines data using the Data Envelopment Analysis (DEA) function on the R statistical software packages. The upper bound on the area of landslides decreases as the number of ridge pixels increases. The same trend was seen in all the five sites, and the upper bound derived from one site is hardly exceeded by those derived from all other sites. By using the upper bound distribution function, the landslide distribution will not be missed.
Highlights
Landslides are dangerous geohazards that can destroy or isolate residential areas and block roads and rivers
The relationship between the area of landslides and the number of ridge pixels was analyzed, and we propose a method for estimating the upper bound distribution of landslide topographies based on extracted ridgelines data using the Data Envelopment Analysis (DEA) function on the R statistical software packages
The relationship between the area of landslides and the number of ridge pixels was analyzed, and we developed a method to estimate the area of landslide in each 900 m × 900 m grid using AW3D30 data and the landslide inventory dataset in Shikoku, Japan
Summary
Landslides are dangerous geohazards that can destroy or isolate residential areas and block roads and rivers. In the 2015 Nepal Gorkha earthquake, a huge landslide with an estimated volume of around 15 × 106 m3 destroyed Langtan village, killing 350 people [2]. Another landslide during the Gorkha earthquake dammed the Marshyangdi River, producing a new lake. To assess the probability of landsliding, numerous methods have been developed. Those are largely separated into three categories: heuristic approaches, deterministic approaches, and statistical approaches [3]. In order to estimate the probability of landslide occurrences, statistical approaches are considered appropriate if there is enough data [4,5]. Since the occurrence of landslides is Geosciences 2020, 10, 115; doi:10.3390/geosciences10040115 www.mdpi.com/journal/geosciences
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