Landslide Detection Using a Saliency Feature Enhancement Technique From LiDAR-Derived DEM and Orthophotos

Abstract

This study proposes a new landslide detection technique that is semi-automated and based on a saliency enhancement approach. Unlike most of the landslide detection techniques, the approach presented in this paper is simple yet effective and does not require landslide inventory data for training purposes. It comprises several steps. First, it enhances potential landslide pixels. Then, it removes the image background using slope information derived from a very high-resolution LiDAR-based (light detection and ranging) digital elevation model (DEM). After that, morphological analysis was applied to remove small objects, separate landslide objects from each other, and fill the gaps between large bare soil objects and urban objects. Finally, landslide scars were detected using the Fuzzy C-means (FCM) clustering algorithm. The proposed method was developed based on datasets acquired over the Kinta Valley area in Malaysia and tested on another area with a different environment and topography (i.e., Cameron Highlands). The results showed that the proposed landslide detection technique could detect landslides in the training area with a Prediction Accuracy, Kappa index, and Mean Intersection-Over-Union (mIOU) of 71.12%, 0.81, and 68.52%, respectively. The Prediction Accuracy, Kappa index, and mIOU of the method based on the test dataset were 65.78%, 0.68, and 56.14%, respectively. These results show that the proposed method can be used for landslide inventory mapping and risk assessments.

Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation

“…The study area was chosen in a tropical area in the Cameron Highlands in the state of Pahang, Malaysia (Figure 1). This region has experienced numerous landslides and floods in the past, which were mainly caused by heavy and prolonged rainfall [2]. The predominant land cover of the area is a rainforest with dense vegetation.…”

“…Remote Sens. 2021, 13, x FOR PEER REVIEW 4 of 25 floods in the past, which were mainly caused by heavy and prolonged rainfall [2]. The predominant land cover of the area is a rainforest with dense vegetation.…”

“…The inventories can be prepared through field surveys, past landslide events from news and government records, and remote sensing data. In this research, a landslide inventory identified 35 landslide locations (shallow landslides) in the Cameron Highlands, Malaysia (Figure 1), taken from the previous projects [2,3,29,41].…”

“…In addition, the light detection and ranging (LiDAR) data was used to derive the DEM over a 25 km 2 area, and other associated conditioning parameters. The data was captured on the 15th of January 2015 using an airborne laser scanning system (RIEGL) with a point spacing of 8 points per square meter and an altitude of 1510 m [2,42].…”

“…Landslides are among the common natural disasters that cause considerable damage to life and property in various regions around the globe, with governments and other agencies having to spend a substantial amount to monitor and mitigate the disaster [1,2]. Although landslide-related damage cannot be entirely avoided, these events can be predicted to some extent so that damage to human life and property can be minimized [2]. In general, susceptibility maps are used to perform hazard and risk assessments in landslideprone areas.…”