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

Biswajeet Pradhan,Maher Ibrahim Sameen,Abdullah M Alamri,Mustafa Ridha Mezaal,Husam A H Al-Najjar

doi:10.1109/access.2020.3006914

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.

Highlights

Landslide is a destructive natural geohazard that poses significant damage to human life and property every year worldwide, for instance, 187 casualties were recorded by landslide events in Iran, imposing US$ 12,700,000 up to September 2007 [1]
To separate the potential landslide regions from other regions, the result of image saliency enhancement was classified into two classes with the threshold of 0.54 - 1.75% selected by the quantile classification scheme utilising Arc GIS software addressed by [34] (Fig. 5b)
The main data used are LiDAR-derived slope map and orthophotos and the proposed landslide detection technique could be a useful tool for landslide mapping and monitoring when inventories are scarce as well as there is a need of quick response time

Summary

Introduction

Landslide is a destructive natural geohazard that poses significant damage to human life and property every year worldwide, for instance, 187 casualties were recorded by landslide events in Iran, imposing US$ 12,700,000 up to September 2007 [1]. In Malaysia, from 1973 to 2007, landslides caused losses of approximately one Billion US$ for the period from 1973 to 2007 with over 100 deaths [3]. Landslide inventory maps show locations, along with attribute information, of landslides that occurred in a particular area [9]. These sorts of complete inventory data do not always exist (i.e. impacts of landslides). A complete landslide inventory database contains precise locations, type, and volume of mobilized materials, date of occurrence, and the impacts of past landslides.

Objectives

Methods

Results

Discussion

Conclusion