Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features

Abstract

Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in recent decades. By providing a highly detailed digital elevation model (DEM), airborne laser scanning (LiDAR) allows effective landslide identification, especially in forested areas. In the present study, object-based image analysis (OBIA) was applied to landslide detection by utilizing LiDAR-derived data. In contrast to previous investigations, our analysis was performed on forested and agricultural areas, where cultivation pressure has degraded specific landslide geomorphology. A diverse variety of aspects that influence OBIA accuracy in landslide detection have been considered: DEM resolution, segmentation scale, and feature selection. Finally, using DEM delivered layers and OBIA, landslide was identified with an overall accuracy (OA) of 85% and a kappa index (KIA) equal to 0.60, which illustrates the effectiveness of the proposed approach. In the end, a field investigation was performed in order to evaluate the results achieved by applying an automatic OBIA approach. The advantages and challenges of automatic approaches for landslide identification for various land use were also discussed. Final remarks underline that effective landslide detection in forested areas could be achieved while this is still challenging in agricultural areas.

Methods of Landslide Detection using GIS and Remote Sensing Images

“…Figure 1 and table 2 shows the landslides detection accuracy of various methods recently proposed. The results shows that the method proposed in [19] and [22] are having poor detection accuracy among all the methods. The approach recently introduced in [21] achieved the higher detection accuracy.…”

“…In [22], another recent OBIA based proposed for the landslide detection by using LiDAR-derived data. They designed a framework consist of phrases such as digital elevation model (DEM) derivatives preparation, segmentation of multi-resolution, SVM based classification, and the post-processing for refinement in outcomes.…”

Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software

“…GEOBIA has already demonstrated its potential in landslide mapping [7,15]. Commonly, high-resolution optical and multispectral data are combined with HRDTM derivatives for landslide detection [6,[16][17][18][19].…”

“…However, these approaches have the drawback of failing to detect landslides in forests, and of relying on recent observations of landslide events and on cloud-free optical data. Until now, only few authors based landslide detection solely on LiDAR HRDTM-derived variables [7,15,21]. The use of LiDAR HRDTM-derived data avoids the above-mentioned limitations of optical remote-sensing imagery (cloud-free data, landslides under forest).…”

“…Moreover, previous landslide detection procedures were mostly implemented in commercial software environments such as Definiens eCognition [7,15,16,18,21], ESRI ArcGIS [13,15], the ENVI image analysis software [13,15], or MathWorks MATLAB [9,21]. Only few authors made their algorithms publicly available [9,18], or used computing environments released under open source licenses [11].…”