A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives

Abstract

Landslide inventory maps are critical to understand the factors governing landslide occurrence and estimate hazards or sediment delivery to channels. Numerous semi-automated approaches for landslide inventory mapping have been proposed to improve the efficiency and objectivity of the process, but these methods have not been widely adopted by practitioners because of the use of input parameters without physical meaning, a lack of transparency in machine-learning based mapping techniques, and limitations in resulting products, which are not ordinarily designed or tested on a large-scale or in diverse geologic units. To this end, this work presents a new semi-automated method, called the Scarp Identification and Contour Connection Method (SICCM), which adapts to diverse geologic settings automatically or semi-automatically using interventions driven by simple inputs and interpretation from an expert mapper. The applicability of SICCM for use in landslide inventory mapping is demonstrated for three diverse study areas in western Oregon, USA by assessing the utility of the results as a landslide inventory, evaluating the sensitivity of the algorithm to changes in input parameters, and exploring how geology influences the resulting landslide inventory results. In these case studies, accuracies exceed 70%, with reliability and precision of nearly 80%. Conclusions of this work are that (1) SICCM efficiently produces meaningful landslide inventories for large areas as evidenced by mapping 216 km2 of landslide deposits with individual deposits ranging in size from 58 to 1.1 million m2; (2) results are predictable with changes to input parameters, resulting in an intuitive approach; (3) geology does not appear to significantly affect SICCM performance; and (4) the process involves simplifications compared with more complex alternatives from the literature.

Remote Sensing for Assessing Landslides and Associated Hazards

“…This technology has highly advanced the accuracy of landslide inventory maps (Schulz 2004;Ardizzone et al 2007), the monitoring of surface displacement, and provides essential data for landslide susceptibility (i.e., DEM derivatives with slope, surface roughness, curvature calculation in Van Den Eeckhaut et al 2012). The gain of information by LiDAR also concerns detailed morphological features investigation at the sub-meter scale (Lissak et al 2014;Bunn et al 2019). But exploration of this kind of Very High-Resolution (VHR) data can be limited by the cost of surveys.…”

“…Nevertheless, the quality of the visual interpretation highly depends on the complexity of the terrain, the vegetation cover, and on the acquisition procedures. For areas of dense vegetation, the use of a LiDAR-derived elevation model (3D models: pointcloud, 3D meshes, DEMs) helps to identify undercovered features (Chigira et al 2004;Mckean and Roering 2004;Ardizzone et al 2007;Van Den Eeckhaut et al 2012;Razak et al 2013;Lissak et al 2014;Pawluszek and Borkowski 2016;Bunn et al 2019;Görüm 2019). DEM derivatives such as slope values, aspect, roughness, orientation, openness, and Sky View Factor indicators can be calculated to highlight morphological features induced by landslides and extract hazard boundaries.…”

“…For this purpose, Machine Learning (ML) and Deep Learning (DL) approaches can be useful. Whereas these approaches were few employed for landslide assessment until the early 2000s (Bui et al 2019), today they are developed for various applications: landslide triggering prediction (Farahmand and AghaKouchak 2013), landslide displacement prediction (Lian et al 2013;Zhao and Du 2016), landslide detection (Stumpf and Kerle 2011a, b;Chen et al 2014;Moosavi et al 2014;Bunn et al 2019;Ghorbanzadeh et al 2019).…”

“…Object-based approach is a good alternative to detect landslides (Stumpf and Kerle 2011a, b;Kurtz et al 2014;Moosavi et al 2014;Li et al 2015;Casagli et al 2016Casagli et al , 2017Bunn et al 2019) from various data sources (as well as Very High-Resolution optical data than LiDAR-derived DEM). This technique is based on 2 steps: the image segmentation and the image classification.…”

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

“…Focusing only on HRDTM-derived data for semi-or fully automated landslide detection, several promising techniques have emerged in recent years: Booth et al [9] and Kasai et al [10] used filter techniques such as Fourier and wavelet transform [9] or eigenvalue ratios [10] to detect landslides based on their topographic signature. Leshchinsky et al [11] and Bunn et al [12] developed and extended the contour connection method, a vector-based method to detect different landslide features using a mesh of nodes and connecting lines. Pawłuszek et al [13] detected landslides using a pixel-based classification scheme, and benchmarked different classifiers while also accounting for different grid resolutions.…”