Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study)

Abstract

Landslides are a significant geohazard, which frequently result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping using GIS and remote sensing can help communities prepare for these damaging events. Current mapping efforts utilize a wide variety of techniques and consider multiple factors. Unfortunately, each study is relatively independent of others in the applied technique and factors considered, resulting in inconsistencies. Further, input data quality often varies in terms of source, data collection, and generation, leading to uncertainty. This paper investigates if lidar-derived data-sets (slope, slope roughness, terrain roughness, stream power index, and compound topographic index) can be used for predictive mapping without other landslide conditioning factors. This paper also assesses the differences in landslide susceptibility mapping using several, widely used statistical techniques. Landslide susceptibility maps were produced from the aforementioned lidar-derived data-sets for a small study area in Oregon using six representative statistical techniques. Most notably, results show that only a few factors were necessary to produce satisfactory maps with high predictive capability (area under the curve >0.7). The sole use of lidar digital elevation models and their derivatives can be used for landslide mapping using most statistical techniques without requiring additional detailed data-sets that are often difficult to obtain or of lower quality.

Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland

“…Some researchers assume that the accuracy of the created susceptibility map increases proportionally with the quantity of LCFs used (Jebur et al 2014). Other scientists state that a small number of LCFs is satisfactory to produce landslide susceptibility maps with a reasonable quality (Jebur et al 2014;Mahalingam et al 2016). The investigations of Kingsbury et al (1992) present that the additional factors (soil type, land use, slope aspect, proximity to watercourses) did not increase the reliability of the susceptibility maps and are suitable to a particular study area only.…”

“…The investigations of Kingsbury et al (1992) present that the additional factors (soil type, land use, slope aspect, proximity to watercourses) did not increase the reliability of the susceptibility maps and are suitable to a particular study area only. Therefore, no specific rule exists to define how many conditioning factors are sufficient for the susceptibility analysis on a given study area (Pourghasemi et al 2013;Mahalingam et al 2016). Moreover, various factors have a different impact on landslide occurrence; therefore, MCDA provides the possibility to include expert opinion to describe their impact.…”

“…Hence, many researchers tested different approaches, which analyse the spatial distribution of landslides with different LCFs (Bui et al 2016;Mahalingam et al 2016). Nowadays, the significance of each LCF can be easily validated using geographic information system (GIS).…”

Integrated Use of Aerial Photographs and LiDAR Images for Landslide and Soil Erosion Analysis: A Case Study of Wakamow Valley, Moose Jaw, Canada

“…It is an increasingly popular technique which is being widely used for analysis and mapping of landslides over a traditional labour extensive field survey [18]. It is well documented that high-resolution satellite and aerial photographs are not providing sufficient information which is normally required for slope analysis.…”

An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method

“…From these epicenters, an epicenter proximity map was prepared using the Euclidean distance function, comprising five classes: <2 km, 2-4 km, 4-6 km, 6-10 km, and >10 km, as shown in Figure 5b. A natural break method [47] was employed to classify the epicenter proximity. This is a data clustering method designed to determine the best arrangement of values in different classes [48].…”