High-resolution DEMs in the study of rainfall- and earthquake-induced landslides: Use of a variable window size method in digital terrain analysis

Iwahashi, Junko; Kamiya, Izumi; Yamagishi, Hisakazu

doi:10.1016/j.geomorph.2012.02.002

Cited by 34 publications

(16 citation statements)

References 41 publications

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“…Physically-based models for landslide susceptibility can incorporate rainfall as a dynamic input of the model [272,273]. The use of ground-based meteorological radars for rainfall prediction and early warning in near real-time landslide forecasting is a very promising application.…”

Section: Triggering Factorsmentioning

confidence: 99%

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

et al. 2014

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Landslides represent major natural hazards, which cause every year significant loss of lives and damages to buildings, properties and lifelines. In the last decades, a significant increase in landslide frequency took place, in concomitance to climate change and the expansion of urbanized areas. Remote sensing techniques represent a powerful tool for landslide investigation: applications are traditionally divided into three main classes, although this subdivision has some limitations and borders are sometimes fuzzy. The first class comprehends techniques for landslide recognition, i.e., the mapping of past or active slope failures. The second regards landslide monitoring, which entails both ground deformation measurement and the analysis of any other changes along time (e.g., land use, vegetation cover). The third class groups methods for landslide hazard analysis and forecasting. The aim of this paper is to give an overview on the applications of remote-sensing techniques for the three categories of landslide investigations, focusing on the achievements of the last decade, being that previous studies have already been exhaustively reviewed in the existing literature. At the end of the paper, a new classification of remote-sensing techniques that may be pertinently adopted for investigating specific typologies of soil and rock slope failures is proposed.

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Section: Triggering Factorsmentioning

confidence: 99%

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

et al. 2014

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“…Instead, such models overlook the effect of the type of landslide analyzed. Previously, Chang et al [42] and Li et al [70] studied multi-event landslide types for areas in Taiwan and China. However, studies pertaining to the Japanese archipelago is limited to multi-event landslide analysis with the exception of Iwahashi et al [71] and others [72].…”

Section: Discussionmentioning

confidence: 99%

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

et al. 2019

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Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.

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“…Surface metrics are quantitative measures of continuous variables (McGarigal et al, 2009;Cushman et al, 2010). Environmental applications of surface metrics have occurred in mountainous regions (Nogami, 1995;Riley et al, 1999;Dorner et al, 2002;McGarigal et al, 2009;Iwahashi et al, 2012) or on the sea floor (McCormick, 1994;Brock et al, 2004;Wilson et al, 2007;Wedding et al, 2008;Walker et al, 2009;Zawada and Brock, 2009;Zawada et al, 2010;Friedman et al, 2012), with only limited applications in riverine settings (Everson and Boucher, 1998;Aberle et al, 2010;Legleiter, 2014b).…”

Section: Introductionmentioning

confidence: 99%

Measuring floodplain spatial patterns using continuous surface metrics at multiple scales

2015

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High-resolution DEMs in the study of rainfall- and earthquake-induced landslides: Use of a variable window size method in digital terrain analysis

Cited by 34 publications

References 41 publications

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

Measuring floodplain spatial patterns using continuous surface metrics at multiple scales

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