Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this latent effect provides valuable imaging support on the unobserved rainfall pattern.     

GIS‐based regional landslide susceptibility mapping: a case study in southern California

Landslides threaten lives and property throughout the United States, causing in excess of $2 billion in damages and 25–50 deaths annually. In regions subjected to urban expansion caused by population growth and/or increased storm intensities caused by changing climate patterns, the economic and society costs of landslides will continue to rise. Using a geographic information system (GIS), this paper develops and implements a multivariate statistical approach for mapping landslide susceptibility. The presented susceptibility maps are intended to help in the design of hazard mitigation and land development policies at regional scales. The paper presents (a) a GIS‐based multivariate statistical approach for mapping landslide susceptibility, (b) several dimensionless landslide susceptibility indexes developed to quantify and weight the influence of individual categories for given potential risk factors on landslides and (c) a case study in southern California, which uses 11 111 seismic landslide scars collected from previous efforts and 5389 landslide scars newly digitized from local geologic maps. In the case study, seven potential risk factors were selected to map landslide susceptibility. Ground slope and event precipitation were the most important factors, followed by land cover, surface curvature, proximity to fault, elevation and proximity to coastline. The developed landslide susceptibility maps show that areas classified as having high or very high susceptibilities contained 71% of the digitized landslide scars and 90% of the seismic landslide scars while only occupying 26% of the total study area. These areas mostly have ground slopes higher than 46% and 2‐year, 6‐hour precipitation greater than 51 mm. Only 12% of digitized landslides and less than 1% of recorded seismic landslides were located in areas classified as low or very low susceptibility, while occupying 42% of the total study region. These areas mostly have slopes less than 27% and 2‐year, 6‐hour precipitation less than 41 mm. Copyright © 2007 John Wiley & Sons, Ltd.     

Spatio-temporal analysis and simulation on shallow rainfall-induced landslides in China using landslide susceptibility dynamics and rainfall I-D thresholds

An empirical simulation method to simulate the possible position of shallow rainfall-induced landslides in China has been developed. This study shows that such a simulation may be operated in real-time to highlight those areas that are highly prone to rainfall-induced landslides on the basis of the landslide susceptibility index and the rainfall intensity-duration (I-D) thresholds. First, the study on landslide susceptibility in China is introduced. The entire territory has been classified into five categories, among which high-susceptibility regions (Zone 4- ‘High’ and 5-‘Very high’) account for 4.15% of the total extension of China. Second, rainfall is considered as an external triggering factor that may induce landslide initiation. Real-time satellite-based TMPA 3B42 products may provide real rainfall spatial and temporal patterns, which may be used to derive rainfall duration time and intensity. By using a historical record of 60 significant past landslides, the rainfall I-D equation has been calibrated. The rainfall duration time that may trigger a landslide has resulted between 3 hours and 45 hours. The combination of these two aspects can be exploited to simulate the spatiotemporal distribution of rainfall-induced landslide hazards when rainfall events exceed the rainfall I-D thresholds, where the susceptibility category is ‘high’ or ‘very high’. This study shows a useful tool to be part of a systematic landslide simulation methodology, potentially providing useful information for a theoretical basis and practical guide for landslide prediction and mitigation throughout China.     

STUDY ON THE DISTRIBUTION PATTERN OF EARTHQUAKE- TRIGGERED LANDSLIDES BASED ON SEISMIC LANDSLIDE SUSCEPTIBILITY ANALYSIS: A CASE STUDY OF LANDSLIDES TRIGGERED BY THE MS6.5 LUDIAN EARTHQUAKE IN 2014

On August 3, 2014, an M_W6.5 earthquake occurred in Ludian County, Yunnan Province, which triggered significant landslides and caused serious ground damages and casualties. Compared with the existing events of earthquake-triggered landslides, the spatial distribution of co-seismic landslides during the Ludian earthquake showed a special pattern. The relationship between the co-seismic landslides and the epicenter or the known faults is not obvious, and the maximum landslide density doesn't appear in the area near the epicenter. Peak ground acceleration (PGA), which usually is used to judge the limit boundary of co-seismic landslide distribution, cannot explain this distribution pattern. Instead of correlating geological and topographic factors with the co-seismic landslide distribution pattern, this study focuses on analyzing the influence of seismic landslide susceptibility on the co-seismic distribution. Seismic landslide susceptibility comes from a calculation of critical acceleration values using a simplified Newmark block model analysis and represents slope stability under seismic loading. Both DEM (SRTM 90m)and geological map (1 ︰ 200^￣￣000)are used as inputs to calculate critical acceleration values. Results show that the most susceptible slopes with the smallest critical accelerations are generally concentrated along the banks of rivers. The stable slopes, which have the larger critical accelerations and are comparably stable, are in the places adjacent to the epicenter. Comparison of the distribution of slope stability and the real landslides triggered by the 2014 M_W6.1 Ludian earthquake shows a good spatial correlation, meaning seismic landslide susceptibility controls the co-seismic landslide distributions to a certain degree. Moreover, our study provides a plausible explanation on the special distribution pattern of Ludian earthquake triggered landslides. Also the paper discusses the advantages of using the seismic landslide susceptibility as a basic map, which will offer an additional tool that can be used to assist in post-disaster response activities as well as seismic landslides hazards zonation.     

Factors controlling landslide frequency–area distributions

A power-law relation for the frequency–area distribution (FAD) of medium and large landslides (e.g. tens to millions of square meters) has been observed by numerous authors. But the FAD of small landslides diverges from the power-law distribution, with a rollover point below which frequencies decrease for smaller landslides. Some studies conclude that this divergence is an artifact of unmapped small landslides due to lack of spatial or temporal resolution; others posit that it is caused by the change in the underlying failure process. An explanation for this dilemma is essential both to evaluate the factors controlling FADs of landslides and power-law scaling, which is a crucial factor regarding both landscape evolution and landslide hazard assessment. This study examines the FADs of 45 earthquake-induced landslide inventories from around the world in the context of the proposed explanations. We show that each inventory probably involves some combination of the proposed explanations, though not all explanations contribute to each case. We propose an alternative explanation to understand the reason for the divergence from a power-law. We suggest that the geometry of a landslide at the time of mapping reflects not just one single movement but many, including the propagation of numerous smaller landslides before and after the main failure. Because only the resulting combination of these landslides can be observed due to a lack of temporal resolution, many smaller landslides are not taken into account in the inventory. This reveals that the divergence from the power-law is not necessarily attributed to the incompleteness of an inventory. This conceptual model will need to be validated by ongoing observation and analysis. Also, we show that because of the subjectivity of mapping procedures, the total number of landslides and total landslide areas in inventories differ significantly, and therefore the shapes of FADs also differ considerably. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Landslide susceptibility analysis based on citizen reports

Landslide susceptibility estimates are essential for reducing the risk posed by landslides to social and economic well-being. However, estimates of landslide susceptibility depend on reliable landslide inventories whose production requires extensive field or remote sensing efforts. Further, most inventories are not updated through time and thus may not capture the influence of changes in climate and/or land use. Inventories based on citizen reports of landslide occurrence, have the potential to overcome these limitations. Such an inventory can be produced from citizen reports to a 311-phone and online system, a nationwide database that updates real-time and records reported landslides location and timing. Whereas this landslide inventory is promising, it has not used for landslide susceptibility analyses and may be associated with spatial uncertainties and reporting biases. In this study we explore the use of 311-based landslide inventory for landslide susceptibility estimates in Pittsburgh, PA, USA, where landslide risk is among the highest in the nation. We compare the 311-based inventory to field-validated inventories through a multi-pronged approach that combines field validation of 311-reported landslides, probabilistic analysis of the association between landslides and the underlying topographic and geologic factors, and spatial filtering. Our results show that: (a) approximately 70% of the 311-reported landslides are associated with an identifiable landslide in the field; (b) the spatial uncertainty of the 311-reported landslides is 104 ± 25 m; (c) 311-reported landslides differ from other inventories in that they are primarily associated with proximity to roads, however, field-correction of 311-reported landslide locations rectifies this anomaly; (d) a simple spatial filter, scaled by the uncertainty in location as determined from a subset of the 311-data, can increase the consistency between the 311-reported inventory and field-validated inventories. These results suggest that 311-based landslide inventories can improve susceptibility estimates at a relatively low cost and high temporal resolution.     

基于不同核函数的2010年玉树地震滑坡空间预测模型研究

基于统计学习理论与地理信息系统(GIS)技术的地震滑坡灾害空间预测是一个重要的研究方向,其可以对相似地震条件下地震滑坡的发生区域进行预测.2010年4月14日07时49分(北京时间),青海省玉树县发生了M_w6.9级大地震,作者基于高分辨率遥感影像解译与现场调查验证的方法,圈定了2036处本次地震诱发滑坡,这些滑坡大概分布在一个面积为1455.3 km²的矩形区域内.本文以该矩形区域为研究区,以GIS与支持向量机(SVM)模型为基础,开展基于不同核函数的地震滑坡空间预测模型研究.应用GIS技术建立玉树地震滑坡灾害及相关滑坡影响因子空间数据库,选择高程、坡度、坡向、斜坡曲率、坡位、水系、地层岩性、断裂、公路、归一化植被指数(NDVI)、同震地表破裂、地震动峰值加速度(PGA)共12个因子作为地震滑坡预测因子.以SVM模型为基础,基于线性核函数、多项式核函数、径向基核函数、S形核函数等4类核函数开展地震滑坡空间预测研究,分别建立了玉树地震滑坡危险性指数图、危险性分级图、预测结果图.4类核函数对应的模型正确率分别为79.87%,83.45%,84.16%,64.62%.基于不同的训练样本开展模型训练与讨论工作,表明径向基核函数是最适用于该地区的地震滑坡空间预测模型.本文为地震滑坡空间预测模型中核函数的科学选择提供了依据,也为地震区的滑坡防灾减灾工作提供了参考.     

基于高分卫星遥感影像的地震应急滑坡编目与分布特征探讨——以2017年8月8日九寨沟7.0级地震为例

地震应急是减轻地震灾害的重要途径之一。地震应急工作具有时间紧迫、事关重大的特点。2017年8月8日四川九寨沟M_S7.0级地震发生后,为快速、准确地提供地震引发的滑坡灾害分布,本研究基于震后第一天获取到的高分辨率遥感影像（高分二号卫星影像、北京二号卫星影像）,通过人工目视解译的方法初步建立了四川九寨沟地震滑坡编目。结果表明,该地震至少触发了622处同震滑坡,分布在沿使用影像边界框定的面积为3919km2的区域内。本研究还利用这个地震滑坡编目,统计了九寨沟地震滑坡数量和滑坡点密度（LND）与地形（坡度、坡向）、地震（地震烈度、震中距）等因素的关系。结果表明九寨沟地震滑坡多发生在坡度为20°—50°的区域内,滑坡的易发性随着坡度的增加而增加。受地震波传播方向的影响,E、SE向是地震滑坡较易发生的坡向。滑坡的易发程度和地震烈度呈正相关,即随着烈度的增大,滑坡易发性增大。滑坡易发性还随着震中距增加而降低,这是由于地震波能量随震中距的增加而衰减导致的。     

基于分形的巴谢河流域滑坡空间分布格局及其影响因素分析

定量研究区域滑坡空间分布规律,揭示不同类型滑坡的分布格局,对预测和评价滑坡危险性有重要指导意义。基于ArcGIS空间分析功能及分形理论的关联维数和盒计维数,分析了巴谢河流域黄土滑坡及黄土-泥岩滑坡的空间分布格局及其影响因素。结果表明：区域滑坡个体关联具有多尺度分形,黄土滑坡与黄土-泥岩滑坡分别在8 km、12 km尺度上存在阈值,滑坡个体在该阈值尺度前后呈现不同的相关程度,且黄土滑坡个体空间的关联程度和聚集程度均高于黄土-泥岩滑坡;黄土-泥岩滑坡分布范围广、形态复杂,其面积展布盒计维数大于黄土滑坡;地层岩性及坡度对两类滑坡分布格局的影响较大,沟壑密度次之,起伏度影响较小。     

GIS支持下的地震诱发滑坡危险区预测研究

为了满足对地震诱发滑坡危险区预测的不断增长的迫切要求，灾害评价成为帮助决策过程重要的基础工具之一。即使地震滑坡危险性各组份的评价很困难，但地理信息可辅助提出这种灾害制图的有关方法。描述了用于地理信息系统识别和定量计算不同地震滑坡危险区的技术方法，确定了地震烈度、地形坡度、岩土体类型和现存滑坡密度共4个因子参与的地震诱发滑坡危险性分析。在ARC／INFO DRID支持下，进行叠合分析，由此编制了云南省地震诱发滑坡危险区预测图。由地貌学家提出的地震诱发滑坡预测为规划和工程师提供了对区域规划和建筑工程有价值的技术方法。     

Investigating Landslides Caused by Earthquakes – A Historical Review

Post-earthquake field investigations of landslide occurrence have provided a basis for understanding, evaluating, and mapping the hazard and risk associated withearthquake-induced landslides. This paper traces thehistorical development of knowledge derived from these investigations. Before 1783, historical accounts of the occurrence of landslides in earthquakes are typically so incomplete and vague that conclusions based on these accounts are of limited usefulness. For example, the number of landslides triggered by a given event is almost always greatly underestimated. The first formal, scientific post-earthquake investigation that included systematic documentation of the landslides was undertaken in the Calabria region of Italy after the 1783 earthquake swarm. From then until the mid-twentieth century, the best information on earthquake-induced landslides came from a succession ofpost-earthquake investigations largely carried out by formal commissions that undertook extensive ground-based field studies. Beginning in the mid-twentieth century, when the use of aerial photography became widespread, comprehensive inventories of landslide occurrence have been made for several earthquakes in the United States, Peru, Guatemala, Italy, El Salvador, Japan, and Taiwan. Techniques have also been developed for performing ``retrospective' analyses years or decades after an earthquake that attempt to reconstruct the distribution of landslides triggered by the event. The additional use of Geographic Information System (GIS) processing and digital mapping since about 1989 has greatly facilitated the level of analysis that can applied to mapped distributions of landslides. Beginning in 1984, syntheses of worldwide and national data on earthquake-induced landslides have defined their general characteristics and relations between their occurrence and various geologic and seismic parameters. However, the number of comprehensive post-earthquake studies of landslides is still relatively small, and one of the most pressing needs in this area of research is for the complete documentation of landslides triggered by many more earthquakes in a wider variety of environments.     

断裂性质与滑坡分布的关系——以汶川地震中的大型滑坡为例

越来越多的地震滑坡相对于地震断层的不对称分布震例让人们意识到断层上盘效应的存在。 然而,目前有关断裂运动方式与滑坡空间分布关系的研究还不够充分和深入。在收集大量地震滑坡震例资料并获得其分布规律的基础上,建立了一个简化的断层模型,以地震波在地表与断层面之间反射传播特性为基础,探讨断层倾角改变对地表地震动强度的影响。进而,以汶川地震触发的大型滑坡为例,研究了断层的几何特征和运动方式对诱发滑坡空间分布的影响。结果表明,断层的倾角对滑坡空间分布范围具有控制作用,随着倾角的增加,垂直断层走向的滑坡分布范围逐渐减小;并且,大型滑坡的初始坡面受到断裂运动方向的影响,与断裂运动方向一致的坡面更容易发生滑坡。所获结果不仅有助于提高区域性地震滑坡危险区域的预测精度,而且对认识大型滑坡的滑动机制、主控因素以及可能的滑动规模、滑距等也起到促进作用。通过对滑坡崩塌的认识来辅助提高对地质构造、地震断层等的认识,应是地震诱发滑坡崩塌研究的新的意义所在。     

基于GIS和专家知识的滇西南地区滑坡敏感性模糊逻辑推理方法

为了充分识别和有效减轻滑坡灾害风险,对滇西南南涧(约470 km²)和凤庆—昌宁(约2300 km²)两个研究区开展了基于GIS和专家知识的滑坡敏感性模糊逻辑评价研究。通过检查模型计算得到的历史滑坡点敏感性值与整个研究区域的滑坡敏感性平均值是否不同来评价本方法的性能,用Z值检查来测试差异的统计显著性。计算结果显示,南涧地区的Z值为4.1,相应的P值小于0.001,表明通过模型计算得到的滑坡敏感性值是该区域滑坡事件发生的良好指标;凤庆—昌宁地区的Z值为8.93,相应的P值小于0.001。在此基础上,采用自然断点法对滑坡敏感性值进行分类,根据分类结果将滑坡敏感性水平划分成5个等级:极低(0.0~0.001)、较低(0.001~0.051)、中等(0.051~0.394)、较高(0.394~0.557)和极高(0.557~1.0)。敏感性极低和较低的地区没有发现历史滑坡记录;敏感性极高地区的历史滑坡密度约是敏感性较高地区的4倍,约为敏感性中等地区的10倍。凤庆—昌宁地区的研究结果表明,从区域专家群中提取的滑坡敏感性与环境因子关系的知识可以外延到滇西南其它地区。     

Electrical imaging of sliding geometry and fluids associated with a deep seated landslide (La Clapière,France)

This paper deals with the applicability of electrical resistivity tomography (ERT) for the recognition of large landslide structures at depths, which have never previously been imaged accurately. One of the most studied and instrumented deep landslides in Europe is taken as an example: the La Clapière landslide. The first stage of the study consisted of an accurate geological mapping taking into account a morphological analysis of gravitational deformations. This allowed a very fine definition of the landslide structure, that could be compared with three provided ERT profiles performed within the landslide body. Very good correlations were obtained for the determination of sub‐horizontal structures and associated fluid circulations. It confirmed the position of the sliding surface that reached a maximum depth of 100 m. Forward computing was however necessary to determine the influence and then the presence of vertical discontinuities. It supports the use of ERT as an efficient tool for large scale landslide imaging, such as deep seated landslides. Copyright © 2010 John Wiley & Sons, Ltd.     

Spatial analysis of the reliability of transport networks subject to rainfall‐induced landslides

A methodology is developed to examine the susceptibility of a transport system to rainfall‐induced landslides and is demonstrated for part of the UK rail network with regard to the potential changes that might occur with climate change. A mathematical model is given for the system failure and a statistical model is formulated for the joint distribution of rainfall at different points along the railway line. These are used to investigate the response of earth embankments along the railway line to current and future climate scenarios, including the effects of rainfall and evapotranspiration on slope hydrology and stability. It is shown that, for the system of clay embankments in question, the moisture profile through the embankment at the end of the summer months has a critical effect on system stability, both in terms of expected failure timing and probability of failure. Further, it is seen that, with changing climate, the system stability is likely to increase unless the degradation of embankment material properties, another potential effect of changed climate, is taken into account. The spatial distribution of failures is also likely to change. Copyright © 2008 John Wiley & Sons, Ltd.     

Landslide inventories and their statistical properties

Landslides are generally associated with a trigger, such as an earthquake, a rapid snowmelt or a large storm. The landslide event can include a single landslide or many thousands. The frequency–area (or volume) distribution of a landslide event quanti?es the number of landslides that occur at different sizes. We examine three well‐documented landslide events, from Italy, Guatemala and the USA, each with a different triggering mechanism, and ?nd that the landslide areas for all three are well approximated by the same three‐parameter inverse‐gamma distribution. For small landslide areas this distribution has an exponential ‘roll‐over’ and for medium and large landslide areas decays as a power‐law with exponent ‐2·40. One implication of this landslide distribution is that the mean area of landslides in the distribution is independent of the size of the event. We also introduce a landslide‐event magnitude scale m_L = log(N_LT), with N_LT the total number of landslides associated with a trigger. If a landslide‐event inventory is incomplete (i.e. smaller landslides are not included), the partial inventory can be compared with our landslide probability distribution, and the corresponding landslide‐event magnitude inferred. This technique can be applied to inventories of historical landslides, inferring the total number of landslides that occurred over geologic time, and how many of these have been erased by erosion, vegetation, and human activity. We have also considered three rockfall‐dominated inventories, and ?nd that the frequency–size distributions differ substantially from those associated with other landslide types. We suggest that our proposed frequency–size distribution for landslides (excluding rockfalls) will be useful in quantifying the severity of landslide events and the contribution of landslides to erosion. Copyright © 2004 John Wiley & Sons, Ltd.     

Use of LIDAR‐derived images for mapping old landslides under forest

Large, deep‐seated landslides are common features in the Flemish Ardennes (Belgium). As most of these old (>100 years) landslides are located under forest in this hilly region, aerial photograph interpretation is not an appropriate landslide mapping method. This study tested the potential of LIDAR (Light Detection and Ranging) images for mapping old landslides under forest. Landslide inventory maps were created for a 125 km² area by applying the expert knowledge of seven geomorphologists to LIDAR‐derived hillshade, slope and contour line maps in a GIS environment. Each of the seven LIDAR‐based landslide inventories was compared (i) with the other six, (ii) with a detailed field survey‐based inventory, and (iii) with a comparable study in which topographic data were extracted from a topographical map. The combination of the percentage of field landslides indicated by an expert and the percentage of positional discrepancies (expressed in terms of positional mismatch) were used to evaluate the quality of the LIDAR‐based inventory maps. High‐quality LIDAR‐derived landslide inventory maps contain more than 70 per cent of the landslides mapped during the field survey, and have positional discrepancies smaller than 70 per cent when compared with the field survey‐based inventory map. Four experts and the combination map of all experts satisfied these criteria. Together the seven experts indicated all landslides mapped in the field. Importantly, LIDAR enabled the experts to find ten new landslides and to correct the boundaries of eleven (of the 77) landslides mapped during the field survey. Hence, this study showed that large‐scale LIDAR‐derived maps analysed by experienced geomorphologists can significantly improve field survey‐based inventories of landslides with a subdued morphology in hilly regions. Copyright © 2006 John Wiley & Sons, Ltd.     

Analysis of historical data for a better understanding of post-construction landslides at an artificial waterway

With growing urbanization, an increasing number of landslides worldwide are linked to construction works, especially along transportation routes. To get a better understanding of these post-construction landslides at artificial waterways, archive studies have been conducted, in addition to analysis of historical scientific literature and technical reports. The resultant database is used to describe the occurrence and the characteristics of the landslides at the Mittelland Canal, and to evaluate aspects of conditioning and preparatory factors, potential triggers and applied mitigation measures. The Mittelland Canal has been chosen because it is one of the most important artificial waterways in Europe, and the high number of landslides that occur there is unique. In particular, landslides at the Mittelland Canal were frequently reported in the first half of the 20^th century. The susceptibility to landslides is strongly influenced by material characteristics, as a large number of landslides is linked to sites where fine-grained mudstone is abundant, which quickly decomposes under immersion in water or under dynamic stress. In addition, landslides can be linked to the creation of over-steepened slopes. Construction works can also be considered another preparatory factor for landslides. In addition, some landslides in World War II appear to be connected to the blast of aerial bombings. Precipitation seems to play a minor role in triggering landslides. The applied mitigation measures were developed recently, marking the importance of the canal as a transportation route. © 2020 John Wiley & Sons, Ltd.     

How does forest structure affect root reinforcement and susceptibility to shallow landslides?

Forests can decrease the risk of shallow landslides by mechanically reinforcing the soil and positively influencing its water balance. However, little is known about the effect of different forest structures on slope stability. In the study area in St Antönien, Switzerland, we applied statistical prediction models and a physically‐based model for spatial distribution of root reinforcement in order to quantify the influence of forest structure on slope stability. We designed a generalized linear regression model and a random forest model including variables describing forest structure along with terrain parameters for a set of landslide and control points facing similar slope angle and tree coverage. The root distribution measured at regular distances from seven trees in the same study area was used to calibrate a root distribution model. The root reinforcement was calculated as a function of tree dimension and distance from tree with the root bundle model (RBMw). Based on the modelled values of root reinforcement, we introduced a proxy‐variable for root reinforcement of the nearest tree using a gamma distribution. The results of the statistical analysis show that variables related to forest structure significantly influence landslide susceptibility along with terrain parameters. Significant effects were found for gap length, the distance to the nearest trees and the proxy‐variable for root reinforcement of the nearest tree. Gaps longer than 20 m critically increased the susceptibility to landslides. Root reinforcement decreased with increasing distance from trees and is smaller in landslide plots compared to control plots. Furthermore, the influence of forest structure strongly depends on geomorphological and hydrological conditions. Our results enhance the quantitative knowledge about the influence of forest structure on root reinforcement and landslide susceptibility and support existing management recommendations for protection against gravitational natural hazards. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimating landslide susceptibility areas considering the uncertainty inherent in modeling methods

Landslides are one of the most dangerous types of natural disasters, and damage due to landslides has been increasing in certain regions of the world because of increased precipitation. Policy decision makers require reliable information that can be used to establish spatial adaptation plans to protect people from landslide hazards. Researchers presently identify areas susceptible to landslides using various spatial distribution models. However, such data are associated with a high amount of uncertainty. This study focuses on quantifying the uncertainty of several spatial distribution models and identifying the effectiveness of various ensemble methods that can be used to provide reliable information to support policy decisions. The area of study was Inje-gun, Republic of Korea. Ten models were selected to assess landslide susceptibility. Moreover, five ensemble methods were selected for the aggregated results of the 10 models. The uncertainty was quantified using the coefficient of variation and the uncertainty map we developed revealed areas with strongly differing values among single models. A matrix map was created using an ensemble map and a coefficient of variation map. Using matrix analysis, we identified the areas that are most susceptible to landslides according to the ensemble model with a low uncertainty. Thus, the ensemble model can be a useful tool for supporting decision makers. The framework of this study can also be employed to support the establishment of landslide adaptation plans in other areas of the Republic of Korea and in other countries.