Development of an empirical model for rainfall-induced hillside vulnerability assessment: a case study on Chen-Yu-Lan watershed,Nantou, Taiwan

In Taiwan, the hillside is about 70 % of total area. These areas also have steep topography and geological vulnerability. When an event of torrential rain comes during a typhoon, the landslide disasters usually occur at these areas due to the long duration and high intensity of rainfall. Therefore, a design which considers the potential landslide has become an important issue in Taiwan. In this study, a temporal characteristic of landslide fragility curve (LFC) was developed, based on the geomorphological and vegetation factors using landslides at the Chen-Yu-Lan watershed in Taiwan, during Typhoon Sinlaku (September 2008) and Typhoon Morakot (August 2009). This study addressed an effective landslide hazard assessment process, linking together the post-landslide damage and post-rainfall data for LFC model. The Kriging method was used to interpolate the rainfall indices (R ₀, R, I) for numerical analysis. Remote sensing data from SPOT images were applied to analyze the landslide ratio and vegetation conditions. The 40-m digital elevation model was used for slope variation analysis in the watershed, and the maximum likelihood estimate was conducted to determine the mean and standard deviation parameters of the proposed empirical LFC model. This empirical model can express the probability of exceeding a damage state for a certain classification (or conditions) of landslides by considering a specific hazard index for a given event. Finally, the vulnerability functions can be used to assess the loss from landslides, and, in the future, to manage the risk of debris flow in the watershed.     

Application and verification of a fractal approach to landslide susceptibility mapping

Landslide susceptibility mapping is essential for land-use activities and management decision making in hilly or mountainous regions. The existing approaches to landslide susceptibility zoning and mapping require many different types of data. In this study, we propose a fractal method to map landslide susceptibility using historical landslide inventories only. The spatial distribution of landslides is generally not uniform, but instead clustered at many different scales. In the method, we measure the degree of spatial clustering of existing landslides in a region using a box-counting method and apply the derived fractal clustering relation to produce a landslide susceptibility map by means of GIS-supported spatial analysis. The method is illustrated by two examples at different regional scales using the landslides inventory data from Zhejiang Province, China, where the landslides are mainly triggered by rainfall. In the illustrative examples, the landslides from the inventory are divided into two time periods: The landslides in the first period are used to produce a landslide susceptibility map, and those in the late period are taken as validation samples for examining the predictive capability of the landslide susceptibility maps. These examples demonstrate that the landslide susceptibility map created by the proposed technique is reliable.     

A simplified three-dimensional shallow landslide susceptibility framework considering topography and seismicity

Shallow landslides are a prevalent concern in mountainous or hilly regions that can result in severe societal, economic, and environmental impacts. The challenge is further compounded as the size and location of a potential slide is often unknown. This study presents a generalized approach for the evaluation of regional shallow landslide susceptibility using an existing shallow landslide inventory, remote sensing data, and various geotechnical scenarios. The three-dimensional limit equilibrium model derived in this study uses a raster-based approach that uniquely integrates tree root reinforcement, earth pressure boundary forces, and pseudo-static seismic accelerations. Contributions of this methodology include the back-calculation of soil strength from a landslide inventory, sensitivity analyses regarding landslide size-pixel size relationships, and the determination of shallow landslide susceptibility for a landscape or infrastructure considering various root, water, and seismic conditions using lidar bare-earth DEMs as a topographic input. Using a distribution of inventoried landslide points and random points in non-landslide locales, the proposed methodology demonstrated reasonable correlation between regions of high landslide susceptibility and observed shallow landslides within a 150-km² region of the Oregon Coast Range when the water-height ratio was 0.5. The method may be improved by considering spatial hydrologic conditions and geology more explicitly.     

Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography

Large deep-seated landslides can be reactivated during intense events, and they can evolve into destructive failures. They are generally difficult to recognize in the field, especially when they develop in densely forested areas. A detailed and constantly updated inventory map of such phenomena, and the recognition of their topographic signatures is absolutely a key tool for landslide risk mitigation.The aim of this work is to test in forested areas, the performance of the new automatic and objective methodology developed by Tarolli et al. (2012) for geomorphic features extraction (landslide crowns) from high resolution topography (LiDAR derived Digital Terrain Models – DTMs). The methodology is based on the detection of landslides through the use of thresholds obtained by the statistical analysis of variability of landform curvature. The study was conducted in a high-risk area located in the central-south Taiwan, where an accurate field survey on landsliding processes and a high-quality set of airborne laser scanner elevation data are available. The area has been chosen because some of the deep-seated landslides are located near human infrastructures and their reactivation is highly dangerous. Thanks to LiDAR’s capability to detect the bare ground elevation data in forested areas, it was possible to recognize in detail landslide features also in remote regions difficult to access. The results, if compared with the previous work of Tarolli et al. (2012), mainly focused on shallow landslides, and in a not forested area, indicate that for deep-seated landslides, where the crowns are more evident, and they are present at large scale, the tested methodology performs better (higher quality index). The method can be used to interactively assist the interpreter/user on the task of deep-seated landslide hazard mapping, and risk assessment planning of such regions.     

Landslide hazard mapping using logistic regression model in Mackenzie Valley,Canada

A logistic regression model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards in a mountainous environment. A case study is conducted in the mountainous southern Mackenzie Valley, Northwest Territories, Canada. To determine the factors influencing landslides, data layers of geology, surface materials, land cover, and topography were analyzed by logistic regression analysis, and the results are used for landslide hazard mapping. In this study, bedrock, surface materials, slope, and difference between surface aspect and dip direction of the sedimentary rock were found to be the most important factors affecting landslide occurrence. The influence on landslides by interactions among geologic and geomorphic conditions is also analyzed, and used to develop a logistic regression model for landslide hazard mapping. The comparison of the results from the model including the interaction terms and the model not including the interaction terms indicate that interactions among the variables were found to be significant for predicting future landslide probability and locating high hazard areas. The results from this study demonstrate that the use of a logistic regression model within a GIS framework is useful and suitable for landslide hazard mapping in large mountainous geographic areas such as the southern Mackenzie Valley.     

Landslide dam formation susceptibility analysis based on geomorphic features

Global climate change has increased the frequency of abnormally high rainfall; such high rainfall events in recent years have occurred in the mountainous areas of Taiwan. This study identifies historical earthquake- and typhoon-induced landslide dam formations in Taiwan along with the geomorphic characteristics of the landslides. Two separate groups of landslides are examined which are classified as those that were dammed by river water and those that were not. Our methodology applies spatial analysis using geographic information system (GIS) and models the geomorphic features with 20?×?20 m digital terrain mapping. The Spot 6 satellite images after Typhoon Morakot were used for an interpretation of the landslide areas. The multivariate statistical analysis is also used to find which major factors contribute to the formation of a landslide dam. The objective is to identify the possible locations of landslide dams by the geomorphic features of landslide-prone slopes. The selected nine geomorphic features include landslide area, slope, aspect, length, width, elevation change, runout distance, average landslide elevation, and river width. Our four geomorphic indexes include stream power, form factor, topographic wetness, and elevation–relief ratio. The features of the 28 river-damming landslides and of the 59 non-damming landslides are used for multivariate statistical analysis by Fisher discriminant analysis and logistic regression analysis. The principal component analysis screened out eleven major geomorphic features for landslide area, slope, aspect, elevation change, length, width, runout distance, average elevation, form factor, river width, stream power, and topography wetness. Results show that the correctness by Fisher discriminant analysis was 68.0 % and was 70.8 % by logistic regression analysis. This study suggests that using logistic regression analysis as the assessment model for identifying the potential location of a landslide dam is beneficial. Landslide threshold equations applying the geomorphic features of slope angle, angle of landslide elevation change, and river width (H _L/W _R) to identify the potential formation of natural dams are proposed for analysis. Disaster prevention and mitigation measures are enhanced when the locations of potential landslide dams are identified; further, in order to benefit such measures, dam volume estimates responsible for breaches are key.     

Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods

Landslides are among the most common and dangerous natural hazards in mountainous regions that can cause damage to properties and loss of lives. Landslide susceptibility mapping (LSM) is a critical tool for preventing or mitigating the negative impacts of landslides. Although many previous studies have employed various statistical methods to produce quantitative maps of the landslide susceptibility index (LSI) based on inventories of past landslides and contributing factors, they are mostly ad hoc to a specific area and their success has been hindered by the lack of a methodology that could produce the right mapping units at proper scale and by the lack of a general framework for objectively accounting for the differing contribution of various preparatory factors. This paper addresses these issues by integrating the geomorphon and geographical detector methods into LSM to improve its performance. The geomorphon method, an innovative pattern recognition approach for identifying landform elements based on the line of sight concept, is adapted to delineate ridge lines and valley lines to form slope units at self-adjusted spatial scale suitable for LSM. The geographical detector method, a spatial variance analysis method, is integrated to objectively assign the weights of contributing factors for LSM. Applying the new integrated approach to I-Lan, Taiwan produced very significant improvement in LSI mapping performance than a previous model, especially in highly susceptible areas. The new method offers a general framework for better mapping landslide susceptibility and mitigating its negative impacts.     

Statistical approach to earthquake-induced landslide susceptibility

Susceptibility analysis for predicting earthquake-induced landslides has most frequently been done using deterministic methods; multivariate statistical methods have not previously been applied. In this study, however, we introduce a statistical methodology that uses the intensity of earthquake shaking as a landslide triggering factor. This methodology is applied in a study of shallow earthquake-induced landslides in central western Taiwan. The results show that we can accurately interpret landslide distribution in the study area and predict the occurrence of landslides in neighboring regions. This susceptibility model is capable of predicting shallow landslides induced during an earthquake scenario with similar range of ground shaking, without requiring the use of geotechnical, groundwater or failure depth data.     

Earthquake-induced landslide hazard and vegetation recovery assessment using remotely sensed data and a neural network-based classifier: a case study in central Taiwan

A catastrophic earthquake with a Richter magnitude of 7.3 occurred in the Chi-Chi area of Nantou County on 21 September 1999. Large-scale landslides were generated in the Chiufenershan area of Nantou County in central Taiwan. This study used a neural network-based classifier and the proposed NDVI-based quantitative index coupled with multitemporal SPOT images and digital elevation models (DEMs) for the assessment of long-term landscape changes and vegetation recovery conditions at the sites of these landslides. The analyzed results indicate that high accuracy of landslide mapping can be extracted using a neural network-based classifier, and the areas affected by these landslides have gradually been restored from 211.52 ha on 27 September 1999 to 113.71 ha on 11 March 2006, a reduction of 46.24%, after six and a half years of assessment. In accordance with topographic analysis at the sites of the landslides, the collapsed and deposited areas of the landslide were 100.54 and 110.98 ha, with corresponding debris volumes of 31,983,800 and 39,339,500 m³. Under natural vegetation succession, average vegetation recovery rate at the sites of the landslides reached 36.68% on 11 March 2006. The vegetation recovery conditions at the collapsed area (29.17%) are shown to be worse than at the deposited area (57.13%) due to topsoil removal and the steep slope, which can be verified based on the field survey. From 1999 to 2006, even though the landslide areas frequently suffered from the interference of typhoon strikes, the vegetation succession process at the sites of the landslides was still ongoing, which indicates that nature, itself, has the capability for strong vegetation recovery for the denudation sites. The analyzed results provide very useful information for decision-making and policy-planning in the landslide area.     

Landslide susceptibility mapping using GIS-based multi-criteria decision analysis,support vector machines,and logistic regression

Identification of landslides and production of landslide susceptibility maps are crucial steps that can help planners, local administrations, and decision makers in disaster planning. Accuracy of the landslide susceptibility maps is important for reducing the losses of life and property. Models used for landslide susceptibility mapping require a combination of various factors describing features of the terrain and meteorological conditions. Many algorithms have been developed and applied in the literature to increase the accuracy of landslide susceptibility maps. In recent years, geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) have been successfully applied in the production of landslide susceptibility maps. In this study, the MCDA and SVR methods were employed to assess the shallow landslide susceptibility of Trabzon province (NE Turkey) using lithology, slope, land cover, aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data. Performances of the methods were compared with that of widely used logistic regression model using ROC and success rate curves. Results showed that the MCDA and SVR outperformed the conventional logistic regression method in the mapping of shallow landslides. Therefore, multi-criteria decision method and support vector regression were employed to determine potential landslide zones in the study area.     

俯冲带地区压扭断裂型地震触发滑坡及其剥蚀厚度空间分布规律分析

2010年1月12日海地MW 7.0级地震触发了大量的滑坡。我们基于GIS与遥感技术构建了3类详细完备的海地地震滑坡编录图,分别为单体滑坡面分布数据,滑坡中心点位置数据与滑坡后壁点位置数据。结果表明海地地震触发了30828处滑坡,这些滑坡大致分布在一个面积为3192.85km2的区域内,滑坡覆盖面积为15.736km2。基于滑坡中心点密度(LCND)、滑坡后壁点密度(LTND)、滑坡面积百分比(LAP)与滑坡剥蚀厚度(LET)这4个衡量指标,使用统计分析方法,分析了海地地震滑坡及其剥蚀厚度与地震参数、地形参数、公路参数的关系。分析结果表明滑坡与坡度、地震动峰值加速度(PGA)存在大致的正相关关系; 与距离恩里基约芭蕉园断裂、距离水系存在大致的负相关关系; 滑坡沿着恩里基约芭蕉园断裂距离的统计结果表明,震中以西距离震中22~26km与8~12km的区域,与震中以东距离震中6~18km的区域是地震滑坡易发区域; 斜坡曲率值越接近0,也就是坡面较平的斜坡越不容易在地震条件下发生滑动; LCND、LTND、LAP与LET高值对应的高程区间为200~1200m; 滑坡发生的优势坡向为E方向; 滑坡的发生与距离震中、距离公路没有太明确的关系。     

Evolution of landslide hotspots in Taiwan

Among the disasters facing Taiwan, earthquakes and typhoons incur the greatest monetary losses, and landslide disasters inflict the greatest damage in mountainous areas. The nationwide landslide susceptibility map gives an indication of where landslides are likely to occur in the future; however, there is no objective index indicating the location of landslide hotspots. In this study, we used statistical analysis to locate landslide hotspots in catchments in Taiwan. Global and local spatial autocorrelation analysis revealed the existence of landslide clusters between 2003 and 2012 and identified a concentration of landslide hotspots in the eastern part of Central Taiwan. The extreme rainfall brought by typhoon Morakot also led to the formation of new landslide hotspots in Southern Taiwan. This study provides a valuable reference explaining changes in landslide hotspots and identifying areas of high hotspot concentration to facilitate the formulation of strategies to deal with landslide risk.     

Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal

Mountainous areas in Nepal are prone to landslides, resulting in an enormous loss of life and property every year. As a first step towards mitigating or controlling such problems, it is necessary to prepare landslide susceptibility maps. Various methodologies have been proposed for landslide susceptibility mapping. This study applies the weight of evidence method to the Tinau watershed in west Nepal. A landslide susceptibility map is prepared on the basis of field observations and available data of geology, land use, topography and hydrology. Predicted susceptibility levels are found to be in good agreement with the locations of past landslides. The results show that about 30?% of the area is highly susceptible to landsliding. The present results provide useful information to the authorities concerning the landslide susceptibility zones and possible improvements for disaster management activities and sustainable development.     

Quality assessment of the Italian Landslide Inventory using GIS processing

Landslides constitute one of the most important natural hazards in Italy as they are widespread and result in considerable damage and fatalities every year. The Italian Landslide Inventory (IFFI) Project was launched in 1999 with the aim of identifying and mapping landslides over the entire Italian territory. The inventory currently holds over 480,000 landslides and has been available by means of Web services since 2005. The aim of this study is to define quality indices for evaluation of the homogeneity and completeness of the IFFI database. In order to estimate the completeness of the landslide attribute information, a heuristic approach has been used to assign weighting values to significant parameters selected from the landslide data sheet. The completeness and homogeneity of the landslide mapping has been evaluated by means of three different methods: an area-frequency distribution analysis; the proximity of the landslides surveyed to urban areas; variation of the landslide index within the same lithology. The quality indices have allowed identification of areas with a high level of completeness and critical areas in which the data collected have been underestimated or are not very accurate. The quality assessment of collected and stored data is essential in order to use the IFFI database for definition and implementation of landslide susceptibility models and for land use planning and management.     

The determination of statistical and deterministic hydrological landslide-triggering thresholds

 Hydrological landslide-triggering thresholds separate combinations of daily and antecedent rainfall or of rainfall intensity and duration that triggered landslides from those that failed to trigger landslides. They are required for the development of landslide early warning systems. When a large data set on rainfall and landslide occurrence is available, hydrological triggering thresholds are determined in a statistical way. When the data on landslide occurrence is limited, deterministic models have to be used. For shallow landslides directly triggered by percolating rainfall, triggering thresholds can be established by means of one-dimensional hydrological models linked to the infinite slope model. In the case of relatively deep landslides located in topographic hollows and triggered by a slow accumulation of water at the soil-bedrock contact, simple correlations between landslide occurrence and rainfall can no longer be established. Therefore real-time failure probabilities have to be determined using hydrological catchment models in combination with the infinite slope model. Received: 15 October 1997 · Accepted: 25 June 1997     

Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China

In the Three Gorges of China, there are frequent landslides, and the potential risk of landslides is tremendous. An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of lives and properties caused by these landslides. This paper presents landslide susceptibility mapping on the Zigui-Badong of the Three Gorges, using rough sets and back-propagation neural networks (BPNNs). Landslide locations were obtained from a landslide inventory map, supported by field surveys. Twenty-two landslide-related factors were extracted from the 1:10,000-scale topographic maps, 1:50,000-scale geological maps, Landsat ETM + satellite images with a spatial resolution of 28.5 m, and HJ-A satellite images with a spatial resolution of 30 m. Twelve key environmental factors were selected as independent variables using the rough set and correlation coefficient analysis, including elevation, slope, profile curvature, catchment aspect, catchment height, distance from drainage, engineering rock group, distance from faults, slope structure, land cover, topographic wetness index, and normalized difference vegetation index. The initial, three-layered, and four-layered BPNN were trained and then used to map landslide susceptibility, respectively. To evaluate the models, the susceptibility maps were validated by comparing with the existing landslide locations according to the area under the curve. The four-layered BPNN outperforms the other two models with the best accuracy of 91.53 %. Approximately 91.37 % of landslides were classified as high and very high landslide-prone areas. The validation results show sufficient agreement between the obtained susceptibility maps and the existing landslide locations.     

Earthquake triggered landslide–Indian scenario

Landslide initiation due to earthquake is one of the most prevalent seismic hazard, which claims hundreds of lives in the Himalayan mountainous terrains of India. Number of landslides, maximum distance from the epicentre and total landslide area/volume are correlatable with earthquake magnitudes. Application of globally accepted earthquake triggered landslide parameter models do not match well with published data for the Himalayan earthquake triggered landslides. Considering the incompleteness of landslide inventories for most of the Himalayan earthquakes, development of regression equations show that in the Himalayan environment, landslide may trigger even with imperciptable earthquakes affecting longer distances having earthquake magnitude of more than 8 M with potential to affect more areas than the global expectations.     

Suitability analysis for topographic factors in loess landslide research: a case study of Gangu County,China

Loess Plateau is one of the ecologically fragile regions in China. It is one of the slippery strata of which landslides often developed. The formation and development of landslides are mainly affected by various natural environments, triggering factors, the vulnerability of landslide-bearing bodies, and topography has a controlling effect on landslides and determines landslide distribution. As important environmental elements, the selection and reclassification of topographic factors are the basis for loess landslide vulnerability map. In this study, our research suggests an effective workflow to select and analyze the topographic factors in the loess landslides. Nine hazard-formative environmental factors [e.g., slope, aspect, slope shape (SS), slope of slope (SOS), slope of aspect (SOA), surface amplitude (SA), surface roughness (SR), incision depth (ID) and elevation variation coefficient (EVC)] are prepared for landslide suitability analysis. The models of certainty factor, sensitivity index and correlation coefficient are combined to select and analyze the suitability of these factors. Four topographic factors (i.e., slope, SOS, SS and SR) were ultimately selected to carry out the landslide vulnerability mapping with other factors. Our results showed that most of the landslides were located in medium and high classes and accounting for 75.3%, and these places also coincided with higher economies and intense human activities. Our research also suggested that in situ measurements are necessary to determine how to reclassify these topographic factors and how many grades these topographic factors divided, which would further improve the reliability of landslide vulnerability map for the decision makers to deal with the possible future landslides in terms of safety and human activities.     

A GIS method for landslide inventory and susceptibility mapping in the Río El Estado watershed,Pico de Orizaba volcano,México

In volcanic terrains, dormant stratovolcanoes are very common and can trigger landslides and debris flows continually along stream systems, thereby affecting human settlements and economic activities. It is important to assess their potential impact and damage through the use of landslide inventory maps and landslide models. In Mexico, numerous geographic information systems (GIS)-based applications have been used to represent and assess slope stability. However, there is no practical and standardized landslide mapping methodology under a GIS. This work provides an overview of the ongoing research project from the Institute of Geography at the National Autonomous University of Mexico that seeks to conduct a multi-temporal landslide inventory and produce a landslide susceptibility map by using GIS. The Río El Estado watershed on the southwestern flank of Pico de Orizaba volcano, the highest mountain in Mexico, is selected as a study area. The geologic and geomorphologic factors in combination with high seasonal precipitation, high degree of weathering, and steep slopes predispose the study area to landslides. The method encompasses two main levels of analysis to assess landslide susceptibility. First, the project aims to derive a landslide inventory map from a representative sample of landslides using aerial orthophotographs and field work. Next, the landslide susceptibility is modelled by using multiple logistic regression implemented in a GIS platform. The technique and its implementation of each level in a GISs-based technology is presented and discussed.     

The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area,China

Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.