Validation of a region-wide model of landslide susceptibility in the Manawatu–Wanganui region of New Zealand

Since European settlement 160 years ago, much of the indigenous forest in New Zealand hill country has been cleared for pastoral agriculture, resulting in increased erosion and sedimentation. To prioritise soil conservation work in the Manawatu–Wanganui region, we developed a model of landslide susceptibility. It assigns high susceptibility to steep land not protected by woody vegetation and low susceptibility everywhere else, following the commonly used approach for identifying inappropriate land use. A major storm on 15–16 February 2004 that produced many landslides was used to validate the model. The model predicted hills at risk to landsliding with moderate accuracy: 58% of erosion scars in the February storm occurred on hillsides considered to be susceptible. The model concept of slope thresholds, above which the probability of landsliding is high and below which the probability is low, is not adequate because below 30° the probability of landsliding is approximately linearly related to slope. Thus, reforestation of steep slopes will need to be combined with improved vegetation management for soil conservation on moderate slopes to significantly reduce future landsliding.     

Predictive landslide susceptibility assessment using the weight-of-evidence method in north-central Garhwal Himalaya,India

Landslides are frequent natural disasters in mountainous regions, particularly in the Himalayas in India during the southwest monsoon season. Although scientific study of landslides has been in progress for years, no significant achievement has been made to preclude landsliding and allay disasters. This research was undertaken to understand the areal distribution of landslides based on geological formations and geomorphological processes, and to provide more precise information regarding slope instability and mechanisms of failure. After completing a landslide inventory, prepared through field investigation and satellite image analysis, 493 landslides, comprising 131 investigated in the field and 362 identified from satellite imagery, were identified and mapped. The areal distribution of these landslides shows that sites more prone to landsliding have moderate to steep slopes, the lithology of the Lesser Himalayan formations, and excavations for road corridors. Landslide susceptibility zones were delineated for the area using the weight-of-evidence method on the basis of the frequency and distribution of landslides. Weights of selected variables were computed on the basis of severity of triggering factors. The accuracy of landslide susceptibility zones, calculated statistically (R² = .93), suggests high accuracy of the model for predicting landsliding in the area.     

Relationships between rainfall and landsliding in the Middle Hills,Nepal

Many studies have documented major landslide events in mountain areas following heavy rainfall amounts. In the Himalaya, landslides occur during every monsoon period, but the role of rainfall in triggering these failures is not clear. This paper reports the results of a three-year study (1991-1993) into landsliding in the Likhu Khola drainage basin, Middle Hills, Nepal. Considerable annual variability in numbers, types and sizes of landslides was noted. Some of this variability can be explained by fluctuations in rainfall amounts and intensities, but many landslides were explained more easily by other controlling factors. In situations where slopes are extensively terraced for agriculture, with some terraces being intensely irrigated and others not, relationships between landsliding and rainfall amounts are complex and no simple explanations can be made.     

汶川地震灾区滑坡空间特征变化分析

“5.12”汶川大地震触发了大量滑坡,给人民群众生命财产和社会经济发展造成了巨大损失。基于GIS空间分析方法,结合震前和震后的滑坡编目数据,对滑坡与坡度、坡向、高程、岩土类型、道路、河流和断裂带等7个孕灾环境因素的空间分布关系进行统计分析。结果表明：滑坡与孕灾环境因素的空间分布关系受地震的影响比较大。相比于震前,震后滑坡发生的优势坡度、优势岩土类型、优势距离缓冲区等均发生了很大的变化;并且坡向、距道路距离、距河流距离等因素对滑坡有明显地趋势性影响。     

Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area

A landslide susceptibility evaluation is vital for disaster management and development planning in the Yangtze River Three Gorges Reservoir Area. In this study, with the support of remote sensing and Geographic Information System, 4 factor groups comprising 10 separate subfactors of landslide-related data layers were selected to establish a susceptibility evaluation model based on the back-propagation neural network including slope, aspect, plan curvature, strata and lithology, distance to faults, land use/land cover, Normalized Difference Vegetation Index, Normalized Difference Water Index, distance from roads, and effect of rivers. During model development, a three-layered interconnected neural network structure of 10 (input layer) × 20 (hidden layer) × 1 (output layer) was used for evaluating the landslide susceptibility in Guojiaba. At the same time, a back-propagation algorithm was applied to calculate the weights between the input layer and the hidden layer and between the hidden layer and the output layer. The results showed that the effect of slope has the highest weight value (0.2051), which is more than two times that of the other factors, followed by strata and lithology (0.1213) and then the effect of rivers (0.1201). At the end of the susceptibility evaluation, the area was divided into four zones such as very high, high, moderate and low susceptibility. For verification, the receiver operating characteristic curve for the back-propagation neural network-derived landslide susceptibility evaluation model was drawn, and the results showed that the area under the receiver operating characteristic curve was 0.8790 and the prediction accuracy was 88%. Furthermore, the results obtained from this article were then verified by comparing with the existing landslide historical data and multiple field-verified results. Lastly, the landslide susceptibility map will help decision makers in risk management, site selection, site planning, and the design of control engineering.     

Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands

During the last decade, slope failures were reported in a 500 km² study area in the Geba–Werei catchment, northern Ethiopia, a region where landslides were not considered an important hazard before. Field observations, however, revealed that many of the failures were actually reactivations of old deep-seated landslides after land use changes. Therefore, this study was conducted (1) to explore the importance of environmental factors controlling landslide occurrence and (2) to estimate future landslide susceptibility. A landslide inventory map of the study area derived from aerial photograph interpretation and field checks shows the location of 57 landslides and six zones with multiple landslides, mainly complex slides and debris flows. In total 14.8% of the area is affected by an old landslide. For the landslide susceptibility modelling, weights of evidence (WofE), was applied and five different models were produced. After comparison of the models and spatial validation using Receiver Operating Characteristic curves and Kappa values, a model combining data on elevation, hillslope gradient, aspect, geology and distance to faults was selected. This model confirmed our hypothesis that deep-seated landslides are located on hillslopes with a moderate slope gradient (i.e. 5°–13°). The depletion areas are expected on and along the border of plateaus where weathered basalts rich in smectite clays are found, and the landslide debris is expected to accumulate on the Amba Aradam sandstone and upper Antalo limestone. As future landslides are believed to occur on inherently unstable hillslopes similar to those where deep-seated landslides occurred, the classified landslide susceptibility map allows delineating zones where human interventions decreasing slope stability might cause slope failures. The results obtained demonstrate that the applied methodology could be used in similar areas where information on the location of landslides is essential for present-day hazard analysis.     

汶川地震灾区滑坡空间特征变化分析简

“5.12”汶川大地震触发了大量滑坡,给人民群众生命财产和社会经济发展造成了巨大损失。基于GIS空间分析方法,结合震前和震后的滑坡编目数据,对滑坡与坡度、坡向、高程、岩土类型、道路、河流和断裂带等7个孕灾环境因素的空间分布关系进行统计分析。结果表明：滑坡与孕灾环境因素的空间分布关系受地震的影响比较大。相比于震前,震后滑坡发生的优势坡度、优势岩土类型、优势距离缓冲区等均发生了很大的变化;并且坡向、距道路距离、距河流距离等因素对滑坡有明显地趋势性影响。     

Susceptibility and triggering scenarios at a regional scale for shallow landslides

The work aims at identifying susceptible areas and pluviometric triggering scenarios at a regional scale in Calabria (Italy), with reference to shallow landsliding events. The proposed methodology follows a statistical approach and uses a database linked to a GIS that has been created to support the various steps of spatial data management and manipulation. The shallow landslide predisposing factors taken into account are derived from (i) the 40-m digital terrain model of the region, an  15,075 km² extension; (ii) outcropping lithology; (iii) soils; and (iv) land use. More precisely, a map of the slopes has been drawn from the digital terrain model. Two kinds of covers [prevalently coarse-grained (CG cover) or fine-grained (FG cover)] were identified, referring to the geotechnical characteristics of geomaterial covers and to the lithology map; soilscapes were drawn from soil maps; and finally, the land use map was employed without any prior processing.Subsequently, the inventory maps of some shallow landsliding events, totaling more than 30,000 instabilities of the past and detected by field surveys and photo aerial restitution, were employed to calibrate the relative importance of these predisposing factors.The use of single factors (first level analysis) therefore provides three different susceptibility maps. Second level analysis, however, enables better location of areas susceptible to shallow landsliding events by crossing the single susceptibility maps.On the basis of the susceptibility map obtained by the second level analysis, five different classes of susceptibility to shallow landsliding events have been outlined over the regional territory: 8.9% of the regional territory shows very high susceptibility, 14.3% high susceptibility, 15% moderate susceptibility, 3.6% low susceptibility, and finally, about 58% very low susceptibility.Finally, the maps of two significant shallow landsliding events of the past and their related rainfalls have been utilized to identify the relevant pluviometric triggering scenarios. By using 205 daily rainfall series, different triggering pluviometric scenarios have been identified with reference to CG and FG covers: a value of 365 mm of the total rainfall of the event and/or 170 mm/d of the rainfall maximum intensity and a value of 325 mm of the total rainfall of the event and/or 158 mm/d of the rainfall maximum intensity are able to trigger shallow landsliding events for CG and FG covers, respectively.The results obtained from this study can help administrative authorities to plan future development activities and mitigation measures in shallow landslide-prone areas. In addition, the proposed methodology can be useful in managing emergency situations at a regional scale for shallow landsliding events triggered by intense rainfalls; through this approach, the susceptibility and the pluviometric triggering scenario maps will be improved by means of finer calibration of the involved factors.     

The landslide response of alpine basins to post-Little Ice Age glacial thinning and retreat in southwestern British Columbia

The role of post-Little Ice Age (LIA) Neoglacial retreat on landslide activity is investigated in 19 alpine basins along the upper Lillooet River Valley, British Columbia. We examine how Neoglacial scouring and glacial recession have modified hillslope form and slope stability, and construct a decision-making flowchart to identify landslide hazards associated with glacial retreat. This work is based on field mapping, GIS analysis, statistical associations between landslides and terrain attributes, and a comparison between Neoglaciated and non-Neoglaciated terrain within each basin.The bedrock landslide response to glacial retreat varies appreciably according to lithology and the extent of glacial scour below the LIA trimline. Valleys carved in weak Quaternary volcanics show significant erosional oversteepening and contain deep-seated slope movement features, active rock fall, rock slides, and rock avalanches near glacial trimlines. Basins in stronger granitic rock rarely show increased bedrock instability resulting from post-LIA retreat, except for shallow-seated rock slides along some trimlines and failures on previously unstable slopes. In surficial materials, landslides associated with post-LIA retreat originate in till or colluvium, as debris slides or debris avalanches, and are concentrated along lateral moraines or glacial trimlines.Significant spatial association was also observed between recent catastrophic failures, gravitational slope deformation, and slopes that were oversteepened then debuttressed by glacial erosion. Eight out of nine catastrophic rock slope failures occurred just above glacial trimlines and all occurred in areas with a previous history of deep-seated gravitational slope movement, implying that this type of deformation is a precursor to catastrophic detachment.     

基于信息量与逻辑回归模型的次生滑坡灾害敏感性评价——以汶川县北部为例

次生滑坡灾害的影响是震后较长时间里人们持续关注的焦点,对其开展敏感性评价具有重要意义。选取5.12地震的重灾区汶川县北部作为研究区,利用遥感与地理信息技术提取地震滑坡信息,在全面分析滑坡与高程、坡度、坡向、岩性、断裂带、地震烈度以及水系等7个影响因子相关特性的基础上,采用信息量法与逻辑回归模型进行灾害敏感性评价,将研究区划分为极轻度、轻度、中度、高度和极高危险5个级别,并对不同模型的适用性开展分析和对比。结果表明,逻辑回归模型在描述区域滑坡灾害危险度总体特征方面稍具优势。     

Mapping landslide susceptibility from small datasets: A case study in the Pays de Herve (E Belgium)

A landslide susceptibility map is proposed for the Pays de Herve (E Belgium), where large landslides affect Cretaceous clay outcrop areas. Based on a Bayesian approach, this GIS-supported probabilistic map identifies the areas most susceptible to deep landslides. The database is comprised of the source areas of ten pre-existing landslides (i.e. a sample of 154 grid cells) and of six environmental data layers, namely lithology, proximity to active faults, slope angle and aspect, elevation and distance to the nearest valley-floor. A 30-m-resolution DEM from the Belgian National Geographical Institute is used for the analysis. Owing to the small size of the sample, a special cross-validation procedure of the susceptibility map is performed, which uses in an iterative way each of the landslides to test the predictive power of the map derived from the other landslides. Four different sets of variables are used to produce four susceptibility maps, whose prediction curves are compared. While the prediction rates associated with the models not involving the “proximity to active fault” criterion are comparable to those of the models considering this variable, strong weaknesses inherent in the fault data on which the latter rely suggest that the final susceptibility map should be based on a model that excludes any reference to fault. This highlights the difference between a triggering factor and determining factors, and in the same time broadens the scope of the produced map. A single reactivated slide is also used to test the possibility of predicting future reactivation of existing landslides in the area. Finally, the need for geomorphological control over the mathematical treatment is underlined in order to obtain realistic prediction maps.     

GIS支持下三峡库区秭归县滑坡灾害空间预测

基于GIS空间分析和统计模型相结合进行区域评价与空间预测是滑坡灾害研究的重要方向之一。以三峡库区秭归县为研究区,选择坡度、坡向、边坡结构、工程岩组、排水系统、土地利用和公路开挖作为评价因子。为提高模型的预测精度、可信度和推广能力,利用窗口采样规则降低训练样本之间的空间相关性。建立Logistic回归模型,对滑坡灾害与评价因子进行定量相关性分析。计算研究区滑坡灾害易发性指数,对其进行聚类分析,绘制滑坡易发性分区图,其中高、中易发区占整个研究区面积的38.9%,主要分布在人类工程活动频繁和靠近排水系统的区域。经过验证,该模型的预测精度达到77.57%。     

Changes in land cover and shallow landslide activity: A case study in the Spanish Pyrenees

The Pyrenees, like many other mountain areas in Europe, have experienced depopulation and land abandonment during the 20th century. This has encouraged vegetation recovery in formerly occupied areas, including reforestation to promote woodland. The objective of this study is to analyse the effects of these changes on shallow landsliding, a process responsible for erosion and land degradation in many mountain areas. A sequence of aerial images reveals a slight decrease in the landslide occurrence rate in the last half of the 20th century and a parallel increase in the landslide extinction rate, i.e. the rate at which evidence of landslide activity is removed by vegetation colonisation. A logistic regression routine was used to assess the influence of land use and vegetation recovery in the occurrence of shallow landslides. The result shows that the former arable fields on the valley slopes still facilitate landsliding, even after land abandonment and revegetation by shrubs or trees. A shift in the topographic location of landslides was also detected, pointing to an increased importance of water redistribution in the slopes after prolonged rainfall periods.     

Landslide susceptibility zonation in the Rio Mendoza Valley, Argentina

A qualitative landslide susceptibility zonation map (scale 1:100,000) of a sector of the Río Mendoza Valley was prepared by overlapping thematic maps of conditioning factors. The main parameters affecting the distribution of landslides in the area were ranked according to slope instability. Geomorphological surveys and field observations enabled identification of 300 historical or prehistoric landslides. A landslide inventory was developed classifying the processes involved in slope instability, and analysing the degree of activity. The two most influential factors in decreasing slope stability were lithology and slope angle.     

基于专家知识的滑坡危险性模糊评估方法

滑坡发生的影响因素众多, 其危险性与各因素之间的关系多呈非线性关系, 同时各因素之 间也存在或强或弱的相关性, 而目前的危险性评价方法难以体现这些要求。本文提出了一种借助 滑坡专家知识并利用模糊推理理论进行滑坡危险性评价的方法。该方法通过建立了①坡度与岩 层倾角之差和坡向与岩层倾向之差、②坡度和岩性、③临空面和岩性、④坡形和岩性等四种环境 因子组合, 以此将不同环境因子之间的相关性融入各组合模型中, 并将四种组合所得的模糊危险 度进行叠加用于滑坡危险度的模糊评价。环境组合模型中的参数利用专家经验给出。将该方法应 用于三峡库区云阳- 巫山段, 得到了滑坡危险性的分级分布图。从滑坡危险性分布图上可清楚发 现, 本方法所计算出的危险性值在滑坡发生的地区明显高于未发生滑坡的地区, 该结果可以用于 城镇建设和重要基础规划设施的参考。     

Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)

In this article a statistical multivariate method, i.e., rare events logistic regression, is evaluated for the creation of a landslide susceptibility map in a 200 km² study area of the Flemish Ardennes (Belgium). The methodology is based on the hypothesis that future landslides will have the same causal factors as the landslides initiated in the past. The information on the past landslides comes from a landslide inventory map obtained by detailed field surveys and by the analysis of LIDAR (Light Detection and Ranging)-derived hillshade maps. Information on the causal factors (e.g., slope gradient, aspect, lithology, and soil drainage) was extracted from digital elevation models derived from LIDAR and from topographical, lithological and soil maps. In landslide-affected areas, however, we did not use the present-day hillslope gradient. In order to reflect the hillslope condition prior to landsliding, the pre-landslide hillslope was reconstructed and its gradient was used in the analysis. Because of their limited spatial occurrence, the landslides in the study area can be regarded as “rare events”. Rare events logistic regression differs from ordinary logistic regression because it takes into account the low proportion of 1s (landslides) to 0s (no landslides) in the study area by incorporating three correction measures: the endogenous stratified sampling of the dataset, the prior correction of the intercept and the correction of the probabilities to include the estimation uncertainty. For the study area, significant model results were obtained, with pre-landslide hillslope gradient and three different clayey lithologies being important predictor variables. Receiver Operating Characteristic (ROC) curves and the Kappa index were used to validate the model. Both show a good agreement between the observed and predicted values of the validation dataset. Based on a qualified judgement, the created landslide susceptibility map was classified into four classes, i.e., very high, high, moderate and low susceptibility. If interpreted correctly, this classified susceptibility map is an important tool for the delineation of zones where prevention measures are needed and human interference should be limited in order to avoid property damage due to landslides.     

Spatially and temporally distributed modeling of landslide susceptibility

Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km² Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly influenced by natural processes and human activities in space and time; while results from simulated outputs show the potential for decision-making in effective forest planning by using various management scenarios and controlling factors that influence landslide susceptibility. Such a process-based tool could be used to deal with real-dynamic systems to help decision-makers to answer complex landslide susceptibility questions.     

GIS and ANN model for landslide susceptibility mapping

Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage line are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.     

GIS and ANN model for landslide susceptibility mapping

1 IntroductionThe population growth and the expansion of settlements and life-lines over hazardous areas exert increasingly great impact of natural disasters both in the developed and developing countries. In many countries, the economic losses and casualties due to landslides are greater than commonly recognized and generate a yearly loss of property larger than that from any other natural disasters, including earthquakes, floods and windstorms. Landslides in mountainous terrain often occur a…     

基于遥感与地理信息系统的分布式斜坡稳定性定量评估模型

文章介绍一种进行斜坡稳定性定量研究的分布式模型——SINMAP模型。该模型以水文学理论为基础,耦合稳定状态水文模型TOPMODEL与大范围斜坡稳定性模型,在充分考虑各种影响因素的基础上,对研究区域进行斜坡稳定性评价。选取汉江江口流域作为试验研究区,以DEM、遥感影象、各种专题图件及地面考察资料作为信息源,利用SINMAP方法获得可视化的研究区地表稳定性指数专题图。经实际资料检验表明,该模型可获取较高的预测精度,尤其在流域尺度上具有极大的应用价值。