Landslide inventory and susceptibility modelling using geospatial tools,in Hunza-Nagar valley,northern Pakistan

A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known for its frequent and devastating landslides. In this paper, we have developed a landslide inventory map for Hunza-Nagar valley by using the visual interpretation of the SPOT-5 satellite imagery and mapped a total of 172 landslides. The landslide inventory was subsequently divided into modelling and validation data sets. For the development of landslide susceptibility map seven discrete landslide causative factors were correlated with the landslide inventory map using weight of evidence and frequency ratio statistical models. Four different models of conditional independence were used for the selection of landslide causative factors. The produced landslides susceptibility maps were validated by the success rate and area under curves criteria. The prediction power of the models was also validated with the prediction rate curve. The validation results shows that the success rate curves of the weight of evidence and the frequency models are 82% and 79%, respectively. The prediction accuracy results obtained from this study are 84% for weight of evidence model and 80% for the frequency ratio model. Finally, the landslide susceptibility index maps were classified into five different varying susceptibility zones. The validation and prediction result indicates that the weight of evidence and frequency ratio model are reliable to produce an accurate landslide susceptibility map, which may be helpful for landslides management strategies.     

Landslide Susceptibility Mapping along Bhalubang–Shiwapur Area of Mid-Western Nepal Using Frequency Ratio and Conditional Probability Models

Roads constructed in fragile Siwaliks are prone to large number of instabilities. Bhalubang–Shiwapur section of Mahendra Highway lying in Western Nepal is one of them. To understand the landslide causative factor and to predict future occurrence of the landslides, landslide susceptibility mapping(LSM) of this region was carried out using frequency ratio(FR) and weights-of-evidence(W of E) models. These models are easy to apply and give good results. For this, landslide inventory map of the area was prepared based on the aerial photo interpretation, from previously published/unpublished reposts, and detailed field survey using GPS. About 332 landslides were identified and mapped, among which 226(70%) were randomly selected for model training and the remaining 106(30%) were used for validation purpose. A spatial database was constructed from topographic, geological, and land cover maps. The reclassified maps based on the weight values of frequency ratio and weights-of-evidence were applied to get final susceptibility maps. The resultant landslide susceptibility maps were verified andcompared with the training data, as well as with the validation data. From the analysis, it is seen that both the models were equally capable of predicting landslide susceptibility of the region(W of E model(success rate = 83.39%, prediction rate = 79.59%); FR model(success rate = 83.31%, prediction rate = 78.58%)). In addition, it was observed that the distance from highway and lithology, followed by distance from drainage, slope curvature, and slope gradient played major role in the formation of landsides. The landslide susceptibility maps thus produced can serve as basic tools for planners and engineers to carry out further development works in this landslide prone area.     

支持向量机与Newmark模型结合的地震滑坡易发性评估研究

Newmark位移模型是研究地震滑坡易发性的经典模型,机器学习方法支持向量机模型也越来越多的应用到滑坡易发性评估研究。本文将Newmark位移模型与支持向量机模型相结合,建立基于物理机理的地震滑坡易发性评估模型并应用于2008年汶川地震重灾区汶川县。从震后遥感影像目视解译出汶川县1900处地震诱发滑坡,并将其随机划分为70%的训练数据集和30%的验证数据集。选择地形起伏度、坡度、地形曲率、与构造断裂带距离、与水系距离、与道路距离6个因子与Newmark位移值共同作为地震滑坡易发性影响因素。利用ROC曲线和模型不确定性等指标对模型结果进行评估,并与二元统计模型频率比和多元统计模型Logistic回归的结果进行对比。结果表明：与频率比和Logistic回归模型相比,支持向量机模型的正确率最高,训练集和验证集ROC曲线下的面积分别为0.876和0.851。将模型应用于绘制汶川县地震滑坡易发性图,结果显示滑坡易发性图与实际的滑坡点位分布一致性较高,有80.4%的滑坡位于极高和高易发区。这说明支持向量机与Newmark位移方法结合建立的地震滑坡易发性评估模型有较高的预测价值,可以为滑坡风险评估和管理提供依据。     

Evaluating effectiveness of frequency ratio,fuzzy logic and logistic regression models in assessing landslide susceptibility: a case from Rudraprayag district,India

Rudraprayag in Garhwal Himalayan division is one of the most vulnerable districts to landslides in India. Heavy rainfall, steep slope and developmental activities are important factors for the occurrence of landslides in the district. Therefore, specific assessment of landslide susceptibility and its accuracy at regional level is essential for disaster management and proper land use planning. The article evaluates effectiveness of frequency ratio, fuzzy logic and logistic regression models for assessing landslide susceptibility in Rudraprayag district of Uttarakhand state, India. A landslide inventory map was prepared and verified by field data. Fourteen landslide parameters and generated inventory map were utilized to prepare landslide susceptibility maps through frequency ratio, fuzzy logic and logistic regression models. Landslide susceptibility maps generated through these models were classified into very high, high, medium, low and very low categories using natural breaks classification. Receiver operating characteristics (ROC) curve, spatially agreed area approach and seed cell area index (SCAI) method were used to validate the landslide models. Validation results revealed that fuzzy logic model was found to be more effective in assessing landslide susceptibility in the study area. The landslide susceptibility map generated through fuzzy logic model can be best utilized for landslide disaster management and effective land use planning.     

霍西煤矿区地表滑坡灾害敏感性数值建模与等级划分

本文以山西省霍西煤矿区为研究区,利用遥感和GIS方法对滑坡灾害的敏感性进行了数值建模与定量评价。利用交叉检验方法构建了径向基核函数支持向量机滑坡敏感性评价模型,并基于拟合精度对模型进行了定量评价;对各评价因子在模型中的重要性进行对比分析;基于空间分辨率为30m的评价因子,通过径向基核函数支持向量机模型获得了霍西煤矿区滑坡敏感性指数值,并利用分位数法将霍西煤矿区的滑坡敏感性分为极高、高、中和低4个等级。结果表明：拟合精度建模阶段和验证阶段分别为87.22%和70.12%;与滑坡敏感性关系最密切的5个评价因子依次是岩性、距道路距离、坡向、高程和土地利用类型;极高和高敏感区域分布了93.49%的滑坡点,面积占总面积的50.99%,是比较合理的分级方案。本研究不仅可以为研究区人工边坡调查和煤矿资源合理开采提供借鉴,对相似矿区的相关工作也具有参考价值。     

Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed,Gansu Province,China

Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups, (i) training dataset and (ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages, distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.     

GIS-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine

The loess area in the northern part of Baoji City, Shaanxi Province, China is a region with frequently landslide occurrences. The main aim of this study is to quantitatively predict the extent of landslides using the index of entropy model(IOE), the support vector machine model(SVM) and two hybrid models namely the F-IOE model and the F-SVM model constructed by fractal dimension. First, a total of 179 landslides were identified and landslide inventory map was produced, with 70%(125) of the landslides which was optimized by 10-fold crossvalidation being used for training purpose and the remaining 30%(54) of landslides being used for validation purpose. Subsequently, slope angle, slope aspect, altitude, rainfall, plan curvature, distance to rivers, land use, distance to roads, distance to faults, normalized difference vegetation index(NDVI), lithology, and profile curvature were considered as landslide conditioning factors and all factor layers were resampled to a uniform resolution. Then the information gain ratio of each conditioning factors was evaluated. Next, the fractal dimension for each conditioning factors was calculated and the training dataset was used to build four landslide susceptibility models. In the end, the receiver operating characteristic(ROC) curves and three statistical indexes involving positive predictive rate(PPR), negative predictive rate(NPR) and accuracy(ACC) were applied to validate and compare the performance of these four models. The results showed that the F-SVM model had the highest PPR, NPR, ACC and AUC values for training and validation datasets, respectively, followed by the F-IOE model.Finally, it is concluded that the F-SVM model performed best in all models, the hybrid model built by fractal dimension has advantages than original model, and can provide reference for local landslide prevention and decision making.     

Optimization of causative factors using logistic regression and artificial neural network models for landslide susceptibility assessment in Ujung Loe Watershed,South Sulawesi Indonesia

Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR(FSLR), ANN, and their combination(FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher(92.59%) than LR(82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve(AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR-ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.     

Regional scale rainfall- and earthquake-triggered landslide susceptibility assessment in Wudu County, China

Wudu County in northwestern China frequently experiences large-scale landslide events.High-magnitude earthquakes and heavy rainfall events are the major triggering factors in the region.The aim of this research is to compare and combine landslide susceptibility assessments of rainfalltriggered and earthquake-triggered landslide events in the study area using Geographical Information System(GIS) and a logistic regression model.Two separate susceptibility maps were produced using inventories reflecting single landslide-triggering events,i.e.,earthquakes and heavy rain storms.Two groups of landslides were utilized: one group containing all landslides triggered by extreme rainfall events between 1995 and 2003 and the other group containing slope failures caused by the 2008 Wenchuan earthquake.Subsequently,the individual maps were combined to illustrate the locations of maximum landslide probability.The use of the resulting three landslide susceptibility maps for landslide forecasting,spatial planning and for developing emergency response actions are discussed.The combined susceptibility map illustrates the total landslide susceptibility in the study area.     

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.     

Landslide Susceptibility Assessment of the Youfang Catchment using Logistic Regression

A detailed landslide susceptibility map was produced in the Youfang catchment using logistic regression method with datasets developed for a geographic information system(GIS).Known as one of the most landslide-prone areas in China, the Youfang catchment of Longnan mountain region,which lies in the transitional area among QinghaiTibet Plateau, loess Plateau and Sichuan Basin, was selected as a representative case to evaluate the frequency and distribution of landslides.Statistical relationships for landslide susceptibility assessment were developed using landslide and landslide causative factor databases.Logistic regression(LR)was used to create the landslide susceptibility maps based on a series of available data sources: landslide inventory; distance to drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index, and land use.The quality of the landslide susceptibility map produced in this paper was validated and the result can be used fordesigning protective and mitigation measures against landslide hazards.The landslide susceptibility map is expected to provide a fundamental tool for landslide hazards assessment and risk management in the Youfang catchment.     

Landslide inventory and susceptibility assessment using multiple statistical approaches along the Karakoram highway,northern Pakistan

China-Pakistan Economic Corridor(CPEC)is a framework of regional connectivity,which will not only benefit China and Pakistan but will have positive impact on Iran,Afghanistan,India,Central Asian Republic,and the region.The surrounding area in CPEC is prone to frequent disruption by geological hazards mainly landslides in northern Pakistan.Comprehensive landslide inventory and susceptibility assessment are rarely available to utilize for landslide mitigation strategies.This study aims to utilize the high-resolution satellite images to develop a comprehensive landslide inventory and subsequently develop landslide susceptibility maps using multiple techniques.The very high-resolution(VHR)satellite images are utilized to develop a landslide inventory using the visual image classification techniques,historic records and field observations.A total of 1632 landslides are mapped in the area.Four statistical models i.e.,frequency ratio,artificial neural network,weights of evidence and logistic regression were used for landslide susceptibility modeling by comparing the landslide inventory with the topographic parameters,geological features,drainage and road network.The developed landslides susceptibility maps were verified using the area under curve(AUC)method.The prediction power of the model was assessed by the prediction rate curve.The success rate curves show 93%,92.8%,92.7%and 87.4%accuracy of susceptibility maps for frequency ratio,artificial neural network,weights of evidence and logistic regression,respectively.The developed landslide inventory and susceptibility maps can be used for land use planning and landslide mitigation strategies.     

Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria

Rainfall induced landslides are a common threat to the communities living on dangerous hill-slopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence (WoE) method was applied to calculate the positive (presence of landslides) and negative (absence of landslides) factor weights. A combination of analytical hierarchical process (AHP) and fuzzy membership standardization (weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren’s algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of WoE, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.     

基于逻辑回归模型和3S技术的思南县滑坡易发性评价

区域滑坡易发性评价对滑坡灾害防治具有重要意义，贵州省思南县由于其特殊的自然地理和地质条件，受滑坡地质灾害的影响非常严重，因此，非常有必要对思南县的滑坡易发性进行评价。在滑坡编录的基础上，采用由RS、GIS和GPS组成的3S技术，获取了思南县的数字高程模型、坡度、坡向、剖面曲率、坡长、岩土类型、地表湿度指数、距离水系的距离、植被覆盖度和地表建筑物指数10个滑坡影响因子；再在频率比和相关性分析的基础上，利用逻辑回归模型对思南县的滑坡易发性进行了评价并绘制了易发性分布图。结果表明:利用逻辑回归模型预测思南县滑坡易发性的准确率(AUC值)达到0.797，较为准确地预测出了思南县滑坡分布规律；极高和高滑坡易发区主要分布在高程低于600 m、地表坡度较大且以软质岩类为主的区域；而极低和低滑坡易发区主要分布在高程较高、地表坡度较小且以硬质岩类为主的区域。      

基于信息量模型和数据标准化的滑坡易发性评价

本文以北川曲山-擂鼓片区为研究区,将坡度、坡向、高程、地层、距断层的距离、距水系的距离和距道路的距离作为该区域滑坡易发性评价因子。采用信息量模型计算了各项评价因子的信息量值,并运用4种标准化模型对信息量值进行标准化处理。各评价因子的权重由层次分析法（AHP）确定。在GIS中将权重值和各评价因子的标准化信息量值,进行叠加计算得到区域滑坡总信息量值,并基于自然断点法对其进行重分类,将研究区划分为极高易发区、高易发区、中易发区、低易发区和极低易发区5级易发区。将基于4种标准化模型和信息量模型得到的滑坡易发性评价结果进行了对比分析,结果表明：基于最值标准化信息量模型的滑坡易发性评价结果的ROC曲线下面积AUC值为0.807,高于其余模型的AUC值,说明最值标准化信息量模型的滑坡易发性评价效果最好。极高易发区面积占研究区面积的20.03%,离断层和水系较近,主要分布地层为寒武系、志留系和三迭系。研究结果可为区内滑坡风险评价和灾害防治提供参考。     

Mapping landslide susceptibility at the Three Gorges Reservoir,China, using gradient boosting decision tree,random forest and information value models

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.     

GIS based spatial data analysis for landslide susceptibility mapping

Landslide susceptibility map delineates the potential zones for landslides occurrence. The paper presents a statistical approach through spatial data analysis in GIS for landslide susceptibility mapping in parts of Sikkim Himalaya. Six important causative factors for landslide occurrences were selected and corresponding thematic data layers were prepared in GIS. Topographic maps,satellite image,field data and published maps constitute the input data for thematic layer preparation. Numerical weights for different categories of these factors were determined based on a statistical approach and the weighted thematic layers were integrated in GIS environment to generate the landslide susceptibility map of the area. The landslide susceptibility map classifies the area into five different landslide susceptible zones i.e.,very high,high,moderate,low and very low. This map was validated using the existing landslide distribution in the area.     

Geometrical and Geotechnical Characteristics of Landslides in Korea under Various Geological Conditions

The goal of this study is to determine the geometrical and geotechnical characteristics of landslides under various geological conditions using detailed field surveys, laboratory soil tests and precipitation records. Three study areas are selected to consider different rocks, including gneiss in Jangheung, granite in Sangju and sedimentary rocks in Pohang, South Korea. Many landslides have occurred in these three areas during the rainy season.Precipitation records indicate that landslides occurring in the gneiss area of Jangheung and granite area of Sangju may be influenced by the hourly rainfall intensity rather than cumulative rainfall.However, landslides occurring in the sedimentary rock area of Pohang may be influenced by hourly rainfall intensity and cumulative rainfall. To investigate the factors that influence these types of landslides, a detailed landslide survey was performed and a series of laboratory soil tests were conducted.According to the detailed field survey, most landslides occurred on the flanks of mountain slopes, and the slope inclination where they occurred mostly ranged from 26 to 30 degrees, regardless of the geological conditions. The landslide in the gneiss area of Jangheung is larger than the landslides in the granite area of Sangju and sedimentary rock area of Pohang.Particularly, the landslide in the sedimentary rock area is shorter and shallower than the landslides in the gneiss and granite areas. Thus, the shape and size of the landslide are clearly related to the geological conditions. According to the integrated soil property and landslide occurrence analyses results, the average dry unit weight of the soils from the landslide sites is smaller than that of the soils obtained from the nonlandslide site. The average coefficient of permeability of soils obtained from the landslide sites is greater than that of soils obtained from the non-landslide sites with the same geology. These results indicate that the soils from the landslide sites are more poorly graded or looser than the soils from the non-landslide sites.     

Landslide susceptibility assessment using the certainty factor and analytic hierarchy process

A new approach combining the certainty factor (CF) and analytic hierarchy process (AHP) methods was proposed to assess landslide susceptibility in the Ziyang district, which is situated in the Qin-Ba Mountain region, China. Landslide inventory data were collected based on field investigations and remote sensing interpretations. A total of 791 landslides were identified. A total of 633 landslides were randomly selected from this data set as the training set, and the remaining landslides were used for validation as the test set. Nine factors, including the slope angle, slope aspect, slope curvature, lithology, distance to faults, distance to streams, precipitation, road network intensity degree and land use were chosen as the landslide causal factors for further susceptibility assessment. The weight of each factor and its subclass were calculated by AHP and CF methods. Landslide susceptibility was compared between the bivariate statistical method and the proposed CF-AHP method. The results indicate that the distance to streams, distance to faults and lithology are the most dominant causal factors associated with landslides. The susceptibility zonation was categorized into five classes of landslide susceptibility, i.e., very high, high, moderate, low and very low level. Lastly, the relative operating characteristics (ROC) curve was used to validate the accuracy of the new approach, and the result showed a satisfactory prediction rate of 78.3%, compared to 69.2% obtained with the landslide susceptibility index method. The results indicate that the CF-AHP combined method is more appropriate for assessing the landslide susceptibility in this area.     

Relationships between landslide types and topographic attributes in a loess catchment, China

Topographic attributes have been identified as the most important factor in controlling the initiation and distribution of shallow landslides triggered by rainfall.As a result,these landslides influence the evolution of local surface topography.In this research,an area of 2.6 km 2 loess catchment in the Huachi County was selected as the study area locating in the Chinese Loess Plateau.The landslides inventory and landslide types were mapped using global position system(GPS) and field mapping.The landslide inventory shows that these shallow landslides involve different movement types including slide,creep and fall.Meanwhile,main topographic attributes were generated based on a high resolution digital terrain model(5 m × 5 m),including aspect,slope shape,elevation,slope angle and contributing area.These maps were overlaid with the spatial distributions of total landslides and each type of landslides in a geographic information system(GIS),respectively,to assess their spatial frequency distributions and relative failure potentials related to these selected topographic attributes.The spatial analysis results revealed that there is a close relation between the topographic attributes of the postlandsliding local surface and the types of landslide movement.Meanwhile,the types of landslide movement have some obvious differences in local topographic attributes,which can influence the relative failure potential of different types of landslides.These results have practical significance to mitigate natural hazard and understandgeomorphologic process in thick loess area.