Baige Landslide Research Articles

Using a single remote-sensing image to calculate the height of a landslide dam and the maximum volume of a lake

Abstract. Landslide dams are caused when landslide materials block rivers. After the occurrence of large-scale landslides, it is necessary to conduct a large-scale investigation of barrier lakes and a rapid risk assessment. Remote sensing is an important means to achieve this goal. However, at present, remote sensing is only used for the monitoring and extraction of hydrological parameters, without predicting the potential hazard of the landslide dam. The key parameters of the barrier dam, such as the dam height and the maximum volume, still need to be obtained based on a field investigation, which is time consuming. Our research proposes a procedure that is able to calculate the height of the landslide dam and the maximum volume of the barrier lake using a single remote-sensing image and a pre-landslide DEM. The procedure includes four modules: (a) determining the elevation of the lake level, (b) determining the elevation of the bottom of the dam, (c) calculating the highest height of the dam and (d) predicting the lowest crest height of the dam and the maximum volume. Finally, a sensitivity analysis of the parameters used during the procedure and an analysis of the influence of the image resolution is carried out. This procedure is mainly demonstrated through the Baige landslide dam and the Hongshiyan landslide dam. A single remote-sensing image and a pre-landslide DEM are used to predict the height of each dam and the key parameters of the dam break, which are in good agreement with the measured data. This procedure can effectively support quick decision making regarding hazard mitigation.

A new algorithm for landslide dynamic monitoring with high temporal resolution by Kalman filter integration of multiplatform time-series InSAR processing

• Provide a new scheme for landslide dynamic monitoring and prediction. • Integrate multiplatform time-series InSAR processing by Kalman filter. • Compensate the deficiency of the space-borne InSAR in landslide temporal resolution. Free and open access to the earth observation synthetic aperture radar (SAR) satellite images has enabled the implementation of landslide time-series monitoring. However, with the restriction of the low revisit period of satellite, it is difficult to meet the requirements of landslide dynamic monitoring with high temporal resolution. Moreover, with the multiplication of SAR satellite platforms, a corresponding data integration algorithm is needed to obtain the complementary displacement information from every single observation orbit. In this work, an integrated algorithm for landslide multi-source displacement optimization estimation based on Kalman filter (KF) is proposed to improve the temporal resolution and realize the dynamic monitoring and prediction of landslide movement. Specifically, the landslide migration coordinate system is established firstly, and the interferometric SAR (InSAR) displacement results in line of sight (LOS) are projected to the downslope direction. Then, with the introduction of the acceleration variable into the process covariance matrix of prediction model and the observation noise variance weight determination, the downslope displacements of multiplatform InSAR observations are dynamically integrated into a unified time series by KF dynamic prediction and correction. So as to achieve the high temporal resolution monitoring of landslide. For validation purpose, the Baige landslide in Tibet, China is selected as the test area, and 55 Sentinel-1A ascending images, 42 Sentinel-1A descending images, and 10 ALOS-2 PALSAR-2 images collected over this area from May 2017 to April 2019 are used to estimate the high temporal time series. The temporal resolution of landslide monitoring is successfully improved from 12 days with a single orbit to the shortest 1 day and repeatedly 2–5 days with multiple platforms, and the prediction of subsequent displacement is also realized. Prospectively, with the continuous multiplication of the InSAR satellite platforms, this proposed algorithm can provide high temporal monitoring data for landslide and better assist relevant emergency response, which is necessary for the dynamic monitoring and early warning of landslide.

Probabilistic hazard assessment of landslide-induced river damming

Landslide-induced river damming poses a considerable threat to the safety of humans and infrastructure. Prediction of landslide-induced river damming is of great significance for quantitative risk assessment and emergency response planning. However, owing to the large uncertainties embedded in the input parameters and dynamic numerical models, a reliable physically based prediction of landslide dam formation is still challenging. In this study, we proposed a probabilistic framework to predict rockslide-induced river damming and its associated barrier lake. Both parameter and model uncertainties were considered to reproduce the run-out process and the deposition behavior of the Baige landslide based on dynamic numerical simulation, while calibrating the input parameters through a sequential Bayesian back analysis. The first slide of the Baige landslide was considered to calibrate the input parameters with observations at several deposition depths. The proposed method was validated by predicting the second slide-induced river damming using the calibrated results from the first slide. Furthermore, the second slide with deposition depth observations was employed to update the input parameters again. Through the sequential Bayesian back analysis, the probability of the river damming induced by the potentially unstable rock masses was predicted, which yielded a hazard zonation map of the barrier lake exceeding various water levels. This hazard zonation map may be employed to guide quantitative risk assessment and corresponding emergency response plans for future landslide-induced upstream backwater-inundation.

Cause of the Baige Landslides: Long-Term Cumulative Coupled Effect of Tectonic Action and Surface Erosion

Abstract In 2018, successive large-scale landslides occurred near the village of Baige in the Jinshajiang suture zone on the Qinghai–Tibet Plateau, posing substantial threat both to the lives and property of residents and to the engineering facilities along the upper and lower reaches of the Jinsha River. In this study, field investigation data, remote sensing images, aerial photography, microanalysis of rocks, and mechanical experiments were considered to ascertain the material and geomorphic conditions of the landslides and to explore the failure mechanism and process of evolution of the landslides under the coupled effect of exogenic and endogenic factors. Located in the Jinshajiang suture zone, the Baige landslides occurred close to the western boundary fault zone. The geological structure of the slope and the formation of landslides are controlled by the specific environmental tectonic dynamics of the suture zone. Tectonic damage has dramatically influenced the strength and hydraulic characteristics of the rock mass, and the rock’s uniaxial compressive strength and resistance to water disintegration have deteriorated markedly as a result. Strong weathering of the plateau and repeated groundwater infiltration have further reduced the mechanical properties of the unique rock and soil with inferior original strength characteristics. The intensive crustal uplift and the rapid river erosion have markedly altered the free face of the damaged rock mass, thereby increasing the gravitational potential energy of the rock mass. The long-term cumulative coupled effect of tectonic movement and surface denudation represents the fundamental mechanism of the Baige landslides. The Jinshajiang suture zone continues to face the risk of further massive landslides similar to those that occurred in Baige.

Landslide dam breaching and outburst floods: A numerical model and its application

The damages caused by the breach of landslide dams can be mitigated by making an emergency plan based on the fast and accurate forecast of the resultant outburst floods. This study proposed an advanced physically-based breach model, which can effectively predict the breach evolution and the outflow hydrograph. Variations of soil erodibility along the dam depth are specially considered in this model. To avoid infinite occurrence of soil erosion, an arctangent erosion model is proposed to calculate the erosion rate, two advantages of which against the existing erosion models are demonstrated: better representation of the erosion process and simplified requirement of only one input parameter. The rate of the breach lateral enlargement is considered to vary along the depth, and the breach shape is regarded as a rectangle. The breach model is applied in two case studies of the Tangjiashan landslide dam and the Baige landslide dam. The predictions of breaching geometric parameters and the outflow hydrographic parameters agree well with the field observations. Comparing with the two widely-used landslide dam-break models, the proposed numerical method predicts changes of the lake level and the breach bottom more accurately. Results also prove the importance and necessity of considering soil erodibility along the dam depth. Moreover, sensitivity analysis is also conducted. It can be found that the parameter related to the arctangent erosion model significantly affects the breach evolution and the associated outflow hydrograph, while the influences of the broad-crested weir flow formula and the lateral enlargement rate is not that obvious. The numerical results are not sensitive to the velocity step adopted in the numerical scheme, facilitating the application of the model in the rapid risk assessment for landslide dams.

On three-dimensional SPH modelling of large-scale landslides

Lagrangian particle-based smoothed particle hydrodynamics (SPH) is increasingly widely used in landslide modelling. This paper investigates four important issues not addressed by previous studies on SPH modelling of large-scale landslides, i.e., convergence property, influence of constitutive parameters, scale effect and friction reduction, and influence of different treatments of the viscous effect. The GPU-acceleration technique is employed to achieve high-resolution three-dimensional (3D) modelling. The Baige landslide is investigated by comparing numerical results with field data, and detailed analyses on the four issues are provided. Suggestions on particle resolution, constitutive parameter, and formulations of viscous discretization are also presented for future SPH modelling of large-scale landslides.

Flood assessment and early warning of the reoccurrence of river blockage at the Baige landslide

Flood assessment and early warning of the reoccurrence of river blockage at the Baige landslide

Locking effect of granodiorite porphyry veins on the deformation of Baige landslide (eastern Tibetan plateau, Tibet)

Locking effect of granodiorite porphyry veins on the deformation of Baige landslide (eastern Tibetan plateau, Tibet)

Application of Slope Radar (S-SAR) in Emergency Monitoring of the “11.03” Baige Landslide

On October 11 and November 3, 2018, the disaster chain of landslide-barrier lake occurred twice in Baige Village, Xizang Province. After the second sliding of the landslide, the danger of the landslide dam was eliminated by the manual excavation of the drain grooves. During this period, a ground-based interferometric synthetic aperture radar (GB-InSAR) called “S-SAR” was utilized for real-time monitoring and analyzing 48 selected target pixels on the residual deformation bodies of landslides (divided into K1, K2, and K3 deformation zones) for 8 days. Through the real-time deformation map of pixels in the monitoring area obtained by S-SAR, the ranges of five strong deformation regions were identified and delineated. Based on the apparent cumulative deformation-time curve of each target pixel, the overall deformation law of K1, K2, and K3 deformation zones could be monitored and analyzed in real time. Based on a curve graph of the deformation rate, acceleration, and time of each target pixel, the K1, K2, and K3 deformation zones were within a uniform deformation stage. Taking the target pixel point and the corresponding time in which the deformation rate and deformation acceleration had a large, abrupt jump at the same time as the position and time of the near-slip failure, the 11 positions and moments of the near-slip failure were counted. The results presented here may represent a workable reference for emergency monitoring and early warning of similar sudden geological disasters.

Hazard Prediction Method of Landslide Damming and Analysis of a Typical Application

Landslide damming occurs frequently, affects a wide range of areas, and is difficult to predict, thus threatening the safety of human life, properties, and engineering structures. However, the studies on early identification remain at an early stage. This paper proposes a hazard prediction method for landslide damming, which includes prediction models of river blockage, landslide dam geometry, landslide dam stability, and dam breach time. On this basis, the landslide damming hazard can be predicted via ten parameters: the repose angle of the landslide mass, the landslide volume, the landslide discharge, the river discharge, the reservoir capacity of the barrier lake, the bottom width of the valley, two slope angles on both sides of the valley, the valley bed inclination, and the sliding surface dip-angle. The proposed method was examined in relation to a typical case, namely, the Baige landslide damming that occurred in 2018 in China, with the application process presented integrally. The results indicated that the river blockage model predicted that the Baige landslide could form a huge landslide dam, which was fully in line with the actual situation. The landslide dam geometry model provided the predicted value of the height, width, and length, as well as the downstream and upstream angles, of the Baige landslide dam, with the maximum relative error between the predicted values and the true values less than 20%. The prediction results of the landslide dam stability model indicated that when the reservoir capacity of the barrier lake is 280 million m3, the dam could breach. In fact, the dam breach time model predicted that the dam could breach within 38 h after the landslide dam formation, which was 11% less than the actual breach time of the Baige landslide dam. The current study provides a useful theoretical reference and technical support for the early identification, prediction, and warning of the disaster chain of landslide-induced river blocking.

Landslide-lake outburst floods accelerate downstream hillslope slippage

Abstract. The Jinsha River, which has carved a 2–4 km deep gorge, is one of the largest SE Asian rivers. Two successive landslide-lake outburst floods (LLFs) occurred after the 2018 Baige landslides along the river. Using Sentinel-2 images, we examined the LLF impacts on downstream river channels and adjacent hillslopes over a 100 km distance. The floods increased the width of the active river channel by 54 %. Subsequently, major landslides persisted for 15 months in at least nine locations for displacements >2 m. Among them, three moving hillslopes ∼80 km downstream from the Baige landslides slumped more than 10 m 1 year after the floods. Extensive undercuts by floods probably removed hillslope buttresses and triggered a deformation response, suggesting strong and dynamic channel–hillslope coupling. Our findings indicate that infrequent catastrophic outburst flooding plays an important role in landscape evolution. Persistent post-flood hillslope movement should be considered in disaster mitigation in high-relief mountainous regions.

Geo-structures and deformation-failure characteristics of rockslide areas near the Baige landslide scar in the Jinsha River tectonic suture zone

Geo-structures and deformation-failure characteristics of rockslide areas near the Baige landslide scar in the Jinsha River tectonic suture zone

Study on the Creep-Sliding Mechanism of the Giant Xiongba Ancient Landslide Based on the SBAS-InSAR Method, Tibetan Plateau, China

The geohazards associated with strongly deformed and reactivated large-scale ancient landslide are analyzed through a study of the Xiongba ancient landslide. The SBAS-InSAR method, combined with remote sensing interpretation, was used to obtain the Xiongba ancient landslide surface deformation characteristics, on the western bank of the Jinsha River, during the period from October 2017 to June 2020. Two large strong deformation zones were discovered in this study, H1 and H2, which were located at the front edge of the Xiongba landslide. The maximum cumulative deformation in the H1 deformation zone was approximately 204 mm, and the deformation in the H2 deformation zone was approximately 302 mm. Influenced by the Jinsha River erosion, the Baige landslide-dammed lake-dam breakage-debris (LDLDB) flow/flood hazard chains, which occurred 75 km upstream reaches in October and November 2018, and the erosion of the foot of the Xiongba ancient landslide foot resulted in notably enhanced deformation. The creep rate in the H1 deformation zone was 14~16 times that before the Baige landslide hazard chains occurred, and the hazard chains caused sliding in the H2 zone. The Xiongba ancient landslide is undergoing retrogressive reactivation. The Xiongba ancient landslide is currently experiencing continuously creep-sliding, and the deformation rate in some areas is accelerating, which may induce a large-scale reactivation of the Xiongba ancient landslide and an LDLDB hazard chain.

Detecting the Vegetation Change Related to the Creep of 2018 Baige Landslide in Jinsha River, SE Tibet Using SPOT Data

It is vital to monitor the post-seismic landslides economically and effectively in high-mountain regions for the long term. The landslide creep could cause a subtle change of the overlying vegetation after the earthquake, which will lead to the change of vegetation spectral characteristics in optical remote sensing data. The optical remote sensing technique can be used to monitor the landslide creep areas with dense vegetation in a large range at a low cost because it is easy to obtain multi-temporal, multiple-scale, and multi-spectral information. We identified and extracted the vegetation change area before the 2018 Baige landslide by the high-resolution optical remote sensing data. Firstly, the image fusion method was used to improve the accuracy of change detection. Then, vegetation coverage before the landslide was calculated. The vegetation change was identified, and qualitative and quantitative methods were used to analyze the spatio-temporal changes of vegetation coverage. Our results indicate that the creep distance of the landslide is about 50 m and the vegetation in the back scarp area and the main sliding area display a significant downward trend with time closing to the landslide comparing with that in the reference area. The vegetation change in the remote sensing image has an excellent spatio-temporal correlation with the landslide creep. This study provides a possible way and perspective for monitoring post-seismic landslide disasters.

Large-scale field model tests of landslide dam breaching

The breaching of landslide dams is a severe geological disaster, and the resulting breaching floods significantly threaten people in downstream areas. In this study, a large-scale physical model was constructed in a natural river channel using unconsolidated dam material with a wide grain size distribution. Terrestrial laser scanning was used to capture three-dimensional geometric information of the landslide dam during the breaching process. The results showed that the dominant erosion pattern during dam breaching was surface progressive erosion. The breaching process could be divided into three stages based on different erosion characteristics: headward erosion, rapid erosion, and attenuated erosion. Longitudinal and lateral evolution models were proposed based on the field test results. The longitudinal evolution model describes the erosion at the dam crest and the sedimentation at the dam toe during the breaching process by defining the erosion and rotation points. The lateral evolution model revealed different lateral erosion characteristics at different cross-sections. Both the longitudinal and lateral evolution models were validated using two dam break events of the Baige landslide. The evolution models proposed in this study provide a scientific reference for numerical simulations of dam breaching and the prevention and mitigation of landslide dams.

Numerical simulation of landslide dam overtopping failure considering headward erosion

Landslide dams often fail shortly after the formation, resulting in huge risks to the downstream residents and properties. Fast and accurate forecasting of the breaching process and discharge hydrograph is important for a decision making to mitigate the damages. Morphological data indicate that the longitudinal length of most landslide dams is much larger than their transverse length. Additionally, headward erosion is more intense in landslide dams than manmade dams during the overtopping failure process. However, most existing models neglect this phenomenon. The objective of this study is to build up a dam-break model considering the headward erosion process, assuming that the change rate of downstream slope-angle is consistent with the rate of breach vertical erosion. And a dam-break numerical model to simulate the overtopping failure process of landslide dams considering the headward erosion was developed based on the theories of hydraulics, hydrology, soil mechanics, sediment dynamics, and etc. Nevertheless, the breach final bottom elevation, which can only be measured after the dam failure, is directly applied in currently available dam break models. To overcome this problem and for the prediction purpose of a newly formed landslide dam, we proposed an empirical model to estimate the breach final bottom elevations of high, medium and low erodibility landslide dams based on the statistical analysis of worldwide landslide dam failure cases collected recently. To verify the effectiveness of proposed dam-break numerical model, the ‘11.03′ Baige landslide dam which locates at the border of Tibet Autonomous Region and Sichuan province was selected as a case study. Results of this study show that the relative errors in the simulated peak discharge, time to the peak, and the total flood volume are 1.59%, 3.50% and 0.53%, respectively, which meet the accuracy requirements of hydrological forecasting. The relative error in the prediction of breach final bottom elevation is 4.28%, which indicates that the accuracy of the simulation is satisfactory. Finally, a sensitivity analysis indicates that the downstream slope affects the prediction of the breach and flood hydrograph significantly, and the excavation of diversion channel is an effective measure to reduce the peak discharge of dam overtopping failure.

Mapping Outburst Floods Using a Collaborative Learning Method Based on Temporally Dense Optical and SAR Data: A Case Study with the Baige Landslide Dam on the Jinsha River, Tibet

Outburst floods resulting from giant landslide dams can cause devastating damage to hundreds or thousands of kilometres of a river. Accurate and timely delineation of flood inundated areas is essential for disaster assessment and mitigation. There have been significant advances in flood mapping using remote sensing images in recent years, but little attention has been devoted to outburst flood mapping. The short-duration nature of these events and observation constraints from cloud cover have significantly challenged outburst flood mapping. This study used the outburst flood of the Baige landslide dam on the Jinsha River on 3 November 2018 as an example to propose a new flood mapping method that combines optical images from Sentinel-2, synthetic aperture radar (SAR) images from Sentinel-1 and a Digital Elevation Model (DEM). First, in the cloud-free region, a comparison of four spectral indexes calculated from time series of Sentinel-2 images indicated that the normalized difference vegetation index (NDVI) with the threshold of 0.15 provided the best separation flooded area. Subsequently, in the cloud-covered region, an analysis of dual-polarization RGB false color composites images and backscattering coefficient differences of Sentinel-1 SAR data were found an apparent response to ground roughness’s changes caused by the flood. We carried out the flood range prediction model based on the random forest algorithm. Training samples consisted of 13 feature vectors obtained from the Hue-Saturation-Value color space, backscattering coefficient differences/ratio, DEM data, and a label set from the flood range prepared from Sentinel-2 images. Finally, a field investigation and confusion matrix tested the prediction accuracy of the end-of-flood map. The overall accuracy and Kappa coefficient were 92.3%, 0.89 respectively. The full extent of the outburst floods was successfully obtained within five days of its occurrence. The multi-source data merging framework and the massive sample preparation method with SAR images proposed in this paper, provide a practical demonstration for similar machine learning applications using remote sensing.

Time Series Offsettracking of Baige Landslide Based on Sentinel-2 and Landsat 8

Objectives: Time-series offset tracking is a critical technique for landslide monitoring with optical images, which has been applied worldwide successfully in the recent years. The continued monitoring of landslide can provide early warning and emergency informationfor the nature hazard prevention. However, the resolution in the temporal domain is restricted by the single-platform datasets, especially in the case of bad weather. To solve this issue, this paper used the multi-source optical images, including Sentinel-2 and Landsat 8, to generate the long time-series deformation of landslides. The high resolution of deformation in the temporal domain can help us know more about the development process of the landslide. Methods: The method is executed based on the idea of small baseline subset (SBAS) in the interferometric synthetic aperture radar (InSAR) technique, aiming to reduce the influence of the temporal baseline and noise on the signal noise ratio (SNR). Meanwhile, to obtain the time-series deformation of the multi-platform optical images, we used a weighted singular value decomposition (SVD) method to obtain the time-series deformation of the multi-platform optical images. In this paper, a collection of 11 Sentinel-2 and 16 Landsat 8 datasets are used to generate two-dimensional horizontal time-series deformations of Baige landslide from 2014-11 to 2018-09. First, we calculated the time-series offset tracking of the single platform, i.e., Sentinel-2 and Landsat 8, and the dual-platform by considering the selection of the image pair, search window size, step length, and the threshold of SNR respectively. And a time-series deformation of 27 images are obtained with offset tracking technique of the dual-platform. Second, the accuracy of the results for the single-platform and the dual-platforms is evaluated by two commonly used methods:stable area statistics with a priori knowledge and cross-validation of independent datasets. Then, the developmental stages of Baige landslide is divided based on the long time-series deformation of the dual-platform, the creep deformation theory, and the tangential angle of the time-series curve.Finally, the relationship between precipitation and deformation is discussedto analyze the causes of the landslide. Results: The results show that:①the time-series deformation of the dual-platform can obtain more details compared the results of the single platform. ②the deformation accuracy of the dual-platform is higher than that of the single platform. For example, the improvements of the dual-platform in the east-west direction (°E/°W) and the north-south (°N/°S) direction are 3.02% and 5.37% respectively compared to the Sentinel-2. In the Landsat 8 case, the results are 3.61% and 0.40% respectively. During the time span, the maximum accumulated deformation and the deformation rate reach to 42.9 m and 9.06 m/a respectivley. In addition, the deformation accuracy of the Sentinel-2 is higher than that of the Landsat8inthe similar situation of observations.③the pre-event of Baige landslide can be divided into the initial stage, the uniform stage, the initial acceleration, and the medium-term acceleration. The corresponding average deformation ratesof each stage are 7.05 mm/d, 10.27 mm/d, 26.80 mm/d, and 65.79 mm/d respectively. The landslide is going through the medium-term acceleration stage until the 5, Feb. 2018.④the correlation between the deformation and precipitation is 0.88, which indicates that heavy rainfall is one of the major causesfor the Baige landslide. Conclusions: Although the deformation resolution of the multi-platform optical image is higher than that of the single platform, the fusion accuracy is limited by the accuracy of different individual platforms caused by the sensitivity for the topography. Hence, in the future work, we will focus on the combined estimation of the multi-platform based on the variance component estimation.

The characteristics, induced factors, and formation mechanism of the 2018 Baige landslide in Jinsha River, Southwest China

On October 11 and November 3, 2018, two successive landslides happened at the same location near Baige Village in Tibet of China and dammed the Jinsha River twice. The induced factors and formation mechanism of the Baige landslide are still being questioned. In this paper, high-resolution topographic data, multi-temporal remote sensing images (including the unmanned aerial vehicle (UAV) 3D image, remote sensing, and Geographical Information Systems (GIS) techniques), rainfall data, seismic records, comprehensive field investigations, and numerical simulations are used to identify the main characteristics, the induced factors, and the formation mechanism of the event. The results show that deep-seated gravitational slope deformations play an important role in the evolution process of the Baige landslide; river downcutting is a prerequisite for the progressive evolution of the gravitational slope deformation behavior of the Baige landslide, whereas earthquake plays a catalytic role in the development of the gravitational slope deformation of the Baige landslide. The Baige landslide would be a typical case study for understanding the formation mechanism of large-scale landslides along the Jinsha River.

Dynamic process analysis of the Baige landslide by the combination of DEM and long-period seismic waves

In this study, based on the frictional velocity-weakening law used in continuum modeling, an adapted Hertz-Mindlin contact model between particles and the ground surface is established to accurately simulate the landslide dynamics process. Moreover, the long-period seismic waves indicating the dynamic characteristics of large landslides are adopted to determine the computational parameters for the discrete element method (DEM). By combining the force-time functions obtained from the long-period seismic waves and numerical modeling, the dynamic parameters and reasonableness of the simulation results of the first Baige landslide on October 11, 2018, in eastern Tibet, China, have been validated. The friction coefficient obtained in this study is also in agreement with the relationship between the volume and friction coefficients reported in the literature. Taking the same model and parameters, the dynamic process leading to the second Baige landslide on November 3, 2018, is analyzed. The modeled profile and position of the maximum thickness of the deposits arising from the two landslides both match the observations well. It is demonstrated that this combination procedure is feasible for the prediction and identification of areas susceptible to large landslides.