Landslide Identification Research Articles

Mask R-CNN–Based Landslide Hazard Identification for 22.6 Extreme Rainfall Induced Landslides in the Beijiang River Basin, China

Landslides triggered by extreme precipitation events pose a significant threat to human life and property in mountainous regions. Therefore, accurate identification of landslide locations is crucial for effective prevention and mitigation strategies. During the prolonged heavy rainfall events in Guangdong Province between 21 May and 21 June 2022, shallow and clustered landslides occurred in the mountainous regions of the Beijiang River Basin. This research used high-resolution satellite imagery and integrated the Mask R-CNN algorithm model with spectral, textural, morphological and physical characteristics of landslides in remote sensing imagery, in addition to landslide-influencing factors and other constraints, to interpret the landslides induced by the event through remote sensing techniques. The detection results show that the proposed methodology achieved a high level of accuracy in landslide identification, with a precision rate of 81.91%, a recall rate of 84.07% and an overall accuracy of 87.28%. A total of 3782 shallow landslides were detected, showing a distinct clustered distribution pattern. The performance of Mask R-CNN, Faster-CNN, U-Net and YOLOv3 models in landslide identification was further compared, and the effects of setting the rotation angle and constraints on the identification results of the Mask R-CNN algorithm model were investigated. The results show that each model improves the evaluation indices, but the Mask R-CNN model has the best detection performance; the rotation angle can effectively improve the generalization ability and robustness of the model, and the landslide-inducing factor data and texture feature sample data are the best for landslide identification. The research results provide valuable references and technical support for deepening our understanding of the distribution patterns of rainfall-triggered shallow and cluster landslides in the Beijiang River Basin.

Rapid 3D lidar change detection for geohazard identification using GPU-based alignment and M3C2 algorithms

Topographic change detection is increasingly being used to identify and monitor landslides and other geohazards in support of risk-informed decision-making. Expanding change detection from site specific to regional scales enables increasingly proactive asset management and contributes to improving the resilience of infrastructure to extreme events. It is widely known that change detection precision can be improved by applying three-dimensional algorithms, such as iterative closest point (ICP) and M3C2, directly to raw point clouds. However, this also increases the computational requirements compared to alternatives such as digital elevation model differencing. This study presents a novel graphics processing unit (GPU)-based implementation of the ICP-M3C2 workflow to address this limitation. In the proposed algorithm, point cloud data segments are automatically queued and served to the working GPU, which efficiently performs point cloud processing operations, while the central processing unit (CPU) performs data management operations in parallel. The developed method is estimated to be up to 54 times faster than CPU-based versions of the same algorithm. In this study, we present how the workflow has been applied to six regional-scale landslide identification and monitoring case studies in which landslides are causing the disruption of pipelines, highways, and rail corridors. Overall, in 2021 and 2022, over 17 500 linear km of change detection were processed using the demonstrated method.

Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China

Landslides are a natural disaster that exists widely in the world and poses a great threat to human life and property, so it is of great importance to identify and locate landslides. Traditional manual interpretation can effectively identify landslides, but its efficiency is very low for large interpreted areas. In this sense, a landslide recognition method based on the Dual Graph Convolutional Network (DGCNet) is proposed to identify the landslide in remote sensing images quickly and accurately. The remote sensing image (regional remote sensing image) of the northern mountainous area of Tuergen Township, Xinyuan County, Xinjiang Province, was obtained by GeoEye-1 (spatial resolution: 0.5 m). Then, the DGCNet is used to train the labeled images, which finally shows good accuracy of landslide recognition. To show the difference with the traditional convolutional network model, this paper adopts a convolution neural network algorithm named GoogLeNet for image recognition to carry out a comparative analysis, the remote sensing satellite images (single terrain image) of Xinyuan County, Xinjiang Province is used as the data set, and the prediction accuracy is 81.25%. Compared with the GoogLeNet model, the DGCNet model has a larger identification range, which provides a new method for landslide recognition of large-scale regional remote sensing images, but the performance of DGCNet is highly dependent on the quality and characteristics of the input image. If the input data quality is poor or the image structure is unclear, the model’s performance may decline.

Interferometric Synthetic Aperture Radar Applicability Analysis for Potential Landslide Identification in Steep Mountainous Areas with C/L Band Data

Landslides frequently occur in the mountainous area of southwest China, resulting in infrastructure damage, as well as a loss of life and property. The use of interferometric synthetic aperture radar (InSAR) technology has become increasingly popular due to its wide coverage, high precision, and efficiency in identifying potential landslides in steep mountainous regions to mitigate risks. This study focused on the Mao County region in China and utilized a small baseline subset of InSAR (SBAS−InSAR) technology with Sentinel-1 and ALOS-2 data to identify the potential landslides and analyze their applicability. To ensure accuracy, the findings were verified using optical image and field surveys. Additionally, a comparative analysis was performed on C-band and L-band SAR data to examine differences in the coherence, geometric distortion, and displacement results, revealing that the L-band has clear advantages in the coherence, suitable observation coverage, and displacement results, while C-band can detect relatively slight displacements. This study aimed to determine the applicability of different SAR satellites for early landslide identification in steep mountainous areas, which can serve as a technical reference for selecting appropriate SAR data and enhancing InSAR identification abilities for potential landslides in the future.

Early detection of potential landslides along high‐speed railway lines: A pressing issue

AbstractEarly detection of landslides is important for prevention and mitigation of landslide disasters. Especially, accurately identifying potential landslides along high‐speed railway is becoming a pressing issue for operation safety of high‐speed railway. Here, 161 Sentinel‐1A satellite images from March 2017 to September 2022 were acquired to detect potential landslides along the Lanzhou‐Urumqi high‐speed railway (LUHR) in Qinghai. Results show that rainfall is the main cause for the transition from secondary creep (steady state) to tertiary creep (accelerated) at the Jiujiawan landslide. The hot spot analysis method was used to cluster the average line‐of‐sight deformation velocity along the LUHR, effectively extracting five deformation zones that may pose a threat. Optical satellite image analysis revealed four potential landslides and one unstable slope. Furthermore, the rescaled range analysis documented that the Hurst exponent is greater than 0.8 for all four potential landslide instability points, implying that the identified potential landslide areas are still in motion and may pose a threat to the operation of the LUHR. On this basis, we propose a framework for early identification and monitoring of landslides along the high‐speed railway lines that supports landslide hazard managing with the goal to reduce landslide risk.

Identification of Landslides Controlling and Triggering Factors using the Analytical Hierarchy Process (AHP) in Dermasuci Village, Pangkah District, Tegal Regency, Central Java Province - Indonesia

The National Disaster Management Agency (BNPB) stated that in 2022 around 634 landslides occurred in Indonesia. One of the regions in Indonesia that experienced landslide disasters is Dermasuci Village, Pangkah District, Tegal Regency, Central Java Province. The first landslide disaster occurred on February 5, 2022, and the second landslide occurred on February 10, 2022. The landslide disasters caused damage to 61 residents’ houses and four road sections. The objective of this study is to identify the controlling and triggering factors that mainly contributed to the landslide event. These two factors are determined based on the order of their static and dynamic properties using the analytical hierarchy process (AHP) method. Controlling and triggering factors of the landslide in the study area are determined based on geological and engineering geological conditions which were obtained from surface mapping results. The controlling factors obtained are the rock bedding inclination, geological structure in the form of faults, slope inclination, engineering properties of soil and rock, land use, and groundwater table. The triggering factors that are known and commonly occur are rainfall in the form of antecedents and intensity, and earthquakes that can induce landslides.

A landslide susceptibility assessment method based on auto-encoder improved deep belief network

Abstract The landslide susceptibility assessment is an essential part of landslide disaster risk identification and prevention. However, the binarization of the hidden layer limits the parameterization ability of the conditional probability of visible layer, making the training process of restricted Boltzmann machine more difficult and further limiting the accuracy and efficiency of deep belief network (DBN) model in landslide susceptibility assessment. Therefore, this study proposed a landslide susceptibility assessment method based on Auto-Encoder (AE)-modified DBN. Zhouqu County, Gansu Province in the People’s Republic of China, was selected as the study area. Historical landslides in Zhouqu County were identified using small baseline subset interferometric synthetic aperture radar technology and optical image. Landslide factors were screened based on a geographical detector and stepwise regression method. The Logcosh loss function and determinant coefficient R 2 index were used to evaluate the training process of the AE model, and the balanced cross entropy loss function was used to evaluate the entire network training process. In addition, the area under the curve (AUC) of the synthetical index model (SIM), support vector machine (SVM), and multilayer perceptron (MLP) were compared and evaluated. The results indicated that the proposed model could significantly improve the accuracy of landslide susceptibility assessment. The AUC value of the proposed model was 0.31, 0.12, and 0.11 higher than that of SIM, SVM, and MLP, respectively. Therefore, the improved DBN model based on AE proposed is reliable for early landslide identification and prediction.

A process-oriented approach for identifying potential landslides considering time-dependent behaviors beyond geomorphological features

Geomorphological features are commonly used to identify potential landslides. Nevertheless, overemphasis on these features could lead to misjudgment. This research proposes a process-oriented approach for potential landslide identification that considers time-dependent behaviors. The method integrates comprehensive remote sensing and geological analysis to qualitatively assess slope stability, and employs numerical analysis to quantitatively calculate aging stability. Specifically, a time-dependent stability calculation method for anticlinal slopes is developed and implemented in discrete element software, incorporating time-dependent mechanical and strength reduction calculations. By considering the time-dependent evolution of slopes, this method highlights the importance of both geomorphological features and time-dependent behaviors in landslide identification. This method has been applied to the Jiarishan slope (JRS) on the Qinghai-Tibet Plateau as a case study. The results show that the JRS, despite having landslide geomorphology, is a stable slope, highlighting the risk of misjudgment when relying solely on geomorphological features. This work provides insights into the geomorphological characterization and evolution history of the JRS and offers valuable guidance for studying slopes with similar landslide geomorphology. Furthermore, the process-oriented method incorporating time-dependent evolution provides a means to evaluate potential landslides, reducing misjudgment due to excessive reliance on geomorphological features.

SE-YOLOv7 Landslide Detection Algorithm Based on Attention Mechanism and Improved Loss Function

With the continuous development of computer vision technology, more and more landslide identification detection tasks have started to shift from manual visual interpretation to automatic computer identification, and automatic landslide detection methods based on remote sensing satellite images and deep learning have been gradually developed. However, most existing algorithms often have the problem of low precision and weak generalization in landslide detection. Based on the Google Earth Engine platform, this study selected landslide image data from 24 study areas in China and established the DN landslide sample dataset, which contains a total of 1440 landslide samples. The original YOLOv7 algorithm model was improved and optimized by applying the SE squeezed attention mechanism and VariFocal loss function to construct the SE-YOLOv7 model to realize the automatic detection of landslides in remote sensing images. The experimental results show that the mAP, Precision value, Recall value, and F1-Score of the improved SE-YOLOv7 model for landslide identification are 91.15%, 93.35%, 94.54%, and 93.94%, respectively. At the same time, through a field investigation and verification study in Qianyang County, Baoji City, Shaanxi Province, comparing the detection results of SE-YOLOv7, it is concluded that the improved SE-YOLOv7 can locate the landslide location more accurately, detect the landslide range more accurately, and have fewer missed detections. The research results show that the algorithm model has strong detection accuracy for many types of landslide image data, which provides a technical reference for future research on landslide detection based on remote sensing images.

Improving Railway Alignment Selection in Mountainous Areas with Complex Vegetation: A Multisource Data Landslide Identification Approach for Assisted Decision-Making Research

In order to provide important assistance for the scientific and effective route selection of future planned railways in the research area and to quickly and accurately identify the distribution range of landslides, thereby proactively mitigating the impact of geological hazards on railways under earthquake conditions, this study aims to shift the risk threshold for geological hazards and provide a scientific basis for the accurate planning and route selection of railways in mountainous areas. Jiuzhaigou was selected as the research area and postearthquake surface deformation information in the study area was obtained through Sentinel-1 satellite radar data. Based on Sentinel-2 optical remote sensing imagery, the changes in vegetation indices in the study area before and after the earthquake were analyzed in depth. The concept of vegetation index difference was proposed as a characteristic parameter for landslide information interpretation and a method combining surface deformation information was developed for landslide information interpretation. According to this method, the study area experienced a deformation subsidence of up to 14.93 cm under the influence of the earthquake, with some areas experiencing an uplift of approximately 6.0 cm. The vegetation index difference in the research area ranged from −1.83502 to 1.45366. The total number of landslides extracted is 12.034 km2 and 164 landslide points are marked, with an overall recognition accuracy of 92.6% and a Kappa coefficient of 0.876. The research results provide new research ideas for landslide information interpretation and can be used to assist in the decision-making of mountain railroad alignment options.

Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images

Currently, researchers commonly use convolutional neural network (CNN) models for landslide remote sensing image recognition. However, with the increase in landslide monitoring data, the available multimodal landslide data contain rich feature information, and existing landslide recognition models have difficulty utilizing such data. A knowledge graph is a linguistic network knowledge base capable of storing and describing various entities and their relationships. A landslide knowledge graph is used to manage multimodal landslide data, and by integrating this graph into a landslide image recognition model, the given multimodal landslide data can be fully utilized for landslide identification. In this paper, we combine knowledge and models, introduce the use of landslide knowledge graphs in landslide identification, and propose a landslide identification method for remote sensing images that fuses knowledge graphs and ResNet (FKGRNet). We take the Loess Plateau of China as the study area and test the effect of the fusion model by comparing the baseline model, the fusion model and other deep learning models. The experimental results show that, first, with ResNet34 as the baseline model, the FKGRNet model achieves 95.08% accuracy in landslide recognition, which is better than that of the baseline model and other deep learning models. Second, the FKGRNet model with different network depths has better landslide recognition accuracy than its corresponding baseline model. Third, the FKGRNet model based on feature splicing outperforms the fused feature classifier in terms of both accuracy and F1-score on the landslide recognition task. Therefore, the FKGRNet model can make fuller use of landslide knowledge to accurately recognize landslides in remote sensing images.

Identifying Potential Landslides in Steep Mountainous Areas Based on Improved Seasonal Interferometry Stacking-InSAR

Landslides are a major concern in the mountainous regions of southwest China, leading to significant loss of life and property damage. Therefore, it is crucial to identify potential landslides for early warning and mitigation. stacking-InSAR, a technique used for landslide identification in a wide area, has been found to be faster than conventional time-series InSAR. However, the dense vegetation in southwest China mountains has an adverse impact on the coherence of stacking-InSAR, resulting in more noise and inaccuracies in landslide identification. To address this problem, this paper proposes an improved seasonal interferometry stacking-InSAR method. It uses Sentinel-1 satellite data from 2017 to 2022. The study area is the river valley section of the G213 road from Wenchuan County to Mao County. The study reveals the characteristics of seasonal decoherence in the steep mountainous region, and identifies a total of 21 potential landslides using the improved method. Additionally, optical satellite imagery and LiDAR data were used to assist in the identification of potential landslides. The results of the conventional stacking-InSAR method and the improved seasonal interferometry stacking-InSAR method are compared, showing that the latter is more effective in noise suppression caused by low coherence. Their standard deviations were reduced by 46%, 22%, 10%, and 14%, respectively, using the quantitative statistics for the four tested areas. The proposed method provides an efficient and effective approach for detecting potential landslides in the mountainous regions of southwest China. It can serve as a valuable technical reference for future landslide identification studies in this area.

Landslide Identification in Human-Modified Alpine and Canyon Area of the Niulan River Basin Based on SBAS-InSAR and Optical Images

Landslide identification in alpine and canyon areas is difficult due to the terrain limitations. The main objective of this research was to explore the method of combining small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), multi-temporal optical images and field surveys to identify potential landslides in the human-modified alpine and canyon area of the Niulan River in southwestern China based on terrain visibility analysis. The visibility of the terrain is analyzed using the different incident and heading angles of the Sentinel satellite’s ascending and descending orbits. Based on the SAR image data of Sentinel-1A satellites from 2016 to 2019, the SBAS-InSAR method was used to identify landslides, and then multi-temporal optical images were used to facilitate landslide identification. Field surveys were carried out to verify the identification accuracy. A total of 28 landslides were identified, including 13 indicated by SBAS-InSAR, 8 by optical imaging and 7 by field investigation. Many landslides were induced by the impoundment and fluctuation of reservoir water. The comparison and verification of typical landslide monitoring data and reservoir water fluctuations revealed that a sudden drop of reservoir water had a great influence on landslide stability. These research results can facilitate a comprehensive understanding of landslide distribution in the reservoir area and guide the follow-up landslide risk management.

Landslide surface horizontal displacement monitoring based on image recognition technology and computer vision

The deformation and displacement of landslides has always been the priority of landslide monitoring and early warning. With the development of image processing technology in recent years, the analysis of landslide surface deformation through image data has become a popular method and different methods of image analysis are rapidly increasing. However, the current method of analyzing landslide deformation and displacement using a single image processing method tends to analyze the displacement of the local deformation area of the landslide, which lacks overall monitoring of the slope and is difficult to use for pre-disaster monitoring of landslide hazards, easily leading to missed detection of landslide potential hazard points. Therefore, this paper combines image recognition technology and computer vision (IR-CV) to propose a method for monitoring and identifying the deformation displacement of landslide surfaces, which has been validated by indoor landslide model tests and unmanned aerial vehicle field simulation displacement recognition tests. In the model tests, the IR-CV method successfully identified the displacements generated during the deformation and damage of the landslide model while using the model deformation time series image data recorded by the camera to analyze the overall deformation of the surface of the landslide model. Further, in this paper, a field simulation displacement identification test was conducted using an unmanned aerial vehicle (UAV), and the orthophoto data obtained by the UAV verified that the IR-CV method can be used for overall deformation displacement identification of landslides in the field. The results of indoor model tests and field UAV simulation tests show that the monitoring method of IR-CV can effectively identify the deformation and displacement of landslide surfaces, which has good application prospects in pre-disaster monitoring and displacement identification of landslides, thus providing a cost-effective and feasible solution for monitoring the deformation and displacement of landslide surfaces.

Geomorphological transformations and future deformation estimations of a large potential landslide in the high-order position area of Diexi, China

Landslides in high-order position areas pose a serious threat to residents located below such areas. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based on landslides that have already occurred; however, the investigation of potential landslides is of higher value. In this study, a progressive potential landslide identification method is proposed with the application of SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) and subsequently combined with Google Earth, GF-1 (Gaofen-1), GF-2 (Gaofen-2), ZY-3 (Ziyuan-3) and UAV (Unmanned Aerial Vehicle) imagery, and DEM (Digital Elevation Model) for further validation. The proposed method is employed to determine the potential landslide in Tuanjiecun by integrating the multi-period data to simultaneously analyze the evolution and mechanism of the potential landslide. The long short-term memory method is then adopted to predict the evolution trend based on accumulative deformation from SBAS-InSAR. The results suggest that the deformation of Tuanjiecun potential landslide will increase to −238.57 mm. This article provides a new approach for disaster prevention and mitigation by determining a potential landslide using composite remote sensing and predicting the development of the potential landslide on indirect contact monitoring.

Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR

The Dadu River travels in the mountainous areas of southwestern China, one of regions with the most hazards that has long suffered from frequent geohazards. The early identification of landslides in this region is urgently needed, especially after the recent Luding earthquake (MS 6.8). While conventional ground-based monitoring techniques are limited by the complex terrain conditions in these alpine valley regions, space interferometric synthetic aperture radar (InSAR) provides an incomparable advantage in obtaining surface deformation with high precision and over a wide area, which is very useful for long-term and slow geohazard monitoring. In this study, more than 500 Sentinel-1 SAR images with four frames acquired during 2017~2022 were collected to detect the hidden landslide regions from the Jinchuan to Ebian Section along the Dadu River, based on joint-scatterer InSAR (JS-InSAR) and small baseline subset (SBAS) techniques. The results showed that our method could be successfully applied for landslide monitoring in complex mountainous regions. Furthermore, 143 potential landslide regions spreading over an 800 km area along the Dadu River were extracted by integrating the deformation measurements and optical images. Our study can provide a reference for large-scale geological hazard surveys in mountainous areas, and the InSAR technique will be encouraged for the local government in future long-term monitoring applications in the Dadu River Basin.

Karst landslides detection and monitoring with multiple SAR data and multi-dimensional SBAS technique in Shuicheng, Guizhou, China

Shuicheng District is a karst mountain area, located in Guizhou Province, China. Its fragile stratum and frequent underground mining activities makes it prone to landslides. Owning to its wide coverage and frequent revisit, the InSAR technology has advantages in potential landslide identification and deformation monitor. However, affected by dense vegetation and atmospheric delay, it is much difficult to get sufficient effective targets to derive the deformation in this area. Besides, deformation derived from single orbit SAR data can result in the missing identification of some potential landslides and the misinterpreting of the real kinematics process of landslides. In this study, the multi-source SAR data, atmospheric error correction by quadratic tree image segmentation method, and phase-stacking method were selected to derive the surface deformation of this area. Besides, DS-InSAR and MSBAS method were combined to derive the deformation of Pingdi landslide. First, the potential landslides in this area were identified, surface deformation result, optical remote sensing images and geomorphological features were jointly considered. Then, the landslide distribution characteristics was analyzed in terms of slope, elevation and stratum. After that, the deformation along the LOS direction was acquired using the DS-InSAR method. The MSBAS method was used to retrieve the two-dimensional deformation of Pingdi landslide. Finally, the comprehensive analysis of triggering factors and failure process were conducted according to the spatial-temporal deformation characteristics and field investigation. The results indicated that landslides in Shuicheng district were mostly located in the junction of T1 and P3 stratum and mining related. Mining activity was the main cause of the Pingdi landslide deformation, the precipitation was the driving factor of the landslide instability. The research provides an insight into the explore the unstable slope distribution characteristic and the failure process of the landslides.

Identification and evaluation of the high mountain upper slope potential landslide based on multi-source remote sensing: the Aniangzhai landslide case study

Identification and evaluation of the high mountain upper slope potential landslide based on multi-source remote sensing: the Aniangzhai landslide case study

Persistent Scatterer Interferometry (PSI) Technique for the Identification and Monitoring of Critical Landslide Areas in a Regional and Mountainous Road Network

A reliable road network is a vital local asset, connecting communities and unlocking economic growth. Every year landslides cause serious damage and, in some cases, the full disruption of many road networks, which can last from a few days to even months. The identification and monitoring of landslides with conventional methods on an extended and complex road network can be a rather difficult process, as it requires a significant amount of time and resources. The road network of the Chania regional unit on the island of Crete in Greece is a typical example, as it connects, over long distances, many remote mountainous villages with other local communities, as well as with the main urban centers, which are mainly located across the shore. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor slow-moving and ongoing landslides over large and complex areas such as those of the mountainous road networks. This study applied PSI in the Chania regional unit, using the novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing BEYOND of the Institute of Astronomy and Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (BEYOND) for the rapid identification of the areas, most critical to landslide in a local road network. The application of P-PSI speeded up the total required processing time by a factor of five and led to the rapid identification and monitoring of 235 new slow-moving landslides. The identified landslides were correlated with a pre-existing landslide inventory and open access visual data to create a complete landslide inventory and a relative landslide inventory map, thus offering a valuable tool to local stakeholders.

Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas

It is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas, this paper established an integrated identification method, including sliding prone area identification based on regional geological environment analysis, target area identification of potential landslides in terms of comprehensive remote sensing methods, and landslide recognition through engineering geological survey. The Miaoyuan catchment in Quzhou City, Zhejiang Province, southeastern China, was taken as an example to validate the identification methods. Particularly, the Shangfang landslide was successfully studied in terms of comprehensive methods, such as geophysical survey, drilling, mineral and chemical composition analysis, and microstructure scanning of the sliding zone. In order to assess the landslide risk, the potential runout of the Shangfang landslide was evaluated in a quantitative simulation. This paper suggests a methodology to identify potential landslides from a large area to a specific slope covered by dense vegetation.