Landslide Area Research Articles

Automatic detection of landslide impact areas using Google Earth Engine

This research introduces an effective framework for automatically detecting landslide impact areas using Google Earth Engine (GEE). The Asia–Pacific region frequently experiences earthquakes and heavy rainfall, leading to frequent landslides that cause loss of life and property. Focusing on landslide catalogues from Taiwan and Japan, the study proposes an automatic landslide detection process using a new method termed multi-bitemporal images (MBTIs), which involves the collection of accumulated changes over time. First, set the event date and collect all images before and after the event. Second, analyse the change pixels in bi-temporal images. Third, review all change pixels to determine the total amount of accumulated changes. This method includes all bi-temporal image sets in the analysis, unlike traditional methods that only use single pairs of bi-temporal images. Clouds are filtered using a pixel-based approach and machine learning techniques. The landslide areas are analysed statistically, and appropriate thresholds for automatic landslide detection are suggested. Using reproducibility, which indicates the percentage of bi-temporal images that detect vegetation loss in mountainous areas, the proposed method achieves a 99% reduction in false positives with a reproducibility requirement of 24.21%, while maintaining true positives at 66.89%. This study analyzed 28–720 bi-temporal image sets from various regions using Sentinel-2 data, revealing that subsequent landslides can be 7–293 times larger than co-seismic landslides. In comparison, subsequent landslides were found to be 3–12 times larger than rainfall-induced landslides. Additionally, the impact of earthquake event on subsequent landslides is 2.3–24.4 times greater than that of rainfall-induced event. By using GEE, the accumulation of hundreds of satellite images can be completed within 15 min, depending on the processing requirements.

Spatiotemporal dynamics of landslide susceptibility under future climate change and land use scenarios

Abstract Mountainous landslides are expected to worsen due to environmental changes, yet few studies have quantified their future risks. To address this gap, we conducted a comprehensive analysis of the eastern Hindukush region of Pakistan. A geospatial database was developed, and logistic regression was employed to evaluate baseline landslide susceptibility for 2020. Using the latest CMIP6 models under three Shared Socioeconomic Pathways (SSPs) and the CA-Markov model, we projected future rainfall and land use/land cover patterns for 2040, 2070, and 2100, respectively. Our results reveal significant changes in future rainfall and land use patterns, particularly in the long-term future (2070 and 2100). Future landslide susceptibility was then predicted based on these projections. By 2100, high-risk landslide areas are expected to increase substantially under all SSP scenarios, with the largest increases observed under SSP5-8.5 (56.52%), SSP2-4.5 (53.55%), and SSP1-2.6 (22.45%). By 2070, high-risk areas will rise by 43.08% (SSP1-2.6), 40.88% (SSP2-4.5), and 12.60% (SSP5-8.5). However, by 2040, the changes in high-risk areas are minimal compared to the baseline, with increases of 9.45% (SSP1-2.6), 1.69% (SSP2-4.5), and 7.63% (SSP5-8.5). These findings provide crucial insights into the relationship between environmental changes and landslide risks and support the development of climate risk mitigation, land use planning, and disaster management strategies for mountainous regions.

Ultra-High Sensitivity Real-Time Monitoring of Landslide Surface Deformation via Triboelectric Nanogenerator.

Monitoring surface deformation is crucial for the early warning of landslides, facilitating timely preventive measures. Triboelectric nanogenerator (TENG) demonstrates great potential for self-powered distributed monitoring in remote and power-scarce landslide areas. However, landslides deform typically at a rate of a few millimeters per day (mm d-1), making it challenging for TENG to directly monitor the deformation process. Herein, a method for monitoring surface deformation of landslides by constructing an ultra-low-speed triboelectric displacement sensor (US-TDS) is reported. Utilizing a force storage-release device and an accelerator, the US-TDS can produce obvious sensing signals at a linear input speed of 4.32mm d-1. The coefficient of determination (R2) for the fitting curve of the pulse signals within the speed range of 21.6 to 129.6mm d-1 reaches 0.999. Moreover, US-TDS can detect deformation displacement as small as 0.0382mm. The stability of US-TDS displacement measurements is confirmed at a speed of 108mm d-1, with relative errors under 1%. Ultimately, a real-time monitoring and early warning system for landslide surface deformation is constructed and verified through a combination of indoor simulations and outdoor experiments. This work provides a feasible solution for the scientific monitoring and early warning of the landslide development.

Deep learning algorithms based landslide vulnerability modeling in highly landslide prone areas of Tamil Nadu, India

Deep learning algorithms based landslide vulnerability modeling in highly landslide prone areas of Tamil Nadu, India

Distribution-agnostic landslide hazard modelling via Graph Transformers

In statistical applications, choosing a suitable data distribution or likelihood that matches the nature of the response variable is required. To spatially predict the planimetric area of a landslide population, the most tested likelihood corresponds to the Log-Gaussian case. This causes a limitation that hinders the ability to accurately model both very small and very large landslides, with the latter potentially leading to a dangerous underestimation of the hazard. Here, we test a distribution-agnostic solution via a Graph Transformer Neural Network (GTNN) and implement a loss function capable of forcing the model to capture both the bulk and the right tail of the landslide area distribution. An additional problem with this type of data-driven hazard assessment is that one often excludes slopes with landslide areas equal to zero from the regression procedure, as this may bias the prediction towards small values. Due to the nature of GTNNs, we present a solution where all the landslide area information is passed to the model, as one would expect for architectures built for image analysis. The results are promising, with the landslide area distribution generated by the Wenchuan earthquake being suitably estimated, including both zeros, the bulk and the extremely large cases. We consider this a step forward in the landslide hazard modelling literature, with implications for what the scientific community could achieve in light of a future space–time and/or risk assessment extension of the current protocol.

A Novel Method for Identifying Landslide Surface Deformation via the Integrated YOLOX and Mask R-CNN Model

The detection of landslide areas and surface characteristics is the prerequisite and basis of landslide hazard risk assessment. The traditional method relies mainly on manual field identification, and discrimination is based on the lack of unified quantitative standards. Thus, the use of neural networks for the quantitative identification and prediction of landslide surface deformation is explored. By constructing an integrated model based on YOLO X-CNN and Mask R-CNN, a deep learning-based feature detection method for landslide surface images is proposed. First, the method superimposes Unmanned Aerial Vehicle (UAV) oblique photography data (UOPD) and Internet heterosource image data (IHID) to construct a landslide surface image dataset and landslide surface deformation database. Second, an integrated model suitable for small- and medium-scale target detection and large-scale target edge extraction is constructed to automatically identify and extract landslide surface features and to achieve rapid detection of landslide surface features and accurate segmentation and deformation recognition of landslide areas. The results show that the detection accuracy for small rock targets is greater than 80% and that the speed is 57.04 FPS. The classification and mask segmentation accuracies of large slope targets are approximately 90%. A speed of 7.89 FPS can meet the needs of disaster emergency response; this provides a reference method for the accurate identification of landslide surface features.

IC-IE-AKS-O: an automatic recognition method for coastal slope landslide areas

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

Benchmarking data handling strategies for landslide susceptibility modeling using random forest workflows

Machine learning (ML) algorithms are frequently used in landslide susceptibility modeling. Different data handling strategies may generate variations in landslide susceptibility modeling, even when using the same ML algorithm. This research aims to compare the combinations of inventory data handling, cross validation (CV), and hyperparameter tuning strategies to generate landslide susceptibility maps. The results are expected to provide a general strategy for landslide susceptibility modeling using ML techniques. The authors employed eight landslide inventory data handling scenarios to convert a landslide polygon into a landslide point, i.e., the landslide point is located on the toe (minimum height), on the scarp (maximum height), at the center of the landslide, randomly inside the polygon (1 point), randomly inside the polygon (3 points), randomly inside the polygon (5 points), randomly inside the polygon (10 points), and 15 m grid sampling. Random forest models using CV–nonspatial hyperparameter tuning, spatial CV–spatial hyperparameter tuning, and spatial CV–forward feature selection–no hyperparameter tuning were applied for each data handling strategy. The combination generated 24 random forest ML workflows, which are applied using a complete inventory of 743 landslides triggered by Tropical Cyclone Cempaka (2017) in Pacitan Regency, Indonesia, and 11 landslide controlling factors. The results show that grid sampling with spatial CV and spatial hyperparameter tuning is favorable because the strategy can minimize overfitting, generate a relatively high-performance predictive model, and reduce the appearance of susceptibility artifacts in the landslide area. Careful data inventory handling, CV, and hyperparameter tuning strategies should be considered in landslide susceptibility modeling to increase the applicability of landslide susceptibility maps in practical application.

Predicting landslide extent of satellite images based on image classification extraction

Abstract Landslide is a common geological disaster and the most destructive natural disaster at home and abroad. Therefore, automatic interpretation of landslide high-risk areas and potential landslide areas through different models and methods is one of the basic ways to prevent and reduce possible disasters. In this study, we improve the NDWI method by using the multispectral image data from the French satellite SPOT 5. Combined with infrared band sensors, the Normalized Difference Water Index (NDWI) of large exposed landslides is extracted by improving the NDWI index method by utilizing the characteristics of near-infrared (NIR) and short-wave infrared (SWIR) bands and replacing the green-wave band or the mid-infrared (MIR) band with the short-wave infrared (SWIR) band, which can be applied to the water resources management of high-risk areas, potential landslide areas, and potential landslide areas, and the water environment monitoring of water resources and disaster emergency management. We calculate the 2014 satellite images with a total area of 11351.6 ha, and after the NDWI index, the dark blue color represents the high-risk landslide area of 1609.1 ha (14175%), the light blue color represents the potential landslide area of 3971.773 ha (34.989%), and the brown color represents the vegetation area of 5770.727 ha (50.836%).

An enhanced neighborhood differential method for potential landslide identification from stacking-InSAR results

Stacking-InSAR is widely used for identifying potential landslide. However, in Stacking-InSAR results, there are often challenges to clearly identify the potential landslide due to wide-scale system errors, especially the elevation-related atmospheric disturbances, leading to low precision of potential landslide identification. This paper introduces an enhanced neighborhood differential method that employs a concept of step size to identify potential landslide from Stacking-InSAR. Applied to the mountainous regions in the northwestern part of Sichuan Province, China, this method successfully identified 117 potential landslides, verified through field investigations. Compared to traditional Stacking-InSAR, this method additionally identified 34 potential landslides. Further analysis indicates that the optimal step size in the neighborhood differential method should ensure that one differential pixel is located inside the potential landslide area while the other is outside, thereby making the step size closely related to the spatial extent of the potential landslide. The results indicate that this method could effectively reduce wide-scale system error in Stacking-InSAR results, providing an important technical reference for the rapid and efficient identification of potential landslide over a wide mountainous area in the future.

Numerical simulation on the prevention and control of landslide-induced wave disasters in Wangjiashan landslide area of Baihetan reservoir

The risk management of landslide surges after water storage in large reservoirs is a major challenge in reservoir management. The water storage in the reservoir area of the Baihetan hydropower station on the Jinsha River brings the potential of Wangjiashan landslide activation. This paper proposes a mitigation measurement based on slope cutting for the Wangjiashan landslide and evaluates the feasibility and effectiveness of the management scheme through numerical simulation. Results indicate that when water storage is in a normal level, after an earthquake, the landslide will impact the Baihetan Reservoir with a maximum velocity of 8.52 m/s, generating waves with a maximum wave height of 15.63 m. By removing the upper part of the landslide, the volume of the landslide is reduced from 611 × 10 4 m 3 to 369 × 10 4 m 3 , it will cut down the maximum wave height in the Xiangbiling residential area to 3.09 m, which significantly reduces the risk of landslide surge waves. It further establishes a disaster prevention and control plan for the landslide-induced wave disaster. Overall, this study provides important theoretical and practical references for the risk management of landslide surge waves and offers valuable insights for addressing similar issues in the future.

Establishing a Landslide Traces Inventory for the Baota District, Yan’an City, China, Using High-Resolution Satellite Images

The Baota District of Yan’an City, located in the Loess Plateau, is an important patriotic education base in China. The region’s fragile geological environment and frequent geological disasters pose significant threats to the production and livelihood of residents. Establishing a landslide traces inventory can provide crucial assistance for studying regional land disaster distribution patterns and implementing disaster prevention and mitigation measures. However, the Baota District has not yet established a comprehensive and detailed landslide traces inventory, resulting in a lack of clear understanding and comprehensive knowledge regarding the threats and impacts of landslide disasters in the area. Therefore, this study employed high-resolution satellite images, applying a human–computer interactive visual interpretation method in conjunction with field survey verifications, to develop the most detailed and comprehensive landslide traces inventory for the Baota District to date. The results indicate that within the 3556 km2 area of the Baota District, there are 73,324 landslide traces, with an average landslide density of 20.62 km-2 and a total landslide area of 769.12 km2, accounting for 21.63% of the total land area. These landslides are relatively evenly distributed throughout the district, with a higher concentration in the east compared to the west. Most of the landslides are small in size. This study can support disaster prevention and mitigation efforts in the Baota District and serve as a reference for establishing landslide inventories in other regions of the Loess Plateau.

The Identification and Influence Factor Analysis of Landslides Using SBAS-InSAR Technique: A Case Study of Hongya Village, China

On 1 September 2022, a landslide in Hongya Village, Weiyuan Town, Huzhu Tu Autonomous County, Qinghai Province, caused significant casualties and economic losses. To mitigate such risks, InSAR technology is employed due to its wide coverage, all-weather operation, and cost-effectiveness in detecting landslides. In this study, focusing on the landslide in Hongya Village, SBAS-InSAR and Sentinel-1A satellite data from July 2021 to September/October 2022 were used to accurately identify the areas of active landslides and to analyze the landslide deformation trends, in combination with the geological characteristics of the landslides and rainfall data. The results showed that strong deformation was detected in the middle and back of the landslide in Hongya Village, with a maximum deformation rate of approximately -13 mm/year. The surface of the landslide consisted of mainly Upper Pleistocene wind-deposited loess, which is extremely sensitive to water. The deformation of the landslide was closely related to the rainfall, and the deformation of the landslide increased with the increase in rainfall. The research results prove that the combination of ascending and descending orbit data based on SBAS-InSAR technology is highly feasible in the field of landslide deformation monitoring and is of great practical significance for landslide disaster prevention and mitigation.

Remote sensing identification of shallow landslide based on improved otsu algorithm and multi feature threshold

In low-resolution remote sensing images under complex lighting conditions, there is a similarity in spectral characteristics between non-landslide areas and landslide bodies, which increases the probability of misjudgment in the identification process of shallow landslide bodies. In order to further improve the accuracy of landslide identification, a shallow landslide remote sensing identification method based on an improved Otsu algorithm and multi-feature threshold is proposed for the temporary treatment project of the Yangjunba disaster site in Leshan City. Using Retinex theory, remote sensing images are enhanced with local linear models and guided filtering; then, multi-feature scales and sliding window calculations of opening and closing transformations identify potential landslide areas, which are finally segmented using the Otsu algorithm. Through experimental verification, the method proposed in this article can clearly segment the target object and background after binary segmentation of remote sensing images. The recognition rate of shallow landslide bodies is not less than 95%, indicating that the method proposed in this article is relatively accurate in identifying shallow landslide bodies in the research area and has good application effects.

Surface wave monitoring using ambient noise for detecting temporal variations in underground structures in landslide area

The temporal variation in subsurface structure of a landslide area was monitored using spatial autocorrelation (SPAC) as a simple and robust seismic observational method. The SPAC method is often used in civil engineering to estimate one-dimensional (1D) subsurface structures. However, in this study, we monitor the temporal variation in the SPAC coefficient deviation to evaluate environmental change effects and changes in the subsurface velocity structure. We used a seismic array comprising 10 seismometers in a landslide-prone area in Morimachi Town, western Shizuoka Prefecture, Japan, and continuous observations were performed from October 2020 – May 2022. We obtained a 1D velocity structural model as a reference using the multi-mode SPAC (MMSPAC) method with the averaged SPAC coefficients at different distances for all observation periods. We calculated the daily variation in SPAC coefficient relative to the average for all observation periods. We then applied cluster analysis to the SPAC coefficient deviation, which revealed weekly changes likely due to human activity and the effect of stream surges on nearby streams. After removing stream surge clusters and correcting for the weekly change, we reapplied cluster analysis to identify two major clusters. The differences between the two clusters can be attributed to structural changes in the very near surface (∼5 m) and deeper parts (∼20 m), likely influenced by shallow groundwater due to rainfall. By investigating the location of the landslide mass near the observation site, we suggest that structural changes around 20 m deep may correspond to the depth of potential slip surfaces.

RegionGrow3D: A Deterministic Analysis for Characterizing Discrete Three‐Dimensional Landslide Source Areas on a Regional Scale

AbstractRegional‐scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics‐based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three‐dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad‐scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km2) using 3D, limit‐equilibrium (LE)‐based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back‐calculated from inventoried landslides using 3D LE‐based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA.

DIPHORM: An Innovative DIgital PHOtogrammetRic Monitoring Technique for Detecting Surficial Displacements of Landslides

Monitoring surface displacements of landslides is essential for evaluating their evolution and the effectiveness of mitigation works. Traditional methods like robotic total stations (RTSs) and GNSS provide high-accuracy measurements but are limited to discrete points, potentially missing the broader landslide’s behavior. On the contrary, laser scanner surveys offer accurate 3D representations of slopes and the possibility of inferring their movements, but they are often limited to infrequent, high-cost surveys. Monitoring techniques based on ground-based digital photogrammetry may represent a new, robust, and cost-effective alternative. This study demonstrates the use of multi-temporal images from fixed and calibrated cameras to achieve the 3D reconstruction of landslide displacements. The method presented offers the important benefit of obtaining spatially dense displacement data across the entire camera view and quasi-continuous temporal measurement. This paper outlines the framework for this prototyping technique, along with a description of the necessary hardware and procedural steps. Furthermore, strengths and weaknesses are discussed based on the activities carried out in a landslide case study in northeastern Italy. The results from the photo-monitoring are reported, discussed, and compared with traditional topographical data, validating the reliability of this new approach in monitoring the time evolution of surface displacements across the entire landslide area.

Method for Landslide Area Detection Based on EfficientNetV2 with Optical Image Converted from SAR Image Using pix2pixHD with Spatial Attention Mechanism in Loss Function

A method for landslide area detection based on EfficientNetV2 with optical image converted from SAR image using pix2pixHD with a spatial attention mechanism in the loss function is proposed. Meteorological landslides such as landslides after heavy rains occur regardless of day or night and weather conditions. Meteorological landslides such as landslides are easier to visually judge using optical images than SAR images, but optical images cannot be observed at night, in the rain, or on cloudy days. Therefore, we devised a method to convert SAR images, which allow all-weather observation regardless of day or night, into optical images using pix2pixHD, and to learn about landslide areas using the converted optical images to build a trained model. We used SAR and optical images derived from Sentinel-1 and -2, which captured landslides caused by the earthquake on 14 April 2016, as training data, and constructed a learning model that classifies landslide areas using EfficientNetV2. We evaluated the superiority of the proposed method by comparing it with a learning model that uses only SAR images. As a result, it was confirmed that the F1-score and AUC were 0.3396 and 0.2697, respectively, when using only SAR images, but were improved by 1.52 to 1.84 times to 0.6250 and 0.4109, respectively, when using the proposed method.

Landslide Recognition Based on Machine Learning Considering Terrain Feature Fusion

Landslides are one of the major disasters that exist worldwide, posing a serious threat to human life and property safety. Rapid and accurate detection and mapping of landslides are crucial for risk assessment and humanitarian assistance in affected areas. To achieve this goal, this study proposes a landslide recognition method based on machine learning (ML) and terrain feature fusion. Taking the Dawan River Basin in Detuo Township and Tianwan Yi Ethnic Township as the research area, firstly, landslide-related data were compiled, including a landslide inventory based on field surveys, satellite images, historical data, high-resolution remote sensing images, and terrain data. Then, different training datasets for landslide recognition are constructed, including full feature datasets that fusion terrain features and remote sensing features and datasets that only contain remote sensing features. At the same time, different ratios of landslide to non-landslide (or positive/negative, P/N) samples are set in the training data. Subsequently, five ML algorithms, including Extreme Gradient Boost (XGBoost), Adaptive Boost (AdaBoost), Light Gradient Boost (LightGBM), Random Forest (RF), and Convolutional Neural Network (CNN), were used to train each training dataset, and landslide recognition was performed on the validation area. Finally, accuracy (A), precision (P), recall (R), F1 score (F1), and intersection over union (IOU) were selected to evaluate the landslide recognition ability of different models. The research results indicate that selecting ML models suitable for the study area and the ratio of the P/N samples can improve the A, R, F1, and IOU of landslide identification results, resulting in more accurate and reasonable landslide identification results; Fusion terrain features can make the model recognize landslides more comprehensively and align better with the actual conditions. The best-performing model in the study is LightGBM. When the input data includes all features and the P/N sample ratio is optimal, the A, P, R, F1, and IOU of landslide recognition results for this model are 97.47%, 85.40%, 76.95%, 80.95%, and 71.28%, respectively. Compared to the landslide recognition results using only remote sensing features, this model shows improvements of 4.51%, 35.66%, 5.41%, 22.27%, and 29.16% in A, P, R, F1, and IOU, respectively. This study serves as a valuable reference for the precise and comprehensive identification of landslide areas.

Centrifuge tests on the deformation law of pipelines crossing slopes with different water contents

The deformation law of pipelines crossing landslide areas is an important prerequisite for the scientific design of pipelines. Most current studies find it difficult to simulate the real stress and strain state of pipelines on site. This study conducted two centrifuge model tests to observe pipeline failure indicators (deformation, stress, strain) and slope failure indicators (crack width, soil pressure, landslide displacement) in response to changes in acceleration. The experimental findings align with the results obtained from numerical simulations. The findings indicate that slope failure with high moisture content is more serious. At a moisture content of 15%, the slope has a maximum displacement of 70 mm in close reach to the buried pipeline. The pipeline has a “bimodal distribution” of strain along its axial length. The largest amount of deformation occurs at a distance of L/4 from the center of the pipeline, and the deformation value is 9000 με. Moreover, the maximum deformation in the mid-span is 21 mm, which corresponds to the result accumulated from the numerical simulation. Thus, the pipeline needs to improve the disaster prevention capabilities at the mid-span and L/4 of the pipeline.