Slow-moving Landslides Research Articles

A fast and robust method for detecting trend turning points in InSAR displacement time series

Ground deformation monitoring is a crucial task in geohazard management to ensure the safety of lives and infrastructure. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced technique for measuring small displacements on the Earth’s surface. Estimated PS-InSAR time series acquired by Sentinel-1 satellites provide a great opportunity for effective monitoring of ground deformation in recent years. However, challenges arise when processing these time series due to their non-uniform sampling, noise from atmosphere and preprocessing issues including phase unwrapping and others. Therefore, estimating the location and direction of trend turning in such time series, as an indicator of ground deformation, is not an easy task. In this work, a sequential turning point detection method (STPD) is proposed and compared with other change point detection methods. Using a large set of simulated time series with various noise types, it is shown that STPD outperforms other methods in terms of overall accuracy and root mean square error for location and direction of trend turnings. As a case study, STPD is applied to detect turning points within PS-InSAR time series for the province of Frosinone in Italy and classified using topography and land cover/use. In addition, an area susceptible to landslides is selected to estimate the starting dates of potential slow-moving landslides. It is also shown that the turning points in the local precipitation time series have a high correlation with the ones in the PS-InSAR time series, indicating that precipitation is a major triggering factor of the displacements in the area. The STPD can rapidly and effectively detect locations and directions of trend turnings and is freely available online in both MATLAB and python.

Simulation of cyclic slow-to-fast landslides based on the coupled creep and frictional slip model

Soft rock slopes may undergo long-term cyclic slow-to-fast landslides due to the softening of clay minerals and frictional sliding under hydromechanical disturbance. We proposed a meso/macroscale cyclic slow-to-fast landslide model by coupling creep and frictional sliding model to address the abovementioned problem. The model can reveal the macroscopic catastrophic phenomena caused by the accumulation of mesoscale damage in soft rocks, thus providing a scientific basis for the simulation of cyclic slow-to-fast landslides. For modeling validation, we applied the model to the Spriana soft rock slope, which undergoes long-term instability under periodic groundwater level variation. By adopting the coupled finite element method and distinct element method (FEM-DEM), the model can mimic the stable creep and fast sliding in different phases of matured slow-moving landslides. The difficulties in simulating cyclic accelerated sliding are solved, where the simulation error of displacement is less than 5.7 % compared with field monitoring data. It may serve as a prediction tool for cyclic slow-to-fast landslides.

Satellite monitoring of reinforced concrete buildings in areas affected by slow-moving landslides

Slow-moving landslides are very spread phenomena in Italy and worldwide. The study of their impact on the existing structures is of great interest, since they can provoke more or less serious consequences for structures, facilities and human life safety. Traditional monitoring techniques on site are usually adopted for landslides. The innovative Differential Interferometry Synthetic Aperture Radar (DInSAR) techniques, based on the use of data derived from satellite images, constitute an advanced technology that allows to remotely monitor the deformation of large portions of land over long periods of time, with relatively low costs. These techniques are able to well capture displacements slowly evolving in time; for this reason, they fit very well with slow-moving landslides. This work follows a previous study by some of the authors, where the structural health monitoring of a reinforced concrete infilled building within a landslide-affected area was made. A finite element model of the structure was created, assuming a non-linear behavior for the structural elements, focusing on the elastic part up to yielding, while infills were modeled as diagonal struts. DInSAR data derived from the processing of COSMO-SkyMed images in the period 2012-2016 were used in order to investigate the evolution of the infills damage over the years. In this work, the monitoring period has been extended by processing new COSMO-SkyMed images, up to 2022. The prediction of the future expected displacements, shown in the previous study, has been now compared to the real displacements detected. According to them, the analysis of the expected damage on the infills has been checked.

An Embedding Swin Transformer Model for Automatic Slow-Moving Landslide Detection Based on InSAR Products

An Embedding Swin Transformer Model for Automatic Slow-Moving Landslide Detection Based on InSAR Products

Detecting slow-moving landslides in parts of Darjeeling-Sikkim Himalaya, NE India : quantitative constraints from PSInSAR and its relation to the structural discontinuitiesの紹介

Detecting slow-moving landslides in parts of Darjeeling-Sikkim Himalaya, NE India : quantitative constraints from PSInSAR and its relation to the structural discontinuitiesの紹介

Impact assessment of unsustainable airport development in the Himalayas using remote sensing: A case study of Pakyong Airport, Sikkim, India

Ground deformation is a widespread phenomenon that accelerates due to anthropogenic land development. Thus reclaimed/created land is more vulnerable to deformation and subsidence, especially in mountain areas. Advanced Differential Synthetic Aperture Radar Interferometry (A-DInSAR) can be used to monitor such projects. The Pakyong Airport is an engineering feat, constructed by cutting a mountain and converting it into a tabletop in a landslide-prone zone and seismically active region of the Sikkim Himalayas. The cutting of hill slopes for airport construction and other anthropogenic activities has increased slope instability in the region. This paper studies the slow-moving landslides in the airport neighbourhood using A-DInSAR on Sentinel-1 time series data consisting of 64 images of ascending track and 82 images of the descending track. The time period of monitoring was from October 2014 to April 2018 (43 months). The images have been connected using the Minimum Spanning Tree graph for interferogram generation for estimating deformation. The atmospheric noise was removed, and the results enabled the identification of deformation (in line-of-sight) on the airstrip as well as in the neighbouring area, both the upslope and downslope of the airport. The deformation rates estimated were up to ±90 mm/year in Pakyong from both tracks. We could successfully capture such land movement associated with the Pakyong Airport construction and help assess the impacts of infrastructure construction on the slope stability of the area. The controlling factors such as precipitation, seismicity, geology and others were analysed with respect to the deformation obtained. This study helps in assessing the land deformation after construction (cutting and filling of the slope) in the area. The deformation detected in this study needs to be addressed for the safety of the residents as well as for the infrastructure present in the area.

Step-like displacement prediction and failure mechanism analysis of slow-moving reservoir landslide

Landslides triggered by extreme rainfall due to global climate change are becoming more frequent. The Earth surface processes activity and landform evolution caused by landslide movement seriously affect human survival and the environment. In recent years, the reservoir slope located in the Three Gorges reservoir (TGR) area has been subject to the combined effects of extreme rainfall and reservoir regulation, which have been highly susceptible to inducing landslides. Reservoir landslides usually show step-like slow movement characteristics. The causes of slow-moving landslides are complex, the mechanisms behind them are difficult to grasp, and it may evolve into violent landslide disasters. Therefore, accurately predicting step-like displacement in slow-moving landslides is an effective solution for risk reduction. To achieve the accurate prediction of the step-like displacement of slow-moving landslides and the analysis of the failure mechanism, this study establishes a prediction model applicable to step-like displacement prediction of slow-moving reservoir landslides is developed based on 16 years of continuously monitored cumulative displacement of the Jiuxianping landslide, combined with the Empirical Mode Decomposition (EMD) method and the Improved Particle Swarm Optimization (IPSO) optimized the Long short-term memory (LSTM) neural network. This approach can effectively improve step-like displacement prediction accuracy. In particular, this approach can better represent the association between the step displacement of slow-moving landslides with environmental characteristics (e.g., rainfall, reservoir level). The study reveals that there are differences in the mechanisms and influencing conditions that cause the deformation of different areas of the landslide. Additionally, the reservoir water level fluctuation and seasonal rainfall are identified as the primary reasons contributing to the stepped displacement of the Jiuxianping landslide. The method provides a basis for the step-like displacement prediction, instability mechanism and landform evolution study of slow-moving reservoir landslides and provides valuable guidance for the prevention and control of similar reservoir geomorphic hazards worldwide.

Input-parameter optimization using a SVR based ensemble model to predict landslide displacements in a reservoir area – A comparative study

For reservoir landslides with limited in situ monitoring data, improving the accuracy of an ensemble prediction model through historical displacement-related factor deconstruction and model optimization is challenging. Unfortunately, even though various support vector regression (SVR) based ensemble prediction models have been proposed for this purpose, the optimal combination of new algorithms and their degree of improved prediction accuracy is still unclear. Based on four evaluation indicators, this paper presents a comparative study of a typical landslide displacement prediction model containing four important nodes (landslide displacement decomposition, inducing factor frequency-component extraction, input parameter selection, and optimization algorithm selection) vital to prediction accuracy. The empirical mode decomposition (EMD) series model was adopted to decompose the landslide displacement and extract the frequency component of the factors that affect the landslide movement. Four trajectory similarity judgment models were used to select input variables for the SVR-based prediction model. Nine swarm intelligence (SI) algorithms were used to help optimize the landslide prediction model. A case study shows that the SVR-based ensemble landslide prediction model works well in predicting the displacement of a slow-moving landslide. The combination of EEMD-CEEMDAN-LCSS-PSO yields the best prediction performance, with MAPE, RMSE, MAE, and R2 values of 0.004, 2.38, 8.64, and 0.96, respectively. The findings of this study can impact further studies of landslide ensemble prediction model optimization with insights into the algorithm selection, parameter setting, evaluation measures, and experimental settings.

Velocity distribution and movement of multiple slow-moving landslides characterized by an optimized MTInSAR workflow

Slow-moving landslides can be found worldwide, exhibiting gradual movement over years to decades, with velocities ranging from millimeters to meters per year. However, slow-moving landslides can undergo transformation into rapid and catastrophic events, leading to severe damage to human society. Given the presence of >2500 pre-existing slow-moving landslides identified and cataloged using LiDAR bare ground data in Taiwan, it is crucial to efficiently assess their activity prior to the typhoon and heavy rainfall seasons. Therefore, a key research objective is to enhance the ability to detect landslide velocities and movements over large areas spanning tens of thousands of square kilometers. This study aims to establish a workflow that employs the multitemporal interferometric synthetic aperture radar (MTInSAR) technique for the detection and monitoring of multiple slow-moving landslides across wide regions. Specifically, we propose an optimized analysis package called “multi-snap2stamps,” based on the existing snap2stamps method, to generate interferograms for multiple regions simultaneously. Focusing on the analysis of nine pre-existing slow-moving landslides, with velocities <1.6 m/year (classified as very slow or extremely slow landslides according to established criteria), distributed across Taiwan with diverse terrain and geological conditions, this study investigates the characteristics of these deep-seated landslides using the proposed MTInSAR analysis workflow in both spatial and temporal domains. The results reveal that two slow-moving landslide sites exhibit significant seasonal signals in the time series, while seven sites do not display such signals. Additionally, the MTInSAR time series effectively captures accelerated movement in the Shiding-T001 landslide, which corresponds well with in situ inclinometer monitoring data. This study demonstrates the effective analysis of velocity distribution and movement characteristics of slow-moving landslides using the proposed MTInSAR workflow. Furthermore, it highlights the potential of this workflow for improving landslide motion detection over vast areas of interest in the future.

Prediction of landslide velocity and displacement from groundwater level changes considering the shear rate-dependent friction of sliding zone soil

In reactivated slow-moving landslides, the prediction of velocity and displacement is a crucial issue for understanding landslide kinematics and realizing warning systems. A variety of data-based numerical models have been used to predict landslide displacement; however, kinematics-based models, which consider the shear behaviors of soils within the sliding zone that fundamentally control landslide movement, have been relatively limited. In the present research, the Tangjiao landslide in the Three Gorges Reservoir (TGR) in China was taken as a case study. The monitored ground displacement from March 2007 to September 2016 and deformation signs observed in the field survey show that the Tangjiao landslide experienced slow movement with the ground displacement increasing stepwise in response to seasonal rainfall and periodic fluctuation of the reservoir water level. We performed a rate-stepped continuous ring shear test on two specimens remolded from the sliding zone soil at shear rates ranging from 0.01 to 10 mm/s. The test results show that the residual strengths of both specimens show a nonmonotonic shear rate dependency, i.e., a weak negative shear rate dependency (fitted by an exponential law) followed by a significant positive dependency (fitted by a linear law). The transition from a negative shear rate dependency to a positive shear rate dependency may be attributed to the change in shear mode when the shear rate exceeds a critical value. This complex shear rate-dependent residual behavior regulates the movement of the Tangjiao landslide in the reactivated state. On this basis, a kinematics-based model for predicting landslide velocity and displacement was proposed by taking into account the shear rate-dependent friction of the sliding zone soil. This model can reproduce the velocity and displacement of the Tangjiao landslide from the change in groundwater level (i.e., stress perturbations in this paper). The results suggest that by using this model, the well-investigated material parameters can enhance the prediction reliability and accuracy of landslide behavior. Our proposed approach, with the ability to reliably predict landslide velocity and displacement, such as the Tangjiao landslide in this work, has important implications for predicting movement trends and developing early warning systems for reactivated slow-moving landslides, especially translational landslides under similar geological conditions.

Landslide susceptibility assessment in multiple urban slope settings with a landslide inventory augmented by InSAR techniques

Landslide susceptibility assessment (LSA) evaluates the likelihood of landslide occurrences and can help mitigate and prevent landslide risks. Recently, there have been vast applications of data-driven LSA methods owing to the increased availability of high-quality satellite data and landslide inventories. However, two issues remain to be addressed, as follows: (a) Items in a landslide inventory are mainly historical landslides from the interpretation of optical images and site investigation, resulting in predictive models trained with these items being insensitive to undetectable slope movements, such as slow-moving landslides that have not yet occurred; (b) Most study areas contain a variety of landslide-prone geographical settings that a single model can not accommodate well. Considering the complex landslide causes in Hong Kong with a land area of approximately 1108 km2, we proposed the utilization of multi-temporal InSAR techniques to generate weak landslide samples from slopes with ground surface movements for landslide inventory augmentation; and meta-learn intermediate representations for the fast adaptation of LSA models corresponding to different landslide-prone geographical settings. Besides, we performed feature permutation to identify dominant landslide-predisposing factors. The LSA results in Hong Kong revealed that slope deformation in several mountainous areas is closely associated with the occurrence of recorded landslides. By augmenting the landslide inventory using InSAR techniques, the proposed method enhanced the LSA models' capacity to identify slow-moving landslides and achieved better statistical performance. The discussion highlights that slope and stream power index (SPI) are the key landslide-predisposing factors in Hong Kong, but the dominant landslide-predisposing factors will vary under different geographical conditions. By comparison with the methods that treat LSA as a binary classification problem, such as support vector machine, multilayer perceptron, deep belief network, and random forest based LSA methods, the proposed method entails a fast-learning strategy and outperforms these methods in data-driven model evaluation indicators, e.g., by 3–6% in accuracy, 2–6% in precision, 1–2% in recall, 3–5% in F1-score, and approximately 10% in Cohen Kappa. The information about the relative importance of landslide predisposing factors, derived through feature permutation, can foster guidance for targeted landslide prevention schemes, such as constructing and maintaining slope consolidation facilities in areas where slope is the dominant landslide-predisposing factor.

Standing on the shoulder of a giant landslide: A six-year long InSAR look at a slow-moving hillslope in the western Karakoram

In this work, we investigate a slow-moving, large landslide (∼20 km2) in the Chitral district in Northern Pakistan, near several villages. The slow-moving landslide was reported more than four decades ago but has never been examined afterward. Interferometric Synthetic Aperture Radar (InSAR) analyses, using Sentinel-1 data that span a period of six years, allowed us to retrieve the spatio-temporal pattern of hillslope deformation. We combined both ascending and descending orbits to identify vertical and horizontal deformations. Our results showed that the crown is moving relatively fast in comparison to the nearby regions; 30 mm/year and 40 mm/year in downward and eastward directions, respectively. Also, step-like deformations observed over the crown reflect a deep-seated landslide. At the footslope, on the other hand, we captured relatively high deformations but in an upward direction; specifically 30 mm/year and 30 mm/year in upward and eastward directions, respectively. We have discussed the possible roles of meteorologic and anthropogenic factors causing hillslope deformation occurred during the six-year period under consideration. We observed a seasonal deformation patterns that might be mainly interpreted to be governed by the influence of snowmelt due to increasing temperatures during the start of spring. Overall, the same mechanism might be present in many other hillslopes across the whole Hindukush-Himalayan-Karakoram range, where seasonal snowmelt is an active agent. In this context, this research provides a case study shedding a light on the hillslope deformation mechanism at the western edge of the Himalayan range.

Potential sliding zone recognition method for the slow-moving landslide based on the Hurst exponent

The abrupt occurrence of the Zhongbao landslide is totally unexpected, resulting in the destruction of local infrastructure and river blockage. To review the deformation history of the Zhongbao landslide and prevent the threat of secondary disasters, the small baseline subsets (SBAS) technology is applied to process 59 synthetic aperture radar (SAR) images captured from Sentinel-1A satellite. Firstly, the time series deformation of the Zhongbao landslide along the radar line of sight (LOS) direction is calculated by SBAS technology. Then, the projection transformation is conducted to determine the slope displacement. Furthermore, the Hurst exponent of the surface deformation along the two directions is calculated to quantify the hidden deformation development trend and identify the unstable deformation areas. Given the suddenness of the Zhongbao landslide failure, the multi-temporal interferometric synthetic aperture radar (InSAR) technology is the ideal tool to obtain the surface deformation history without any monitoring equipment. The obtained deformation process indicates that the Zhongbao landslide is generally stable with slow creep deformation before failure. Moreover, the Hurst exponent distribution on the landslide surface in different time stages reveals more deformation evolution information of the Zhongbao landslide, with partially unstable areas detected before the failure. Two potential unstable areas after the Zhongbao landslide disaster are revealed by the Hurst exponent distribution and verified by the GNSS monitoring results and deformation mechanism discussion. The method combining SBAS-InSAR and Hurst exponent proposed in this study could help prevent and control secondary landslide disasters.

Assessment of Internal and External Factors Causing Slow-Moving Landslides

There have been limited studies on slow-moving landslides in South Korea despite their frequent occurrence. Moreover, a national slow-moving landslide hazard information system (SMLHIS) is needed. Herein, we conducted an overlap analysis of 15 slow-moving landslide areas with clear occurrence timings with national landslide hazard maps (LHMs) using the geographic information system data. Additionally, internal and external factors causing slow-moving landslides were analyzed. The results of the overlap analysis showed that slow-moving landslide areas occurred in low-hazard and excluded non-hazard areas on the LHM. The study of internal factors revealed that slow-moving landslides occurred mainly in the Gyeongsang supergroup, which has sedimentary rock type and sandy loam. The analysis of external factors, e.g., rainfall, showed that slow-moving landslides occurred during intensive rainfall, with continuous and 15-day antecedent rainfall exceeding 100 and 200 mm, respectively. The longer the continuous rainfall duration before a slow-moving landslide; the greater the rainfall on the day of the landslide; the greater the maximum hourly rainfall; the greater the 3-, 5-, and 7-day antecedent rainfalls; and the greater the rainfall intensity during the landslide, the greater the size of the slow-moving landslide. This study provides information for developing a national SMLHIS, presenting novel perspectives for slow-moving landslide research.

Influence of altitude, slope, and waterway characteristics on the occurrence of slow-moving landslides in South Korea

Forest soil sediment disasters, categorized as landslides, slow-moving landslides (land creep), and debris flows, result in destruction of forests and properties, loss of life, and damage to infrastructures. Therefore, it is imperative to evaluate factors that cause slow-moving landslides and establish a national-level hazard information system. In this study, we used geographic information system (GIS) to investigate the difference between landslides and slow-moving landslides by overlapping and analyzing landslide hazard map with slow-moving landslide sites. Slow-moving landslide areas (SLAs) and non-SLAs were characterized to compare the effects of altitude and slope in slow-moving landslide events. Finally, we performed a hydrographical analysis of waterways to compare the SLAs and non-SLAs. Grade 3 slow-moving landslide sites were found to be the most prevalent among all landslide hazard grades (1–5), including those in regions outside the specified grades. The altitude and slope of SLAs were 5 m higher and 1° steeper than those of non-SLAs, respectively. The waterways in non-SLAs (205.4 m) were longer than those in SLAs (85.2 m). Our study highlights the importance of altitude, slope, and waterways as triggers for slow-moving landslides and provides an empirical basis for establishing national hazard information systems for these events.

Proposed vulnerability assessment model for masonry buildings on slow-moving landslides based on physical models and field observations

Proposed vulnerability assessment model for masonry buildings on slow-moving landslides based on physical models and field observations

Experimental investigation of masonry building damage caused by surface tension cracks on slow-moving landslides

Experimental investigation of masonry building damage caused by surface tension cracks on slow-moving landslides

Construction on Slow-Moving Landslides: Effects of Excavation on Neighboring Structures

Construction on Slow-Moving Landslides: Effects of Excavation on Neighboring Structures

Case study of an active landslide at the flank of a water reservoir and its response during earthquakes

Slow-moving active landslides are a widespread phenomenon and often cause major damage to infrastructure. Normally, such slow slides do not cause fatalities, but this can change during extreme events such as a heavy rain or a strong earthquake. In particular their response to earthquakes is only partially understood, while documented field observation of such cases are rare. Considering their mobile pre-seismic state, one would expect active slides to be highly susceptible to an acceleration during earthquakes. When such a landslide affects a flank of a water reservoir, the consequences of its collapse can be devastating due to subsequent tsunami waves overtopping the dam. In this article, a case study of a deep-seated slow-moving landslide with a volume of about 5 million m3 at the flank of a water reservoir in the Swiss Alps is presented. Extensive field investigations and monitoring allow a profound understanding of the kinematics of the landslides and its dependency on the hydrological conditions. Together with laboratory ring shear tests, this forms the basis for quantifying rate effects in the shear zone over a very wide velocity range and allows formulating and calibrating an accurate mechanical landslide model. The material point method is used to investigate potential landslide scenarios during different earthquakes. The simulations show rather small co-seismic displacements and even for pessimistic scenarios, a threat from tsunami waves seems unlikely. This implies that slow-moving active landslides can be less susceptible to co-seismic acceleration than stable slopes due to their ductile behaviour.

Multitemporal Analysis of Slow-Moving Landslides and Channel Dynamics through Integrated Remote Sensing and In Situ Techniques

The relationships between hillslope and fluvial processes were studied in a mountainous area of the Northern Apennines (Italy) where intermittent landslide activity has interacted for a long time with river morphodynamics. The aim of the study was to analyse such relationships in two study sites of the Scoltenna catchment. The sites were analysed in detail and monitored through time. A long-term analysis was carried out based on multitemporal photointerpretation of aerial photos. Slope morphological changes and land use modifications since 1954 were detected and compared with the evolution of the channel morphology. A short-term analysis was also performed based on two monitoring campaigns accomplished in 2021 and 2022 in order to detect possible slope displacements and channel-bed-level changes. The techniques used are global navigation satellite systems and drone photogrammetry accompanied by geomorphological surveys and mapping. The multitemporal data collected allowed us to characterise slope surface deformations and quantify morphological changes. The combination of various techniques of remote and proximal sensing proved to be a useful tool for the analysis of the surface deformations and for the investigation of the interaction between slope and fluvial dynamics, showing the important role of fluvial processes in the remobilisation of the landslide toe causing the displacement of a significant volume of sediment into the stream.