Landslide Development Research Articles

Regional activity of landslide movements affecting the bank of meandering river: Insights using tree-ring based methods

Landslides on the banks of meander bends of lower rivers pose a potential risk to the navigability of rivers, but also to their potentially dangerous damming. To properly understand the relationship of river action on the stability of adjacent landslides, it is necessary to analyse the historical evolution and spatial extent of landslide activity. In this study, the results of a regional reconstruction of landslide movements on the lower reaches of the Váh River (central Slovakia) are presented, which allows for a more comprehensive disclosure of the river-slide relationship compared to classical case studies. Using data from a 408 tree-ring series of 204 disturbed trees, 43 reactivations of five landslides were dated over the last ca. 45 years. Analysis of the relationship between the spatial extent of landslide activity and distance from the river revealed that in two cases there was a direct relationship where landslide activity gradually decreased with increasing distance from the river (these were the landslides closest to the river). Given the absence of erosional forms on the riverbank, it is likely that the landslides are not being destabilized by lateral river erosion and the relief of their faces, but rather by the loading of their bodies due to rising groundwater during floods. On the other hand, one landslide (furthest from the river) showed increasing landslide activity towards the main scarp. This pattern is consistent with the retrogressive landslide development generally expected for other landslides in the wider region. For the remaining two landslides, no significant relationship between spatial landslide activity and distance from the river was found, which, however, cannot rule it out, but rather suggests that the influences on the initiation of landslide movements in this case are very complex and probably combine both the influence of groundwater level fluctuations and the occurrence of critical rainfall events.

Construction of Rules for Recognizing Landslides Along Highways Induced By Rainfall

After the "7-20" concentrated rainfall, many landslides occurred along the highways in Jiaozuo City, which seriously threatened the transportation safety and brought serious economic losses to the government and the residents along the highways. In order to investigate the development pattern and distribution characteristics of landslides along highways under extreme weather, we took Jiaozuo City Highway as the research object, based on the Sentinel-2 remote sensing data, and on the basis of analyzing the spectral characteristics of landslide hazards, we used the band ratio method to establish the identification rules of loess landslides along highways; based on the topography, geology, human impacts, and meteorological and hydrological selected 11 impact factors, and used geoprospectors to investigate the The driving factors of landslides. The following conclusions and understandings were obtained: (1) a total of 256 landslides along the highway in Jiaozuo City were identified, and the overall identification accuracy of 69.9% was obtained through visual interpretation and on-site research; (2) the ratio of the surface true reflectance of red and green light fused with the slope (>15°) can be used to carry out the identification of landslides in large-scale post-disaster situations; (3) the human activities and topography have a greater impact on landslide development; (4) the identification rule has a greater impact on the development of landslides with similar geological backgrounds. rule is useful for the identification and prevention of landslides along highways in low-hill loess-covered areas with similar geologic backgrounds.

A research on the relationship between landslide area changes and environmental factors in the southern Tibetan plateau

Introduction: Landslides are known to be one of the most frequent types of geological disasters. However, there is not an established method for large-scale, rapid, and high-precision landslide extraction. The quantitative impact of environmental changes on landslide development is also not well understood, which hinders accurate assessments and decision-making in environmental and disaster response. The polar regions, including the Antarctic, the Arctic, and the Tibetan plateau (TP), sensitive to global environmental changes, are significantly affected by global warming. This leads to extensive landslide development, particularly in the southern TP. This research focuses on new landslides in the southern TP, exploring extraction methods and the relationship between landslides and environmental factors.Methods: Utilizing the Google Earth Engine (GEE) and an improved Otsu threshold segmentation algorithm, we processed remote sensing images with 10 m resolution to identify landslide areas. The proposed Normalized Landslide Bare-soil Separation Index (NDLBSI) achieved an 87% pre-extraction accuracy in extracting landslides from Sentinel-2 images from 2019 to 2023. For the pre-extraction results, manual interpretation and correction were carried out, and a model correlating annual landslide changes with environmental factors was established based on least squares multivariate statistical methods.Results: Results show that a significant increase in landslide areas in the southern TP over the past 5 years, correlating with the watershed-wide increase in annual average temperature and vegetation cover, along with a decrease in snow cover area.Discussion: These changes could affect soil and rock moisture, influencing soil stability and landslide occurrence. The study provides valuable insights for large-scale landslide detection and understanding the environmental factors influencing landslides, which is of some significance for landslide hazards early warning.

Research on the three-dimensional spatio-temporal dynamic evolution and kinematic characteristics of loess landslides induced by strong earthquakes

Research on the three-dimensional spatio-temporal dynamic evolution and kinematic characteristics of loess landslides induced by strong earthquakes

The study of the natural frequency evolution and numerical simulation of retrogressive landslides

A retrogressive landslide is influenced by the cyclical fluctuations in reservoir water levels is considered a common natural disaster. Tension cracks are important indicators for assessing landslide status in the case of retrogressive landslides. Displacement monitoring is a commonly used method and provides an intuitive reflection of the landslide deformation; however, it does not directly indicate the depth of the tension cracks. Based on the principles of vibrational dynamics, a retrogressive landslide is proposed to be initially classified as a single-mass spring oscillator model before the development of cracks. Following the development of tension cracks, the model can be classified as a double-mass spring oscillator model. The model patterns are verified through numerical simulations using ABAQUS. Based on the numerical simulations, with an increase in the number of reservoir water cycle fluctuations, the displacement and stress of the landslide exhibit periodic growth. However, during displacement growth, the tension cracks do not necessarily increase. As the tension cracks deepen, the landslide transitions from a single-mass spring oscillator model to a double-mass spring oscillator model, with the appearance of a second-order natural frequency. Moreover, as the tension cracks deepen, the numerical values of the natural frequency change. The maximum change in first-order natural frequency is 3.5 Hz. The maximum change in second-order natural frequency is 4.5 Hz. The variation in the natural frequency can reflect the depth of development of the landslide's tension cracks and, consequently, indicate changes in the stability state of the landslide.

Landslides of China's Qinling

AbstractThe Qinling Mountains in China frequently experience geological disasters, with large‐scale landslides being particularly prominent, causing severe economic losses to the local area. To gain a comprehensive understanding of the geological disaters distribution in the region, we conducted extensive research on the entire Qinling Mountains, covering an area of approximately 380,000 km2. By employing methods such as literature review, data collection, and interpretation of remote sensing images, we have successfully created a database of landslides. The inventory of landslides includes a total of 169,888 large‐scale landslides, covering a combined area of approximately 1575 km2. The average size of these landslides is approximately 92,734 m2. The scale of these landslides varies widely, with the smallest individual landslide covering an area of 166.25 m2 and the largest reaching 12.9 km2. Upon examining areas with frequent landslides, it was observed that landslides are usually densely distributed along riverbanks or within valleys. Landslide development is also dense in areas prone to frequent historical earthquakes. This comprehensive database provides essential data to support the analysis of spatial distribution patterns of large‐scale landslides in the Qinling Mountains. It also facilitates landslide assessments and serves as a reference for the prevention and control of landslide disasters in the area.

A three-dimensional smoothed particle hydrodynamics analysis of multiple retrogressive landslides in sensitive soil

Numerical modelling and analysis of three-dimensional multiple retrogressive landslides of Saint-Jude landslide in sensitive clay is conducted. The investigations of the three-dimensional effects in determining the extent of landslides are concerned. To accurately simulate the extremely large deformation caused by progressive soil failure, a special form of SPH formulation known as finite particle method (FPM) is adopted. A hypoelastic–plastic model with strain softening is implemented to capture the key feature of the mechanical behaviour of the sensitive clay in undrained condition. The multiple progressive failure process, the final run-out stretch, and the retrogression distance of the Saint-Jude landslide are compared quantitatively with the data from site investigation. Furthermore, three-dimensional effects and influences of various factors, including slope morphology and soil properties, are discussed for better understanding of the development of retrogressive landslides in sensitive soil.

Seismic landslide hazard assessment using improved seismic motion parameters of the 2017 Ms 7.0 Jiuzhaigou earthquake, Tibetan Plateau

Seismic landslide hazard assessment plays a very important guiding role during urgent earthquake relief. In August 2017, an Ms 7.0 earthquake in Jiuzhaigou County, Sichuan Province, China, triggered thousands of landslides. Based on the analysis of geological settings and coseismic landslide characteristics, the Newmark model is used to complete the seismic landslide hazard assessment. Three seismic motion parameters, namely, peak ground acceleration (PGA), traditional Arias intensity (Arias_P), and improved Arias intensity (Arias_C), are adopted. A publicly published coseismic landslide catalog is used as the validation samples. The results show that the coseismic landslides are mainly distributed in the deep gullies and steep mountainous slopes on the north and south sides of the epicenter. The seismic landslide hazard accuracy based on Arias_C is the best, followed by that based on PGA and Arias_P. The spatial distribution of seismic landslide hazards based on Arias_C shows an almost standard elliptical ring and is in good agreement with that of coseismic landslides. These results fully reflect the combined influence of the epicenter and seismogenic fault on landslide development. The middle seismic landslide hazard and over are mainly located at areas with seismic intensity of VII degree and above. The Arias intensity is very suitable for rapid seismic landslide hazard assessment in emergency situations. The study results can provide scientific and technological support for rapid earthquake relief and have reference significance for future seismic landslide hazard assessment.

Impact of initial slide on downslope seafloor with a weak layer

Submarine landslides are a major offshore geohazard posing increasing threat to offshore and coastal developments. A submarine landslide may usually involve multistage failure triggered by the first failure. During submarine landslide evolution, the emergent sliding mass may act as an impact load on the downslope seafloor potentially leading to surficial erosion of sediments or deeper ploughing failure with the presence of a weak layer. The study presents a comprehensive analysis of seafloor instability and post-failure behaviors subjected to the first slide mass impact, using a large deformation finite element (LDFE) method. Different failure patterns are investigated and the corresponding critical impact loads for triggering the seafloor instability are analyzed. The parameters controlling the strain softening behaviors of soils and the undrained shear strength in the weak layer have significant effects on the critical condition for the seafloor failure. The dynamic inertia of the slide mass, however, has little effect on the results. An empirical equation for assessing the seafloor instability subjected to the first slide impact, with respect to the impact load from the initial slide and the gravity stress relative to the shear strength in the weak layer, is proposed based on a parametric study.

Model test study on natural frequency evolution and early warning of reservoir landslides under water level fluctuations

Model test study on natural frequency evolution and early warning of reservoir landslides under water level fluctuations

Modeling of landslides susceptibility prediction using deep belief networks with optimized learning rate control

To overcome critical issues in landslide susceptibility modeling, a multifactor landslide susceptibility prediction model based on deep belief networks (DBN) with optimized learning rate control (LRC-DBN) was introduced in this study. The LRC-DBN model was applied to predict landslide susceptibility in Zhidan County, Shaanxi and its performance was compared against random forest, support vector machine and logistic regression models. The results show that the LRC-DBN model achieves a maximum AUC value of 0.941, which demonstrating higher predictive performance. The interpretability of LRC-DBN reveals that the development of loess landslides is more likely in areas with elevations ranging from 894.7 m to 998.5 m, slopes ranging from 49.6° to 64°, terrain undulations spanning 31.7 m to 91.0 m, rock type T3y and terrain humidity ranging from 2.4 to 3.9. The result provides invaluable insights for local landslide prevention endeavors and can assist decision-making in urban planning.

Bedding Slope Destabilization under Rainfall: A Case Study of Zhuquedong Slope in Hunan Province, China

The soft interlayer and rock structure play a significant role in controlling the deformation of the bedding slope, and it is necessary to consider the phenomenon of the sudden change of local response in these key parts under rainfall conditions, and then to clarify the mechanism of rainfall infiltration and damage mechanism of such slopes. In this paper, a large red-layered flat-dipping bedding landslide was selected as the research object, and numerical calculations based on the Van Genuchten model for saturated–unsaturated flow were performed in order to investigate the hydrological response and distribution patterns of water within the slope during rainfall. Moreover, stability analysis was performed based on the seepage field results and secondary development of FLAC3D, and the landslide evolution process was simulated and reproduced using the constitutive model of double-variables and the strength reduction method (SRM). The results showed that the effects of heavy rainfall on the water distribution and stability of the highway slope are significant, while the effects on the natural slope are not significant. There are three phases of the slope destabilization: flexure and uplift state, deformation exacerbation state and shear failure state. The slope destabilization mechanism is a typical “sliding-bending-shearing” type. The results of the study can provide a theoretical basis for the study of the seepage, stability analysis and destabilization mechanism of bedding slopes.

Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network

Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (Dn) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. Dn outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (Ia) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides.

Long‐Term Landslide Evolution and Restoration After the Wenchuan Earthquake Revealed by Time‐Series Remote Sensing Images

AbstractA powerful earthquake, particularly in mountainous regions, has the ability to trigger an immense number of landslides, resulting in severe damages. However, effectively assessing their recoveries in the years following the mainshocks has not yet been attempted due to the lack of continuously updated landslide inventories. Therefore, we proposed a novel detection framework utilizing time‐series Landsat images from 2000 to 2021, which enabled us to achieve a long‐term landslide inventory in the seismic zone for the first time. The response patterns of landslide development under different conditions were explored in detail. The restoration models were quantitatively established, revealing that the recoveries of post‐earthquake landslide activity and vital environmental indicators require a range from ∼5.29 to ∼25.00 years. This pioneering research is highly beneficial in comprehending the impact of earthquake‐induced hazards on short‐term land surface damage and long‐term ecological equilibrium.

The Mechanism of Mineral Dissolution on the Development of Red-Bed Landslides in the Wudongde Reservoir Region

The Wudongde reservoir region exhibits a notable prevalence of landslides within the red-bed reservoir stratum. The red bed is a clastic sedimentary rock layer dominated by red continental deposits. It is mainly composed of sandstone, mudstone, and siltstone. The lithology is diverse and uneven. In this study, we delve into the impact of mineral dissolution on the development of red-bed landslides in the reservoir area by utilizing the Xiaochatou landslide as a representative case study. Considering the inherent susceptibility of red-bed formations to erosion, collapse, and softening when exposed to water, an investigation was conducted to examine the consequences of mineral dissolution on landslides occurring in these areas. We conducted a mineral analysis and an identification of rock samples from the Xiaochatou landslide site, revealing alternating layers of sandstone and mudstone. Sandstone and conglomerate specimens were immersed in deionized water, and advanced techniques such as scanning electron microscopy (SEM), ion chromatography (IC), and inductively coupled plasma (ICP) analysis were used to examine the effects of water immersion. We also employed the hydrogeochemical simulation software PHREEQC to understand the dissolution mechanism of gypsum during soaking. Our findings reveal that sandstone and conglomerates harbor a notable quantity of gypsum, which readily dissolves in water. Prolonged immersion leads to erosion cavities within the sandstone, thereby augmenting its permeability. The concentration of SO42− ions in the soaking solution emerges as the highest, followed by Ca2+ and Na+. The notable significance is the dissolution of gypsum, whose intricate mechanism is contingent upon diverse environmental conditions. Variations in ion concentration profoundly influence the saturation index (SI) value, with the pH value playing a crucial role in shifting the reaction equilibrium. Regarding the deformation mode of the landslide, it manifests as a combination of sliding compression and tension cracking. The fracture surface of the landslide assumes a step-like configuration. As the deformation progresses, the mudstone layer takes control over the sliding process, causing the sandstone to develop internal narrow-top and wide-bottom cracks, which propagate upward until the stability of the slope rock mass is compromised, resulting in its rupture. In this manuscript, we delve into the dissolution traits of red-bed soft rock in the Wudongde reservoir area, using a landslide case as a reference. We simulate this rock’s dissolution under environmental water influences, examining its interaction with diverse water types through rigorous experiments and simulations. This study’s importance lies in its potential to shed light on the crucial engineering characteristics of red-bed soft rock.

Landslide Mapping and Causes of Landslides in the China–Nepal Transportation Corridor Based on Remote Sensing Technology

The China–Nepal Transportation Corridor is vital to the country’s efforts to build a land trade route in South Asia and promote the Ring-Himalayan Economic Cooperation Belt. Due to the complex geological structure and topographical environment of the Qinghai–Tibet Plateau, coupled with the impact of climate change, the frequent occurrence of geological disasters has increased the operational difficulty of the China–Nepal Highway and the construction difficulty of the China–Nepal Railway. However, to date, there has been no systematic study of the spatial distribution of landslides along the entire route within the area, the factors influencing landslides at different scales, or the causes of landslides under different topographic backgrounds. There is an even greater lack of research on areas threatened by potential landslides. This study comprehensively applies remote sensing, mathematical statistics, and machine learning methods to map landslides along the China–Nepal transportation corridor, explore the influencing factors and causes of different types of landslides, and investigate the distribution characteristics of potential landslides. A total of 609 historic landslides have been interpreted in the study area and were found to be distributed along faults and locally concentrated. The strata from which landslides develop are relatively weak and are mainly distributed within 2 km of a fault with a slope between 20° and 30°. The direction of slope for the majority of landslides is south to south-west, and their elevation is between 4000 and 5000 m. In addition, we discovered a power law relationship between landslide area and volume (VL = 2.722 × AL1.134) and determined that there were 47 super-large landslides, 213 large landslides, and 349 small and medium-sized landslides in the area, respectively. Slope is the most significant influencing factor for the development of landslides in the area. Apart from slope, faults and strata significantly influence the development of large and medium-small landslides, respectively. We have identified 223 potential landslides in the region, 15 of which directly threaten major transport routes, mainly in the Renbu Gorge section of the China–Nepal Highway and the proposed China–Nepal Railway section from Peikucuo to Gyirong County. In addition, we also discussed the causes of landslides within three geomorphic units in the region. First, the combined effects of faulting, elevation, and relatively weak strata contribute to the development of super-large and large landslides in the Gyirong basin and gorge. Second, the relatively weak strata and the cumulative damaging effects of earthquakes promote the development of small and medium-sized landslides in the Xainza-Dinggye rift basin. Third, under the combined effect of the hanging wall effect of thrust faults and the relatively weak material composition, landslides of various types have developed in the Nagarzê mountain. It is worth noting that potential landslides have developed in all three geomorphic units mentioned above. This study provides data and theory to assist in the accurate mitigation and control of landslide hazards in the corridor.

Physics-informed data assimilation model for displacement prediction of hydrodynamic pressure-driven landslide

Due to the complex and non-linear characteristics of landslide evolution, the deformation law of landslides is always hard to be predicted. Considering the dynamical evolution process of landslide physical mechanism, a Physics-Informed Data Assimilation (PIDA) method is first proposed for landslide displacement forecasting. It can enhance the prediction ability of the physical forecasting model, and solves the problem of ignoring the physical significance of landslides in mathematical prediction model. Additionally, the superiority of Particle Filter (PF) in nonlinear and non-Gaussian system determines its application in complex problem. Aiming at the hydrodynamic pressure-driven landslide, the Sliding Zone Deterioration (SZD) model is constructed as the state equation of the PF algorithm. And the observation equation is formed by the relationship between landslide deformation and monitoring datasets. The physics-informed landslide displacement prediction model serves as a bridge, consisting of the shear strength parameter dynamic updated model and physical mechanism analysis model, for key parameters feedback, modification, and update. Comparing the results to traditional Long Short-Term Memory (LSTM) and Back Propagation Neural Network (BPNN) methods, the PIDA method combining triggering and instability mechanisms performs better in accuracy. Moreover, it enables dynamic and reliable landslide deformation prediction.

Spatial distributions and multi-factor driving mechanism of landslide in southern Liaodong Peninsula

Coastal areas are characterized by high levels of urbanization and also the most active and highly sensitive to geological activities. Due to the impact of global climate change, landslides occur frequently in coastal areas, which have significantly impacted the sustainable socio-economic development of the region. Therefore, studying the spatial distribution of landslides and the factors that drive them holds immense practical significance for the geohazards prevention and mitigation work and optimization of ecological security patterns in coastal areas. This study takes the southern Liaodong Peninsula as the study area and analyzes the spatial distribution characteristics and influence mechanism of landslides. We first applied the Getis-Ord Gi*, kernel density analysis techniques, and Ripley’s K function to explore the spatial distribution characteristics of landslides in the study area at different scales. Then, we employed the information value method to test for the linkage relationships between the driving factors and landslides. Finally, we utilized the geographical detector to reveal the potential impacts and interaction of the driving factors on landslides. The results showed that: (1) landslides in the Southern Liaodong Peninsula exhibit strong spatial clustering characteristics, and have a spatial scale effect; (2) the high susceptibility areas are mainly concentrated in the southern hilly regions and the mountainous regions in the northern parts of the study area; (3) The information value method reveals that there is an optimal combination of factors driving landslides. (4) The factor detector analysis reveals that the primary driver of landslide spatial distribution is the geomorphological types (q-value of 39.10%). (5) The interaction detector indicates that the interplay of all driving factor pairs exhibited an enhanced effect, which is not a simple superposition effect but bivariate and non-linear. The collective influence of natural factors and human activities holds a more substantial influence over landslide development and distribution. The research offers guidance for the execution of landslide prevention initiatives and the establishment of preservation policies for the ecological environment in the southern Liaodong Peninsula region.

Interaction of landslide spatial patterns and river canyon landforms: Insights into the Three Parallel Rivers Area, southeastern Tibetan Plateau

The interaction and mechanism of landslide spatial patterns and river canyon landforms are significant for understanding geomorphic evolution in intensive tectonic alpine environments. This study focuses on the Three Parallel Rivers Area (TPRA) in the southeastern Tibetan Plateau encompassing three parallel rivers (the Nujiang, Lancang, and Jinsha Rivers), to examine the synergistic evolution of geomorphic features and landslides. The analysis revealed a pattern of landslide aggregation in the river valley characterized by the sequence Nujiang > Lancang > Jinsha Rivers. This pattern aligns closely with the distribution of geomorphic indices (local relief, surface erosion index, and threshold slope gradient) in the valleys. As local relief, normalized surface erosion index and normalized threshold slope gradient increase, the mean values of normalized landslide area density (NLAD) rise from around 0.11 to 0.39, 0.16 to 0.48, and 0.10 to 0.21, respectively. Concurrently, the mean values of normalized frequency of landslide dams (NFLD) increase from around 0.05 to 0.24, 0.12 to 0.22, and 0.02 to 0.17, respectively. Additionally, knickpoints could induce upstream suppression and downstream promotion of landslides showcasing the feedback of landslides on the valley landscape. Our findings indicate that the landform formation process in the southeastern Tibetan Plateau orogen is intricately linked to a substantial landsliding response and the observed mass movements vividly mirror the landform formation pattern. These results hold potential implications for understanding the dynamic equilibrium between uplift and surface erosion in the region. This study enhances our understanding of the interaction and mechanisms of landslides and valley landforms.

Landslide development model based on tree-ring data and quantification of the magnitude of landslide movements

Analysis of landslide events by studying anomalies in tree-ring series of disturbed trees provides unique data on past landslide behaviour. However, a comprehensive spatio-temporal model of landslide evolution using dendrogeomorphic data has not yet been constructed. In terms of determining landslide hazard, one of the key issues, besides the frequency of movements, is their magnitude. Thus, this study is aimed at constructing a model of landslide area development including quantification of landslide displacements. Moreover, not only standard macroscopic growth disturbance but also quantitative anatomical parameters of tree rings were used as a basis for these objectives. The research was carried out on a landslide of the Váh river bank (Slovakia) using data from 32 disturbed black locust (Robinia pseudoacacia L.) individuals. Anatomical analysis revealed a reduction in the vessel lumen area in response to landslide movements. The response was associated with tension wood formation or damage to the tree root system. The reconstructed chronology of landslide movements revealed the main phase of landslide in 2017 and its subsequent evolution. Based on the chronological information and the morphology of the landslide body, a spatio-temporal model of the landslide evolution from the initial slope failure to the present was constructed. The model showed the interrelationships between vertical displacement of the main block, compression and tension deformations within the landslide body, and lateral erosion of the landslide toe by the river. Quantification of the magnitude of landslide displacements was realized from exposed roots in tension cracks but also from the ruptured tree trunk. The limitations and advantages of both approaches are discussed.