Search Results (738)

Earthquake-triggered landslides pose significant hazards to lives and infrastructure. While existing seismic landslide models primarily focus on seismic and terrain variables, they often overlook the dynamic nature of hydrologic conditions, such as seasonal soil moisture variability. This study addresses this gap by incorporating satellite-based soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission into the assessment of seismic landslide occurrence. Using landslide inventories from five major earthquakes (Nepal 2015, New Zealand 2016, Papua New Guinea 2018, Indonesia 2018, and Haiti 2021), a balanced global dataset of landslide and non-landslide cases was compiled. Exploratory analysis revealed a strong association between elevated pre-event soil moisture and increased landslide occurrence, supporting its relevance in seismic slope failure. Moreover, a Random Forest model was trained and tested on the dataset and demonstrated excellent predictive performance. To assess the generalizability of the model, a leave-one-earthquake-out cross-validation approach was also implemented, in which the model trained on four events was tested on the fifth. This approach outperformed comparable models that did not consider soil moisture, such as the United States Geological Survey (USGS) seismic landslide model, confirming the added value of satellite-based soil moisture data in improving seismic landslide susceptibility assessments. Full article

In recent years, large-scale linear infrastructure developments have been developed across hundreds of kilometers of permafrost regions on the Qinghai–Tibet Plateau. The implementation of major engineering projects, including the Qinghai–Tibet Highway, oil pipelines, communication cables, and the Qinghai–Tibet Railway, has spurred intensified research into permafrost dynamics. Climate warming has accelerated permafrost degradation, leading to a range of geological hazards, most notably widespread thermokarst landslides. This study investigates the spatiotemporal distribution patterns and influencing factors of thermokarst landslides in Qinghai Province through an integrated approach combining field surveys, remote sensing interpretation, and statistical analysis. The study utilized multi-source datasets, including Landsat-8 imagery, Google Earth, GF-1, and ZY-3 satellite data, supplemented by meteorological records and geospatial information. The remote sensing interpretation identified 1208 cryogenic hazards in Qinghai’s permafrost regions, comprising 273 coarse-grained soil landslides, 346 fine-grained soil landslides, 146 thermokarst slope failures, 440 gelifluction flows, and 3 frost mounds. Spatial analysis revealed clusters of hazards in Zhiduo, Qilian, and Qumalai counties, with the Yangtze River Basin and Qilian Mountains showing the highest hazard density. Most hazards occur in seasonally frozen ground areas (3500–3900 m and 4300–4900 m elevation ranges), predominantly on north and northwest-facing slopes with gradients of 10–20°. Notably, hazard frequency decreases with increasing permafrost stability. These findings provide critical insights for the sustainable development of cold-region infrastructure, environmental protection, and hazard mitigation strategies in alpine engineering projects. Full article

This study applies the Weighted Overlay Analysis (WOA) method integrated with GIS to assess landslide susceptibility along Al Figrah Road in Al-Madinah Al-Munawarah, western Saudi Arabia. Seven key conditioning factors, elevation, slope, aspect, drainage density, lithology, soil type, and precipitation were integrated using high-resolution remote sensing data and expert-assigned weights. The output susceptibility map categorized the region into three zones: low (93.5 million m²), moderate (271.2 million m²), and high risk (33.1 million m²). Approximately 29% of the road corridor lies within the low-risk zone, 48% in the moderate zone, and 23% in the high-risk zone. Ten critical sites with potential landslide activity were detected along the road, correlating well with the high-risk zones on the map. Structural weaknesses in the area, such as faults, joints, foliation planes, and shear zones in both igneous and metamorphic rock units, were key contributors to slope instability. The findings offer practical guidance for infrastructure planning and geohazard mitigation in arid, mountainous environments and demonstrate the applicability of WOA in data-scarce regions. Full article

Landslides are a common geological hazard in mountainous areas, causing significant damage to ecosystems and production activities. Near-natural ecological restoration is considered an effective strategy for post-landslide recovery. To investigate the impact of near-natural restoration strategies on the recovery of plant communities and soil in landslide-affected areas, we selected landslide plots in Lantian County at 1, 6, and 11 years post-landslide as study sites, surveyed plots undergoing near-natural restoration and adjacent undisturbed control plots (CK), and collected and analyzed data on plant communities and soil properties. The results indicate that vegetation succession followed a path from “human intervention to natural competition”: species richness peaked at 1 year post-landslide (D_m = 4.2). By 11 years, dominant species prevailed, with tree species decreasing to 4.1 ± 0.3, while herbaceous diversity increased by 200% (from 4 to 12 species). Soil recovery showed significant temporal effects: total nitrogen (TN) and dehydrogenase activity (DHA) exhibited the greatest increases after 1 year post-landslide (132% and 232%, respectively), and by 11 years, the available nitrogen (AN) in restored plots recovered to 98% of the CK levels. Correlations between plant and soil characteristics strengthened over time: at 1 year, only 6–9 pairs showed significant correlations (p < 0.05), increasing to 21–23 pairs at 11 years. Near-natural restoration drives system recovery through the “selection of native species via competition and activation of microbial functional groups”. The 6–11-year period post-landslide is a critical window for structural optimization, and we recommend phased dynamic regulation to balance biodiversity and ecological functions. Full article

Rainfall infiltration is a key factor affecting the stability of the slope. To study the impact of rainfall on the instability mechanism and stability of slopes, this paper employs numerical simulation to establish a rainfall infiltration slope model and conducts a saturated–unsaturated slope flow and solid coupling numerical analysis. By combining the strength reduction method with the calculation of slope stability under rainfall infiltration, the safety factor of the slope is obtained. A comprehensive analysis is conducted from the perspectives of the seepage field, displacement field and other factors to examine the impact of heavy rainfall patterns and rainfall intensities on the instability mechanism and stability of the slope. The results indicate that heavy rainfall causes the transient saturation zone within the landslide body to continuously move upward, forming a continuous sliding surface inside the slope, which may lead to instability and sliding of the soil in the upper part of the slope toe. The heavy rainfall patterns significantly affect the temporal and spatial evolution of pore water pressure, displacement and safety factors of the slope. Pore water pressure and displacement show a positive correlation with the rainfall intensity at various times during heavy rainfall events. The pre-peak rainfall pattern causes the largest decrease in the safety factor of the slope, and the slope failure occurs earlier, which is the most detrimental to the stability of the slope. The rainfall intensity is inversely proportional to the safety factor. As the rainfall intensity increases, the decrease in the slope’s safety factor becomes more significant, and the time required for slope instability is also shortened. The results of this study provide a scientific basis for analyzing rainfall-induced slope instability and failure. Full article

As is already known, some currently stable landslides may have been activated in the past along a pre-existing sliding surface and reached the residual strength there, as a consequence of high-cumulative displacements. After a fairly long period of quiescence, these landslides can reactivate due to a temporary increase in destabilising forces capable of mobilising the residual strength along the same sliding surface again. Some recent studies have suggested that, under certain conditions, the strength mobilised upon reactivation may slightly exceed the residual value and then decay towards the latter as the displacement progresses. Regarding this matter, many previous studies have hypothesised that some geotechnical variables could affect the recovered strength more significantly: the length of the ageing time, the vertical stress, the stress history, and the speed with which the reactivation occurs. The aim of this research is to confirm whether such recovery of strength upon reactivation is possible and which geotechnical parameters have the greatest influence on the process. To this end, laboratory tests were carried out with the Bromhead ring shear apparatus on normally consolidated saturated samples of both natural soils and clays provided by industry (bentonite and kaolin). The coupling effect of the ageing time, the vertical stress, and the reactivation speed on the mobilised strength upon reactivation were investigated, starting from a pre-existing residual state of these samples. Within the limits of this research, the results seem to confirm that all three geotechnical variables are influential, with a greater impact on the reactivation speed and, subordinately, on the ageing time for long quiescence periods. Therefore, it is concluded that a quiescent landslide could show a reactivated strength slightly higher than the residual value if the destabilising action could arise with a certain rapidity. Conversely, if the destabilising action occurs very slowly, the mobilised strength could correspond to the residual value. The experimental results of this research may find some application in the design of strengthening works for a stable quiescent landslide that could experience a fairly rapid increase in destabilising actions, such as in the case of seismic stress, morphological modification of the slope, or a rising water table. Full article

Root reinforcement in soil plays a critical role in maintaining forest slope stability. However, accurately estimating the reinforcement provided by the entire root system of a mature tree remains a time-intensive task. Previous experimental studies on root reinforcement have predominantly focused on small trees, leaving a knowledge gap concerning larger specimens. This study integrates field pullout test data of individual roots, analyses of root geometry distribution within root systems, and theoretical frameworks, including root distribution and Root Bundle Models, to develop methods for estimating root reinforcement across varying tree sizes. The findings indicate that root system reinforcement in large trees is substantially greater than in smaller counterparts. The methodology proposed herein provides forest management professionals with a practical tool for evaluating root reinforcement in dominant forest trees, thereby facilitating improved assessment of landslide risks in forested slopes. Full article

The soil water characteristic curve (SWCC) is a key foundation in unsaturated soil mechanics describing the relationship between matric suction and water content, which is crucial for studies on effective stress, permeability coefficients, and other soil properties. In natural environments, colluvial and residual soils typically exhibit high pore heterogeneity, and previous studies have shown that the SWCC is closely related to the distribution of pore sizes. The SWCC of soils may display either a unimodal or bimodal distribution, leading to different hydraulic behaviors. Past unsaturated slope stability analyses have used the unimodal SWCC model, but this assumption may result in evaluation errors, affecting the accuracy of seepage and slope stability analyses. This study proposes a novel bimodal hydro-mechanical coupling model to investigate the influence of bimodal SWCC representations on rainfall-induced seepage behavior and stability of unsaturated slopes. By fitting the unimodal and bimodal SWCCs with experimental data, the results show that the bimodal model provides a higher degree of fit and smaller errors, offering a more accurate description of the relationship between matric suction and effective saturation, thus improving the accuracy of soil hydraulic property assessment. Furthermore, the study established a hypothetical slope model and used field data of landslides to simulate the collapse of Babaoliao in Chiayi County, Taiwan. The results show that the bimodal model predicts slope instability 1 to 3 h earlier than the unimodal model, with the rate of change in the safety factor being about 16.6% to 25.1% higher. The research results indicate the superiority of the bimodal model in soils with dual-porosity structures. The bimodal model can improve the accuracy and reliability of slope stability assessments. Full article

Accurate prediction of landslide susceptibility is a key component of disaster risk reduction and early warning systems. Traditional landslide susceptibility prediction methods often face challenges in capturing complex nonlinear and spatio-temporal relationships inherent in geospatial data. In this study, we propose a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Spatial Attention Mechanism (SAM) hybrid deep learning model designed for spatial landslide susceptibility prediction. The model is trained on a comprehensive dataset comprising 19,898 samples, constructed from landslide records and 16 influencing factors in Kumamoto Prefecture, Japan. The input dataset is processed in tabular format using Microsoft Excel and includes variables such as topography, meteorology, soil characteristics, and human activity. The proposed model leverages Convolutional Neural Networks (CNN) to extract spatial features, Long Short-Term Memory networks (LSTM) to model temporal dependencies, and a Spatial Attention Mechanism (SAM) to enhance feature weighting dynamically. Experimental results demonstrate that the CNN–LSTM–SAM–Attention model significantly outperforms traditional machine learning approaches in terms of accuracy, precision, recall, F1 score, ROC–AUC, and PR–AUC. This substantial improvement is attributed to the model’s enhanced capability in capturing complex spatio-temporal patterns and dynamically weighting critical spatial features through the integrated Spatial Attention Mechanism (SAM). This study highlights the potential of deep learning-based approaches for improving the reliability of spatial landslide susceptibility prediction in complex terrain and dynamic climatic conditions. Full article

Based on ascending and descending orbit SAR data from 2017–2025, this study analyzes the long time-series deformation monitoring and slip pattern of an active-layer detachment thaw slump, a typical active-layer detachment thaw slump in the permafrost zone of the Qinghai-Tibetan Plateau, by using the small baseline subset InSAR (SBAS-InSAR) technique. In addition, a three-dimensional displacement deformation field was constructed with the help of ascending and descending orbit data fusion technology to reveal the transportation characteristics of the thaw slump. The results show that the thaw slump shows an overall trend of “south to north” movement, and that the cumulative surface deformation is mainly characterized by subsidence, with deformation ranging from −199.5 mm to 55.9 mm. The deformation shows significant spatial heterogeneity, with its magnitudes generally decreasing from the headwall area (southern part) towards the depositional toe (northern part). In addition, the multifactorial driving mechanism of the thaw slump was further explored by combining geological investigation and geotechnical tests. The analysis reveals that the thaw slump’s evolution is primarily driven by temperature, with precipitation acting as a conditional co-factor, its influence being modulated by the slump’s developmental stage and local soil properties. The active layer thickness constitutes the basic geological condition of instability, and its spatial heterogeneity contributes to differential settlement patterns. Freeze–thaw cycles affect the shear strength of soils in the permafrost zone through multiple pathways, and thus trigger the occurrence of thaw slumps. Unlike single sudden landslides in non-permafrost zones, thaw slump is a continuous development process that occurs until the ice content is obviously reduced or disappears in the lower part. This study systematically elucidates the spatiotemporal deformation patterns and driving mechanisms of an active-layer detachment thaw slump by integrating multi-temporal InSAR remote sensing with geological and geotechnical data, offering valuable insights for understanding and monitoring thaw-induced hazards in permafrost regions. Full article

Shallow landslides are one of the main geological hazards that occur during heavy rainfall in Yuexi County every year, posing potential risks to the personal and property safety of local residents. A rainfall-induced shallow landslide named Baishizu No. 15 landslide in Yuexi Country was taken as a case study. Based on the field geological investigation, combined with physical and mechanical experiments in laboratory as well as numerical simulation, the failure mechanism induced by rainfall infiltration was studied, and the movement process after landslide failure was inverted. The results show that the pore-water pressure within 2 m of the landslide body increases significantly and the factory of safety (F_s) has a good corresponding relationship with rainfall, which decreased to 0.978 after the heavy rainstorm on July 5 and July 6 in 2020. The maximum shear strain and displacement are concentrated at the foot and front edge of the landslide, which indicates a “traction type” failure mode of the Baishizu No. 15 landslide. In addition, the maximum displacement during landslide instability is about 0.5 m. The residual strength of soils collected from the soil–rock interface shows significant rate-strengthening, which ensures that the Baishizu No. 15 landslide will not exhibit high-speed and long runout movement. The rate-dependent friction coefficient of sliding surface was considered to simulate the movement process of the Baishizu No. 15 landslide by using PFC^2D. The simulation results show that the movement velocity exhibited obvious oscillatory characteristics. After the movement stopped, the landslide formed a slip cliff at the rear edge and deposited as far as the platform at the front of the slope foot but did not block the road ahead. The final deposition state is basically consistent with the on-site investigation. The research results of this paper can provide valuable references for the disaster prevention, mitigation, and risk assessment of shallow landslides on residual soil slopes in the Dabie mountainous region. Full article

Under the influence of extreme climatic conditions, landslide disasters occur frequently in the Loess Plateau due to complex geological structures, loose soil, and frequent intense rainfall. These events are often concealed, posing significant challenges for disaster prevention. High-resolution optical remote sensing combined with field surveys can improve identification accuracy; however, concerns persist regarding issues such as omission and misidentification during hazard identification and monitoring processes. To address these challenges, this study proposes an integrated remote-sensing identification approach, focusing specifically on the central region of Tianshui, a typical landslide-prone area within the Loess Plateau. Utilizing Sentinel-1 and JL1LF01A remote-sensing imagery collected from 2022 to 2023, we conducted ground deformation monitoring through the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique. By integrating deformation results with optical imagery features indicative of potential landslide sites, a comprehensive identification method was developed to precisely detect potential landslide hazards. Verification of the identified sites was subsequently performed using the Google Earth platform, resulting in the establishment of a final dataset of potential landslide hazards within the study area. This outcome clearly demonstrates the high applicability and accuracy of the integrated remote-sensing identification method in the context of landslide hazard assessment. Furthermore, this research provides a solid scientific foundation for geological hazard identification efforts and plays a critical guiding role in disaster prevention and mitigation in Tianshui City, thereby enhancing the region’s capacity to withstand disaster risks and effectively safeguarding local lives and property. Full article

Essential oils are complex mixtures of apolar components, mainly phenylpropanoids, monoterpenes, and sesquiterpenes. Vetiver (Vetiveria zizanioides (L.) Nash) is a non-endemic grass in several tropical regions, widely used for slope stabilization and erosion control because of its long and deep roots that help to bind the soil together, preventing landslides and soil loss. From these roots, vetiver essential oil is obtained, which is extracted and produced worldwide and highly valued for its diverse range of bioactive substances used by the cosmetics and perfume industries. These substances, present in a very complex mixture, are difficult to isolate. Zizanoic acid is a very rare substance in nature and also very interesting because of the biological properties already described. In the present study, zizanoic acid was selectively isolated with 84–87% purity from vetiver commercial essential oils, in which it was present at less than 10%, using KOH-impregnated silica gel column chromatography alone. The experiments were monitored using GC-MS and UHPLC-HRMS, and the isolated substances (zizanoic and valerenic acids) were further determined by NMR experiments. The whole methodology and analytical approach proved to be very efficient for natural product complex mixture analysis and also very selective, allowing for a distinct capacity to recover carboxylic acids from complex biological samples. Full article

Wildfires pose a significant threat to the natural and built environment and may alter the hydrologic cycle in burned areas increasing the risk of flooding, erosion, debris flows, and shallow landslides. In this paper, we investigate the feasibility of using differential interferometric synthetic aperture radar (DInSAR) to interpret changes in ground surface elevation following the 2017 Eagle Creek Wildfire in Oregon, USA. We show that DInSAR is capable of measuring ground surface displacements in burned areas not obscured by vegetation cover and that interferometric coherence can differentiate between areas that experienced different burn severities. The distribution of projected vertical displacement was analyzed, suggesting that different areas experience variable rates of change, with some showing little to no change for up to four years after the fire. Comparison of the projected vertical displacements with cumulative precipitation and soil moisture suggests that increases in precipitation and soil moisture are related to periods of increased vertical displacement. The findings of this study suggest that DInSAR may have value where in situ instrumentation is infeasible and may assist in prioritizing areas at high-risk of erosion or other changes over large geographical extents and measurement locations for deployment of instrumentation. Full article

This review examines the application of the geophysical methods for Transportation Infrastructure Slope Monitoring (TISM). In contrast to existing works, which address geophysical methods for natural landslide monitoring, this study focuses on their application to infrastructure assets. It addresses the key aspects regarding the geophysical methods most employed, the subsurface properties revealed, and the design of monitoring systems, including sensor deployment. It evaluates the benefits and challenges associated with each geophysical approach, explores the potential for integrating geophysical techniques with other methods, and identifies the emerging technologies. Geophysical techniques such as Electrical Resistivity Tomography (ERT), Multichannel Analysis of Surface Waves (MASW), and Fiber Optic Cable (FOC) have proven effective in monitoring slope stability and detecting subsurface features, including soil moisture dynamics, slip surfaces, and material heterogeneity. Both temporary and permanent monitoring setups have been used, with increasing interest in real-time monitoring solutions. The integration of advanced technologies like Distributed Acoustic Sensing (DAS), UAV-mounted sensors, and artificial intelligence (AI) promises to enhance the resolution, accessibility, and predictive capabilities of slope monitoring systems. The review concludes with recommendations for future research, emphasizing the need for integrated monitoring frameworks that combine geophysical data with real-time analysis to improve the safety and efficiency of transportation infrastructure management. Full article