Search Results (899)

Surface deformation monitoring is essential for controlling instability processes such as urban infrastructure deformation, mining-induced subsidence, and landslide deformation. However, missing data often disrupt the continuity of the various deformation time series and compromise the reliability of monitoring results. This issue is particularly critical in long-term landslide studies, where conventional missing data imputation methods often neglect the nonlinear characteristics of slope deformation and fail to account for external influences under complex environmental conditions. To address these limitations, this study proposes a deep learning-based imputation method that integrates multi-source monitoring data. A Seq2Seq LSTM (sequence-to-sequence long short-term memory) model is constructed to reconstruct missing deformation values, and a posterior correction module is integrated to optimize the preliminary outputs, thereby enhancing imputation accuracy. The proposed approach is validated using a case study of the southern dump slope landslide at the Hesigewula South Open-Pit Coal Mine in Inner Mongolia, China. Experimental results on the test set demonstrate that the Seq2Seq LSTM–Posterior Correction model significantly outperforms traditional methods such as linear regression and baseline LSTM models. This method offers an effective solution to data gaps in landslide deformation monitoring, demonstrating strong potential for accurate nonlinear imputation in complex environments and providing a practical approach for long-term InSAR-based landslide studies in areas affected by missing SAR data. Full article

The upper Mapocho River basin, located in central Chile, has been affected by numerous landslides in the past, which may become more frequent due to a projected increase in intense precipitation events in the context of climate change. Against this background, this study aimed to analyze the ground deformation associated with an active landslide area in the Yerba Loca basin using the SBAS–DInSAR technique with PAZ and Sentinel-1 images acquired during two time periods, 2019–2021 and 2018–2022, respectively. Using PAZ imagery, the estimated vertical displacement velocity (subsidence) was as high as 9.6 mm/year between 2019 and 2021 in the area affected by the Yerba Loca multirotational slide in August 2018. Analysis of Sentinel-1 images indicated a vertical displacement velocity reaching −94 mm/year between 2018 and 2022 in the Yerba Loca landslide, suggesting continued activity in this area. It, therefore, may collapse again soon, affecting tourism services and the local ecosystem. By focusing on a mountainous region, this study demonstrates the usefulness of radar imagery for investigating landslides in remote or hard-to-reach areas, such as the mountain sector of central Chile. Full article

The Zigui Basin, located in the Three Gorges Reservoir Area, has developed numerous landslides due to its interlayering of sandstone and mudstone, geological structure, and reservoir operations. This study identifies a fourth type of landslide failure mode: an oblique-dip slope wedge (OdSW) landslide, based on the Wanshuitian landslide. Following four heavy rainfall events from 3 to 13 July 2024, this landslide exhibited significant deformation on the 17th and was completely destroyed within 40 min. The dimensions of the landslide were 350 m in length, 160 m in width, and 20 m in thickness, with a volume estimated at 8.0 × 10⁵ m³. The characteristics of landslide deformation and the changes in moisture content within the shallow slide body were ascertained using unmanned aerial vehicles, moisture meters, and mobile phone photography. The landslide was identified to have occurred within the weathered residual layer of mudstone, situated between two sandstone layers, with the eastern boundary defined by an inclined rock layer. Upon transitioning into the accelerated deformation stage, the landslide initially exhibited uniform overall sliding deformation, culminating in accelerated deformation destruction. The dip structure created terrain disparities, resulting in a step-like terrain on the left bank and gentler slopes on the right bank, with interbedded soil and rock in a shallow layer, because the interlayered soft and hard geological conditions caused varied weathering and erosion patterns on the riverbank slopes. The interbedded weak–hard stratum layer fostered the development of the oblique-dip slope wedge landslide. Based on the improved Green–Ampt model, we developed a stability prediction methodology for an oblique-dip slope wedge landslide and determined the rainfall infiltration depth threshold of the Wanshuitian landslide (9.8 m). This study aimed not merely to sharpen the evolution mechanism and stability prediction of the Wanshuitian landslide but also to formulate more effective landslide-monitoring strategies and emergency management measures. Full article

Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential landslide-prone zones, with a focus on the Tung-An tribal settlement in the eastern part of the village. Using high-resolution satellite imagery from SPOT 6/7 (2013–2023) and Pléiades (2019–2023), we derived annual NDVI layers to monitor vegetation dynamics across the landscape. Long-term vegetation trends were evaluated using the Mann–Kendall test, while spatiotemporal clustering was assessed through Emerging Hot Spot Analysis (EHSA) based on the Getis-Ord Gi* statistic within a space-time cube framework. The results revealed statistically significant NDVI increases in many valley-bottom and mid-slope regions, particularly where natural regeneration or reduced disturbance occurred. However, other valley-bottom zones—especially those affected by recurring debris flows—still exhibited declining or persistently low vegetation. In contrast, persistent low or declining NDVI values were observed along steep slopes and debris-flow-prone channels, such as the Nanshan and Mei Creeks. These zones consistently overlapped with known landslide paths and cold spot clusters, confirming their ecological vulnerability and geomorphic risk. This study demonstrates that integrating NDVI trend analysis with spatiotemporal hot spot classification provides a robust, scalable approach for identifying slope hazard areas in data-scarce mountainous regions. The methodology offers practical insights for ecological monitoring, early warning systems, and disaster risk management in Taiwan and other typhoon-affected environments. By highlighting specific locations where vegetation decline aligns with landslide risk, the findings can guide local authorities in prioritizing slope stabilization, habitat conservation, and land-use planning. Such targeted actions support the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by reducing disaster risk, enhancing community resilience, and promoting the long-term sustainability of mountain ecosystems. Full article

Despite the widespread deployment of inclinometers and GPS, an engineering gap remains for a low-cost, seepage-sensitive landslide early-warning technique. To explore the application of self-potential (SP) in landslide monitoring and early warning, a series of physical simulations were conducted, focusing on slope rainfall and slope cracking conditions. The self-potential signals were monitored using a custom-built STM32-based acquisition system, which provided continuous, real-time data with minimal noise. The relationship between self-potential signals and internal changes within the landslide body was analyzed, revealing that SP signals are highly sensitive to seepage, saturation, and structural changes within the slope. During slope rainfall simulations, the self-potential signals responded rapidly to changes in rainfall intensity, capturing the dynamic nature of seepage and saturation changes. A dynamic early-warning model was developed based on statistical methods, including sliding t-tests/Pettitt mutation tests and Mahalanobis distance test, to detect early signs of landslide instability. The model successfully identified significant changes in SP signals that corresponded to the onset of landslide movement, demonstrating the potential of self-potential for real-time landslide monitoring and early warning. This study highlights the effectiveness of self-potential monitoring in detecting early signs of landslide instability and suggests that SP signals can be a valuable addition to existing landslide monitoring systems. Full article

Displacement back-analysis is a crucial approach to enhance the effectiveness of landslide monitoring data. To improve the computational efficiency and reliability of large-scale three-dimensional landslide displacement inversion, this study develops a novel Landslide Displacement Intelligent Dynamic Inversion Framework (LDIDIF), which integrates the Bayesian displacement back-analysis (BBA) approach, a Long Short-Term Memory (LSTM) surrogate model, and the RANdom SAmple Consensus (RANSAC) algorithm. Specifically, BBA is employed to dynamically calibrate geotechnical parameters with uncertainty, the LSTM model replaces traditional numerical simulations to reduce computational cost, and RANSAC filters inlier observations to enhance the robustness of the inversion model. A case study of the Dawanzi GNSS landslide is conducted. Results show that the LSTM surrogate model achieves prediction errors below 2 mm and enhances computational efficiency by approximately 50,000 times. The RANSAC algorithm effectively identifies and removes GNSS outliers. Notably, LDIDIF significantly reduces the uncertainty of shear strength parameters within the slip zone, yielding a calibrated displacement precision better than 10 mm. The calibrated model reveals that the landslide is buoyancy-driven and that frontal failure may trigger progressive deformation in the rear slope. These findings offer valuable insights for landslide early warning and reservoir operation planning in the Dawanzi area. Full article

Critical infrastructure, such as transport networks, energy facilities, and urban installations, is increasingly vulnerable to natural hazards and climate change. Remote sensing technologies, namely satellite imagery, offer solutions for monitoring, evaluating, and enhancing the resilience of these vital assets. This paper explores how applications based on synthetic aperture radar (SAR) and optical satellite imagery contribute to the protection of critical infrastructure by enabling near real-time monitoring and early detection of natural hazards for actionable insights across various European critical infrastructure sectors. Case studies demonstrate the integration of remote sensing data into geographic information systems (GISs) for promoting situational awareness, risk assessment, and predictive modeling of natural disasters. These include floods, landslides, wildfires, and earthquakes. Accordingly, this study underlines the role of remote sensing in supporting long-term infrastructure planning and climate adaptation strategies. The presented work supports the goals of the European Union (EU-HORIZON)-sponsored ATLANTIS project, which focuses on strengthening the resilience of critical EU infrastructures by providing authorities and civil protection services with effective tools for managing natural hazards. Full article

Coseismic landslides are geological events in which slopes, either on the verge of instability or already in a fragile state, experience premature failure due to seismic shaking. On 5 September 2022, an Ms 6.8 earthquake struck Luding County, Sichuan Province, China, triggering numerous landslides that caused severe casualties and property damage. This study systematically interprets 13,717 coseismic landslides in the Luding earthquake’s epicentral area, analyzing their spatial distribution concerning various factors, including elevation, slope gradient, slope aspect, plan curvature, profile curvature, surface cutting degree, topographic relief, elevation coefficient variation, lithology, distance to faults, epicentral distance, peak ground acceleration (PGA), distance to rivers, fractional vegetation cover (FVC), and distance to roads. The analytic hierarchy process (AHP) was improved by incorporating frequency ratio (FR) to address the subjectivity inherent in expert scoring for factor weighting. The improved AHP, combined with the Pearson correlation analysis, was used to identify the dominant controlling factor and assess the landslide susceptibility. The accuracy of the model was verified using the area under the receiver operating characteristic (ROC) curve (AUC). The results reveal that 34% of the study area falls into very-high- and high-susceptibility zones, primarily along the Moxi segment of the Xianshuihe fault and both sides of the Dadu river valley. Tianwan, Caoke, Detuo, and Moxi are at particularly high risk of coseismic landslides. The elevation coefficient variation, slope aspect, and slope gradient are identified as the dominant controlling factors for landslide development. The reliability of the proposed model was evaluated by calculating the AUC, yielding a value of 0.8445, demonstrating high reliability. This study advances coseismic landslide susceptibility assessment and provides scientific support for post-earthquake reconstruction in Luding. Beyond academic insight, the findings offer practical guidance for delineating priority zones for risk mitigation, planning targeted engineering interventions, and establishing early warning and monitoring strategies to reduce the potential impacts of future seismic events. Full article

Deep learning-based landslide detection in optical remote sensing images has been extensively studied. However, several challenges remain. Over time, factors such as vegetation cover and surface weathering can weaken the distinct characteristics of landslides, leading to blurred boundaries and diminished texture features. Furthermore, obtaining landslide samples is challenging in regions with low landslide frequency. Expanding the acquisition range introduces greater variability in the optical characteristics of the samples. As a result, deep learning models often struggle to achieve accurate landslide identification in these regions. To address these challenges, we propose a multi-scale target attention YOLO model (MSTA-YOLO). First, we introduced a receptive field attention (RFA) module, which initially applies channel attention to emphasize the primary features and then simulates the human visual receptive field using convolutions of varying sizes. This design enhances the model’s feature extraction capability, particularly for complex and multi-scale features. Next, we incorporated the normalized Wasserstein distance (NWD) to refine the loss function, thereby enhancing the model’s learning capacity for detecting small-scale landslides. Finally, we streamlined the model by removing redundant structures, achieving a more efficient architecture compared to state-of-the-art YOLO models. Experimental results demonstrated that our proposed MSTA-YOLO outperformed other compared methods in landslide detection and is particularly suitable for wide-area landslide monitoring. Full article

Landslide displacement prediction is crucial for disaster mitigation, yet traditional methods often fail to capture the complex, non-stationary spatiotemporal dynamics of slope evolution. This study introduces an enhanced prediction framework that integrates multi-scale signal processing with dynamic, geology-aware graph modeling. The proposed methodology first employs the Maximum Overlap Discrete Wavelet Transform (MODWT) to denoise raw Global Navigation Satellite System (GNSS)-monitored displacement time series data, enhancing the underlying deformation features. Subsequently, a geology-aware graph is constructed, using the temporal correlation of displacement series as a practical proxy for physical relatedness between monitoring nodes. The framework’s core innovation lies in a dynamic graph optimization model with low-rank constraints, which adaptively refines the graph topology to reflect time-varying inter-sensor dependencies driven by factors like mining activities. Experiments conducted on a real-world dataset from an active open-pit mine demonstrate the framework’s superior performance. The DCRNN-proposed model achieved the highest accuracy among eight competing models, recording a Root Mean Square Error (RMSE) of 2.773 mm in the Vertical direction, a 39.1% reduction compared to its baseline. This study validates that the proposed dynamic graph optimization approach provides a robust and significantly more accurate solution for landslide prediction in complex, real-world engineering environments. Full article

UAV platforms equipped with RTK positioning and LiDAR sensors are increasingly used for landslide monitoring, offering frequent, high-resolution surveys with broad spatial coverage. In this study, we applied high-frequency UAV-based monitoring to the active Baldiola earthflow (Northern Apennines, Italy), integrating 10 UAV–LiDAR and photogrammetric surveys, acquired at average intervals of 14 days over a four-month period. UAV-derived orthophotos and DEMs supported displacement analysis through homologous point tracking (HPT), with robotic total station measurements serving as ground-truth data for validation. DEMs were also used for multi-temporal DEM of Difference (DoD) analysis to assess elevation changes and identify depletion and accumulation patterns. Displacement trends derived from HPT showed strong agreement with RTS data in both horizontal (R² = 0.98) and vertical (R² = 0.94) components, with cumulative displacements ranging from 2 m to over 40 m between April and August 2024. DoD analysis further supported the interpretation of slope processes, revealing sector-specific reactivations and material redistribution. UAV-based monitoring provided accurate displacement measurements, operational flexibility, and spatially complete datasets, supporting its use as a reliable and scalable tool for landslide analysis. The results support its potential as a stand-alone solution for both monitoring and emergency response applications. Full article

Accurate prediction of landslide-induced displacement is essential for the structural integrity and operational safety of buildings and infrastructure situated in geologically unstable regions. This study introduces a novel hybrid predictive framework that synergistically integrates Gaussian Process Regression (GPR) with Temporal Convolutional Neural Networks (TCNs), herein referred to as the GTCN model, to forecast displacement at building monitoring points subject to landslide activity. The proposed methodology is validated using time-series monitoring data collected from the slope adjacent to the Zhongliang Reservoir in Wuxi County, Chongqing, an area where slope instability poses a significant threat to nearby structural assets. Experimental results demonstrate the GTCN model’s superior predictive performance, particularly under challenging conditions of incomplete or sparsely sampled data. The model proves highly effective in accurately characterizing both abrupt fluctuations within the displacement time series and capturing long-term deformation trends. Furthermore, the GTCN framework outperforms comparative hybrid models based on Gated Recurrent Units (GRUs) and GPR, with its advantage being especially pronounced in data-limited scenarios. It also exhibits enhanced capability for temporal feature extraction relative to conventional imputation-based forecasting strategies like forward-filling. By effectively modeling both nonlinear trends and uncertainty within displacement sequences, the GTCN framework offers a robust and scalable solution for landslide-related risk assessment and early warning applications. Its applicability to building safety monitoring underscores its potential contribution to geotechnical hazard mitigation and resilient infrastructure management. Full article

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article

In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal dynamics of landslide events. To address this limitation, we propose a systematic framework for constructing a multi-temporal knowledge graph of landslides that integrates multi-source temporal data, enabling the dynamic tracking of landslide processes. Our approach comprises three key steps. First, we summarize domain knowledge and develop a temporal ontology model based on the disaster chain management system. Second, we map heterogeneous datasets (both tabular and textual data) into triples/quadruples and represent them based on the RDF (Resource Description Framework) and quadruple approaches. Finally, we validate the utility of multi-temporal knowledge graphs through multidimensional queries and develop a web interface that allows users to input landslide names to retrieve location and time-axis information. A case study of the Zhangjiawan landslide in the Three Gorges Reservoir Area demonstrates the multi-temporal knowledge graph’s capability to track temporal updates effectively. The query results show that multi-temporal knowledge graphs effectively support multi-temporal queries. This study advances landslide research by combining static knowledge representation with the dynamic evolution of landslides, laying the foundation for hazard forecasting and intelligent early-warning systems. Full article

Early warning systems depend heavily on the accuracy of landslide displacement forecasts. This study focuses on the Bazimen landslide located in the Three Gorges Reservoir region and proposes a hybrid prediction approach combining support vector regression (SVR) and long short-term memory (LSTM) networks. These models are optimized via the K-Nearest Neighbor (KNN) algorithm. Initially, cumulative displacement data were separated into trend and cyclic elements using a smoothing approach. SVR and LSTM were then used to predict the components, and KNN was introduced to optimize input factors and classify the results, improving accuracy. The final KNN-optimized SVR-LSTM model effectively integrates static and dynamic features, addressing limitations of traditional models. The results show that LSTM performs better than SVR, with an RMSE and MAPE of 24.73 mm and 1.87% at monitoring point ZG111, compared to 30.71 mm and 2.15% for SVR. The sequential hybrid model based on KNN-optimized SVR and LSTM achieved the best performance, with an RMSE and MAPE of 23.11 mm and 1.68%, respectively. This integrated model, which combines multiple algorithms, offers improved prediction of landslide displacement and practical value for disaster forecasting in the Three Gorges area. Full article