Search Results (289)

As one of China’s major megacities, Chongqing is highly vulnerable to rainfall-induced landslides, and the increasing frequency of extreme rainfall driven by climate change further exacerbates risks to infrastructure and public safety. Although numerous studies on landslide susceptibility, quantitative assessments of future landslide frequency under different climate scenarios remain insufficient. This study addresses this gap by integrating high-resolution climate projections with a landslide early-warning model to predict spatiotemporal variations in landslide hazard across Chongqing. Based on regional climate characteristics, the rainy season was divided into three periods: May–June, July, and August–September. Soil moisture variations, together with static geological and topographic factors, were integrated using the information value model to assess the semi-dynamic landslide susceptibilities. On this basis, a regional warning model was then established by linking rainfall thresholds to four geological subregions. High-resolution NEX-GDDP-CMIP6 projections and historical ERA5 0rainfall data were used to quantify changes in exceedance days under four shared socioeconomic pathways (SSPs) from 2021 to 2100. Results indicate a substantial increase in days exceeding the 30% landslide-triggering rainfall threshold, with maximum relative growth of 15.57%. Landslide frequency exhibits pronounced spatial and temporal heterogeneity: increases are observed in May–June and August–September, whereas July trends vary with radiative forcing-decreasing under low-forcing scenarios (SSP1-2.6, SSP2-4.5) and increasing under high-forcing scenarios (SSP3-7.0, SSP5-8.5). The largest increase in frequency reaches 72%, primarily affecting southwestern and central Chongqing. By linking climate projections with rainfall thresholds and semi-dynamic susceptibility assessment, the framework provides a scientific reference for landslide risk prevention and mitigation under future climate scenarios, and offers transferable insights for other mountainous urban regions facing similar hazards. Full article

This study establishes an integrated “Detection–Decomposition–Interpretation” framework for geohazard assessment, with Tangshan City serving as a representative case. Using Sentinel-1 SAR images from 2020 to 2024, regional surface deformation was derived via the Small Baseline Subset InSAR (SBAS-InSAR) technique. Six categories of geohazards were systematically identified and classified: landslides, open-pit slope deformation, mining-induced subsidence, spoil heap deformation, tailings pond deformation, and reclamation settlement. A total of 115 potential hazards were spatially cataloged, revealing distinct zonation characteristics: the northern mountainous area is predominantly affected by landslides and open-pit mining hazards; the central plain exhibits concentrated mining subsidence; and the southern coastal zone is marked by large-scale reclamation settlement. For the southern reclamation area, where settlement mechanisms are complex, the Independent Component Analysis (ICA) method was applied to successfully decompose the deformation signals into three independent components: IC1, representing the dominant long-term irreversible settlement driven by fill consolidation, building loads, and groundwater extraction; IC2, reflecting seasonal deformation coupled with groundwater level fluctuations; and IC3, comprising residual noise. Time series analysis further reveals the coexistence of “decelerating” and “accelerating” settlement trends across different zones, indicative of their respective evolutionary stages—from decaying to actively progressing settlement. This study not only offers a scientific basis for geohazard prevention and control in Tangshan, but also provides a transferable framework for analyzing hazard mechanisms in other complex geographic settings. Full article

In Northern Croatia, the stability of slopes is increasingly compromised by a combination of anthropogenic pressures, seismic activity, and climate-driven changes in precipitation patterns. This study presents an integrated, multi-level investigation of the complex, composite Hum na Sutli landslide to characterize its failure mechanism, identify cascading triggering factors, and provide a quantitative basis for impact assessment and mitigation plan development. By reviewing the existing relevant (geo) data, information on the landslide’s historical background and triggering factors was gathered. Material properties were determined in the field and confirmed via laboratory tests. With the integration of new data and multilevel methodology, including unmanned aerial vehicle (UAV) derived light detection and ranging data (LiDAR) data and Electrical Resistivity Tomography (ERT), the characterization of a landslide type was conducted, and an initial landslide map and model were created. Analyzing precipitation data from over the last 25 years provided insights into the area’s changing precipitation trends, highlighting the importance of continuous monitoring of this site. The presented research results for the Hum na Sutli landslide provide a viable basis for mitigation plan creation. Furthermore, laboratory results establish a correlation in landslide susceptibility between two regional units: the Golubovec and Vrbova formations, based on their similar clay-silt-sand compositions and observed failure mechanisms. The research presented here highlights the benefits of multi-level data analysis, emphasizing the integration of existing data with new high-resolution remote sensing data in order to develop a rapid and reliable initial landslide model. Full article

Landslide disasters pose severe threats to mountainous regions, where accurate monitoring and scientific prediction are crucial for early warning and risk mitigation. This study addresses this challenge by focusing on the Outang Landslide, a representative large-scale bank slope in the Three Gorges Reservoir area known for its significant deformation responses to rainfall and reservoir-level fluctuations. The landslide’s behavior, characterized by notable hysteresis and nonlinear trends, poses a significant challenge to accurate prediction. To address this, we derived high-precision time-series deformation data by applying atmosphere-corrected Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to Sentinel-1A imagery, with validation from GNSS measurements. A systematic analysis was then conducted to uncover the correlation, hysteresis, and spatial heterogeneity between landslide deformation and key influencing variables (rainfall, water level, temperature). Furthermore, we proposed a Spatio-Temporal Enhanced Convolutional Neural Network (STE-CNN), which innovatively converts influencing variables into grayscale images to enhance spatial feature extraction, thereby improving prediction accuracy. The results indicate that: (1) From June 2022 to March 2024, the landslide showed an overall downward displacement trend, with maximum settlement and uplift rates of −49.34 mm/a and 21.77 mm/a, respectively; (2) Deformation exhibited significant correlation, hysteresis, and spatial variability with environmental factors, with dominant variables shifting across seasons—leading to intensified movement in flood seasons and relative stability in dry seasons; (3) The improved STE-CNN outperforms typical prediction models in forecasting landslide deformation.This study presents an integrated methodology that combines InSAR monitoring, multi-factor mechanistic analysis, and deep learning, offering a reliable solution for landslide early warning and risk management. Full article

Landslide displacement prediction is vital for geohazard early warning and infrastructure safety. To address the challenges of modeling nonstationary, nonlinear, and multiscale behaviors inherent in GNSS time series, this study proposes a hybrid predicting framework that integrates Past Decomposable Mixing with an inverted Transformer architecture (PDM-iTransformer). The PDM module decomposes the original sequence into multi-resolution trend and seasonal components, using structured bottom-up and top-down mixing strategies to enhance feature representation. The iTransformer then models each variable’s time series independently, applying cross-variable self-attention to capture latent dependencies and using feed-forward networks to extract local dynamic features. This design enables simultaneous modeling of long-term trends and short-term fluctuations. Experimental results on GNSS monitoring data demonstrate that the proposed method significantly outperforms traditional models, with R² increased by 16.2–48.3% and RMSE and MAE reduced by up to 1.33 mm and 1.08 mm, respectively. These findings validate the framework’s effectiveness and robustness in predicting landslide displacement under complex terrain conditions. Full article

The rugged terrain and dense vegetation in the mountainous area of Luding after the strong earthquake have made geologic hazards hidden and difficult to verify, and there are limitations in the fine-resolution monitoring of small-scale landslides, especially in the area covered by high vegetation. Currently, there is a lack of research on the application of L-band LuTan-1 (LT-1) for landslide detection in the dense vegetation-covered area of the Luding strong earthquake zone, and it is necessary to carry out the analysis of the detection capability of LT-1 for small-scale landslide hazards under the complex terrain and dense vegetation area. In this study, the Stacking-InSAR method was employed using LT-1 and Sentinel-1 satellites to conduct deformation monitoring and landslide detection in the Luding seismic area and to investigate the small-scale landslide detection capability of LT-1 in vegetation-covered areas. The results show that LT-1 and Sentinel-1 identified 23 landslide hazards, and their obvious deformation and landslide characteristics indicate that they are still in an unstable state with a continuous deformation trend. At the same time, through the detection analysis of LT-1’s landslide detection capability under high vegetation cover and small-scale landslide detection capability, the results show that the long wavelength LT-1 can be more effective in landslide hazard identification and monitoring than the short wavelength, and LT-1 with high spatial resolution can be more refined to depict the landslide deformation characteristics in space, which demonstrates the great potential of LT-1 in the refinement of landslide detection. It shows the significant potential of the LT-1 satellite data in landslide detection. Finally, the effects of geometric distortion on landslide detection under different satellite orbits are analyzed, and it is necessary to adopt the combined monitoring method of elevating and lowering orbits for landslide detection to ensure the integrity and reliability of landslide detection. This study highlights the capability of the LT-1 satellite in monitoring landslides in complex mountainous terrain and underscores its potential for detecting small-scale landslides. The findings also offer valuable insights for future research on landslide detection using LT-1 data in similar challenging environments. Full article

The karst terrain of Guangxi, China, characterized by steep slopes and thin residual soils, is highly vulnerable to rainfall-induced shallow landslides. Timely and accurate displacement forecasting is critical for early warning and risk mitigation. However, most existing systems depend on centralized computation, leading to latency and reduced responsiveness. Moreover, conventional forecasting models are often too computationally intensive for edge devices with limited processing resources. To address these constraints, we present EoML-SlideNet, a lightweight forecasting framework designed for resource-limited hardware. It decomposes displacement and triggers into trend and periodic components, then applies the Dual-Band Lasso-Enhanced Latent Variable (DBLE–LV) module to select compact, interpretable features via cross-correlation, LASSO, and VIF screening. A small autoregressive model predicts the trend, while a lightweight neural network captures periodic fluctuations. Their outputs are combined to estimate displacement. All models were evaluated on a single CPU-only workstation to ensure fair comparison. This study introduces floating-point operations (FLOPs), alongside runtime, as practical evaluation metrics for landslide displacement prediction models. A site-specific multi-sensor dataset was developed to monitor rainfall-triggered landslide behavior in the karst terrain of Guangxi. The experimental results show that EoML-SlideNet achieves 2–4 times lower MAE/RMSE than the most accurate deep learning and the lightest baseline models, while offering 3–30 times faster inference. These results demonstrate that low-complexity models can match or surpass the accuracy of deep networks while achieving latency and FLOP levels suitable for edge deployment without dependence on remote servers. Full article

Comprehensive, standardised, multi-temporal inventories of rainfall-induced landslides linked to soil moisture remain scarce, especially in tropical regions. Addressing this gap, we present a multi-source urban inventory for Brazil’s Baixada Santista region (1988–2024). A key advance is the introduction of geographical and temporal confidence classifications, which indicates precisely how each landslide’s location and occurrence date are known, thereby addressing a previously overlooked criterion in Brazil’s landslide data treatment. The inventory comprises 2534 records categorised by spatial (G1–G3) and temporal (T1–T3) confidence. Notable findings include the following: (i) confidence classifications enhance inventory reliability for research and early warning, though precise temporal data remains challenging; (ii) multi-source integration with UAV validation is key to robust inventories in urban tropical regions; (iii) soil moisture complements rainfall-based warnings, but requires local calibration for satellite-derived estimates; (iv) data gaps and biases underscore the need for standardised landslide documentation; and (v) the framework is transferable, providing a scalable model for Brazil and worldwide. Despite limitations, the inventory provides a foundation for (i) susceptibility and hazard modelling; (ii) empirical thresholds for early warning; and (iii) climate-related trend analyses. Overall, the framework offers a sustainable, practical, transferable method for worldwide and contributes to strengthening disaster information systems and early warning capacities. 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

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

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

Field-based landslide mapping is a crucial task for geo-hydrological risk assessment but is often limited by the lack of integrated tools to capture accurate spatial and temporal data. This research investigates a Direct Numerical Cartography (DNC) system’s ability to capture both spatial and temporal landslide features during fieldwork. DNC enables fully digital surveys, minimizing errors and delivering real-time, spatially accurate data to experts on site. We tested an integrated approach combining the Ground Operative System for GIS Input Remote-data Acquisition (GOGIRA) with the Expert-based Multitemporal AI Detector (ExMAD). GOGIRA is a low-cost system for efficient georeferenced data collection, while ExMAD uses AI and multitemporal Sentinel-2 imagery to detect landslide triggering times. Upgrades to GOGIRA’s hardware and algorithms were carried out to improve its mapping accuracy. Field tests in Western Greece compared data to 64 expert-confirmed landslides, with the Range-R device showing a mean spatial error of 50 m, outperforming the tripod-based UGO device at 82 m. Operational factors like line-of-sight obstructions and terrain complexity affected accuracy. ExMAD applied a pre-trained U-Net convolutional neural network for automated temporal trend detection of landslide events. The combined DNC and AI-assisted remote sensing approach enhances landslide inventory precision and consistency while maintaining expert oversight, offering a scalable solution for landslide monitoring. 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