Search Results (375)

Landslide deformation monitoring plays a critical role in geohazard prevention and risk mitigation in mountainous regions, where timely and reliable deformation information is essential for early warning and disaster management. Monitoring landslide deformation in mountainous areas remains a persistent challenge, largely due to rugged topography, dense vegetation cover, and low interferometric coherence—factors that substantially limit the effectiveness of conventional InSAR methods. To address these issues, this study aims to develop a robust time-series InSAR framework for enhancing deformation detection and measurement density under low-coherence conditions in complex mountainous terrain, and accordingly introduces the Sequential Estimation and Total Power-Enhanced Expectation–Maximization Inversion (SETP-EMI) approach, which integrates dual-polarization Sentinel-1 SAR time series within a recursive estimation framework, augmented by polarimetric coherence optimization. This methodology allows for dynamic assimilation of SAR data, improves phase quality under low-coherence conditions, and enhances the extraction of distributed scatterers (DS). When applied to Zhenxiong County, Yunnan Province—a region prone to geohazards with complex terrain—the SETP-EMI method achieved a landslide detection rate of 94.1%. It also generated approximately 2.49 million measurement points, surpassing PS-InSAR and SBAS-InSAR results by factors of 22.5 and 3.2, respectively. Validation against ground-based leveling data confirmed the method’s high accuracy and robustness, yielding a standard deviation of 5.21 mm/year. This study demonstrates that the SETP-EMI method, integrated within a DS-InSAR framework, effectively overcomes coherence loss in densely vegetated plateau regions, improving landslide monitoring and early-warning capabilities in complex mountainous terrain. Full article

Phase consistency is a critical prerequisite for achieving high-precision time-series InSAR deformation retrieval. However, conventional SBAS-InSAR methods provide only limited consideration of phase consistency during the inversion process. Within the SBAS-InSAR workflow, two principal categories of error sources are primarily responsible for phase inconsistency, manifested as non-zero closure phase (NCP): (1) fading biases introduced during multilooking and filtering prior to phase unwrapping; and (2) unwrapping errors caused by large deformation gradients, low coherence, or inappropriate selection of unwrapping algorithms. To address these issues, this study introduces an improved SBAS-InSAR processing workflow, termed NCP-SBAS, designed to improve the accuracy of deformation field estimation and thereby enhance its applicability to geological hazard monitoring. The key idea of the method is to enforce phase consistency as a constraint, jointly accounting for the spatiotemporal characteristics of fading biases and the valid deformation signals, thereby enabling effective correction of NCP. To evaluate the effectiveness of NCP-SBAS, this study conducted a detailed analysis of deformation differences in Hualong County, Qinghai Province, before and after NCP correction, highlighting the significant advantages of the proposed approach. The results indicate that the influence of fading biases on deformation estimates depends on both the magnitude and direction of deformation, while unwrapping errors primarily lead to an underestimation of deformation. In addition, the study provides an in-depth discussion of how fading biases and unwrapping errors affect landslide monitoring and identification. Full article

This study investigates rainstorm-induced highway subgrade slope collapses in the coastal areas of Southeast China. By integrating the seepage–stress coupled finite element method with the strength reduction method, we simulate the entire process of seepage, deformation, and slope collapse under rainstorm conditions, analyzing the variation in the stability factor. The key findings are as follows: (1) During rainstorms, water infiltration increases soil saturation and pore water pressure, while reducing matrix suction and soil shear strength, leading to soil softening. (2) The toe of the subgrade slope first undergoes plastic deformation under rainstorms, which develops upward, and finally the plastic zone connects completely, causing collapse. The simulated landslide surface is consistent with the actual one, revealing the collapse mechanism of the subgrade slope. Additionally, the simulated displacement at the slope toe when the plastic zone connects provides valuable insights for setting warning thresholds in landslide monitoring. (3) The stability factor of the subgrade slope in the case study decreased from 1.24 before the rainstorm to 0.985 after the rainstorm, indicating a transition from a stable state to an unstable state. (4) Parameter analysis shows that heavy downpour or downpour will cause the case subgrade slope to enter an unstable state. The longer the rainfall duration, the lower the stability factor. Analysis of soil parameters indicates that strength parameters, internal friction angle, and effective cohesion exert a significant influence on slope stability, whereas deformation parameters, elastic modulus, and Poisson’s ratio have a negligible effect. Slope collapse can be timely forecasted by predicting the stability factor. Full article

Effective monitoring and early warning of the instability of anti-slide piles in loess landslides depend on identifying the precursory signs of anti-slide pile failure. The acoustic emission (AE) characteristics of concrete anti-slide piles under cyclic loading were studied by using the model box test of the loess landslide–pile system. Cyclic graded loading simulates natural landslide sliding. The synergistic relationship between AE signal characteristics and pile bending moment is established, which reveals the evolution law from micro-damage to macro-damage. The results show that (1) AE ringing count and energy count change in the same way, first stable and then a sudden increase. The evolution of AE dominant frequency and amplitude experiences four stages: low frequency and low amplitude (initial damage), high frequency and low amplitude (stable development), medium frequency and high amplitude (accelerated development), and low frequency and high amplitude (failure). Each stage obviously corresponds to the change in bending moment. (3) The significant increase in the proportion of low-frequency AE energy effectively indicates that the landslide–pile system has entered the state of accelerated deformation and instability, which provides a quantifiable, real-time early warning criterion. This study verifies the feasibility and effectiveness of acoustic emission technology in anti-slide pile damage monitoring and landslide early warning and provides a new technical way for the precursor’s identification and early warning of anti-slide pile instability. Full article

To address the challenges of high cost, complex deployment, and difficulties in real-time early warning for small landslides near residential areas, a portable and low-cost high-frequency monitoring device based on Micro-Electro-Mechanical Systems (MEMSs) was developed, and an advanced warning algorithm based on anomaly intensity factors was constructed. The device is easy to install and can collect and transmit data to the cloud in real time. Equipped with edge computing capabilities, it can independently analyze data in the absence of network connectivity and transmit real-time early warning information to terminals within a range of 5 km. To verify the performance of the system, a large-scale outdoor landslide simulation test site was constructed, where slope failure was induced through artificial rainfall to obtain the complete process data from deformation to failure. The experimental results demonstrate that the proposed early warning algorithm effectively identified different stability levels, providing warnings up to 13 s prior to catastrophic failure, confirming the high sensitivity and reliability of the device. This study offers a cost-effective and efficient approach to landslide monitoring and early warning, with notable prospects for broader implementation in practice. Full article

Landslide risk assessment along high-altitude transportation corridors is critical for infrastructure resilience. This study presents an integrated framework combining Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) deformation data and machine learning (ML) to systematically identify and assess landslide susceptibility along the entire 245.5 km West Kunlun Highway. We first compiled a landslide inventory through visual interpretation and SBAS-InSAR analysis. Subsequently, fourteen causative factors were selected to construct and compare six ML models: random forest (RF), K-nearest neighbours (KNN), artificial neural network (ANN), gradient boosting decision trees (GBDT), support vector machine (SVM), and logistical regression (LR). Research findings indicate that along the Hotan–Kangziva Highway in the Western Kunlun Mountains, there exist 21 potential risk points for small-scale landslides, 12 for medium-scale landslides, and 5 for large-scale landslides, with hazard identification accuracy reaching 80%. The random forest model demonstrated outstanding performance, classifying areas with 5.10%, 4.55% and 4.96% probability as extremely high, high and medium susceptibility, respectively. This work provides a robust methodology and a high-accuracy assessment tool for landslide risk management in the data-scarce Western Kunlun Mountains. Full article

Monitoring landslide displacement and anti-slide pile damage is critical for assessing the stability of progressive loess landslides. To address the challenge of capturing precursor information for loess landslide instability under anti-slide pile reinforcement, this study systematically investigates the damage evolution process of slides (through their “slide-stability-reslide” cycles) and anti-slide piles under acoustic emission (AE) monitoring. Cyclic loading tests were employed to simulate the movement of progressive loess landslides. Based on the core causal logic that “slide displacement induces pile damage, damage generates AE signals, and signals invert displacement status”, a laboratory-scale physical model was designed to simultaneously monitor slide displacement, pile stress, and AE signals. The research results indicate that the dominant frequency and amplitude of AE signals are significantly correlated with slide displacement: with cyclic loading, both the dominant frequency and amplitude exhibit a “low → high → low” characteristic, corresponding to “low/medium-frequency low-amplitude”, “medium/high-frequency medium-high-amplitude” and “low-frequency medium-high-amplitude” signals in the three stages of slide deformation, respectively. The Kaiser and Felicity effects effectively monitor pile damage, and the decrease in Felicity ratio serves as a precursor for landslide early warning. Research results can provide a new methodological framework for early warning systems in pile-reinforced loess landslides. Full article

Stability analysis of landslide areas represents a critical issue in many countries, as landslides can cause large material damage and are a threat to the health and life of inhabitants. This article is aimed at the stability analysis of a built-up locality using a combination of traditional inclinometry with observations carried out using TDR technology (Time Domain Reflectometry) for displacement and groundwater level monitoring. Considering the geological conditions of the site and the occurrence of an old stabilized landslide, groundwater is the main trigger for possible slope deformations. The evaluation of the stability, based on the survey and monitoring outputs, was made using the Finite Element Method. The loss of stability was predicted for a certain uplift of groundwater level and seismic loading, which was lower than normative requirements. The presented case study demonstrates the need for an exhaustive and coordinated survey, as well as the importance of monitoring results and integrated analysis. This careful combination of activities enables us to understand the behavior of the landslide, to evaluate the stability potential of the slope, and to design effective protective measures. Full article

Sand dune movements represent a critical global environment challenge. While previous studies have mainly focused on horizontal deformation, this study applies the bistatic InSAR technique to reconstruct high-precision digital elevation models (DEMs) of the desert terrain, enabling quantitative assessment of the height change in sand dunes by the DEM differential method. Although InSAR has been widely applied to monitor the surface deformation over the urban, mining, and landslide areas, its application in the desert area is still rare. In this study, the northwestern Kubuqi desert, where sand dunes are clearly distributed, was selected as the study area. Using the TanDEM-X bistatic InSAR data acquired on 26 December 2012 and 25 January 2018, we generated high-resolution DEMs with an estimated accuracy of RMSE ≈ 0.9 m in non-dune areas, as validated against ICESat-2 reference data. The high-precision DEM is attributed to the application of a parameterized modeling method, which also facilitates the effective implementation of the DEM differential method. Then, the t-test (i.e., a statistical hypothesis method) was used to estimate a minimum detectable height change (i.e., LoD) of approximately ±0.50 m and confirm the significance of observed elevation changes. Based on this, this reveals a net mean dune height decrease of 1.04 m during the study period. In addition, quantitative investigations on the vegetation coverage and the wind conditions provided further evidence supporting the observed reduction in dune height, suggesting that vegetation stabilization has likely inhibited sediment transport. This study demonstrates the potential of bistatic InSAR for monitoring desert geomorphological processes and provides scientific support for designing effective desertification control strategies. 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

Predicting slope deformation is pivotal for reservoir safety management; however, quantitative attribution to hydrologic–temporal factors with interpretable and hyperparameter-robust models under multi-point temporal dependence is still rare. Hence, we develop an interpretable hybrid framework that couples a Light Gradient Boosting Machine (LightGBM) with a Newton–Raphson-based optimizer (NRBO) for hyperparameter tuning. Unsupervised clustering is first employed to capture intrinsic temporal associations among multiple monitoring points. Subsequently, the NRBO–LightGBM framework is proposed to enhance prediction accuracy and model robustness in slope deformation prediction. Finally, SHAP analysis is integrated to quantify the contribution of influencing factors, thereby strengthening the physical interpretability and credibility of the model. The proposed framework is validated using long-term deformation monitoring data from the Lijiaxia Hydropower Station. Comparative experiments indicate that the NRBO–LightGBM model achieves a 22.8% reduction in RMSE and an 11.4% increase in R² relative to conventional statistical models, improving prediction accuracy with a 21.5% lower RMSE and a 15.5% higher R² compared with the baseline LightGBM. Furthermore, SHAP interpretability analysis elucidates the internal predictive mechanism, revealing that deformation evolution is primarily governed by temporal accumulation and seasonal variations represented by the time variable t and periodic components. Overall, the NRBO–LightGBM model provides high-precision and interpretable deformation prediction for reservoir slopes, effectively bridging predictive performance with mechanistic understanding and offering actionable insights for landslide early warning and risk management. Full article

Subsidence over abandoned goaves is a primary trigger for secondary geological hazards such as surface collapse, landslides, and cracking. This threatens safe mining operations, impairs regional economic progress, and endangers local inhabitants and their assets. At present, goaf areas are mainly treated through grouting. However, owing to the deficiencies of traditional deformation monitoring methods (e.g., leveling and GPS), including their slow speed, high cost, and limited data accuracy influenced by the number of monitoring points, the surface deformation features of goaf zones treated with grouting cannot be obtained in a timely fashion. Therefore, this study proposes a method to analyze the spatio-temporal patterns of surface deformation in grout-filled goaves based on the fusion of Multi-temporal InSAR technologies, leveraging the complementary advantages of D-InSAR, PS-InSAR, and SBAS-InSAR techniques. An investigation was conducted in a coal mine located in Shandong Province, China, utilizing an integrated suite of C-band satellite data. This dataset included 39 scenes from the RadarSAT-2 and 40 scenes from the Sentinel missions, acquired between September 2019 and September 2022. Key results reveal a significant reduction in surface deformation rates following grouting operations: pre-grouting deformation reached up to −98 mm/a (subsidence) and +134 mm/a (uplift), which decreased to −11.2 mm/a and +18.7 mm/a during grouting, and further stabilized to −10.0 mm/a and +16.0 mm/a post-grouting. Time-series analysis of cumulative deformation and typical coherent points confirmed that grouting effectively mitigated residual subsidence and induced localized uplift due to soil compaction and fracture expansion. The comparison with the leveling measurement data shows that the accuracy of this method meets the requirements, confirming the method’s efficacy in capturing the actual ground dynamics during grouting. It provides a scientific basis for the safe expansion of mining cities and the safe reuse of land resources. Full article

The process of reservoir impoundment poses a significant threat to the stability of reservoir bank slopes, potentially triggering new landslides or reactivating ancient ones. Consequently, long-term and stable monitoring of surface deformation in reservoir areas is essential for ensuring safe reservoir operation. SBAS-InSAR technology—characterized by its high precision, multi-temporal capability, and wide spatial coverage—offers an effective means of comprehensively characterizing landslide deformation in such environments. In this study, SBAS-InSAR is applied to monitor landslides in the Xiluodu Reservoir area using Sentinel-1A imagery. Ascending and descending orbit data are jointly inverted to reconstruct the two-dimensional (2D) surface deformation time series. The deformation patterns and their spatiotemporal evolution are analyzed in conjunction with remote sensing imagery, topographic and geological data, and reservoir water level fluctuations. The integrated analysis identifies 10 and 12 significant deformation zones in the vertical and east–west directions, respectively—demonstrating improved detection accuracy compared to single-orbit approaches. Two representative landslides, the Mixiluo and Huanghua landslides, are selected for detailed investigation. Their toe deformation exhibits a pronounced response to both rainfall and reservoir water level variations. These findings provide valuable reference data and technical support for the early identification of reservoir bank landslides and the safe operation of reservoirs in this and similar engineering contexts. Full article

Henan Province, characterized by hills and mountains in its western, northern, and southern regions, is a high-risk area for geohazards in China. In this paper, we are the first to investigate the geohazards over Henan using the basic deformation products of LuTan-1, and we provide the minimum detectable deformation gradients of the products. The basic products consist of deformation field products generated by differential interferometric synthetic aperture radar (InSAR, DInSAR) and time-series deformation products derived from multi-temporal InSAR (MT-InSAR). They were produced using the acquisitions from June 2023 to February 2025. We identified 1620 potential geohazards, including 1340 landslides located in western and southern Henan, 139 ground collapses due to underground mining concentrated in the coal-rich central and eastern regions, and 141 cases of ground deformation located mainly in the agricultural areas of central and northern Henan. DInSAR detected 1470 hazards, while MT-InSAR found 150 more. By calculating the deformation between adjacent pixels, we found that the minimum detectable deformation gradients of the 150 geohazards were less than 0.061 mm/m, which is not detectable by DInSAR. The deformation gradients were greater than 0.017 mm/m and were discovered by MT-InSAR. The overall distribution exhibits a certain pattern, offering a basis for geohazard monitoring. Full article

Landslide deformation monitoring via InSAR is crucial for assessing the risk of hazards. Quick and accurate detection of active deformation zones is crucial for early warning and mitigation planning. While the application of deep learning has substantially improved the detection efficiency, several challenges still persist, such as poor multi-scale perception, blurred boundaries, and limited model generalization. This study proposes Hybrid-SegUFormer to address these limitations. The model integrates the SegFormer encoder’s efficient feature extraction with the U-Net decoder’s superior boundary restoration. It introduces a multi-scale fusion decoding mechanism to enhance context perception structurally and incorporates a self-distillation strategy to significantly improve generalization capability. Hybrid-SegUFormer achieves detection performance (98.79% accuracy, 80.05% F1-score) while demonstrating superior multi-scale adaptability (IoU degradation of only 6.99–8.83%) and strong cross-regional generalization capability. The synergistic integration of its core modules enables an optimal balance between precision and recall, making it particularly effective for complex landslide detection tasks. This study provides a new approach for intelligent interpretation of InSAR deformation in complex mountainous areas. Full article