Search Results (1,537)

Normal faults play a key role in accommodating extensional deformation within the South Tibet Rift. The M_S 6.8 Tingri earthquake of 7 January 2025 therefore provides a rare opportunity to investigate how these normal faults accommodate east–west extension driven by India–Eurasia convergence. Using Sentinel-1 synthetic aperture radar (SAR) imagery, we measured coseismic surface deformation and inverted the slip distribution, revealing a maximum line-of-sight (LOS) displacement of 1.85 m. Combining Bayesian inference with joint fault-slip inversion, we constrain the seismogenic fault as a west-dipping normal fault (strike 183°, dip 42.5°, rake ~–115°), exhibiting a maximum slip of 5.36 m at shallow depth. The derived moment magnitude (M_W 7.12, seismic moment 3.32 × 10¹⁹ N·m) agrees well with the USGS estimate (M_W 7.1). Coulomb stress modeling suggests stress decreases along fault flanks and significant stress loading (>0.01 MPa) at rupture terminations and adjacent north–south trending faults, implying elevated aftershock potential and possible fault triggering. GNSS velocity fields and strain rate inversion indicate a regional stress regime with a principal compressive axis (σ₁) oriented ~341° (NNW) and extensional axis (σ₃) at ~73° (ESE), consistent with east–west extension and north–south shortening. The fault exhibits oblique-normal slip, attributed to the non-orthogonal orientation of the fault plane relative to the stress field, resulting in right-lateral shear. Within the framework of the paired general-shear (PGS) deformation, this oblique slip reflects localized extensional deformation within a distributed dextral shear zone. These findings support a model of strain partitioning under regional shear and provide insights into fault segmentation and kinematics in rift systems. 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

Forest height is a key structural parameter for evaluating ecological functions, biodiversity, and carbon dynamics. While LiDAR and Synthetic Aperture Radar (SAR) provide vertical structure information, their large-scale use is restricted by sparse sampling (LiDAR) and temporal decorrelation (SAR). Optical remote sensing offers complementary spectral information but lacks direct height retrieval. To address these limitations, we developed a multi-modal framework integrating GEDI waveform LiDAR, Sentinel-1 SAR (InSAR and PolSAR), and Sentinel-2 multispectral data, combined with machine learning, to estimate forest canopy height across Jiangsu Province, China. GEDI L2A footprints were used as training labels, and a suite of structural and spectral features was extracted from SAR, GEDI, and Sentinel-2 data as input variables for canopy height estimation. The performance of two ensemble algorithms, Random Forest (RF) and Gradient Tree Boosting (GTB) for canopy height estimation, was evaluated through stratified five-fold cross-validation. RF consistently outperformed GTB, with the integration of SAR, GEDI, and optical features achieving the best accuracy (R² = 0.708, RMSE = 2.564 m). The results demonstrate that InSAR features substantially enhance sensitivity to vertical heterogeneity, improving forest height estimation accuracy. These findings highlight the advantage of incorporating SAR, particularly InSAR with optical data, in enhancing sensitivity to vertical heterogeneity and improving the performance of RF and GTB in estimating forest height. The framework we proposed is scalable to other regions and has the potential to contribute to global sustainable forest monitoring initiatives. Full article

Interferometric Synthetic Aperture Radar (InSAR) technology is crucial for large-scale land subsidence analysis in cultivated areas within hilly and mountainous regions. Accurate prediction of this subsidence is of significant importance for agricultural resource management and planning. Addressing the limitations of existing subsidence prediction methods in terms of accuracy and model selection, this paper proposes a deep neural network prediction model based on Variational Mode Decomposition (VMD) and the Snake Optimizer (SO), termed VMD-SO-CNN-LSTM-MATT. VMD decomposes complex subsidence signals into stable intrinsic components, improving input data quality. The SO algorithm is introduced to globally optimize model parameters, preventing local optima and enhancing prediction accuracy. This model utilizes time–series subsidence data extracted via the SBAS-InSAR technique as input. Initially, the original sequence is decomposed into multiple intrinsic mode functions (IMFs) using VMD. Subsequently, a CNN-LSTM network incorporating a Multi-Head Attention mechanism (MATT) is employed to model and predict each component. Concurrently, the SO algorithm performs global optimization of the model hyperparameters. Experimental results demonstrate that the proposed model significantly outperforms comparative models (traditional Long Short-Term Memory (LSTM) neural network, VMD-CNN-LSTM-MATT, and Sparrow Search Algorithm (SSA)-optimized CNN-LSTM) across key metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Specifically, the reductions achieved are minimum improvements of 29.85% for MAE, 8.42% for RMSE, and 33.69% for MAPE. This model effectively enhances the prediction accuracy of land subsidence in cultivated hilly and mountainous areas, validating its high reliability and practicality for subsidence monitoring and prediction tasks. Full article

On 6 February 2023, an earthquake doublet of Mw 7.8 and Mw 7.5 occurred in southeastern Turkey and caused surface ruptures over 350 km for the eastern Anatolian fault (EAF) and 150 km for the Surgu fault (SF), respectively. Over 3700 Mw > 3.0 aftershocks occurred within 5 months following the earthquake doublet, indicating that postseismic stress adjustment is evident. Here, we utilize InSAR technology to investigate the earthquake doublet in terms of its coseismic and postseismic deformations and to estimate the changes in Coulomb stress. We found that the postseismic surface deformation is consistent with the coseismic rupture, characterized by left-lateral strike-slip movement. The coseismic deformations (>5 m) are concentrated in the central-eastern (Pazarcik and Erkenek) segments in the EAF and the central (Cardak) segment in the SF. Notably, the maximum coseismic slip (up to 10 m) and the largest postseismic slip (∼0.5 m) both occurred on the Cardak segment. Postseismic deformations (>0.05 m) are concentrated in the northeastern Erkenek segment and southwestern Amanos segment of the EAF, as well as the eastern Dogansehir segment of the SF. Compared with the coseismic deformation, the postseismic slip compensated for the insufficient deeper slip of the southwestern Amanos segment of the EAF and the central Cardak segment of the SF. Additionally, the postseismic slip extended the rupture area to both the northeast of the Dogansehir segment along the SF and the epicentral area of the 2020 Mw 6.7 earthquake along the EAF. The postseismic afterslip largely reduced the potential seismic hazard of the seismic gap between the eastern end of the coseismic rupture of the 2023 Mw 7.8 earthquake and the epicentral area of the 2020 Mw 6.7 earthquake, as well as the southwestern Amanos segment of the EAF and the eastern Dogansehir segment of the SF. Full article

Interferometric Synthetic Aperture Radar (InSAR) provides unique advantages for sea-crossing bridge monitoring through continuous, large-scale deformation detection. Dividing monitoring data into specific deformation patterns helps establish the connection between bridge deformation and its underlying mechanisms. However, the classification of complex and nonlinear bridge deformations often requires extensive manual labeling work. To achieve automatic classification of deformation patterns with minimal labeled data, this study introduces a transfer learning approach and proposes an InSAR-based method for deformation pattern recognition of cross-sea bridges. At first, deformation time series of the study area are acquired by PS-InSAR, with GNSS results confirming less than 10% error. Then, six types of deformation are identified, including stable, linear, step, piecewise linear, power law, and temperature-related types. Large amounts of simulated data with labels are generated based on these six types. Subsequently, four models—TCN, Transformer, TFT, and ROCKET—are trained using synthetic data and finely adjusted using few real data. Finally, the final classification results are weighted by the classification results of multiple models. Even though confidence and global consistency of each single model are also calculated, the final result is the combined result of a set of multi-type confidences. ROCKET achieved the highest accuracy on simulation data (96.27%) in these four representative models, while ensemble weighting improved robustness on real data. The methodology addresses supervised learning’s labeled data requirements through synthetic data generation and ensemble classification, producing probabilistic outputs that preserve uncertainty information rather than deterministic labels. The framework enables automatic classification of sea-crossing bridge deformation patterns with minimal labeled data, identifying patterns with distinct dominant factors and providing probabilistic information for engineering decision making. Full article

With the continuous construction and operation of urban subways, rapid changes in various urban elements have occurred, subsequently resulting in land subsidence along subway lines. Compared to the construction period, monitoring and multi-factor analysis of subway deformation during the operational period is relatively limited. In this paper, we examine the issue through the novel lens of socio factor agglomeration. Both Sentinel-1, TerraSAR-X, ascending/descending LuTan-1 images and a Block PS-InSAR method were used to monitor 8-year ground subsidence for Kemulang station on Guangzhou Metro Line 6. Compared with the leveling measurements, the root mean square error (RMSE) of the InSAR results was 2.24 mm. Furthermore, social agglomeration effects such as population concentration, property clustering, commercial aggregation and the intensification of resource consumption were considered to analyze the main reason of ground subsidence, the synergistic process of multiple factors and the mechanism of accelerated subsidence phenomenon. We can find from the results that the fundamental cause of the large-scale land subsidence along the subway line is groundwater over-extraction triggered by population agglomeration, coupled with the response of adverse geological formations. Groundwater over-extraction has caused irreversible damage to the local strata. The research shows that the social agglomeration effect will cause more complex disturbance to the subway and lead to more continuous ground subsidence and more covert safety threat for subway operation, which should not be ignored. Full article

The comprehensive, accurate, and rapid acquisition of large-scale surface deformation using Interferometric Synthetic Aperture Radar (InSAR) technology provides crucial information support for regional eco-geological safety assessments and the rational development and utilization of groundwater resources. The Linfen-Yuncheng Basin in Shanxi Province is one of China’s historically most frequented regions for geological hazards in plain areas, such as land subsidence and ground fissures. This study employed the coherent point targets based Small Baseline Subset (SBAS) time-series InSAR technique to interpret a dataset of 224 scenes of 5 m resolution RADARSAT-2 satellite SAR images acquired from January 2017 to May 2024. This enabled the acquisition of high-resolution spatiotemporal characteristics of surface deformation in the Linfen-Yuncheng Basin during the monitoring period. The results show that the area with a deformation rate exceeding 5 mm/a in the study area accounts for 12.3% of the total area, among which the subsidence area accounts for 11.1% and the uplift area accounts for 1.2%, indicating that the overall surface is relatively stable. There are four relatively significant local subsidence areas in the study area. The total area with a rate exceeding 30 mm/a is 41.12 km², and the maximum cumulative subsidence is close to 810 mm. By combining high-resolution satellite images and field survey data, it is found that the causes of the four subsidence areas are all the extraction of groundwater for production, living, and agricultural irrigation. This conclusion is further confirmed by comparing the InSAR monitoring results with the groundwater level data of monitoring wells. In addition, on-site investigations reveal that there is a mutually promoting and spatially symbiotic relationship between land subsidence and ground fissures in the study area. The non-uniform subsidence areas monitored by InSAR show significant ground fissure activity characteristics. The InSAR monitoring results can be used to guide the identification and analysis of ground fissure disasters. This study also finds that due to the implementation of surface water supply projects, the demand for groundwater in the study area has been continuously decreasing. The problem of ground water over-extraction has been gradually alleviated, which in turn promotes the continuous recovery of the groundwater level and reduces the development intensity of land subsidence and ground fissures. Full article

Accurate monitoring of settlement in high-speed railway embankments is critical for operational safety and long-term serviceability. This study investigates the applicability of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) for quantifying millimeter-scale deformations and emphasizes how surrounding environmental factors influence measurement accuracy. Using 29 TerraSAR-X images acquired between 2016 and 2018, PS-InSAR-derived settlements were compared with precise leveling survey data across twelve representative embankment sections of the Honam High-Speed Railway in South Korea. Temporal and spatial discrepancies between the two datasets were harmonized through preprocessing, allowing robust accuracy assessment using mean absolute error (MAE) and standard deviation (SD). Results demonstrate that PS-InSAR reliably captures settlement trends, with MAE ranging from 1.7 to 4.2 mm across different scenes. However, significant variability in accuracy was observed depending on local land-cover composition. Correlation analysis revealed that vegetation-dominated areas, such as agricultural and forest land, reduce persistent scatterer density and increase measurement variability, whereas high-reflectivity surfaces, including transportation facilities and buildings, enhance measurement stability and precision. These findings confirm that environmental conditions are decisive factors in determining the performance of PS-InSAR. The study highlights the necessity of integrating site-specific land-cover information when designing and interpreting satellite-based monitoring strategies for railway infrastructure management. Full article

The Messara Basin, a critical agricultural region in Crete, Greece, faces escalating geohazards, particularly land subsidence driven by intensive groundwater abstraction. Historical radar interferometry (1992–2009) indicated subsidence up to 20 mm·yr⁻¹, while recent European Ground Motion Service data (2016–2021) show mean vertical velocities reaching −31.2 mm·yr⁻¹. This study provides the first integrated hydrogeological assessment for the Basin, based on systematic field surveys, borehole inventories, and four coordinated campaigns (2021–2023) that established a basin-wide monitoring network of 767 stations. The dataset supports delineation of recharge zones, identification of potentiometric depressions, and mapping of aquifer-stress areas. Results show strong seasonality and extensive cones of depression, with local heads declining to ~−50 m below sea level. Land-use change (1990–2018 CORINE data; 2000–2020 agricultural censuses) combined with updated geological mapping highlights the vulnerability of post-Alpine formations, especially Quaternary and Plio–Pleistocene deposits, to deformation. The combined evidence links pumping-induced head decline with spatially coherent subsidence, delineates hotspots of aquifer stress, and identifies zones of elevated compaction risk. These findings provide a decision-ready baseline to support sustainable groundwater management, including enhanced monitoring, targeted demand controls, and managed aquifer-recharge trials. 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

Peatlands are globally important carbon sinks, yet these are increasingly threatened by climate change and human disturbances. Among degradation indicators, surface subsidence is gradual and challenging to monitor, particularly in alpine peatlands. This study applied SBAS-InSAR techniques to analyze surface deformation in the Zoige peatland of the eastern Qinghai—Tibet Plateau using Sentinel-1 SAR data from 2017 to 2023. The results showed a maximum interannual subsidence of −167.92 mm and a peak seasonal deformation of −144.11 mm, with a cumulative average of −23.99 mm (−3.43 mm·yr⁻¹). Approximately 80.9% of peatlands within the protected area exhibited subsidence. Drainage ditch construction emerged as the dominant driver, while climatic factors such as precipitation and temperature exhibited seasonal effects. Subsidence was more pronounced in drier years and during winter months. These findings highlight the spatial heterogeneity and seasonal dynamics of peatland subsidence and underscore the urgent need for hydrological restoration and long-term monitoring to mitigate degradation in alpine peatland ecosystems. Full article

Hydrological changes in peatland are directly related to peat condition. Restoration projects typically aim to raise the water table to enhance peat development, support ecology and increase carbon storage. Remote monitoring of peatland hydrology is challenging but advantageous for assessing condition and restoration effectiveness. This study explores how temporal Sentinel-1-derived InSAR coherence relates to ground-based measurements of soil moisture, water level and local precipitation at two sites, near-natural (Munsary) and degraded (Knockfin Heights), in the Flow Country, Scotland, alongside regional Wick weather station precipitation data (2015–2024). Stronger seasonal linear relationships were observed between soil moisture and InSAR coherence in spring/summer (R² reaching 0.83 at Munsary subsite C, p < 0.001), with in-phase cross correlation throughout the year. In contrast, the relationship between water level and InSAR coherence was more complex with an out-of-phase relationship for much of the year and a weaker linear correlation. These relationships varied with peatland condition, strongest at the more intact bog (Munsary). InSAR coherence and precipitation were in-phase, but not linearly correlated, and land use/cover had no significant effect. Outcomes suggest that InSAR coherence could, when combined with other data, assist in mapping soil moisture/water level dynamics in blanket peatlands, and identify the timing of precipitation events in areas with non-frontal rainfall. Full article

Mining-induced subsidence boundaries, i.e., the surface areas affected by underground mining, play an important role in surface damage assessment and illegal mining identification. Traditional boundary delineation methods rely on field surveys, which restrict their applicability in regions with limited ground observations. Interferometric Synthetic Aperture Radar (InSAR) technology provides a cost-effective and non-contact solution for delineating subsidence boundaries. However, existing InSAR-based methods for subsidence boundary delineation are susceptible to observation noise and other deformation sources, which reduce the accuracy of boundary identification. To this end, this study proposes a novel method for delineating mining-induced subsidence boundaries by integrating both the magnitude and direction of InSAR-derived deformation gradients, referred to as DMSB-DG. First, time-series line-of-sight (LOS) deformation is obtained based on InSAR technology over mining areas. Then, the Roberts operator is employed to compute the magnitude and direction of the deformation gradients, which serve as the basis for boundary delineation. Finally, the ISODATA clustering algorithm is used, incorporating both the magnitude and direction of the deformation gradients as dual constraints to achieve accurate delineation of mining-affected boundaries. The combination of the two features effectively enhances the completeness and accuracy of boundary delineation. The performance of the proposed DMSB-DG method has been verified by simulation and field data. Specifically, compared with the adaptive mining subsidence boundary delimitation (ASBD) method, the proposed method achieved Kappa coefficients of 91.96% and 87.28%, representing improvements of 21.23% and 27.14% in two field tests, respectively. Furthermore, the influence of ascending and descending SAR images, as well as observational noise, on the performance of the proposed algorithm is also evaluated. The results demonstrate that the proposed method effectively suppresses InSAR noise and other interfering deformations, enabling high-precision delineation of mining impact boundaries. Full article