Search Results (2,237)

The latest advances in remote sensing play a central role in providing Earth observation (EO) data for numerous applications in the scope of reaching environmentally sustainable goals. However, over tropical rainforests, optical imaging is often hindered by extensive cloud coverage, which means that analysis-ready images are mostly restricted to the dry season. In this study, we propose combining radar features extracted from short time series of Sentinel-1 Interferometric Wide Swath (IW) data with a deep learning-based classification scheme to continuously monitor the state of forests. The proposed methodology is based on the joint use of SAR backscatter and interferometric coherences at different temporal baselines to perform pixel-wise classification of land cover classes of interest. However, we show that for a sequence of Sentinel-1 time series, different land cover classes exhibit particular seasonal-dependent variations. Another challenge in performing short-term predictions stems from the fact that ground truths are usually available only on a yearly basis. To address these challenges, we propose a seasonal sampling of the training data, masked by potential deforestation, along with a classification based on a modified U-Net model. The classification results show that overall accuracies above 90% can be achieved throughout the whole year with the proposed method, emerging as a potential tool for mapping rainforests with unprecedented temporal resolution. Full article

Landslide susceptibility mapping (LSM) is a fundamental approach for identifying and predicting areas prone to slope failure. However, most conventional LSM methods are based on time-invariant conditioning factors or long-term-averaged predictors and seldom incorporate slope-kinematic information from deformation observations, thereby limiting their ability to capture evolving slope instability. Moreover, the black-box nature of many models limits interpretability and confidence in their predictions. In this study, we integrate small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) with interpretable machine learning (ML) methods to develop a dynamic LSM framework that improves the accuracy and reliability of susceptibility assessment. First, static LSM was performed using ML algorithms, and SHapley Additive exPlanations (SHAP) was used to quantify and visualize feature importance. Subsequently, SBAS-InSAR was applied to retrieve surface deformation rates. Finally, a dynamic LSM matrix was constructed to integrate InSAR-derived deformation with static susceptibility classes, producing time-varying landslide susceptibility maps. Application of the framework in the Baihetan Reservoir area, Southwest China, demonstrates its practical value. During the static LSM phase, the extreme gradient boosting (XGBoost) model achieved strong predictive performance (the area under the receiver operating characteristic curve (AUC) = 0.8864; accuracy = 0.8315; precision = 0.8947), outperforming the alternative models. SHAP analysis indicates that elevation and distance to rivers are the primary controls on landslide occurrence. Incorporating SBAS-InSAR deformation data into the dynamic LSM matrix effectively captures the spatiotemporal evolution of slope instability. Susceptibility upgrades are observed for multiple inventoried landslides, and the actively deforming Xiaomidi and Gantianba landslides are presented as representative case studies, further supported by multisource observations from satellite imagery, unmanned aerial vehicle (UAV) surveys, and ground-based global navigation satellite system (GNSS) monitoring. Consequently, the proposed dynamic LSM framework overcomes limitations of static approaches by integrating deformation information and enhancing interpretability through explainable artificial intelligence. Full article

Monitoring urban subsidence over ultra-long periods using time-series Interferometric synthetic aperture radar (InSAR) technology is critically important. Conventional approaches, however, face two main limitations: significant atmospheric phase residuals in complex urban settings, and discontinuous temporal time-series with short temporal coverage due to single-platform data constraints. To address these limitations, this study presents a new method for estimating ultra-long-term subsidence time series in urban areas, which combines Interferometric Subset Stacking (ISS) with multi-platform data fusion (DF). The methodology firstly processes TerraSAR-X and Sentinel-1A datasets through differential interferometry and applies ISS for atmospheric phase suppression. Next, bilinear interpolation unifies the spatial resolution and aligns the spatial reference frames of the two datasets. Subsequently, joint modeling derives subsidence velocities. Finally, temporal integration via linear interpolation and moving averaging produces a unified spatio-temporal deformation sequence. Applied to the Beijing region, China, this approach generated a 12-year ultra-long-term subsidence time series result (2012–2024), revealing maximum cumulative subsidence of 1100 mm spatially correlated with groundwater extraction patterns. Validation against Global Navigation Satellite System (GNSS) data showed strong agreement (correlation coefficient: 0.94, Root Mean Square Error (RMSE): 6.3 mm). The method achieved substantial atmospheric reduction—67.7% for Sentinel-1A and 24.1% for TerraSAR-X—representing approximately 15–20% accuracy improvement over conventional Generic Atmospheric Correction Online Service (GACOS) for InSAR. By effectively utilizing multi-platform data, this approach makes fuller use of the available phase information and compensates for the temporal gaps inherent in single-satellite datasets. It thus offers a valuable framework for long-term urban deformation monitoring. Full article

Spaceborne Synthetic Aperture Radar (SAR) interferometry provides long-term displacement measurements, but the quality of Persistent Scatterer (PS) time series depends critically on temporal coherence. Low-coherence points often exhibit auto-uncorrelated behaviours, which may be relevant to discriminate fast phenomena. This work introduces a coherence-based framework that identifies the coherence threshold beyond which PS displacement series retain sufficient reliability to support modelling. The threshold is estimated by analysing how data uncertainty, inferred through Sparse Bayesian Learning (SBL) techniques, varies with coherence and by detecting abrupt changes in this relationship. Once the optimal threshold is established, only the most reliable PS are used to train an SBL regression model linking satellite line-of-sight displacement to soil temperature and surface humidity measured by a low-cost ground sensor. PS-Interferometric SAR (PS-InSAR) time series are derived from COSMO-SkyMed raw images. The SBL model employs compressive-sensing principles and latent-parameter dictionaries of basis functions, whose latent parameters are calibrated through a constrained multi-start optimisation of a normalised residual-based objective function, regularised by a sub-validation dataset. In this work, it is shown that the trained model enables temporally denser reconstruction of displacement histories than the satellite revisit cycle allows and enables continuous soil monitoring by comparing model predictions with newly acquired PS-InSAR data. Full article

In response to the key issues of complex internal structure, significant attenuation of partial discharge (PD) ultrasound signal propagation, and low sensor sensitivity in large oil–immersed power transformers, this paper analyzes the multi–order resonant mode vibration characteristics of the shaft–type fiber optic ultrasound sensor core structure. The displacement distribution patterns of the core structure in both transverse and longitudinal resonant modes are clarified. A strategy using oblique fiber winding rings is proposed to eliminate the problems of strain cancellation and non–accumulation of displacement in transverse and longitudinal resonant modes, which are common in traditional fiber optic ultrasound sensors with parallel fiber windings. Furthermore, design principles are provided to enhance the coverage of the free end and the high–strain regions with semi–high symmetry, as well as the vector–integrated response suitable for multi–order modes. Experimental results show that, in typical PD model detection, the oblique winding sensor exhibits a more prominent response near the high–order resonances of the core, with a detection sensitivity approximately 2.5 times higher than that of the parallel winding structure, and an overall sensitivity at least 7.4 times greater than that of traditional Piezoelectric (PZT) sensors. This demonstrates that the fiber winding method is a key design parameter determining the acoustic–solid coupling efficiency and high sensitivity performance of shaft–type fiber optic interferometric PD sensors, providing a feasible path for high–reliability fiber optic sensing solutions for online monitoring of transformer partial discharges. Full article

Tunnel ahead prospecting is important for reducing construction risks associated with faults, fractured zones, and cavities ahead of the tunnel face, but controlled active-source surveys are often impractical during continuous TBM operation. TBM drilling-noise records provide persistent passive excitation; however, strong nonstationarity and narrowband tonal contamination can hinder stable retrieval of interpretable impulse-like responses. We propose an adaptive impulse reconstruction algorithm that couples CEEMDAN-based mode screening with MDD interferometry. CEEMDAN screening suppresses quasi-stationary tonal components while preserving coherent propagation-related wavefields, producing effective signals suitable for interferometric processing. The MDD stage is stabilized using band-limited inversion, phase-only whitening, and a multi-reference strategy. Numerical experiments with a 3D elastic tunnel model indicate that the proposed workflow yields a more compact and laterally coherent virtual-source gather than correlation-based baselines (CC and PHAT-CC) and single-reference deconvolution interferometry, supporting reflection-oriented interpretation beyond simple wavelet compression. Field measurements from an operating TBM tunnel, together with a hammer-impact benchmark, are consistent with the feasibility of the workflow under real tunneling conditions and with physically plausible moveout behavior in the reconstructed gathers. Full article

Surface subsidence has grown to be a major geological problem for big and medium-sized cities in the context of urbanization and climate change. Changchun, a city of moderate size and rapid development, was chosen as the study region for this project. The Enhanced Permanent Scatterer Interferometric Synthetic Aperture Radar (E-PS-InSAR) technique was used based on Sentinel-1A imagery to gather time-series surface deformation information in order to perform long-term, high-precision monitoring and a mechanistic study of surface deformation in urban–rural integration areas. Subsequently, temperature and land-use type data were then integrated for a thorough investigation using techniques including correlation analysis and functional fitting. The following are the primary conclusions: (1) The E-PS-InSAR technique integrating both PS and DS targets can significantly improve the density of monitoring points compared to traditional methods, providing the complete spatial coverage. (2) Changchun has an average annual subsidence rate of −0.14 mm and an average cumulative subsidence of −0.08 mm. The highest cumulative subsidence is up to −41.31 mm, and the maximum subsidence rate is −17.27 mm/yr. (3) Surface subsidence was correlated with land use types, and cultivated land was the primary contributor to subsidence. (4) Surface subsidence exhibits distinct seasonal fluctuations, and climatic factors exhibit a lagged influence on surface subsidence. These results are crucial for safe infrastructure operation, urban planning, and promptly preventing geological dangers in mid-sized cities. Full article

Transportation infrastructure is vulnerable to heterogeneous deformation, yet such deformation remains insufficiently monitored and characterized in metropolitan regions due to the lack of high-resolution deformation gradient products and comparison with industrial standards. Here, we generated a 45 m resolution interferometric synthetic aperture radar (InSAR) surface displacement time series across the Beijing Plain using Sentinel-1 SAR imagery acquired between 2014 and 2024, and calculated deformation gradients along all ring roads, major expressways, and airport runways. These deformation gradients are compared with national standards to evaluate their structural risks. Our analysis shows that (1) subsidence in the Beijing Plain is concentrated in the northern, eastern, and southern regions, where the northeastern region has been uplifting since 2018 due to the groundwater recovery in Beijing; (2) all ring roads, expressways, and airport runways are relatively stable during our observation period of 2015–2021, except for the central runway of Beijing Capital International Airport, which has accumulated a deformation gradient of 1.9‰ during 2015–2021, exceeding the safety limit of 1.5‰, indicating structural risks. These results demonstrate the effectiveness of high-resolution InSAR time series for monitoring deformation and pinpointing potential structural risks. Full article

Landslide susceptibility maps (LSMs) are crucial for risk mitigation, but integrating Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data is often hampered by a lack of physical interpretation. To address this issue, this study proposes an enhanced modeling framework that integrates multi-source monitoring data by coupling dynamic deformation features. Ground deformation velocity is obtained using MT-InSAR and embedded as dynamic physical constraints into the loss function of a Multi-Layer Perceptron (MLP) model. This approach enables the joint optimization of static geological factors and dynamic deformation characteristics in landslide susceptibility prediction. The proposed framework was applied to Zunyi City, Guizhou Province, China, utilizing an inventory of landslide hazard sites and a dataset of 16 susceptibility factors for model training and evaluation. The results demonstrated that the dynamically constrained model significantly improved predictive performance (AUC = 0.976, an increase of 0.032 compared to the baseline model), and enhanced spatial consistency, reflected by an average increase of 0.0184 in predicted susceptibility for inventoried landslide hazard sites. The framework also outperformed other conventional machine learning models across multiple evaluation metrics. Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that slope (18.68%), DEM (13.26%), rainfall (11.57%), and mining activities (8.79%) were the primary contributing factors in high-susceptibility areas. This study offers a physically interpretable and robust methodology that advances landslide risk assessment and contributes to disaster prevention strategies. Full article

Underground coal mining can induce substantial surface deformation and trigger associated geological hazards. However, the quantitative links between mining-induced deformation, stress redistribution, and the spatial pattern of landslide occurrence remain insufficiently understood, particularly in loess-covered mining regions. Taking the Hecaogou Coal Mine in the Zichang mining area of the Loess Plateau, China, as an example, this study uses a coupled framework that integrates multi-temporal Interferometric Synthetic Aperture Radar (InSAR) observations with three-dimensional FLAC3D numerical simulation. We found that surface deformation is primarily concentrated above and adjacent to the mined-out zones, with maximum cumulative deformation of −169.3 mm during March 2017 and December 2023. The stepwise excavation simulations reveal that vertical displacement and vertical compressive stress in the overlying strata increase continuously as mining advances, thereby promoting tensile–shear failure and surface subsidence, with the subsidence magnitude quantitatively increasing from 3.7 mm at 200 m depth to 162 mm at 1000 m depth. A strong agreement between InSAR-derived deformation and simulated deformation fields is demonstrated, confirming the reliability of the modeled deformation process. We also found that the landslide density exhibits a strong spatial correlation with surface deformation, with high-density zones clustering near the mined-out areas. These findings enhance our understanding of how underground coal mining reshapes surface stability and influences the spatial pattern of landslide occurrences in coal mining regions. Full article

Ground subsidence in mined-out areas has irreversible impacts on residents’ lives and infrastructure, making its monitoring and prediction crucial for ensuring safety, protecting the ecological environment, and promoting sustainable development. This study employed the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to process Sentinel-1A satellite images of Liaoyuan’s Northern New District from August 2022 to March 2025, deriving ground deformation data. The SBAS-InSAR results were validated using unmanned aerial vehicle (UAV) measurements. Monitoring revealed deformation rates ranging from −26.80 mm/year (subsidence) to 13.12 mm/year (uplift) in the area, with a maximum cumulative subsidence of 59.59 mm observed near the Xi’an Sixth District. Based on spatiotemporal patterns, most mining-induced subsidence in the study area is in its late stage, primarily caused by progressive compaction of fractured rock masses and voids within the collapse and fracture zones. Using subsidence data from August 2022 to March 2024, three prediction models—LSTM, GRU, and TCN-GRU—were trained and subsequently applied to forecast subsidence from March 2024 to August 2025. Comparisons between the predictions and SBAS-InSAR measurements showed that all models achieved high accuracy. Among them, the TCN-GRU model yielded predictions closest to the actual values, with a correlation coefficient exceeding 0.95, validating its potential for application in time-series settlement monitoring. Full article

In the rubidium-87 atomic fountain clock, the laser collimating and beam-expanding telescope plays a key role in atomic cooling and manipulation, as well as in realizing the cold-atom fountain. To address the bulkiness of conventional laser collimating and beam-expanding telescopes, which limits system integration and miniaturization, we design and implement a compact laser collimating and beam-expanding telescope. The design employs a Galilean beam-expanding optical path to shorten the optical path length. Combined with optical modeling and optimization, this approach reduces the mechanical length of the telescope by approximately 50%. We present the mechanical structure of a five-degree-of-freedom (5-DOF) adjustment mechanism for the light source and the associated optical elements and specify the corresponding tolerance ranges to ensure their precise alignment and mounting. Based on this 5-DOF adjustment mechanism, we further propose a method for tuning the output beam characteristics, enabling precise and reproducible control of the emitted beam. The experimental results demonstrate that, after adjustment, the divergence angle of the output beam is better than 0.25 mrad, the coaxiality is better than 0.3 mrad, the centroid offset relative to the mechanical axis is less than 0.1 mm, and the output beam diameter is approximately 35 mm. Furthermore, long-term monitoring over 45 days verified the system’s robustness, maintaining fractional power fluctuations within ±1.2% without manual realignment. Compared with the original telescope, all of these beam characteristics are significantly improved. The proposed telescope therefore has broad application prospects in integrated atomic fountain clocks, atomic gravimeters, and cold-atom interferometric gyroscopes. Full article

Interferometric Synthetic Aperture Radar (InSAR) is an advanced imaging geodesy technique for detecting and characterizing surface deformation with high spatial resolution and broad spatial coverage. However, as an inherently post-event observation method, InSAR suffers from limited capability for near-real-time and short-term updates of deformation time series. In this paper, we proposed a data-driven adaptive framework for deformation prediction based on a hybrid deep learning method to accurately predict the InSAR-derived deformation time series and take the Xi’erguazi−Mawo landslide complex (XMLC) as a case study. The InSAR-derived time series was initially decomposed into trend and periodic components with a two-step decomposition process, which were thereafter modeled separately to enhance the characterization of motion kinematics and prediction accuracy. After retrieving the observations from the multi-temporal InSAR method, two-step signal decomposition was then performed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). The decomposed trend and periodic components were further evaluated using statistical hypothesis testing to verify their significance and reliability. Compared with the single-decomposition model, the further decomposition leads to an overall improvement in prediction accuracy, i.e., the Mean Absolute Errors (MAEs) and the Root Mean Square Errors (RMSEs) are reduced by 40–49% and 36–42%, respectively. Subsequently, the Radial Basis Function (RBF) neural network and the proposed CNN-BiLSTM-SelfAttention (CBS) models were constructed to predict the trend and periodic variations, respectively. The CNN and self-attention help to extract local features in time series and strengthen the ability to capture global dependencies and key fluctuation patterns. Compared with the single network model in prediction, the MAEs and RMSEs are reduced by 22–57% and 4–33%, respectively. Finally, the two predicted components were integrated to generate the fused deformation prediction results. Ablation experiments and comparative experiments show that the proposed method has superior ability. Through rapid and accurate prediction of InSAR-derived deformation time series, this research could contribute to the early-warning systems of slope instabilities. Full article

This paper proposes a cooperative localization method based on time-division processing of interferometric measurements, in which the receiver updates the signals from multiple UAVs in separate time slots, thereby reducing spectrum usage and baseband hardware overhead. A Kalman-enhanced tracking loop is designed to achieve high-precision carrier-phase and Doppler estimation under low-SNR conditions. For angle estimation, a time-division update strategy is employed such that the receiver performs full carrier tracking for only one UAV in each time slot, while the carrier phases of the remaining UAVs are extrapolated from the Doppler states estimated in the previous epoch. This avoids the hardware complexity associated with maintaining multiple parallel tracking loops. By fusing the estimated azimuth, elevation, and pseudorange measurements with the master UAV’s high-precision GNSS observations, a factor-graph-based sliding-window cooperative localization algorithm is constructed. Simulation results show that the proposed method improves the RMSE of carrier-phase and Doppler estimation by nearly an order of magnitude compared with the traditional FLL-assisted PLL. The system maintains angle estimation accuracy better than 0.01° within a four-node configuration and achieves centimeter-level ranging accuracy when SNR ≥ 0 dB. In a cooperative flight scenario with one master and three follower UAVs, the method consistently delivers sub-decimeter 3D localization accuracy. Full article

Large-aperture optical elements are increasingly in demand for applications in astronomy, high-power lasers, and aerospace technology, but their manufacturing and testing processes pose significant challenges. In this paper, we propose an ultra-large-aperture digital laser plane interferometric testing technique that combines the two-plate shearing absolute mutual testing method with micro-stress support technology. This method enables high-precision testing of Φ800 mm planar elements and offers advantages such as fast testing speed, high resolution, and precise alignment. Simulation results and comparisons with measurements from a ZYGO interferometer validate the effectiveness of the proposed method. Experimental testing of an Φ800 mm planar element yielded a PV value of 0.0923$\lambda$ and an RMS value of 0.0114$\lambda$ at a wavelength of 632.8 nm. The quantitative results are incorporated into the abstract and conclusions, highlighting the method’s minimal error and high accuracy. This technique provides a novel approach for high-precision testing of large-aperture optical elements. Full article