Search Results (204)

Coal mines play an important role in the global energy supply. Monitoring the displacement of open-pit mines is crucial to preventing geological disasters, such as landslides and surface displacement, caused by high-intensity mining activities. In recent years, multi-temporal Synthetic Aperture Radar Interferometry (InSAR) technology has advanced and become widely used for monitoring the displacement of open-pit mines. However, the scattering characteristics of surfaces in open-pit mining areas are unstable, resulting in few coherence points with uneven distribution. Small BAseline Subset InSAR (SABS-InSAR) technology struggles to extract high-density points and fails to capture the overall displacement trend of the monitoring area. To address these challenges, this study focused on the Shengli West No. 2 open-pit coal mine in eastern Inner Mongolia, China, using 201 Sentinel-1 images collected from 20 May 2017 to 13 April 2024. We applied both SBAS-InSAR and distributed scatterer InSAR (DS-InSAR) methods to investigate the surface displacement and long-term behavior of the open-pit coal mine over the past seven years. The relationship between this displacement and mining activities was analyzed. The results indicate significant land subsidence was observed in reclaimed areas, with rates exceeding 281.2 mm/y. The compaction process of waste materials was the main contributor to land subsidence. Land uplift or horizontal displacement was observed over the areas near the active working parts of the mines. Compared to SBAS-InSAR, DS-InSAR was shown to more effectively capture the spatiotemporal distribution of surface displacement in open-pit coal mines, offering more intuitive, comprehensive, and high-precision monitoring of open-pit coal mines. Full article

In recent decades, the interferometric synthetic aperture radar (InSAR) technique has emerged as a powerful tool for monitoring ground subsidence and geohazards. Various satellite SAR systems with different modes, such as Sentinel-1 and Lutan-1, have produced abundant SAR datasets with wide coverage and large historical archives, which have significantly influenced long-term deformation monitoring applications. However, large-scale InSAR data have posed significant challenges to conventional InSAR methods. These issues include the computational burden and storage of multi-temporal InSAR (MT-InSAR) methods, as well as temporal decorrelation for coherent scatterers with long temporal baselines. In this study, we propose a stepwise MT-InSAR with a temporal coherent scatterer method to address these problems. First, a batch sequential method is introduced in the algorithm by grouping the SAR dataset in the time domain based on the average coherence distribution and then applying permanent scatterer interferometry to each temporal subset. Second, a multi-layer network is employed to estimate deformation for partially coherent scatterers using small baseline subset interferograms, with permanent scatterer deformation parameters as the reference. Finally, the final deformation rate and displacement time series were obtained by incorporating all the temporal subsets. The proposed method efficiently generates high-density InSAR deformation measurements for long-time series analysis. The proposed method was validated using 9 years of Sentinel-1 data with 229 SAR images from Jakarta, Indonesia. The deformation results were compared with those of conventional methods and global navigation satellite system data to confirm the effectiveness of the proposed method. Full article

Synthetic aperture radar interferometry (InSAR) has the advantages of a wide monitoring range, high density, high accuracy, and is not limited by weather conditions, providing a new technical means for landslide research. On 21 August 2021, a landslide occurred in Zhonghai Village, Hanyuan County, Ya’an City, Sichuan Province, China, resulting in nine deaths. For the research area, the Small Baseline Subsets InSAR (SBAS-InSAR) technique was used to extract the spatiotemporal evolution characteristics before the landslide occurred (from 16 January 2019 to 22 May 2020), and the height difference before and after the landslide occurrence was extracted using unmanned aerial vehicle photogrammetry, high-resolution remote sensing images, and digital elevation model data. By analyzing seismic activity, human activities, and rainfall in the study area, the main causes of landslides were discussed. This study not only reduces the losses caused by landslide disasters but also provides a scientific basis and technical support for local governments’ disaster prevention and mitigation work. Full article

Phase denoising constitutes a critical component of the synthetic aperture radar interferometry (InSAR) processing chain, where noise suppression and detail preservation are two mutually constraining objectives. Recently, deep learning has attracted considerable interest due to its promising performance in the field of image denoising. In this paper, a Neighbor2Neighbor denoising network (NBDNet) is proposed, which is capable of simultaneously estimating phase and coherence in both single-look and multi-look cases. Specifically, repeat-pass PALSAR real interferograms encompassing a diverse range of coherence, fringe density, and terrain features are used as the training dataset, and the novel Neighbor2Neighbor self-supervised training framework is leveraged. The Neighbor2Neighbor framework eliminates the necessity of noise-free labels, simplifying the training process. Furthermore, rich features can be learned directly from real interferograms. In order to validate the denoising capability and generalization ability of the proposed NBDNet, simulated data, repeat-pass data from Sentinel-1 Interferometric Wide (IW) swath mode, and single-pass data from Hongtu-1 stripmap mode are used for phase denoising experiments. The results demonstrate that NBDNet performs well in terms of noise suppression, detail preservation and computation efficiency, validating its potential for high-precision and high-resolution topography reconstruction. Full article

The historical City of Venice, with its lagoon, has been severely exposed to repeated marine flooding since historical times due to the combined effects of sea level rise (SLR) and land subsidence (LS) by natural and anthropogenic causes. Although the sea level change in this area has been studied for several years, no detailed flooding scenarios have yet been realized to predict the effects of the expected SLR in the coming decades on the coasts and islands of the lagoon due to global warming. From the analysis of geodetic data and climatic projections for the Shared Socioeconomic Pathways (SSP1-2.6; SSP3-7.0 and SSP5-8.5) released in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), we estimated the rates of LS, the projected local relative sea level rise (RSLR), and the expected extent of flooded surfaces for 11 selected areas of the Venice Lagoon for the years 2050, 2100, and 2150 AD. Vertical Land Movements (VLM) were obtained from the integrated analysis of Global Navigation Satellite System (GNSS) and Interferometry Synthetic Aperture Radar (InSAR) data in the time spans of 1996–2023 and 2017–2023, respectively. The spatial distribution of VLM at 1–3 mm/yr, with maximum values up to 7 mm/yr, is driving the observed variable trend in the RSLR across the lagoon, as also shown by the analysis of the tide gauge data. This is leading to different expected flooding scenarios in the emerging sectors of the investigated area. Scenarios were projected on accurate high-resolution Digital Surface Models (DSMs) derived from LiDAR data. By 2150, over 112 km² is at risk of flooding for the SSP1-2.6 low-emission scenario, with critical values of 139 km² for the SSP5-8.5 high-emission scenario. In the case of extreme events of high water levels caused by the joint effects of astronomical tides, seiches, and atmospheric forcing, the RSLR in 2150 may temporarily increase up to 3.47 m above the reference level of the Punta della Salute tide gauge station. This results in up to 65% of land flooding. This extreme scenario poses the question of the future durability and effectiveness of the MoSE (Modulo Sperimentale Elettromeccanico), an artificial barrier that protects the lagoon from high tides, SLR, flooding, and storm surges up to 3 m, which could be submerged by the sea around 2100 AD as a consequence of global warming. Finally, the expected scenarios highlight the need for the local communities to improve the flood resiliency plans to mitigate the consequences of the expected RSLR by 2150 in the UNESCO site of Venice and the unique environmental area of its lagoon. Full article

The dynamic loads of fluid impact and static loads, such as the gravity of a rock mass during the formation of debris flows, exhibit a coupled effect of mutual influence. Under this coupling effect, surface monitoring points in disaster areas experience displacement. However, existing methods do not consider the dynamic–static coupling effects of debris flows on the surface. Instead, they rely on GNSS or InSAR technology for dynamic or static single-scale monitoring, leading to high Mean Absolute Percentage Error (MAPE) values and low warning accuracy. To address these limitations and improve debris flow warning accuracy, a multi-scale warning method was proposed based on Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) technology. GNSS technology was utilized to correct coordinate errors at monitoring points, thereby enhancing the accuracy of monitoring data. Surface deformation images were generated using InSAR and Small Baseline Subset (SBAS) technology, with time series calculations applied to obtain multi-scale deformation data of the surface in debris flow disaster areas. A debris flow disaster morphology classification model was developed using a support vector mechanism. The actual types of debris flow disasters were employed as training labels. Digital Elevation Model (DEM) files were utilized to extract datasets, including plane curvature, profile curvature, slope, and elevation of the monitoring area, which were then input into the training model for classification training. The model outputted the classification results of the hidden danger areas of debris flow disasters. Finally, the dynamic and static coupling variables of surface deformation were decomposed into valley-type internal factors (rock mass static load) and slope-type triggering factors (fluid impact dynamic load) using the moving average method. Time series prediction models for the variable of the dynamic–static coupling effects on surface deformation were constructed using polynomial regression and particle swarm optimization (PSO)–support vector regression (SVR) algorithms, achieving multi-scale early warning of debris flows. The experimental results showed that the error between the predicted surface deformation results using this method and the actual values is less than 5 mm. The predicted MAPE value reached 6.622%, the RMSE value reached 8.462 mm, the overall warning accuracy reached 85.9%, and the warning time was under 30 ms, indicating that the proposed method delivered high warning accuracy and real-time warning. Full article

Bridge expansion joints are critical components that accommodate the movement of a bridge caused by temperature fluctuations, concrete shrinkage, and vehicular loads. Analyzing the spatiotemporal deformation of these expansion joints is essential for monitoring bridge safety. This study investigates the deformation characteristics of Hongtang Bridge in Fuzhou, China, using synthetic aperture radar interferometry (InSAR). We optimize the network paths to enhance the phase unwrapping process of InSAR. Additionally, to address design matrix bias resulting from inaccurate temperature data, we employ the mixed total least squares method to estimate deformation parameters. Subsequently, we utilize independent component analysis to analyze the spatiotemporal deformation characteristics of the bridge. The average standard deviation of the unwrapped phase and the modeling residuals have been reduced by 87% and 5%, respectively. Our findings indicate that thermal expansion deformation is primarily concentrated in the expansion joints, measuring approximately 0.6 mm/°C. In contrast, the cable-stayed bridge deck exhibits the largest deformation magnitude, exceeding 2.0 mm/°C. This research focuses on bridge structures to identify typical deformation locations and evaluate their deformation characteristics. Such analysis is beneficial for conducting safety assessments of bridges. Full article

Urban areas face an imminent risk of collapse due to structural deficiencies and gradual ground subsidence. Therefore, monitoring surface movements is crucial for detecting abnormal behavior, implementing timely preventive measures, and minimizing the detrimental effects of this phenomenon in residential regions. In this context, interferometric synthetic aperture radar (InSAR) has emerged as a highly effective technique for monitoring slow and long-term ground hazards and surface motions. The first goal of this study is to explore the potential applications of persistent scatterer interferometry (PSI) and small baseline subset (SBAS) algorithms in collapse hotspot detection, utilizing a dataset consisting of 144 Sentinel-1 images. The experimental results from three areas with a history of collapses demonstrate that the SBAS algorithm outperforms PSI in uncovering behavior patterns indicative of collapse and accurately pinpointing collapse points near real collapse sites. In the second phase, this research incorporated an additional dataset of 36 TerraSAR-X images alongside the Sentinel-1 data to compare results based on radar images with different spatial resolutions in the C and X bands. The findings reveal a strong correlation between the TerraSAR-X and Sentinel-1 time series. Notably, the analysis of the TerraSAR-X time series for one study area identified additional collapse-prone points near the accident site, attributed to the higher spatial resolution of these data. By leveraging the capabilities of InSAR and advanced algorithms, like SBAS, this study highlights the potential to identify areas at risk of collapse, enabling the implementation of preventive measures and reducing potential harm to residential communities. Full article

The Fucheng-1 (FC-1) satellite has successfully transitioned from its initial operational phase and is now undergoing a detailed performance assessment for time-series deformation monitoring. This study evaluates the surface deformation monitoring capabilities of the newly launched FC-1 satellite using the interferometric synthetic aperture radar (InSAR) technique, particularly in urban applications. By analyzing the observation data from 20 FC-1 scenes and 20 Sentinel-1 scenes, deformation velocity maps of a university in Mianyang city were obtained using persistent scatterer interferometry (PSI) and distributed scatterer interferometry (DSI) techniques. The results show that thanks to the high resolution of 3 × 3 m of the FC-1 satellite, significantly more PS points and DS points were detected than those detected by Sentinel-1, by 13.4 times and 17.9 times, respectively. The distribution of the major deformation areas detected by both satellites in the velocity maps is generally consistent. FC-1 performs better than Sentinel-1 in monitoring densely structured and vegetation-covered areas. Its deformation monitoring capability at the millimeter level was further validated through comparison with leveling measurements, with average errors and root mean square errors of 1.761 mm and 2.172 mm, respectively. Its high-resolution and high-precision interferometry capabilities make it particularly promising in the commercial remote sensing market. Full article

Reclamation is an effective strategy for alleviating land scarcity in coastal areas, thereby providing additional arable land and opportunities for marine ranching. Monitoring the safety of artificial reclamation embankments is crucial for protecting these reclaimed areas. This study employed synthetic aperture radar interferometry (InSAR) using 224 Sentinel-1A data, spanning from 9 January 2016 to 8 April 2024, to investigate the deformation characteristics of the coastal reclamation embankment in Funing Bay, China. We optimized the phase-unwrapping network by employing ambiguity-detection and redundant-observation methods to facilitate the multitemporal InSAR phase-unwrapping process. The deformation results indicated that the maximum observed land subsidence rate exceeded 50 mm per year. The Funing Bay embankment exhibited a higher level of internal deformation than areas closer to the sea. Time-series analysis revealed a gradual deceleration in the deformation rate. Furthermore, a geotechnical model was utilized to predict future deformation trends. Understanding the spatial dynamics of deformation characteristics in the Funing Bay reclamation embankment will be beneficial for ensuring the safe operation of future coastal reclamation projects. Full article

Orbit error is one of the primary error sources of interferometric synthetic aperture radar (InSAR) and differential InSAR (D-InSAR) measurements, arising from inaccurate orbit determination of SAR platforms. Typically, orbit error in the interferogram can be estimated using polynomial models. However, correcting for orbit errors with significant time-varying characteristics presents two main challenges: (1) the complexity and variability of the azimuth time-varying orbit errors make it difficult to accurately model them using a set of polynomial coefficients; (2) existing patch-based polynomial models rely on empirical segmentation and overlook the time-varying characteristics, resulting in residual orbital error phase. To overcome these problems, this study proposes an automated block adjustment framework for estimating time-varying orbit errors, incorporating the following innovations: (1) the differential interferogram is divided into several blocks along the azimuth direction to model orbit error separately; (2) automated segmentation is achieved by extracting morphological features (i.e., peaks and troughs) from the azimuthal profile; (3) a block adjustment method combining control points and connection points is proposed to determine the model coefficients of each block for the orbital error phase estimation. The feasibility of the proposed method was verified by repeat-pass L-band spaceborne and P-band airborne InSAR data, and finally, the InSAR digital elevation model (DEM) was generated for performance evaluation. Compared with the high-precision light detection and ranging (LiDAR) elevation, the root mean square error (RMSE) of InSAR DEM was reduced from 18.27 m to 7.04 m in the spaceborne dataset and from 7.83~14.97 m to 3.36~6.02 m in the airborne dataset. Then, further analysis demonstrated that the proposed method outperforms existing algorithms under single-baseline and single-polarization conditions. Moreover, the proposed method is applicable to both spaceborne and airborne InSAR data, demonstrating strong versatility and potential for broader applications. Full article

Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction. Full article

Large-scale man-made linear structures like high-speed railway lines have become increasingly important in modern life as a faster and more comfortable transportation option. Subsidence or longitudinal levelling deformation problems along these railway lines can prevent the line from operating effectively and, in some cases, require speed reduction, continuous maintenance or repairs. In this study, the longitudinal levelling deformation of the high-speed railway line passing through Konya province (Central Turkey) was analyzed for the first time using the Persistent Scatter Synthetic Aperture Radar Interferometry (PS-InSAR) technique in conjunction with diagnostic train measurements, and the correlation values between them were found. In order to monitor potential levelling deformation along the railway line, medium-resolution, free-of-charge C-band Sentinel-1 (S-1) data and high-resolution, but paid, X-band Cosmo-SkyMed (CSK) Synthetic Aperture Radar (SAR) data were analyzed from the diagnostic train and reports received from the relevant maintenance department. Comparison analyses of the results obtained from the diagnostic train and radar measurements were carried out for three regions with different deformation scenarios, selected from a 30 km railway line within the whole analysis area. PS-InSAR measurements indicated subsidence events of up to 40 mm/year along the railway through the alluvial sediments of the Konya basin, which showed good agreement with the diagnostic train. This indicates that the levelling deformation of the railway and its surroundings can be monitored efficiently, rapidly and cost-effectively using the InSAR technique. Full article

The movement and deformation of rock strata and the ground surface is a dynamic deformation process that occurs as underground mining progresses. Therefore, the dynamic prediction of three-dimensional surface deformation caused by underground mining is of great significance for assessing potential geological disasters. Synthetic aperture radar interferometry (InSAR) has been introduced into the field of mine deformation monitoring as a new mapping technology, but it is affected by many factors, and it cannot monitor the surface deformation value over the entire mining period, making it impossible to accurately predict the spatiotemporal evolution characteristics of the surface. To overcome this limitation, we propose a new dynamic prediction method (InSAR-DIB) based on a combination of InSAR and an improved Boltzmann (IB) function model. Theoretically, the InSAR-DIB model can use information on small dynamic deformation during mining to obtain surface prediction parameters and further realize a dynamic prediction of the surface. The method was applied to the 1613 (1) working face in the Huainan mining area. The results showed that the estimated mean error of the predicted surface deformation during mining was between 80.2 and 112.5 mm, and the estimated accuracy met the requirements for mining subsidence monitoring. The relevant research results are of great significance, and they support expanding the application of InSAR in mining areas with large deformation gradients. Full article

Multi-track synthetic aperture radar interferometry (InSAR) provides a good approach for the monitoring of long-term multi-dimensional earthquake deformation, including pre-, co-, and post-seismic data. However, the removal of atmospheric errors in both single- and multi-track InSAR data presents significant challenges. In this paper, a method of spatio-temporal correlation analysis using independent component analysis (ICA) is proposed, which can extract multi-track deformation components for the accurate retrieval of earthquake deformation time series. Sentinel-1 data covering the double earthquakes in Turkey and Syria in 2023 are used to demonstrate the effectiveness of the proposed method. The results show that co-seismic displacement in the east–west and up–down directions ranged from −114.7 cm to 82.8 cm and from −87.0 cm to 63.9 cm, respectively. Additionally, the deformation rates during the monitoring period ranged from −137.9 cm/year to 123.3 cm/year in the east–west direction and from −51.8 cm/year to 45.7 cm/year in the up–down direction. A comparative validation experiment was conducted using three GPS stations. Compared with the results of the original MSBAS method, the proposed method provides results that are smoother and closer to those of the GPS data, and the average optimization efficiency is 43.08% higher. The experiments demonstrated that the proposed method could provide accurate two-dimensional deformation time series for studying the pre-, co-, and post-earthquake events of the 2023 Turkey–Syria Earthquakes. Full article