Search Results (215)

The exchange of water, sediment, and nutrients in wetlands occurs through a complex network of channels and overbank flow. Although optical sensors can map channels at high resolution, they fail to identify narrow intermittent channels colonized by vegetation. Here we demonstrate an innovative application of rapid-repeat interferometric synthetic aperture radar (InSAR) to study hydrologic connectivity and tidal influences in Louisiana’s coastal wetlands, which can provide valuable insights into water flow dynamics, particularly in vegetation-covered and narrow channels where traditional optical methods struggle. Data used were from the airborne UAVSAR L-band sensor acquired for the Delta-X mission. We applied interferometric techniques to rapid-repeat (~30 min) SAR imagery of the southern Atchafalaya basin acquired during two flights encompassing rising-to-high tides and ebbing-to-low tides. InSAR coherence is used to identify and differentiate permanent open water channels from intermittent channels in which flow occurs underneath the vegetation canopy. The channel networks at rising and ebbing tides show significant differences in the extent of flow, with vegetation-filled small channels more clearly identified at rising-to-high tide. The InSAR phase change is used to identify locations on channel banks where overbank flow occurs, which is a critical component for modeling wetland hydrodynamics. This is the first study to use rapid-repeat InSAR to monitor tidal impacts on water flow dynamics in wetlands. The results show that the InSAR method outperforms traditional optical remote sensing methods in monitoring water flow in vegetation-covered wetlands, providing high-resolution data to support hydrodynamic models and critical support for wetland protection and management. Full article

For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations. Although ACD and CCD techniques can detect changes, spatial variability outside the primary target area introduces complexity into the analysis. This study presents a robust change detection methodology designed to identify urban changes using KOMPSAT-5 time-series data. A comprehensive preprocessing framework—including coregistration, radiometric terrain correction, normalization, and speckle filtering—was implemented to ensure data consistency and accuracy. Statistical homogeneous pixels (SHPs) were extracted to identify stable targets, and coherence-based analysis was employed to quantify temporal decorrelation and detect changes. Adaptive thresholding and morphological operations refined the detected changes, while small-segment removal mitigated noise effects. Experimental results demonstrated high reliability, with an overall accuracy of 92%, validated using confusion matrix analysis. The methodology effectively identified urban changes, highlighting the potential of KOMPSAT-5 data for post-disaster monitoring and urban change detection. Future improvements are suggested, focusing on the stability of InSAR orbits to further enhance detection precision. The findings underscore the potential for broader applications of the developed SAR time-series change detection technology, promoting increased utilization of KOMPSAT SAR data for both domestic and international research and monitoring initiatives. Full article

The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system’s potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy. Full article

The inversion of ocean currents is a significant challenge and area of interest in ocean remote sensing. Spaceborne along-track interferometric synthetic aperture radar (ATI-SAR) has several advantages and benefits, including precise observations, extensive swath coverage, and high resolution. However, a limited number of spaceborne interferometric synthetic aperture radar (InSAR) systems are operating in orbit. Among these, the along-track baseline length is generally suboptimal, resulting in low inversion accuracy and difficulty in achieving operational stability. One of the approaches involves employing lower-frequency bands such as the L band to increase the baseline length to achieve the optimal baseline for a satellite formation. The LuTan-1 mission, the world’s first L-band distributed spaceborne InSAR system, was successfully launched on 27 February 2022. L-band distributed formation operation provides insight into the development of future spaceborne ATI systems with application to new exploration regimes and under optimal baseline conditions. There are two novel aspects of this investigation: (1) We described the ocean current inversion process and results based on LuTan-1 SAR data for the first time. (2) A cross-track baseline component phase removal method based on parameterized modeling was proposed for distributed InSAR systems. Both qualitative and quantitative comparisons validated the effectiveness and accuracy of the inversion results. 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 thickness estimation of landslides is crucial for better landslide evaluation. Traditional non-contact mass conservation methods using 3D deformation may be unsuitable due to observation limitations. This study proposes a more feasible approach based on 2D deformation from two-track Interferometric Synthetic Aperture Radar (InSAR) observations, applied to the Xiongba landslide. The comparison with geological and drilling measurements confirms the reliability of this method. The mapped InSAR LOS deformation rate fields reveal two regions: a significantly deformed frontal zone and a relatively stable zone. Analysis suggests that surface uplift at the Xiongba-H2 landslide’s front edge results from rock–soil mass pushing in high-deformation areas. The estimated thickness ranges from 10 to 100 m, with an active volume of 6.17 × 10⁷ m³. A thicker region is identified at the front edge along the Jinsha River, posing the potential for further failure. This low-cost, easily implemented approach enhances InSAR’s applicability for landslide analysis and hazard assessment. Full article

Interferometric Synthetic Aperture Radar (InSAR) is a widely used remote sensing technology for Earth observation, enabling the detection and measurement of ground deformation through the generation of interferograms. However, phase noise remains a critical factor that degrades interferogram quality. To address this issue, this study proposes MOMFNet, a deep learning approach for InSAR phase filtering based on multi-objective multi-kernel feature extraction that leverages multi-objective multi-kernel feature extraction. MOMFNet incorporates a multi-objective loss function that accounts for both the spatial and statistical characteristics of the denoising results, while its multi-kernel convolutional feature extraction module captures multi-scale information comprehensively. Furthermore, the introduction of weighted residual blocks allows the model to adaptively adjust the importance of features, improving its ability to accurately identify and suppress noise. To train the MOMFNet network, we developed an interferogram simulation strategy that uses randomly distorted 2D Gaussian surfaces to simulate terrain variations, Perlin noise to model atmospheric turbulence phases, and negative Gaussian noise to generate random training samples at different noise levels. Comparative experiments with traditional denoising methods and other deep learning approaches, through both qualitative and quantitative analyses, demonstrated that MOMFNet excels in noise suppression and phase recovery, particularly in scenarios involving large gradients and random noise. Empirical studies using Sentinel-1 satellite data from the Yanzhou coal mine validated the practical value of MOMFNet, showing that it effectively removes irrelevant noise while preserving critical phase details, significantly improving interferogram quality. This research provides important insights into the application of deep learning for InSAR denoising. 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

InSAR has emerged as a leading technique for studying and monitoring ground movements over large areas and across various geodynamic environments. Recent advancements in SAR sensor technology have enabled the acquisition of dense spatial datasets, providing substantial information at regional and national scales. Despite these improvements, classifying and interpreting such vast datasets remains a significant challenge. InSAR analysts and geologists frequently have to manually analyze the time series from Persistent Scatterer Interferometry (PSI) to model the complexity of geological and tectonic phenomena. This process is time-consuming and impractical for large-scale monitoring. Utilizing Artificial Intelligence (AI) to classify and detect deformation processes presents a promising solution. In this study, vertical ground deformation time series from northeastern Italy were obtained from the European Ground Motion Service and classified by experts into different deformation categories. Convolutional and pre-trained neural networks were then trained and tested using both numerical time-series data and trend images. The application of the best performing trained network to test data showed an accuracy of 83%. Such a result demonstrates that neural networks can successfully identify areas experiencing distinct geodynamic processes, emphasizing the potential of AI to improve PSI data interpretation. Full article

To ensure the safe repurposing of post-mining landscapes, understanding and managing geotechnical risks, particularly ground movements such as settlements on opencast mining dump surfaces, is critical. Satellite-based radar interferometry (InSAR) technology offers highly detailed data on vertical ground movements with a high spatial and temporal resolution. By combining a data-driven approach, using InSAR-generated high-resolution datasets, with model-driven methods such as inverse modeling and classic time–settlement models, the efficient monitoring and prediction of opencast mine dump settlements can be achieved. This dual approach—leveraging advanced data analysis tools and precise modeling—yields valuable insights into spatial settlement behavior. In particular, classic time–settlement models are applied to the InSAR data through least square regression and Taylor approximation. The integration of both approaches enables the more robust, data-validated forecasts of key geotechnical indicators, such as the time to settlement stabilization and the expected maximum settlement over large areas. An application at a mine in central Germany illustrates the method by generating spatial predictions of the settlement behavior over more than 200 ha. In general, the results provide a comprehensive dataset for investigating other factors influencing the settlement behavior of opencast mine dumps. Full article

Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in the identification of landslides. However, it is time-consuming, inefficient, etc., to survey landslides throughout our large country. In the context of massive SAR data, this problem is more obvious. Therefore, based on the current technique of using differential interferogram phase gradient stacking to avoid phase unwrapping errors, a landslide phase gradient dataset has been constructed. To validate the dataset’s effectiveness and applicability, deep learning methods were introduced, applying the dataset to four networks: U-Net, Attention-Unet, Bisenet v2, and Deeplab v3. The results indicate that the phase gradient dataset performs well across different models, with the Attention-Unet network demonstrating the best performance. Specifically, the precision, recall, and accuracy on the test dataset were 0.8771, 0.8712, and 0.9834, respectively, and the accuracy on the validation dataset was 0.8523. Finally, in this paper, the model is applied to landslide identification in Gansu Province, China, during 2022-2023, and a total of 1882 landslides are found. These landslides are mainly concentrated in the south of Gansu Province, where the terrain is relatively undulating. The results show that this method can quickly and accurately realize landslide automatic identification in a wide area and provide technical support for large-scale landslide disaster surveys. 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

Mining-induced subsidence poses a serious hazard to the surrounding environment and infrastructure, necessitating the detection of such subsidence for effective disaster mitigation and the safeguarding of local residents. Fucheng 1 is the first high-resolution mini-satellite interferometric Synthetic Aperture Radar (SAR) launched by China in June 2023. In this study, we used Fucheng 1 SAR images to analyze mining-induced subsidence in Karamay by InSAR Stacking and D-InSAR. The findings were compared with Sentinel-1A imagery to evaluate the effectiveness of Fucheng 1 in monitoring subsidence and its interferometric performance. Analysis revealed significant mining-induced subsidence in Karamay, and the results from Fucheng 1 closely corresponded with those from Sentinel-1A, particularly regarding the extent of the subsidence. It is indicated that the precision of Fucheng 1 SAR imagery has reached leading standards. In addition, due to its higher resolution, the maximum detectable deformation gradient (MDDG) of Fucheng 1 is 2.15 times higher than that of Sentinel images. This study provides data support for the monitoring of mining-induced subsidence in the Karamay and give a theoretical basis for the application of Fucheng 1 in the field of Geohazard monitoring. 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

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management. Full article