Search Results (282)

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

In mountain environments, landslide activity can be assessed through a combination of remote and proximal sensing techniques performed at different scales. The complementarity of methods and the synergistic use of data can be crucial for landslide recognition and monitoring. This paper explored the potential of Multi-Temporal Differential Synthetic Aperture Radar Interferometry (MT-DInSAR) to detect and monitor slope deformations at the basin scale in a catchment area of the Northern Apennines (Italy) and verified the consistency between the landslide classification by the Inventory of Landslide Phenomena in Italy (IFFI) and displacements from the SAR data. In this research, C- and X-band SAR were considered to provide insights into the performances and suitability of sensors operating at different frequencies. This study provides clues about the state of activity of slow-moving landslides and critically assessed its contribution to the IFFI inventory update. Moreover, it demonstrated the benefits of the synergistic use of SAR and geomorphological analysis to investigate slope dynamics in clayey terrains by exemplifying the approach for a relevant case study, the Gaiato landslide. Notwithstanding the widespread use of MT-DInSAR for landslide kinematics investigations, the main limiting factors are discussed along with the expected improvements related to the upcoming new generations of L-band SAR satellites. Full article

The geological environment damage caused by coal mining subsidence has become an important factor affecting the sustainable development of mining areas. Reconstruction of the Coal Mining Subsidence Field (CMSF) is the key to preventing geological disasters, and the needs of CMSF reconstruction cannot be met by solely relying on a single remote sensing technology. The combination of Unmanned Aerial Vehicle (UAV) and Synthetic Aperture Radar (SAR) has complementary advantages; however, the data fusion strategy by refining the SAR deformation field through UAV still needs to be updated constantly. This paper proposed a Prior Weighting (PW) method based on Satellite Aerial (SA) heterogeneous remote sensing. The method can be used to fuse SAR and UAV Light Detection and Ranging (LiDAR) data for ground subsidence parameter inversion. Firstly, the subsidence boundary of Differential Interferometric SAR (DInSAR) combined with the large gradient subsidence of Pixel Offset Tracking (POT) was developed to initialize the SAR preliminary CMSF. Secondly, the SAR preliminary CMSF was refined by UAV LiDAR data; the weights of SAR and UAV LiDAR data are 0.4 and 0.6 iteratively. After the data fusion, the subsidence field was reconstructed. The results showed that the overall CMSF accuracy improved from ±144 mm to ±51 mm. The relative errors of the surface subsidence factor and main influence angle tangent calculated by the physical model and in situ measured data are 1.3% and 1.7%. It shows that the proposed SAR/UAV fusion method has significant advantages in the reconstruction of CMSF, and the PW method contributes to the prevention and control of mining subsidence. Full article

Mass movements represent one of the most significant geohazards worldwide. The aim of this research is to highlight the potential and limitations of the European Ground Motion Service (EGMS) in detecting and monitoring mass movements at a local scale, especially in cases where data from in situ instrumental devices are unavailable. The study area corresponds to the La Miera landslide, located in Asturias (NW Spain). The multidisciplinary methodology applied involved the following steps: (1) downloading, acquiring, and analyzing Sentinel-1 A-DInSAR datasets (2015–2021) through the EGMS; (2) conducting a detailed geomorphological map and identifying evidence of movement; (3) classifying building damage by means of a damage inventory; (4) compiling and analyzing daily rainfall records with respect to deformation time series. Sentinel-1 A-DInSAR results revealed maximum LOS and East–West velocities of −11.6 and −7.9 mm/yr related to the landslide activity. Geomorphological mapping allowed for the updating of the landslide boundaries and its characterization as an active, complex movement. Registered building damage, which ranged from moderate to serious, was correlated with LOS and East–West velocities. The displacement recorded by the EGMS closely corresponds with rainfall periods, while periods of reduced rainfall coincide with the stabilization and recovery phases of displacement. This emphasizes a noteworthy quantitative correlation between rainfall events and EGMS data, evident both spatially and temporally. This work highlights that areas in which the EGMS data indicate deformation but lack in situ instrumental records, geomorphological techniques, and building damage surveys can provide spatial validation of the EGMS displacement, while rainfall records can provide temporal validation. Full article

The surface velocity of the Amery Ice Shelf (AIS) is vital to assessing its stability and mass balance. Previous studies have shown that the AIS basin has a stable multi-year average surface velocity. However, spatiotemporal variations in the surface velocity of the AIS and the underlying physical mechanism remain poorly understood. This study combined offset tracking and DInSAR methods to extract the monthly surface velocity of the AIS and obtained the inter-annual surface velocity from the ITS_LIVE product. An uneven spatial distribution in inter-annual variation in the surface velocity was observed between 2000 and 2022, although the magnitude of variation was small at less than 20.5 m/yr. The increase and decrease in surface velocity on the eastern and western-central sides of the AIS, respectively, could be attributed to the change in the thickness of the AIS. There was clear seasonal variation in monthly average surface velocity at the eastern side of the AIS between 2017 and 2021, which could be attributed to variations in the area and thickness of fast-ice and also to variations in ocean temperature. This study suggested that changes in fast-ice and ocean temperature are the main factors driving spatiotemporal variation in the surface velocity of the AIS. 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

In this study, we investigate the usefulness of the spectral whitening procedure, devised by one of the authors as a preprocessing stage of envelope-detected single-look synthetic aperture radar (SAR) images, in application contexts where phase information is relevant. In the first experiment, each of the raw datasets of an interferometric pair of COSMO-SkyMed images, representing industrial buildings amidst vegetated areas, was individually (1) synthesized by the SAR processor without Fourier-domain Hamming windowing; (2) synthesized with Hamming windowing, used to improve the focalization of targets, with the drawback of spatially correlating speckle; and (3) processed for the whitening of complex speckle, using the data obtained in (2). The interferograms were produced in the three cases, and interferometric coherence and phase maps were calculated through 3 × 3 boxcar filtering. In (1), coherence is low on vegetation; the presence of high sidelobes in the system’s point-spread function (PSF) causes the spread of areas featuring high backscattering. In (2), point targets and buildings are better defined, thanks to the sidelobe suppression achieved by the frequency windowing, but the background coherence is abnormally increased because of the spatial correlation introduced by the Hamming window. Case (3) is the most favorable because the whitening operation results in low coherence in vegetation and high coherence in buildings, where the effects of windowing are preserved. An analysis of the phase map reveals that (3) is likely to be facilitated also in terms of unwrapping. Results are presented on a TerraSAR-X/TanDEM-X (TSX-TDX) image pair by processing the interferograms of original and whitened data using a non-local filter. The main results are as follows: (1) with autocorrelated speckle, the estimation error of coherence may attain 16% and inversely depends on the heterogeneity of the scene; and (2) the cleanness and accuracy of the phase are increased by the preliminary whitening stage, as witnessed by the number of residues, reduced by 24%. Benefits are also expected not only for differential InSAR (DInSAR) but also for any coherent analysis and processing carried out performed on SLC data. Full article

With the gradual expansion of mining scale in open-pit coal mines, slope safety problems are increasingly diversified and complicated. In order to reduce the potential loss caused by slope sliding and reduce the major threat to the safety of life and property of residents in the mining area, this study selected two mining areas in Xinjiang as cases and focused on the relationship between phase noise and deformation. The study predicts the specific time point of slope sliding by analyzing the dynamic history correlation tangent angle between the two. Firstly, the time series data of the micro-variation monitoring radar are used to obtain the small deformation of the study area by differential InSAR (D-InSAR), and the phase noise is extracted from the radar echo in the sequence data. Then, the volume of the deformation body is calculated by analyzing the small deformation at each time point, and the standard deviation of the phase noise is calculated accordingly. Finally, the sliding time of the deformation body is predicted by combining the tangent angle of the ratio of the volume of the deformation body to the standard deviation of the phase noise. The results show that the maximum deformation rates of the deformation bodies in the studied mining areas reach 10.1 mm/h and 6.65 mm/h, respectively, and the maximum deformation volumes are 2,619,521.74 mm³ and 2,503,794.206 mm³, respectively. The predicted landslide time is earlier than the actual landslide time, which verifies the effectiveness of the proposed method. This prediction method can effectively identify the upcoming sliding events and the characteristics of the slope, provide more accurate and reliable prediction results for the slope monitoring staff, and significantly improve the efficiency of slope monitoring and early warning. Full article

The goal of this study was to develop a model describing the relationship between the ground-displacement-caused tremors induced by underground mining, and mining and geological factors using the Random Forest Regression machine learning method. The Rudna mine (Poland) was selected as the research area, which is one of the largest deep copper ore mines in the world. The SAR Interferometry methods, Differential Interferometric Synthetic Aperture Radar (DInSAR) and Small Baseline Subset (SBAS), were used in the first case to detect line-of-sight (LOS) displacements, and in the second case to detect cumulative LOS displacements caused by mining tremors. The best-prediction LOS displacement model was characterized by R² = 0.93 and RMSE = 5 mm, which proved the high effectiveness and a high degree of explanation of the variation of the dependent variable. The identified statistically significant driving variables included duration of exploitation, the area of the exploitation field, energy, goaf area, and the average depth of field exploitation. The results of the research indicate the great potential of the proposed solutions due to the availability of data (found in the resources of each mine), and the effectiveness of the methods used. Full article

Illegal coal mining is prevalent worldwide, leading to extensive ground subsidence and land collapse. It is crucial to define the location and spatial dimensions of these areas for the efficient prevention of the induced hazards. Conventional methods for goaf locating using the InSAR technique are mostly based on the probability integral model (PIM). However, The PIM requires detailed mining information to preset model parameters and does not account for the layered structure of the coal overburden, making it challenging to detect underground goaves in cases of illegal mining. In response, a novel method based on the InSAR technique and the Stratified Optimal Okada Dislocation Model, named S-ODM, is proposed for locating goaves with basic geological information. Firstly, the S-ODM employs a numerical model to establish a nonlinear function between the goaf parameters and InSAR-derived ground deformation. Then, in order to mitigate the influence of nearby mining activities, the goaf azimuth angle is estimated using the textures and trends of the InSAR-derived deformation time series. Finally, the goaf’s dimensions and location are estimated by the genetic algorithm–particle swarm optimization (GA-PSO). The effectiveness of the proposed method is validated using both simulation and real data, demonstrating average relative errors of 6.29% and 7.37%, respectively. Compared with the PIM and ODM, the proposed S-ODM shows improvements of 19.48% and 52.46% in geometric parameters. Additionally, the errors introduced by GA-PSO and the influence of ground deformation monitoring errors are discussed in this study. Full article

Estimating landslide dynamics is vital for the prevention of landslide disasters. Differential interferometric synthetic aperture radar (DInSAR) based on L-band SAR satellites is an effective tool for estimating the dynamics of forested landslides that occur in Japan. High-temporal L-band SAR observations have been planned for the future. Thus, it is necessary to further investigate the specific advantages of high-temporal L-band SAR observations for estimating landslide dynamics. In this study, we used DInSAR data with different time windows to identify active landslides in Hokkaido, Japan. This study is the first attempt to demonstrate the advantages of high-temporal L-band SAR observations for estimating active landslide dynamics. We successfully observed the dynamics of two active landslides, Kounai-1 and Kounai-2, using DInSAR over a time window of 14 days. We present the first spatial observation of the dynamics of Kounai-1 and Kounai-2. In addition, we discuss the dynamics of Kounai-1 and Kounai-2 based on interferograms, and our results suggest that both landslides are subunits of the same landslide, called the Kounai landslide. These results indicate that high-temporal L-band SAR observations can mitigate cycle slips and enable the estimation of active landslide dynamics. Full article

Mining activities in coal mining areas have exacerbated ecological and geological environmental risks. To explore the impact of mineral resources on the ecological and geological environment risk (EGER) in coal mining areas, we developed a novel ecological and geological risk assessment framework. This framework first quantifies the impact of mining activities on the surface of coal mining areas using remote sensing interpretation and Differential Interferometric Synthetic Aperture Radar (DInSAR) technology. Then, this framework selected six indicators, including subsidence, surface occupation and damage, FVC, RSEI, precipitation, and temperatures. The weights of the evaluation indicators were calculated using a coupled weighting model combining the Analytic Hierarchy Process (AHP) and the Entropy Method (EM). This approach was applied to the Wangwa mining area to assess its ecological and geological risks. The results show that the surface subsidence increase year by year. The EGER in the study area was medium and the change rate of the EGER index in Wangwa mining area from 2017 to 2022 was −0.460 to 0.598. The EGER index increased southwest of the study area but reduced in the pre-investigation area and north of the investigation area. This study can support decision-making to reduce the adverse environmental impact of coal mining activities. Full article

Large-scale exploitation of underground mineral resources causes surface collapse, reduces land use efficiency, and brings a series of ecological and environmental problems. This is significantly important for the ecological restoration work of mining areas to accurately extract the subsidence range and depth of coal mine surface and formulate the regulation model suitable for coal mine subsidence areas. In this research, we used Differential Interferometric Synthetic Aperture Radar (D-InSAR) technology to extract the subsidence range of the Zhaogu Mining Area in Henan Province based on multi-source remote sensing data. We constructed the Spectral-Spatial Residual Network (SSRN) to classify the land use information within the subsidence range. Finally, we constructed a fuzzy comprehensive evaluation model based on the improved G1 method that assesses the extent of land damage in the subsidence area. Additionally, a suitable governance model for the subsidence area in the Zhaogu Mining Area is proposed. The results can provide technical support and data reference for the comprehensive treatment of subsidence in the Zhaogu Mining Area. Full article

The Upper Silesian coal basin (USCB) in Poland faces significant ground deformation issues resulting from mining activities conducted without backfill, which can persist for years. These activities can cause damage to surface structures and phenomena such as induced seismicity. Ground deformations can be monitored using differential synthetic aperture radar interferometry (DInSAR). However, various DInSAR approaches have their own advantages and limitations, particularly regarding accuracy and atmospheric filtering. This is especially important for high-frequency displacement signals associated with seismic activity, which can be filtered out. Therefore, this study aims to assess the detectability of mining-induced seismic events using interferometric techniques, focusing on the USCB area. In this experiment, we tested two InSAR approaches: conventional DInSAR without atmospheric filtering and the small baseline subset (SBAS) approach, where the atmospheric phase screen was estimated and removed using high-pass and low-pass filtering. The results indicate that, in most cases, post-seismic ground displacement is not detectable using both methods. This suggests that mining-related seismic events typically do not cause significant post-seismic ground displacement. Out of the 17 selected seismic events, only two were clearly visible in the DInSAR estimated deformation, while for four other events, some displacement signals could neither be definitively confirmed nor negated. Conversely, only one seismic event was clearly detectable in the SBAS displacement time series, with no evidence of induced tremors found for the other events. DInSAR proved to be more effective in capturing displacement signals compared to SBAS. This could be attributed to the small magnitude of the tremors and, consequently, the small size of the seismic sources. Throughout the investigated period, all registered events had magnitudes less than 4.0. This highlights the challenge of identifying any significant influence of low-magnitude tremors on ground deformation, necessitating further investigations. Moreover, SBAS techniques tend to underestimate mining displacement rates, leading to smoothed deformation estimates, which may render post-seismic effects invisible for events with low magnitudes. However, after an in-depth analysis of the 17 seismic events in the USCB, DInSAR was found to be more effective in capturing displacement signals compared to SBAS. This indicates the need for significant caution when applying atmospheric filtering to high-frequency displacement signals. Full article