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Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis. [ABSTRACT FROM AUTHOR]

It is challenging to precisely measure the slow interseismic crustal-deformation rate from Synthetic Aperture Radar (SAR) data. The long-wavelength orbital errors, owing to the uncertainties in satellite orbit vectors, commonly exist in SAR interferograms, which degrade the precision of the Interferometric SAR (InSAR) products and become the main barrier to extracting interseismic tectonic deformation. In this study, we propose a novel temporal-network orbital correction method that is able to isolate the far-fault tectonic deformation from the mixed long-wavelength signals based on its spatio–temporal characteristic. The proposed approach is straightforward in methodology but could effectively separate the subtle tectonic deformation from glaring orbital errors without ancillary data. Both synthetic data and real Sentinel-1 SAR images are used to validate the reliability and effectiveness of this method. The derived InSAR velocity fields clearly present the predominant left-lateral strike-slip motions of the Tuosuo Lake segment of the Kunlun fault in western China. The fault-parallel velocity differences of 5–6 mm/yr across the fault between areas ~50 km away from the fault trace are addressed. The proposed method presents a significantly different performance from the traditional quadratic approximate method in the far field. Through the implementation of the proposed method, the root mean square error (RMSE) between the LOSGPS and our derived descending InSAR LOS (line of sight) measurements is reduced to less than one-third of the previous study, suggesting its potential to enhance the availability of InSAR technology for interseismic crustal-deformation measurement. [ABSTRACT FROM AUTHOR]

The InSAR technique is known to be a powerful tool for precise monitoring of wide areas in terms of displacements. It is conceivable to also use this technique to monitor landslide areas, but geometrical distortions due to ground morphology and land cover could make InSAR processing ineffective for such applications. Because of the computational burden of InSAR processing, it is important to have preliminary knowledge about the possible suitability of the technique for the inspected area before acquiring and processing the data. This paper aims to perform a preliminary analysis of the InSAR sensitivity for the specific case of landslide monitoring. A new approach is proposed considering aspects specific to landslide displacements, which are basically tangent to the slope direction. Pre-processed coherence maps were used to account for the impact of land cover. The whole analysis can be carried out without acquiring cumbersome SAR datasets and can be used as a preliminary step. The Italian Emilia-Romagna region has been considered as the study area, with landslide areas accounting for more than 12% of its territory. The outcomes show that the inspected area has favourable morphological conditions, mainly thanks to its mild slopes and the limited number of landslides facing north, but the land cover has a strong negative impact on the InSAR sensitivity. Nevertheless, 7.5% of the landslide areas have promising conditions for monitoring using radar interferometry. [ABSTRACT FROM AUTHOR]

The bistatic Interferometric Synthetic Aperture Radar (InSAR) system can overcome the physical limitations imposed by the baseline of monostatic dual-antenna InSAR. It provides greater flexibility and can enhance elevation measurement accuracy through a well-designed baseline configuration. Unmanned aerial vehicles (UAVs) equipped with bistatic InSAR, having relatively low cost and high flexibility, are useful for mapping and land resource exploration. However, due to challenges including spatiotemporal synchronization and motion errors, there are limited reports on UAV-borne bistatic InSAR. This paper proposes a comprehensive method for processing data from small UAV-borne bistatic InSAR by integrating two-way synchronization chain signals. The proposed method includes compensation for time and phase synchronization errors, trajectory refinement with synchronized chain and Position and Orientation System (POS) data, high-precision bistatic InSAR imaging, and interferometric processing. Height inversion results based on the proposed method are also provided, which demonstrate the effectiveness of the proposed method in improving the accuracy of interferometric measurement at calibration points from 0.66 m to 0.42 m. [ABSTRACT FROM AUTHOR]

Characterizing the coseismic slip behaviors of earthquakes could offer a better understanding of regional crustal deformation and future seismic potential assessments. On 18 December 2023, an Mw 6.0 earthquake occurred on the Lajishan–Jishishan fault system (LJFS) in the northeastern Tibetan Plateau, causing serious damage and casualties. The seismogenic fault hosting this earthquake is not well constrained, as no surface rupture was identified in the field. To address this issue, in this study, we use Interferometric Synthetic Aperture Radar (InSAR) data to investigate the coseismic surface deformation of this earthquake and invert both ascending and descending line-of-sight observations to probe the seismogenic fault and its slip characteristics. The InSAR observations show up to ~6 cm surface uplift caused by the Jishishan earthquake, which is consistent with the thrust-dominated focal mechanism. A Bayesian-based dislocation modeling indicates that two fault models, with eastern and western dip orientations, could reasonably fit the InSAR observations. By calculating the coseismic Coulomb failure stress changes (∆CFS) induced by both fault models, we find that the east-dipping fault scenario could reasonably explain the aftershock distributions under the framework of stress triggering, while the west-dipping fault scenario produced a negative ∆CFS in the region of dense aftershocks. Integrating regional geological structures, we suggest that the seismogenic fault of the Jishishan earthquake, which strikes NNE with a dip of 56° to the east, may be either the Jishishan western margin fault or a secondary buried branch. The optimal finite-fault slip modeling shows that the coseismic slip was dominated by reverse slip and confined to a depth range between ~5 and 15 km. The released seismic moment is 1.61 × 1018 N·m, which is equivalent to an Mw 6.07 earthquake. While the Jishishan earthquake ruptured a fault segment of approximately 20 km, it only released a small part of the seismic moment that was accumulated along the 220 km long Lajishan–Jishishan fault system. The remaining segments of the Lajishan–Jishishan fault system still have the capability to generate moderate-to-large earthquakes in the future. [ABSTRACT FROM AUTHOR]

As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform's position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS). [ABSTRACT FROM AUTHOR]

A trend in the development of spaceborne Synthetic Aperture Radar (SAR) technology is the shift from a single-satellite repeated observation mode to a multi-satellite collaborative observation mode. However, current multi-satellite collaborative geolocation algorithms face challenges, such as geometric model mismatch and poor baseline estimation accuracy, arising from highly dynamic changes among multi-satellites. This paper introduces a high-precision and efficient geolocation algorithm for a spaceborne bistatic interferometric SAR (BiInSAR) system based on an improved range–Doppler (IRD) model. The proposed algorithm encompasses three key contributions. Firstly, a comprehensive description of the spatial baseline geometric model unique to the bistatic configuration is provided, with a specific focus on deriving the perpendicular baseline expression. Secondly, IRD geolocation functions are established to meet the specific requirements of the bistatic configuration. Then, a novel BiInSAR geolocation algorithm based on the IRD's functions is proposed, which can significantly improve the target geolocation accuracy by modifying the range–Doppler equation to suit the bistatic configuration. Meanwhile, a low-coupling parallel calculation method is proposed, which can improve the calculation speed by two to three times. Finally, the accuracy and efficiency of the algorithm are demonstrated using experimental data acquired by the TH-2 satellite, which is China's first spaceborne BiInSAR system. The experimental results prove that the IRD algorithm exhibits geolocation accuracy with an average error of less than 1 m and a standard deviation of less than 2.5 m while maintaining computational efficiency at a calculation speed of 1,429,678 pixels per second. [ABSTRACT FROM AUTHOR]

Long-distance water transfer is a critical engineering measure to rectify disparities in water resource distribution across regions. The effective operation and safety of such projects are paramount to their success, as localized issues can have cascading consequences, potentially disrupting the entire network. Conventional ground-based monitoring methods have limitations in measuring the deformation of large-scale structures. In this paper, InSAR is employed to monitor the deformation of the Shuangwangcheng (SWC) Reservoir, which features a long embankment dam as part of the South-to-North Water Diversion Project in China. We utilize data from both Sentinel-1 and TerraSAR-X satellites to derive 7-year deformation. Results reveal that the entire dam experiences continuous subsidence, with the maximum deformation in the line-of-sight direction measuring ~160 mm. While minor differential settlements are noted in different sections of the dam, the gradient is not significant due to the dam's substantial length. The InSAR deformation results from multiple geometries demonstrate good consistency, with the highest correlation observed between the Sentinel-1 ascending and descending datasets, exceeding 0.9. Validation against the GNSS observations of the three sites on the SWC Dam shows the accuracy of InSAR displacements is ~8 mm. Water level changes do impact deformation, but consolidation settlement appears to be the primary controlling factor during the monitoring period. This study underscores the potential of InSAR in long-distance water transfer projects and highlights that spatially continuous deformation is the most significant advantage. [ABSTRACT FROM AUTHOR]

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China's second largest inland river basin. It is a typical oasis-type agricultural area in northwest China's arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites' line-of-sight (LOS) direction can be up to −70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone. [ABSTRACT FROM AUTHOR]

The Intermittent Small Baseline Subset approach to Interferometric Synthetic Aperture Radar data was originally devised as a way to recover information from regions with intermittent coherence, making it particularly useful in agricultural regions or those featuring significant vegetation. However, as modern data products grow in size, the increased computational complexity that this methodology demands makes processing more daunting. Here, we present a solution: leveraging the Bifrost data processing framework and GPUs, we analyze Sentinel-1 data covering a large region of northern California and are able to achieve dramatic speed-ups on the order of 300–400 times faster than CPU-bound implementations of ISBAS, processing the entire dataset in only 5 h. [ABSTRACT FROM AUTHOR]

The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500–3700 m with slope angles of 10–30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures. [ABSTRACT FROM AUTHOR]

Land subsidence (LS) is a global phenomenon that has affected several urban centres around the world such as Jakarta (Indonesia), Mexico City (Mexico), Xi'an (China), and Iron County (US). It has mainly been attributed to anthropogenic activities such as groundwater exploitation, especially in unconsolidated aquifer systems rich in highly compressible clay and silt. The platy clay minerals rearrange into horizontal stacks after dewatering, leading to a volume change due to overburden. In this study, land subsidence is investigated in the Quetta and Mastung districts, Balochistan, Pakistan, by employing Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS), and groundwater level (GWL) variations. This study represents the first attempt in Pakistan to measure the long-term land subsidence by fusing GNSS and InSAR data for improved validity. InSAR data from the Sentinel-1 satellite in the Ascending (195 scenes) and Descending (183 scenes) tracks were used to analyse LS from December 2015 to December 2022. High-accuracy Trimble NetRS GNSS receivers were used in five locations from October 2006 to December 2022. An average subsidence ranging from 3.2 cm/y to 16 cm/y was recorded in the valley mainly due to the GWL decline and clay-rich sediments, which are prone to compaction due to dewatering. An accumulative LS of 2 m was recorded by the permanent GNSS station in central Quetta from October 2008 to January 2023 (14.2 years). An acceleration in the subsidence from 12 cm/y to 16.6 cm/y after 2016 was recorded by the continuous GNSS. Additionally, the InSAR and GNSS values were compared for validation, resulting in a good correlation between both techniques. A GWL decline ranging from 1.7 m to 6 m was recorded by the piezometers in Quetta during the period 1987–2022. Large- and small-scale fissures were observed in the study area during the surveys. These fissures are responsible for damage to the city's infrastructure and aquifer contamination. The subsidence profile also agrees with the subsurface lithology. Our assessment concludes that Quetta may be the fastest-sinking metropolitan city in Pakistan. The overexploitation of groundwater and the population explosion may be the main contributing factors for the land subsidence. [ABSTRACT FROM AUTHOR]

Although rock glaciers (RGs) are prevalent in the southwestern Pamirs, systematic studies on them are scarce. This article introduces the first inventory of RGs in the southwestern Pamirs, situated at the western edge of the High Mountain Asia region. The inventory, established through a combination of Google Earth optical imagery and Interferometric Synthetic Aperture Radar (InSAR) techniques, encompasses details on the locations, geomorphological parameters, and kinematic attributes of RGs. A total of 275 RGs were cataloged in an area of 55.52 km2 from 3620 to 5210 m in altitude. Our inventory shows that most RGs in this region are talus-connected (213 landforms), with the highest frequency facing northeast (23%). The distribution of RGs thins from west to east and is more abundant in higher altitudes. The Shakhdara range to the south hosts a denser and more active population of RGs than the Shughnon range to the north, highlighting the influence of topography and precipitation. Overall, RGs in the southwestern Pamirs exhibit high activity levels, with active RGs predominating (58%). A comparison between active and transitional RGs showed no significant differences in elevation, temperature, and slope. Glacier-connected and glacier forefield-connected RGs demonstrated higher line-of-sight (LOS) velocities than talus-connected and debris-mantled slope-connected RGs, underscoring the significant impact of precipitation and meltwater on their activity. [ABSTRACT FROM AUTHOR]

The Jing-Mi Diversion Canal is a large-scale water diversion project in Beijing. Routine monitoring is crucial for the reliability and stability of urban water supply. Compared with traditional monitoring methods, interferometric synthetic aperture radar (InSAR) has the advantages of large scale and high accuracy. Based on the small baseline subset InSAR, 187 ascending and 102 descending SAR images obtained from Sentinel-1 were used to detect the deformation along the diversion canal from 2017 to 2023. The results show that there was a sinking trend along the diversion canal. The subsidence was serious in the first half of the canal, and continued to sink from 2019 to 2020. The subsidence was alleviated in 2023. Combined with leveling measurements, the InSAR deformation monitoring results of important pumping station buildings were verified. The measurement accuracy of InSAR can reach the millimeter level. We extracted the groundwater level time series and subsidence for risky canal segments. Through pixel-by-pixel comparison, it was found that fluctuations in groundwater level would have some impact on surface deformation. Severe local subsidence or uplift deformation occasionally occurred. To ensure the safety of water diversion, the monitoring and maintenance of relevant pump station buildings in risky areas should be increased in the future. [ABSTRACT FROM AUTHOR]

Detecting surface deformation associated with low-magnitude ( M w ≤ 5 ) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the M w = 3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring. [ABSTRACT FROM AUTHOR]

The precise identification of railway subgrade defects remains a significant challenge for the railway industry globally. Due to the limitations of individual monitoring techniques, comprehensive information on subgrade defects cannot be obtained. In fact, the presence of subgrade defects can significantly increase the risk of traffic accidents during high-speed train operations, which may affect the safety of train operations and economic development. The monitoring of subgrade health status is used as a pre-disaster planning method that is urgently required to avoid accidents and guide the maintenance strategy. Therefore, a novel "integrated" holistic monitoring approach for subgrade structures is presented based on satellite remote sensing, a comprehensive inspection vehicle, and a ground-based testing technique. Additionally, the monitoring content is more clearly defined during the service life of the subgrade. The method is used to investigate the location, development trend, and the cause of subgrade defects on the Shanghai–Nanjing high-speed railway. Some new viewpoints are put forward: First, the monitoring content for assessing the health status of the subgrade should encompass the foundation settlement, the track geometry status, and the monitoring of deformation and defects within the subgrade. Second, the mileage points K235 and K299 of the subgrade, as well as K236 and K237 of the bridge–subgrade transition sections, are estimated to be locations with potential defects based on the differential InSAR and track quality index. Third, the result of settlement monitoring and ground-penetrating radar analysis illustrates that sections K235 +540 to +680 and K299 +680 to +750 are diagnosed as defect positions triggered by the rapid drop of water level and engineering activity, respectively. Fourth, the "integrated" holistic monitoring technique for subgrade service status might be expected to be a promising method that can be useful in developing maintenance plans and implementing fault recovery for railway infrastructure. [ABSTRACT FROM AUTHOR]

Subsidence prediction is essential for preventing and controlling geohazards in coal mining areas. However, the Interferometric Synthetic Aperture Radar (InSAR) technique is limited in deriving the goaf displacements with a large gradient and fast deformation rates, hindering its application for potential risk evaluation over the mining areas. In this study, we proposed a three-dimensional and full parameter inversion (TDFPI) model to derive the large-gradient subsidence and then investigate its application for building damage assessment over coal mining areas. By taking the Guotun coal mine as the case study, the TDFPI model was demonstrated to have effectively predicted the large-gradient deformation of the mining areas and successfully evaluated the house damage in Chelou village, which agrees well with our field investigations. Specifically, the predicted subsidence results were validated with high fitting accuracy against field measurements, with RMSE of 0.083 m and 0.102 m, respectively, on observation line A and line F. In addition, the classified damage levels are highly consistent with in situ field surveys for the house cracks in Chelou village, presenting its practicality and effectiveness for building damage evaluation, and thus can provide a useful tool for potential risk assessment and prevention over the mining areas. [ABSTRACT FROM AUTHOR]

Time series dynamic prediction of surface deformation in mining areas can provide reference data for coal mine safety and production, which has important impacts. The combination of interferometric synthetic aperture radar (InSAR) technology and the probability integral method (PIM) is commonly used for predicting deformation. However, most surface subsidence prediction in mining areas is based on the static PIM parameters, failing to achieve the three-dimensional (3D) dynamic deformation prediction. This paper proposed a 3D deformation dynamic prediction model (InSAR-3D-CTPIM) between InSAR deformation observations and dynamic coordinate-time PIM (CTPIM) parameters, which can realize the prediction of east–west, north–south, and vertical series deformation caused by mining. The method has been validated by simulation experiments and real experiments in the mining area of Jiansheng Coal Mine in Baisha Reservoir, Henan Province, China. The results showed that the modeling accuracy was improved by 34.3% compared to the traditional multi-rate model, and the accuracy was improved by 28.5% compared to the vertical deformation obtained by the traditional static PIM method. The InSAR-3D-CTPIM model can be used to predict the evolutionary history of basin-wide surface deformation dynamics in coal mining areas, and provide a reference for the early warning and prediction of geological hazards in coal mining areas. [ABSTRACT FROM AUTHOR]

Three-dimensional coseismic surface deformation fields are important for quantifying the geometric and kinematic characteristics of earthquake rupture faults. However, traditional geodetic techniques are constrained by intrinsic limitations: Interferometric synthetic aperture radar (InSAR) can only extract far-field deformation fields owing to incoherence; global navigation satellite systems (GNSSs) can only acquire displacement at discrete points. The recently developed optical pixel correlation technique, which is based on high-resolution remote sensing images, can acquire near-field coseismic horizontal deformation. In this study, InSAR line-of-sight (LOS) and azimuth direction far-field deformation, horizontal near-field deformation determined using optical pixel correlation based on pre- and post-earthquake GaoFen (GF)-2/7 images, and vertical deformation determined by differencing pre- and post-earthquake GF-7 digital elevation models (DEMs) were combined to comprehensively provide the three-dimensional deformation field of the 2022 Mw 6.6 Menyuan earthquake. The results show that the near-field deformation field calculated by optical pixel correlation quantified displacements distributed over the rupture fault zone, which were not available from the InSAR deformation maps. We identified significant vertical displacements of ~1–1.5 m at a bend region, which were induced by local compressive stress. The maximum uplift (>2.0 m) occurred near the epicenter, on the southern sides of the main and secondary faults along the middle segment of the ruptured Lenglongling fault. In addition, surface two-dimensional strain derived from the displacement maps calculated by optical pixel correlation revealed high strain concentration on the rupture fault zone. The method described herein provides a new tool for a better understanding of the characteristics of coseismic surface deformation and rupture patterns of faults. [ABSTRACT FROM AUTHOR]

With the operation of the world's second-largest hydropower facility, Baihetan Hydropower Station, the risk of landslide deformation has increased. To address these potential threats, we employed Interferometric Synthetic Aperture Radar (InSAR) technology for a large-scale landslide investigation and comprehensively revealed the deformation mechanisms of landslides near the dam site. Our research indicates that the alternating geological features of soft and hard rock layers are the primary causes of landslides, especially the fracturing phenomena of vast amounts of mudstone upon contact with moisture. This leads to the reservoir's left bank's dip-slope being susceptible to slip and tensional failure, while the reservoir's right bank's reverse slope is more prone to plastic flow and tensional damage. Rapid water level changes and altered rainfall patterns are key factors that trigger landslide instability. Furthermore, we also explored the relationship between fault zones, seismic activity, and landslides, particularly noting the fully coupled state of the southern end of the Daliangshan fault zone, which might further exacerbate landslide deformation. [ABSTRACT FROM AUTHOR]

The Haiyuan fault system plays a crucial role in accommodating the eastward expansion of the Tibetan Plateau (TP) and is currently slipping at a rate of several centimeters per year. However, limited seismic activities have been observed using geodetic techniques in this area, impeding the comprehensive investigation into regional tectonics. In this study, the geometric structure and source models of the 2022 Mw 6.7 and the 2016 Mw 5.9 Menyuan earthquakes were investigated using Sentinel-1A SAR images. By implementing an atmospheric error correction method, the signal-to-noise ratio of the 2016 interferometric synthetic aperture radar (InSAR) coseismic deformation field was significantly improved, enabling InSAR observations with higher accuracy. The results showed that the reliability of the source models for those events was improved following the reduction in observation errors. The Coulomb stress resulting from the 2016 event may have promoted the strike-slip movement of the western segment of the Lenglongling fault zone, potentially expediting the occurrence of the 2022 earthquake. The coseismic slip distribution and the spatial distribution of aftershocks of the 2022 event suggested that the seismogenic fault may connect the western segment of the Lenglongling fault (LLLF) and the eastern segment of the Tuolaishan fault (TLSF). Additionally, the western segment of the surface rupture zone of the northern branch may terminate in the secondary branch close to the Sunan-Qilian fault (SN-QL) strike direction, and the earthquake may have triggered deep aftershocks and accelerated stress release within the deep seismogenic fault. [ABSTRACT FROM AUTHOR]

The prediction and prevention of landslide hazard is a challenging topic involving the assessment and quantitative evaluation of several elements: geological and geomorphological setting, rainfalls, and ground motion. This paper presents the multi-approach investigation of the Nevissano landslide (Asti Province, Piedmont, NW Italy). It shows a continuous and slow movement, alongside few paroxysmal events, the last recorded in 2016. The geological and geomorphological models were defined through a field survey. An inventory of the landslide's movements and rainfall records in the period 2000–2016 was performed, respectively, through archive investigations and the application of "Moving Sum of Daily Rainfall" method, allowing for the definition of rain thresholds for the landslide activation (105 mm and 193 mm, respectively, in 3 and 30 days prior to the event). The displacements over the last 8 years (2016–2023) were monitored through an innovative in-continuum monitoring inclinometric system and Earth Observation (EO) data (i.e., relying on Interferometric Synthetic Aperture Radar, or InSAR data): it gave the opportunity to validate the rainfall thresholds previously defined. This study aims to provide information to public authorities for the appropriate management of the site. Moreover, the proposed workflow could be adopted as a guideline for investigating similar situations. [ABSTRACT FROM AUTHOR]

Surface deformation caused by an earthquake is very important to study for a better understanding of the development of geological structures and seismic hazards in an active tectonic area. In this study, we estimated the surface deformation due to an earthquake along an active blind fault using Sentinel-1 SAR data. On 24 September 2019, an earthquake with 5.6 Mw and 10 km depth stroke near Mirpur, Pakistan. The Mirpur area was highly affected by this earthquake with a huge collapse and the death of 34 people. This study aims to estimate the surface deformation associated with this earthquake in Mirpur and adjacent areas. The interferometric synthetic aperture radar (InSAR) technique was applied to study earthquake-induced surface motion. InSAR data consisting of nine Sentinel-1A SAR images from 11 August 2019 to 22 October 2019 was used to investigate the pre-, co- and post-seismic deformation trends. Time series investigation revealed that there was no significant deformation in the pre-seismic time. In the co-seismic time, strong displacement was observed and in post-seismic results, small displacements were seen due to 4.4 and 3.2 Mw aftershocks. Burst overlap interferometry and offset-tracking analysis were used for more sensitive measurements in the along-track direction. Comprehensive 3D displacement was mapped with the combination of LOS and along-track offset deformation. The major outcome of our results was the confirmation of the existence of a previously unpublished blind fault in Mirpur. Previously, this fault line was triggered during the 2005 earthquake and then it was activated on 24 September 2019. Additionally, we presented the co-seismically induced rockslides and some secondary faulting evidence, most of which occurred along or close to the pre-existing blind faults. The study area already faces many problems due to natural hazards where additional surface deformations, particularly because of the earthquake with activated blind fault, have increased its vulnerability. [ABSTRACT FROM AUTHOR]

The time-effective mapping of erosion gullies is crucial for monitoring and early detection of developing erosional progression. However, current methods face challenges in obtaining large-scale erosion gully networks rapidly due to limitations in data availability and computational complexity. This study developed a rapid method for extracting erosion gully networks by integrating interferometric synthetic aperture radar (InSAR) and the relative elevation algorithm (REA) within the Huangfuchuan Basin, a case basin in the northern Loess Plateau, China. Validation in the study area demonstrated that the proposed method achieved an F1 score of 81.94%, representing a 9.77% improvement over that of the reference ASTER GDEM. The method successfully detected small reliefs of erosion gullies using the InSAR-refined DEM. The accuracy of extraction varied depending on the characteristics of the gullies in different locations. The F1 score showed a positive correlation with gully depth (R2 = 0.62), while the fragmented gully heads presented a higher potential of being missed due to the resolution effect. The extraction results provided insights into the erosion gully networks in the case study area. A total of approximately 28,000 gullies were identified, exhibiting pinnate and trellis patterns. Most of the gullies had notable intersecting angles exceeding 60°. The basin's average depth was 64 m, with the deepest gully being 140 m deep. Surface fragmentation indicated moderate erosive activity, with the southeastern loess region showing more severe erosion than the Pisha sandstone-dominated central and northwestern regions. The method described in this study offers a rapid approach to map gullies, streamlining the workflow of erosion gully extraction and enabling efficiently targeted interventions for erosion control efforts. Its practical applicability and potential to leverage open-source data make it accessible for broader application in similar regions facing erosion challenges. [ABSTRACT FROM AUTHOR]

The Xiaojiang Basin ranks among the global regions with the highest density of geological hazards. Landslides, avalanches, and debris flows represent significant threats to the safety of residents and their properties, impeding sustainable development. This study utilized three InSAR techniques to monitor surface deformations in the basin, using the standard deviation of these measurements as a stability threshold to identify potential landslides. A systematic analysis of landslide development characteristics was then conducted. Key findings include the following: (1) The annual average deformation velocity in the basin from 2018 to 2021 ranged from −25.36 to 24.40 mm/year, identifying 212 potential landslides. (2) Deformation analysis of a typical landslide in Caizishan showed consistent detection of significant surface changes by all three InSAR methods. Seasonal deformation linked to summer rainfall exacerbates the movement in elevated landslides. (3) Landslides predominantly occur in fragile geological formations such as sandstone, mudstone, and kamacite on slopes of 20° to 40°. These landslides, typically covering less than 0.1 km2, are mostly found on barren and grassland terrains adjacent to lower debris gullies, with a relative elevation difference of under 300 m and an aspect range of 90° to 270°. A high kernel density value of 0.3 or higher was noted, with 86.8% influenced by regional tectonic activities, including fault zones. The results demonstrate that natural environmental factors primarily drive landslides in the Xiaojiang Basin, which pose significant threats to the safety of nearby residents. This study's insights and outcomes provide valuable references for safeguarding local populations, disaster prevention, and promoting regional sustainable development. [ABSTRACT FROM AUTHOR]

In mountainous regions, high-locality landslides have the characteristics of a latent disaster process with a wide disaster range, which can easily cause large casualties. Therefore, early landslide identification and dynamic stability evaluation are significant. We first used multi-temporal synthetic aperture radar data to detect potential landslides at Yezhi Site Area during the 2015–2020 period, identifying and mapping a total of 18 active landslides. The study area was found to have an average deformation rate between −15 and 10 mm/y during the period. Then, time series and spatiotemporal deformation characteristics of landslides were examined using interferogram stacking and small baseline interferometry techniques. The results show that the majority of the landslide deformations detected exhibit a periodic variation trend, and the study area was in a slow deformation state before 2017. Finally, combined with detection results, Google Earth optical images, and field investigations, it is concluded that the main factors affecting the time series deformation and spatial distribution of landslides in the study area are rainfall, geological factors, and engineering activities. The results of this study provide valuable technical references and support for early identification and dynamic stability evaluation of regional active landslides in complex terrain, especially for high-locality landslides. [ABSTRACT FROM AUTHOR]

Accurate and complete digital elevation models (DEMs) play an important fundamental role in geospatial analysis, supporting various engineering applications, human activities, and scientific research. Interferometric synthetic aperture radar (InSAR) plays an increasingly important role in DEM generation. Nonetheless, owing to its inherent characteristics, gaps often appear in regions marked by significant topographical fluctuations, necessitating an extra void-filling process. Traditional void-filling methods have operated directly on preexisting data, succeeding in relatively flat terrain. When facing mountainous regions, there will always be gross errors in elevation values. Regrettably, conventional methods have often disregarded this vital consideration. To this end, this research proposes a DEM void-filling method based on incorporating elevation outlier detection. It accounts for the detection and removal of elevation outliers, thereby mitigating the shortcomings of existing methods and ensuring robust DEM restoration in mountainous terrains. Experiments were conducted to validate the method applicability using TanDEM-X data from Sichuan, China, Hebei, China, and Oregon, America. The results underscore the superiority of the proposed method. Three traditional methods are selected for comparison. The proposed method has different degrees of improvement in filling accuracy, depending on the void status of the local terrain. Compared with the delta surface fill (DSF) method, the root mean squared error (RMSE) of the filling results has improved by 7.87% to 51.87%. The qualitative and quantitative experiments demonstrate that the proposed method is promising for large-scale DEM void-filling tasks. [ABSTRACT FROM AUTHOR]

Landslide disasters pose a significant threat, with their highly destructive nature underscoring the critical importance of timely and accurate recognition for effective early warning systems and emergency response efforts. In recent years, substantial advancements have been made in the realm of landslide recognition (LR) based on remote sensing data, leveraging deep learning techniques. However, the intricate and varied environments in which landslides occur often present challenges in detecting subtle changes, especially when relying solely on optical remote sensing images. InSAR (Interferometric Synthetic Aperture Radar) technology emerges as a valuable tool for LR, providing more detailed ground deformation data and enhancing the theoretical foundation. To harness the slow deformation characteristics of landslides, we developed the FCADenseNet model. This model is designed to learn features and patterns within ground deformation data, with a specific focus on improving LR. A noteworthy aspect of our model is the integration of an attention mechanism, which considers various monitoring factors. This holistic approach enables the comprehensive detection of landslide disasters across entire watersheds, providing valuable information on landslide hazards. Our experimental results demonstrate the effectiveness of the FCADenseNet model, with an F1-score of 0.7611, which is 9.53% higher than that of FC_DenseNet. This study substantiates the feasibility and efficacy of combining InSAR with deep learning methods for LR. The insights gained from this research contribute to the advancement of regional landslide geological hazard monitoring, identification, and prevention strategies. [ABSTRACT FROM AUTHOR]

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were −90.78 mm and −83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are −35.38 mm/y and −30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning. [ABSTRACT FROM AUTHOR]

Many previous studies have primarily focused on the use of deep learning for interferometric processing or separate recognition purposes rather than targeted measurements of detected wellpads. The present study centered around the integration of deep learning recognition and interferometric measurements for Tengiz oilfield wellpads. This study proposes the optimization, automation, and acceleration of targeted ground deformation wellpad monitoring. Mask Region-based Convolutional Neural Network (R-CNN)-based deep learning wellpad recognition and consequent Small Baseline Subset Synthetic Aperture Radar Interferometry (SBAS-InSAR) analyses were used for the assessment of ground deformation in the wellpads. The Mask R-CNN technique allowed us to detect 159 wells with a confidence level of more than 95%. The Mask R-CNN model achieved a precision value of 0.71 and a recall value of 0.91. SBAS-InSAR interferometric measurements identified 13 wells for Sentinel-1 (SNT1), 8 wells for COSMO-SkyMed (CSK), and 20 wells for TerraSAR-X (TSX) located within the −54–−40 mm/y class of vertical displacement (VD) velocity. Regression analyses for the annual deformation velocities and cumulative displacements (CD) of wells derived from SNT1, CSK, and TSX satellite missions showed a good agreement with R2 > 95. The predictions for cumulative displacements showed that the vertical subsidence processes will continue and reach −339 mm on 31 December 2023, with increasing spatial coverage and the potential to impact a higher number of wells. The hydrological analyses in the Tengiz oilfield clearly demonstrated that water flow has been moving towards the detected hotspot of subsidence and that its accumulation will increase with increasing subsidence. This detected subsidence hotspot was observed at a crossing with a seismic fault that might always be subject to reactivation. The role of this seismic fault should also be investigated as one of the ground deformation-controlling factors, even though this area is not considered seismically active. The primary practical and scientific values of these studies were identified for the operational risk assessment and maintenance needs of oilfield and gas field operators. [ABSTRACT FROM AUTHOR]

Landslides are a common geological disaster, which cause many economic losses and casualties in the world each year. Drawing up a landslide list and monitoring their deformations is crucial to prevent landslide disasters. Interferometric synthetic aperture radar (InSAR) can obtain millimeter-level surface deformations and provide data support for landslide deformation monitoring. However, some landslides are difficult to detect due to the low-coherence caused by vegetation cover in mountainous areas and the difficulty of phase unwrapping caused by large landslide deformations. In this paper, a method based on multi-direction phase gradient stacking is proposed. It employs the differential interferograms of small baseline sets to directly obtain the abnormal region, thereby avoiding the problem where part of landslide cannot be detected due to a phase unwrapping error. In this study, the Sentinel-1 satellite ascending and descending data from 2018 to 2020 are used to detect landslides around Zhouqu County, China. A total of 26 active landslides were detected in ascending data and 32 active landslides in the descending data using the method in this paper, while the SBAS-InSAR detected 19 active landslides in the ascending data and 25 active landslides in the descending data. The method in this paper can successfully detect landslides in areas that are difficult for the SBAS-InSAR to detect. In addition, the proposed method does not require phase unwrapping, so a significant amount of data processing time can be saved. [ABSTRACT FROM AUTHOR]

Slant range geometry plays a crucial role in interpreting synthetic aperture radar (SAR) observations, especially in converting line-of-sight (LOS) surface deformations to actual vertical subsidence. This paper proposes a new conversion model to retrieve vertical settlements of the embankment slopes using the geometrical parameters of the dam and the SAR sensor. The simulation results highlight the impact of slope foreshortening and heading direction of the satellite on deformation retrieval. Various SAR data with different resolutions and bands are used to analyze the model's performance, revealing a high conformity of the model with practical conversion parameters exceeding 80% for TerraSAR-X and Cosmo-SkyMed data. [ABSTRACT FROM AUTHOR]

Land subsidence, resulting from natural or human activities, is a global environmental geological disaster. The Interferometric Synthetic Aperture Radar (InSAR) time-series analysis technique offers high spatial and continuous temporal resolution, providing data and a foundation for investigating regional land subsidence and its evolution mechanism. Beijing Capital International Airport (BCIA) has experienced uneven land subsidence since 1935, together with severe fissures significantly affecting its normal operations. In this study, the time-series InSAR method was successfully applied to monitor the gradual increase in uneven local subsidence and ground fissures activity at BCIA from June 2003 to March 2023. Initially, ENVISAT-ASAR, Cosmo-SkyMed, and Sentinel-1 data were processed by time-series InSAR techniques to generate deformation rate maps and time series for the airport area. Subsequently, a comparison was made between the displacement time series from InSAR and ground leveling measurements to assess the accuracy of InSAR-derived measurements. Through a comprehensive analysis of the distribution characteristics of land subsidence at the airport, a long-standing ground fault was located within the airport was identified. A preliminary discussion on the development status of this ground fissure was carried out based on the visual interpretation of optical images. Lastly, the inducing factors and evolutionary conditions of land subsidence were discussed. This case demonstrates the applicability of InSAR technology in identifying and monitoring geological processes such as land subsidence and ground fissure activities. It provides a scientific approach to exploring and studying the causes and formation mechanisms of land subsidence and ground fissures in the Beijing Capital Airport area. [ABSTRACT FROM AUTHOR]

The endorheic Konya Basin is a vast aggradational plain in Central Anatolia, Türkiye. It occupies a significant portion of Konya Province, covering approximately 50,000 km2. The basin is subjected to intense groundwater withdrawal and extensive agricultural activities with excessive irrigation. These activities have led to human-induced hazards, such as sinkholes and regional land subsidence. Although sinkhole occurrence mainly occurs in the Karapınar area, land subsidence is primarily observed in the central sector of Konya city, with 2 million inhabitants, as well as in various parts of the basin. This study focuses on determining the extent and rate of land subsidence throughout the basin, understanding sinkhole formation, and unraveling their relationship with anthropogenic activities. For this purpose, Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 data from 2014 to 2022 was conducted to identify and assess land subsidence. We also used the land cover data and groundwater-level information to better understand the spatial and temporal patterns of land subsidence and sinkhole occurrence. Additionally, the land cover data were used to resolve spatial–temporal variations in the cultivated area and urbanization, which are the main factors governing groundwater exploitation in the region. Our study identified widespread subsidence zones with rates as high as 90 mm/y. Groundwater overexploitation to sustain extensive agricultural operations is the main cause of the high rate of land subsidence. Additionally, it was discovered that the number of sinkholes has substantially increased due to anthropogenic influences, currently amounting to as many as 660. [ABSTRACT FROM AUTHOR]

Until the Mw 6.6 Luding earthquake ruptured the Moxi section of the Xianshuihe fault (XSHF) on 5 September 2022, the region had not experienced an Mw >6 earthquake since instrumental records began. We used Global Positioning System (GPS) and Sentinel-1 interferometric synthetic aperture radar (InSAR) observations to image the coseismic deformation and constrain the location and geometry of the seismogenic fault using a Bayesian method We then present a distributed slip model of the 2022 Mw6.6 Luding earthquake, a left-lateral strike-slip earthquake that occurred on the Moxi section of the Xianshuihe fault in the southwest Sichuan basin, China. Two tracks (T26 and T135) of the InSAR data captured a part of the coseismic surface deformation with the line-of-sight displacements range from ∼−0.16 m to ~0.14 m in the ascending track and from ~−0.12 m to ~0.10 m in the descending track. The inverted best-fitting fault model shows a pure sinistral strike-slip motion on a west-dipping fault plane with a strike of 164.3°. We adopt a variational Bayesian approach and account for the uncertainties in the fault geometry to retrieve the distributed slip model. The inverted result shows that the maximum slip of ~1.82 m occurred at a depth of 5.3 km, with the major slip concentrated within depths ranging from 0.9–11 km. The InSAR-determined moment is 1.3 × 1019 Nm, with a shear modulus of 30 GPa, equivalent to Mw 6.7. The published coseismic slip models of the 2022 Luding earthquake show apparent differences despite the use of similar geodetic or seismic observations. These variations underscore the uncertainty associated with routinely performed source inversions and their interpretations for the underlying fault model. [ABSTRACT FROM AUTHOR]

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas. [ABSTRACT FROM AUTHOR]

Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose a method combining stacked autoencoder (SAE) and convolutional neural network (CNN) to classify InSAR time series and ease the interpretation of movements. The InSAR time series are classified into five categories, including stable, linear, accelerating, deceleration, and phase unwrapping error (PUE). The accuracy of labeled samples reaches 95.1%, reflecting the performance of the proposed method. This method was applied to the InSAR results for Kunming extracted from 171 ascending Sentinel-1 images from January 2017 to September 2022. The classification map of the InSAR time series shows that stable coherent points dominate around 79.28% of the area, with linear patterns at 10.70%, decelerating at 5.30%, accelerating at 4.72%, and PUE patterns at 3.60%. The results demonstrate that this method can distinguish different ground motion features and detect nonlinear deformation signals on a large scale without human intervention. [ABSTRACT FROM AUTHOR]

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from −73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR. [ABSTRACT FROM AUTHOR]

Affected by geological factors, the scale of surface deformation in a hilly semi-desertification mining area varies. Meanwhile, there is certain dense vegetation on the ground, so it is difficult to construct a high-precision and complete image of a subsidence basin by using a single monitoring method, and hence the laws of the deformation and inversion of mining parameters cannot be known. Therefore, we firstly propose conducting collaborative monitoring by using InSAR (Interferometric Synthetic Aperture Radar), UAV (unmanned aerial vehicle), and 3DTLS (three-dimensional terrestrial laser scanning). The time-series complete surface subsidence basin is constructed by fusing heterogeneous data. In this paper, SBAS-InSAR (Small Baseline Subset) technology, which has the characteristics of reducing the time and space discorrelation, is used to obtain the small-scale deformation of the subsidence basin, oblique photogrammetry and 3D-TLS with strong penetrating power are used to obtain the anomaly and large-scale deformation, and the local polynomial interpolation based on the weight of heterogeneous data is used to construct a complete and high-precision subsidence basin. Compared with GNSS (Global Navigation Satellite System) monitoring data, the mean square errors of 1.442 m, 0.090 m, 0.072 m are obtained. The root mean square error of the high-precision image of the subsidence basin data is 0.040 m, accounting for 1.4% of the maximum subsidence value. The high-precision image of complete subsidence basin data can provide reliable support for the study of surface subsidence law and mining parameter inversion. [ABSTRACT FROM AUTHOR]

Glacial Lake Outburst Flood (GLOF) events, particularly prevalent in Asia's High Mountain regions, pose a significant threat to downstream regions. However, limited understanding of triggering mechanisms and inadequate observations pose significant barriers for early warnings of impending GLOFs. The 2018 Nyalam GLOF event in southern Tibet offers a valuable opportunity for retrospective analysis. By combining optical and radar remote sensing images, meteorological data, and seismicity catalogs, we examined the spatiotemporal evolution, triggering factors, and the outburst mechanism of this event. Our analysis reveals a progressive retreat of 400–800 m for the parent glaciers between 1991 and 2018, increasing the runoff areas at glacier termini by 167% from 2015 to 2018 and contributing abundant meltwater to the glacial lake. In contrast, the lake size shrunk, potentially due to a weakening moraine dam confirmed by SAR interferometry, which detected continuous subsidence with a maximum line-of-sight (LOS) rate of ~120 mm/a over the preceding ~2.5 years. Additionally, temperature and precipitation in 2018 exceeded the prior decade's average. Notably, no major earthquakes preceded the event. Based on these observations, we propose a likely joint mechanism involving high temperatures, heavy precipitation, and dam instability. An elevated temperature and precipitation accelerated glacial melt, increasing lake water volume and seepage through the moraine dam. This ultimately compromised dam stability and led to its failure between 3 August 2018 and 6 August 2018. Our findings demonstrate the existence of precursory signs for impending GLOFs. By monitoring the spatiotemporal evolution of environmental factors and deformation, it is possible to evaluate glacial lake risk levels. This work contributes to a more comprehensive understanding of GLOF mechanisms and is of significant importance for future glacial lake risk assessments. [ABSTRACT FROM AUTHOR]

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research. [ABSTRACT FROM AUTHOR]

In the context of global warming, the accelerated degradation of circum-Arctic permafrost is releasing a significant amount of carbon. InSAR can indirectly reflect the degradation of permafrost by monitoring its deformation. This study selected three typical permafrost regions in North America: Alaskan North Slope, Northern Great Bear Lake, and Southern Angikuni Lake. These regions encompass a range of permafrost landscapes, from tundra to needleleaf forests and lichen-moss, and we used Sentinel-1 SAR data from 2018 to 2021 to determine their deformation. In the InSAR process, due to the prolonged snow cover in the circum-Arctic permafrost, we used only SAR data collected during the summer and applied a two-stage interferogram selection strategy to mitigate the resulting temporal decorrelation. The Alaskan North Slope showed pronounced subsidence along the coastal alluvial plains and uplift in areas with drained thermokarst lake basins. Northern Great Bear Lake, which was impacted by wildfires, exhibited accelerated subsidence rates, revealing the profound and lasting impact of wildfires on permafrost degradation. Southern Angikuni Lake's lichen and moss terrains displayed mild subsidence. Our InSAR results indicate that more than one-third of the permafrost in the North American study area is degrading and that permafrost in diverse landscapes has different deformation patterns. When monitoring the degradation of large-scale permafrost, it is crucial to consider the unique characteristics of each landscape. [ABSTRACT FROM AUTHOR]

The ionospheric effects in repeat-pass SAR interferometry (InSAR) have become a rising concern with the increasing interest in low-frequency SAR. The ionosphere will introduce serious phase errors in the interferogram, which should be properly corrected. In this paper, the influence of the wavenumber shift on the Range Split-Spectrum (RSS) method is analyzed quantitatively. It is shown that the split-spectrum processing deteriorates the coherence of the sub-band interferogram and then greatly reduces the estimation accuracy. The RSS method combined with common band filtering (CBF) can improve the coherence of sub-band interferograms and estimation accuracy, but the estimation is biased due to the RSS model mismatch. To address the problem, a modified truncated singular value decomposition (MTSVD) based multi-sub-band RSS method is proposed in this paper. The proposed method divides the range common spectrum into multiple sub-bands to jointly estimate the ionospheric phase. The performance of the proposed method is analyzed and validated based on simulation experiments. The results show that the proposed method has stronger robustness and higher accuracy. [ABSTRACT FROM AUTHOR]

Climate warming can lead to permafrost degradation, potentially resulting in slope failures such as retrogressive thaw slumps (RTSs). The formation of and changes in RTSs could exacerbate the degradation of permafrost and the environment in general. The mechanisms of RTS progression and the potential consequences on the analogous freeze–thaw cycle are not well understood, owing partly to necessitating field work under harsh conditions and with high costs. Here, we used multi-source remote sensing and field surveys to quantify the changes in an RTS on Eboling Mountain in the Qilian Mountain Range in west-central China. Based on optical remote sensing and SBAS-InSAR measurements, we analyzed the RTS evolution and the underlying drivers, combined with meteorological observations. The RTS expanded from 56 m2 in 2015 to 4294 m2 in 2022, growing at a rate of 1300 m2/a to its maximum in 2018 and then decreasing. Changes in temperature and precipitation play a dominant role in the evolution of the RTS, and the extreme weather in 2016 may also be a primary contributor to the accelerated growth, with an average deformation of −8.3 mm during the thawing period, which decreased slope stability. The RTS evolved more actively during the thawing and early freezing process, with earthquakes having potentially contributed further to RTS evolution. We anticipate that the rate of RTS evolution is likely to increase in the coming years. [ABSTRACT FROM AUTHOR]

To fully comprehend the patterns of land and ecological damage caused by coal mining subsidence, and to scientifically carry out ecological mine restoration and management, it is urgent to accurately grasp the information of coal mining, particularly in complex coaling areas, such as North Anhui, China. In this paper, a space–air–ground collaborative monitoring system was constructed for coal mining areas based on multi-source remote sensing data and subsidence characteristics of coaling areas were investigated in North Anhui. It was found that from 2019 to 2022, 16 new coal mining subsidence areas were found in northern Anhui, with the total area increasing by 8.1%. In terms of land use, water areas were increased by 101.9 km2 from 2012 to 2022, cultivated land was decreased by 99.3 km2, and residence land was decreased by 11.8 km2. The depth of land subsidence in the subsidence areas is divided into 307.9 km2 of light subsidence areas with a subsidence depth of less than 500 mm; 161.8 km2 of medium subsidence areas with a subsidence depth between 500 mm and 1500 mm; and 281.2 km2 of heavy subsidence areas with a subsidence depth greater than 1500 mm. The total subsidence governance area is 191.2 km2, accounting for 26.5% of the total subsidence area. From the perspective of prefecture-level cities, the governance rate reaches 51.3% in Huaibei, 10.1% in Huainan, and 13.6% in Fuyang. The total reclamation area is 68.8 km2, accounting for 34.5% of the subsidence governance area. At present, 276.1 km2 within the subsidence area has reached stable subsidence conditions, mainly distributed in the Huaibei mining area, which accounts for about 60% of the total stable subsidence area. [ABSTRACT FROM AUTHOR]

On 28 December 2020, the area of the city of Petrinja was hit by two strong earthquakes of magnitudes 5.0 and 4.7 on the Richter scale, and the following day, 29 December 2020, the same area was hit by an even stronger earthquake of magnitude 6.2. It was one of the two strongest instrumentally recorded earthquakes that hit the territory of the Republic of Croatia in the last hundred years, and the strongest earthquake in the Banovina area after the great earthquake in 1909. Increased seismic activity in this area is caused by two vertical strike–slip faults, Pokupski and Petrinjski. This article aims to determine the displacements of the Earth's crust caused by seismic activity in this area using GNSS measurements and InSAR techniques and comparing their results. Our study showed that horizontal coseismic displacements of 20 cm and more were limited to a radius of 20 km from the epicenter, with a maximum displacement of around half a meter. Considering the original plate tectonic movements of the region and the time elapsed since the previous earthquake of similar magnitude, the geodynamic movements of the Dinarides area are in substantial part sudden displacements associated with earthquakes. [ABSTRACT FROM AUTHOR]

The increasing incidence of retrogressive thaw slumps (RTSs) in permafrost regions underscores the need for detailed spatial and temporal analysis using InSAR technology to monitor and predict dynamic changes in the process of RTSs. Nevertheless, current InSAR deformation forecasting methods employing deep learning strategies such as the traditional long short-term memory (LSTM) and recent transformer models encounter difficulties in effectively capturing temporal features. Moreover, they are limited in their ability to directly integrate spatial information. In this paper, an innovative deep learning approach named Spacetimeformer is proposed for predicting medium- and short-term InSAR deformation of RTSs in the Chumar River area. This method employs a transformer architecture with a spatiotemporal attention mechanism, which enhances the long-term prediction capabilities of time series models and dynamic spatial modeling. It is applicable to multivariate InSAR spatiotemporal deformation prediction problems. The findings include a list of 72 RTSs compiled based on derived InSAR deformation maps and Sentinel-2 optical images, of which 64 have an average deformation rate exceeding 10 mm/year, indicating signs of permafrost degradation. The density distribution of the displacement maps predicted by the Spacetimeformer model aligned well with the InSAR deformation maps obtained from the small baseline subset (SBAS) method, with the overall prediction deviation controlled within 20 mm. In addition, the point-scale prediction results were compared with LSTM and transformer models. This study indicates that the Spacetimeformer network achieved good results in predicting the deformation of RTSs, with a root mean square error of 1.249 mm. The Spacetimeformer method for deformation prediction with the spacetime mechanism presented in this study can serve as a general framework for multivariate deformation prediction based on InSAR results. It can also quantitatively assess the spatial deformation characteristics and deformation trends of RTSs. [ABSTRACT FROM AUTHOR]

The Austin metropolitan area has experienced unprecedented economic and population growth over the past two decades. This rapid growth is leading communities to settle in areas susceptible to landslides, necessitating a comprehensive analysis of landslide risks and the development of early warning systems. This could be accomplished with better confidence for slow-moving landslides, whose occurrences could be forecasted by monitoring precursory ground displacement. This study employed a combination of ground- and satellite-based observations and techniques to assess the kinematics of slow-moving landslides and identify the controlling and triggering factors that contribute to their occurrence. By closely examining landslide events in the Shoal Creek area, potential failure modes across the study area were inferred. The findings revealed that landslide-prone areas are undergoing creep deformation at an extremely slow rate (up to −4.29 mm/yr). These areas lie on moderate to steep slopes (>22°) and are predominantly composed of clay-rich units belonging to the Del Rio and Eagle Ford formations. Based on the incidents at Shoal Creek, episodes of intense rainfall acting on the landslide-prone areas are determined to be the main trigger for landslide processes in the region. [ABSTRACT FROM AUTHOR]

Beijing, China's capital city, has experienced decades of severe land subsidence due to the long-term overexploitation of groundwater. The implementation of the South-to-North Water Diversion Project (SNWDP) and artificial ecological restoration have significantly changed Beijing's hydro-ecological and geological environment in recent years, leading to a widespread rise in groundwater levels. However, whether the related land subsidence has slowed down or reversed under these measures has not yet been effectively monitored and quantitatively analyzed in terms of time and space. Accordingly, in this study, we proposed using an improved time-series deformation method, which combines persistent scatterers and distributed scatterers, to process Sentinel-1 images from 2015 to 2022 in the Beijing Plain region. We performed a geospatial analysis to gain a better understanding of how the new hydrological conditions changed the pattern of deformation on the Beijing Plain. The results indicated that our combined PS and DS method provided more measurements both in total quantity and spatial density than conventional PSI methods. The land subsidence in the Beijing Plain area has been effectively alleviated from a subsidence region of approximately 1377 km2 in 2015 to only approximately 78 km2 in 2022. Ecological restoration areas in the northeastern part of the Plain have even rebounded over this period, at a maximum of approximately 40 mm in 2022. The overall pattern of ground deformation (subsidence and uplift) is negatively correlated with changes in the groundwater table (decline and rise). Local deformation is controlled by the thickness of the compressible layer and an active fault. The year 2015, when anthropogenic water transfers were eliminated and ecological measures to recharge groundwater were implemented, was the crucial turning point of the change in the deformation trend in the subsidence history of Beijing. Our findings carry significance, not only for China, but also for other areas where large-scale groundwater extractions are causing severe ground subsidence or rebound. [ABSTRACT FROM AUTHOR]

As a city undergoes large-scale construction and expansion, there is an urgent need to monitor the stability of the ground and infrastructure. The time-series InSAR technique is an effective tool for measuring surface displacements. However, interpreting these displacements in a built environment, where observed displacements consist of mixed signals, poses a challenge. This study uses principal component analysis (PCA) and the k-means clustering method for exploring deformation series within an unsupervised learning context. The PCA method extracts the dominant components in deformation series, whereas the clustering method identifies similar deformation series. This method was tested on Kunming City (KMC) using C-band Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 PALSAR-2 data acquired between 2017 to 2022. The experiment demonstrated that the suggested unsupervised learning approach can group PS points with similar kinematic characteristics. Five types of deformation kinematic characteristics were discovered in the three SAR datasets: upward, slight upward, stability, slight downward, and downward. According to the results, less than 20% of points exhibit significant motion trends, whereas 50% show small velocity values but still demonstrate movement trends. The remaining 30% are relatively stable. Similar clustering results were obtained from the three datasets using unsupervised methods, highlighting the effectiveness of identifying spatial–temporal patterns over the study area. Moreover, It was found that clustering based on kinematic characteristics enhances the interpretation of InSAR deformation, particularly for points with small deformation velocities. Finally, the significance of PCA decomposition in interpreting InSAR deformation was discussed, as it can better represent series with noise, enabling their accurate identification. [ABSTRACT FROM AUTHOR]