InSAR Time Series Research Articles

Refined Coseismic Slip and Afterslip Distributions of the 2021 Mw 6.1 Yangbi Earthquake Based on GNSS and InSAR Observations

On 21 May 2021, an Mw 6.1 earthquake occurred in Yangbi County, Dali Bai Autonomous Prefecture, Yunnan Province, with the epicenter located in an unmapped blind fault approximately 7 km west of the Weixi-Qiaohou fault (WQF) on the southeastern margin of the Qinghai–Tibetan Plateau. While numerous studies have been conducted to map the coseismic slip distribution by using the Global Navigation Satellite System (GNSS), Interferometric Synthetic Aperture Radar (InSAR) and seismic data as well as their combinations, the understanding of deformation characteristics during the postseismic stage remains limited, mostly due to the long revisiting time interval and large uncertainty of most SAR satellites. In this study, we refined coseismic slip and afterslip distributions with nonlinear inversions for both fault geometry and relaxation time. First, we determined the fault geometry and coseismic slip distribution of this earthquake by joint inversion for coseismic offsets in the line-of-sight (LOS) direction of both Sentinel-1A/B ascending and descending track images and GNSS data. Then, the descending track time series of Sentinel-1 were further fitted using nonlinear least squares to extract the coseismic and postseismic deformations. Finally, we obtained the refined coseismic slip and afterslip distributions and investigated the spatiotemporal evolution of fault slip by comparing the afterslip with aftershocks. The refined coseismic moment magnitude, which was of Mw 6.05, was smaller than Mw 6.1 or larger, which was inferred from our joint inversion and previous studies, indicating a significant reduction in early postseismic deformation. In contrast, the afterslip following the mainshock lasted for about six months and was equivalent to a moment release of an Mw 5.8 earthquake. These findings not only offer a novel approach to extracting postseismic deformation from noisy InSAR time series but also provide valuable insights into fault slip mechanisms associated with the Yangbi earthquake, enhancing our understanding of seismic processes.

Deformation Monitoring and Analysis of Baige Landslide (China) Based on the Fusion Monitoring of Multi-Orbit Time-Series InSAR Technology.

Due to the limitations inherent in SAR satellite imaging modes, utilizing time-series InSAR technology to process single-orbit satellite image data typically only yields one-dimensional deformation information along the LOS direction. This constraint impedes a comprehensive representation of the true surface deformation of landslides. Consequently, in this paper, after the SBAS-InSAR and PS-InSAR processing of the 30-view ascending and 30-view descending orbit images of the Sentinel-1A satellite, based on the imaging geometric relationship of the SAR satellite, we propose a novel computational method of fusing ascending and descending orbital LOS-direction time-series deformation to extract the landslide's downslope direction deformation of landslides. By applying this method to Baige landslide monitoring and integrating it with an improved tangential angle warning criterion, we classified the landslide's trailing edge into a high-speed, a uniform-speed, and a low-speed deformation region, with deformation magnitudes of 7~8 cm, 5~7 cm, and 3~4 cm, respectively. A comparative analysis with measured data for landslide deformation monitoring revealed that the average root mean square error between the fused landslide's downslope direction deformation and the measured data was a mere 3.62 mm. This represents a reduction of 56.9% and 57.5% in the average root mean square error compared to the single ascending and descending orbit LOS-direction time-series deformations, respectively, indicating higher monitoring accuracy. Finally, based on the analysis of landslide deformation and its inducing factors derived from the calculated time-series deformation results, it was determined that the precipitation, lithology of the strata, and ongoing geological activity are significant contributors to the sliding of the Baige land-slide. This method offers more comprehensive and accurate surface deformation information for dynamic landslide monitoring, aiding relevant departments in landslide surveillance and management, and providing technical recommendations for the fusion of multi-orbital satellite LOS-direction deformations to accurately reconstruct the true surface deformation of landslides.

Landslide displacement prediction using time series InSAR with combined LSTM and TCN: application to the Xiao Andong landslide, Yunnan Province, China

AbstractResearch on landslide displacement prediction based on interferometric synthetic aperture radar (InSAR) deformation data involves two main issues. First, InSAR can provide only one-dimensional deformation data along the satellite’s line of sight (LOS), which cannot truly reflect the deformation of the landslide body in the downward direction along the slope. Second, the use of a single prediction model does not adequately account for both long-term and local changes in landslide displacement, affecting the accuracy of the predictions. To address this, in this study, Long Short-Term Memory networks (LSTM) and temporal convolutional network (TCN) models are combined to construct a method (LSTM-TCN) of landslide displacement prediction. This method can consider the long-term and localized changes in landslide displacement. The method is first based on InSAR technology to obtain surface deformation. The deformation of the landslide is subsequently computed in the downward direction along the slope to obtain the landslide displacement time series data. Next, the LSTM-TCN is used for landslide displacement prediction. Finally, the mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) are used to evaluate the performance of the model. The experiment is conducted on the Xiao Andong landslide in Anshi village, Fengqing County, Lincang City, Yunnan Province, China. The LSTM-TCN model achieves an R2 of 0.75, an RMSE of 0.43 cm, and an MAE of 0.36 cm. Compared with the individual LSTM and TCN models, the LSTM-TCN model exhibits the highest prediction accuracy and the smallest prediction error, which is closer to the true result that in the other models. These results demonstrate that the combined LSTM-TCN model effectively captures the complex features and long-term trends in landslide displacement data, significantly enhancing the accuracy of predictions.

Characterizing the evolution of the Daguangbao landslide nearly 15 years after the 2008 Wenchuan earthquake by InSAR observations

The Daguangbao landslide (DGBL), the largest landslide triggered by the 2008 Ms 8.0 Wenchuan earthquake, has received much attention, but its long-term post-earthquake evolution and driving force of activity are still poorly understood. As the evolutionary behavior of the DGBL is complicated by the influence of mainshock, aftershocks and rainfall, it is of great significance to study the dynamics of the landslide. In this study, a systematic and comprehensive framework for assessing the long-term stability and risk of co-seismic landslides was proposed. Based on ALOS-1 and Sentinel-1 data, time-series InSAR technology was used to reveal the nearly 15-year post-seismic evolution characteristics of the DGBL at different stages, followed by the prediction of the stabilization time, the estimation of the landslide thickness and risk assessment. The first stage was identified as three years after the earthquake (2008–2011). During this stage, ALOS-1 results show that the deformation of DGBL was intense (300 mm/year) with uneven spatial distribution, and an aftershock (Ms 5.3), along with increased rainfall, triggered its acceleration in 2009. The second stage was the period from 2014 to 2022. For this stage, we used the mass conservation approach to invert the thickness of the DGBL, revealing that a new sliding surface and thickness center had formed following the co-seismic failure in 2008. Sentinel-1 time series results indicated that the DGBL remains active even 15 years after the Wenchuan earthquake, but the deformation of DGBL has significantly slowed down (50 mm/year). The stabilization time for different segments of DGBL was predicted to range from 2027 to 2040 according to an exponential model. Beyond the overall trend of recovery, seasonal movements (including localized acceleration in 2021) closely related to rainfall remained evident, but the impact of aftershocks on the DGBL was severely weakened over time. UAV and field survey results suggested that the risk of localized debris flows at DGBL still exists. Our study improves our understanding of the long-term evolutionary pattern of DGBL and provides an important reference for post-earthquake landslide risk assessment and disaster prevention.

Identification of Potential Landslides in the Gaizi Valley Section of the Karakorum Highway Coupled with TS-InSAR and Landslide Susceptibility Analysis

Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment.

Landslide Susceptibility Assessment Based on Multisource Remote Sensing Considering Inventory Quality and Modeling

The completeness of landslide inventories and the selection of evaluation models significantly impact the accuracy of landslide susceptibility assessments. Conventional field geological survey methods and single remote-sensing technology struggle to reliably identify landslides under complex environmental conditions. Moreover, prevalent landslide susceptibility evaluation models are often plagued by issues such as subjectivity and overfitting. Therefore, we investigated the uncertainty in susceptibility modeling from the aspects of landslide inventory quality and model selection. The study focused on Luquan County in Yunnan Province, China. Leveraging multisource remote-sensing technologies, particularly emphasizing optical remote sensing and InSAR time-series deformation detection, the existing historical landslide inventory was refined and updated. This updated inventory was subsequently used to serve as samples. Nine evaluation indicators, encompassing factors such as distance to faults and tributaries, lithology, distance to roads, elevation, slope, terrain undulation, distance to the main streams, and average annual precipitation, were selected on the basis of the collation and organization of regional geological data. The information value and two coupled machine-learning models were formulated to evaluate landslide susceptibility. The evaluation results indicate that the two coupled models are more appropriate for susceptibility modeling than the single information value (IV) model, with the random forest model optimized by genetic algorithm in Group I2 exhibiting higher predictive accuracy (AUC = 0.796). Furthermore, comparative evaluation results reveal that, under equivalent model conditions, the incorporation of a remote-sensing landslide inventory significantly enhances the accuracy of landslide susceptibility assessment results. This study not only investigates the impact of landslide inventories and models on susceptibility outcomes but also validates the feasibility and scientific validity of employing multisource remote-sensing technologies in landslide susceptibility assessment.

Multidisciplinary assessment of seasonal ground displacements at the Hatfield Moors gas storage site in a peat bog landscape

The study aims to analyse ground displacement conditions observed over an Underground Gas Storage (UGS) site located at Hatfield Moors (United Kingdom), with a focus on understanding its implications for decarbonization efforts. The location serves as an active onshore storage site and was used as an analogy to assess ground motion implications around Carbon Capture and Storage (CCS) by the British Geological Survey (BGS) as part of the SENSE (Assuring integrity of CO2 storage sites through ground surface monitoring) project. Given the value of continuous and real-time monitoring of ground movements induced by gas storage activities, the study leverages satellite Interferometric Synthetic Aperture Radar (InSAR) data to assess the environmental impact of UGS operations. Using free and open-source Sentinel-1 satellite data, ground motion patterns over Hatfield Moors are analysed, highlighting displacements ranging from − 5.0 to -10.0 mm/year within the peat bog. In addition, the Time Series (TS) of ground displacement from January 2018 to December 2022 reveals a seasonality in ground motion, with uplift observed in late winter and subsidence in late summer, showing a periodicity of approximately 1 year and a magnitude of +/-10.0 mm. Through in-depth analysis, the study highlights the need to understand the underlying causes of ground fluctuations at gas storage sites. This paper shows that InSAR has the versatility to integrate seamlessly with different monitoring tools and methodologies, opening avenues for comprehensive and holistic analyses. Cross-correlation analyses further elucidate temporal relationships between different datasets by evaluating InSAR time series, UGS injection/withdrawal data and piezometric data. This involves decomposing the TS into distinct components, including trend, seasonality and residuals. The case of Hatfield Moors shows a significant discrepancy between the UGS data and the InSAR TS, while also demonstrating a clear correlation between the groundwater data and the InSAR TS. By integrating insights from geology, hydrology and remote sensing technologies, the study navigates the complexities inherent in areas of overlapping phenomena. Accurate interpretation is essential for informed decision making, particularly at sites such as Hatfield Moors, where the convergence of natural peat motion and storage operations highlights the need for interdisciplinary analysis to understand the underlying causes of ground fluctuation.

Internal deformation of the North Andean Sliver in Ecuador-southern Colombia observed by InSAR

Summary In the Northern Andes, partitioning of oblique subduction of the Nazca plate beneath the South American continent induces a northeastward motion of the North Andean Sliver. The strain resulting from this motion is absorbed by crustal faults, which have produced magnitude 7 + earthquakes historically in the Andean Cordillera of Ecuador and southern Colombia. In order to quantify the strain in that area, we derive a high-resolution surface velocity map using InSAR time-series processing. We analyzed 6 to 8 years of Sentinel-1 data and combined different satellite line-of-sight directions to produce a reliable velocity map in the East direction. We use interpolated GNSS data to express the velocity map with respect to Stable South America and remove the long-wavelength pattern due to the post-seismic deformation following the 2016 Mw 7.8 Pedernales earthquake. The InSAR velocity map finds high E-W shortening strain rates along N-S trending structures within the Western Cordillera and the Interandean valley, with little deformation taking place east of them. This result strengthens the previous proposition of a ∼350 km long Quito-Latacunga tectonic block, forming a restraining bend in the overall right-lateral strike-slip fault system accommodating the northeastward escape motion of the North Andean Sliver. However, the high spatial resolution provided by InSAR indicates that previously proposed boundaries for this block need to be revised. In particular, InSAR results highlight high strain rate (>300 nstrain/yr) along undescribed active structures, south and west of the proposed limits for the Quito-Latacunga block, respectively in Peltetec and Ibarra regions. Interestingly, the two areas with the largest strain rates spatially correlate with the proposed areas of large historical earthquakes. Modeling of the InSAR and GNSS velocities in these areas suggests shallow coupling and high slip rates on structures which, previously, were not identified as active. We also demonstrate a slow-down of the shallow aseismic slip on the Quito fault after the Pedernales earthquake, suggesting that stress changes following large megathrust events might trigger transient slip behaviors on crustal faults. The high-resolution strain map provided by this work provides a new basis for future tectonic models in the Ecuadorian and southern Colombian Andes, and will contribute to the seismic hazard assessment in this highly populated area of the Andes.

Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring

Monitoring displacement at transport infrastructure using Sentinel‑1 Interferometric Synthetic Aperture Radar (InSAR) faces challenges due to the sensor’s medium spatial resolution, which limits the pixel coverage over the infrastructure. Therefore, carefully selecting coherent pixels is crucial to achieve a high density of reliable measurement points and to minimize noisy observations. This study evaluates the effectiveness of various pixel selection methods for displacement monitoring within transport infrastructures. We employ a two-step InSAR time series processing approach. First, high-quality first-order pixels are selected using temporal phase coherence (TPC) to estimate and correct atmospheric contributions. Then, a combination of different pixel selection methods is applied to identify coherent second-order pixels for displacement analysis. These methods include amplitude dispersion index (ADI), TPC, phase linking coherence (PLC), and top eigenvalue percentage (TEP), targeting both point-like scatterer (PS) and distributed scatterer (DS) pixels. Experiments are conducted in two case studies: one in Germany, characterized by dense vegetation, and one in Spain, with sparse vegetation. In Germany, the density of measurement points was approximately 30 points/km², with the longest segment of the infrastructure without any coherent pixels being 2.8 km. In Spain, the density of measurement points exceeded 500 points/km², with the longest section without coherent pixels being 700 meters. The results indicate that despite the challenges posed by medium-resolution data, the sensor is capable of providing adequate measurement points when suitable pixel selection methods are employed. However, careful consideration is necessary to exclude noisy pixels from the analysis. The findings highlight the importance of choosing a proper method tailored to infrastructure characteristics. Specifically, combining TPC and PLC methods offers a complementary set of pixels suitable for displacement measurements, whereas ADI and TEP are less effective in this context. This study demonstrates the potential of Sentinel‑1 InSAR for capturing both regional-scale and localized displacements at transport infrastructure.

Evolution Pattern of Land Subsidence Using InSAR Time-Series Analysis in Bangkapi, Bangkok, Thailand

Subsidence in Bangkok has occurred continuously for a long time. This research applied radar satellite imagery (Sentinel-1 101 images, 2017–2022) to track land subsidence using the time series INSAR technique. The study area was in the Bang Kapi area in Bangkok, which has a high level of cumulative subsidence. The pattern of cumulative subsidence change was analyzed using the standard deviation ellipse (SDE) technique. The results showed that the Bang Kapi area had the highest cumulative subsidence in 2019 (-125.31 mm). The movement of the subsidence center changed every year in different directions with three patterns identifiable. The first pattern showed the evolution of subsidence in a northeast-southwest direction, the second pattern showed subsidence evolution in a northwest-southeast direction, and the third pattern presented evolution that did not appear to be in any specific direction. The size of the subsidence area in 2017 and 2022 increased, whereas between 2018 and 2021, the subsidence area decreased compared to other years during the research period.

A Novel Dataset Replenishment Strategy Integrating Time-Series InSAR for Refined Landslide Susceptibility Mapping in Karst Regions

The accuracy of landslide susceptibility mapping is influenced by the quality of sample data, factor systems, and assessment methods. This study aims to enhance the representativeness and overall quality of the sample dataset through an effective sample expansion strategy, achieving greater precision and reliability in the landslide susceptibility model. An integrated interpretative framework for landslide susceptibility assessment is developed using the XGBoost-SHAP-PDP algorithm to deeply investigate the key contributing factors of landslides in karst areas. Firstly, 17 conditioning factors (e.g., surface deformation rate, land surface temperature, slope, lithology, and NDVI) were introduced based on field surveys, satellite imagery, and literature reviews, to construct a landslide susceptibility conditioning factor system in line with karst geomorphology characteristics. Secondly, a sample expansion strategy combining the frequency ratio (FR) with SBAS-InSAR interpretation results was proposed to optimize the landslide susceptibility assessment dataset. The XGBoost algorithm was then utilized to build the assessment model. Finally, the SHAP and PDP algorithms were applied to interpret the model, examining the primary contributing factors and their influence on landslides in karst areas from both global and single-factor perspectives. Results showed a significant improvement in model accuracy after sample expansion, with AUC values of 0.9579 and 0.9790 for the training and testing sets, respectively. The top three important factors were distance from mining sites, lithology, and NDVI, while land surface temperature, soil erosion modulus, and surface deformation rate also significantly contributed to landslide susceptibility. In summary, this paper provides an in-depth discussion of the effectiveness of LSM in predicting landslide occurrence in complex terrain environments. The reliability and accuracy of the landslide susceptibility assessment model were significantly improved by optimizing the sample dataset within the karst landscape region. In addition, the research results not only provide an essential reference for landslide prevention and control in the karst region of Southwest China and regional central engineering construction planning but also provide a scientific basis for the prevention and control of geologic hazards globally, showing a wide range of application prospects and practical significance.

Ground subsidence in major Philippine metropolitan cities from 2014 to 2020

Land subsidence is recognized as one of the hazards that threaten Metro Manila, Philippines, and other major urban areas worldwide. It has become a significant global issue caused by excessive groundwater extraction, rapid urbanization, and natural sediment compaction, exacerbated by climate change through rising sea levels. This paper presents vertical ground motion rates in Philippine metropolitan cities using Sentinel-1 InSAR time series analysis from 2014 to 2020 through UK COMET’s LiCSAR products and the LiCSBAS package of Morishita et al. (2020). The results revealed a maximum subsidence rate of 109 mm/yr in Bulacan Province in Greater Manila. For the first time, this paper also presents ground motion estimates and the following maximum subsidence rates for other Philippine metropolitan cities: 11 mm/yr in Metro Cebu, 38 mm/yr in Metro Davao, 9 mm/yr in Metro Iloilo, and 29 mm/yr in Legazpi City. Areas with remarkably high subsidence rates are observed as circular to elliptical deformation features in vertical motion maps. Sinking areas mostly coincide with industrial and commercial complexes evident as a contiguous distribution of large and expansive man-made structures with distinct radar reflections. Monitoring this hazard is crucial as it increases the risk of floods, building and infrastructure damage, and economic loss, and exposes residents along the coast to worsening tidal incursions and storm surges due to climate change. InSAR is proposed here as a targeted, long-term deformation monitoring tool, especially related to groundwater usage.

Co-seismic and post-seismic slip associated with the 2021 Mw5.9 Arkalochori, Central Crete (Greece) earthquake constrained by geodetic data and aftershocks

The co-seismic and post-seismic deformation field associated with the Mw5.9 Arkalochori main shock that occurred in central Crete (Greece) on 27 September 2021 is analyzed using Copernicus Sentinel-1A & 1B images, GNSS measurements and seismological data. The fault geometry is constrained through the joint inversion of multiple datasets and the slip distribution for the co-seismic and post-seismic period is obtained using a homogeneous half-space elastic model and the Steepest Descent Method. The results indicate a blind normal fault striking 215° with a 55° dip to the northwest and the co-seismic slip model suggests a nearly circular main slip patch (8 × 6 km2) with a maximum slip of 0.98 m. Post-seismic displacements started rapidly after the main shock followed by a gradual decay as highlighted by the calculated InSAR time series. The temporal evolution of post-seismic slip is described by a simple logarithmic function, decaying faster at the southwest part of the fault. The cumulative afterslip model suggests that the maximum post-seismic slip of 0.23 m occurred within a similar depth range compared to the co-seismic one, yet with a shift towards the southwest. Post-seismic slip inside the main shock rupture area is sustained, highlighting the slow recovery of locking in the co-seismic slip region. Afterslip (seismic or aseismic) played a dominant role in the early post-seismic period acting complementarily to the main rupture. Indications suggest that the spatiotemporal evolution of the productive aftershock sequence may be driven afterslip, alongside other potential factors.

Application of Singular Spectrum Analysis to InSAR time-series for constraining the postseismic deformation due to moderate magnitude earthquakes: the case of 2019 Mw 6 Mirpur earthquake, NW Himalaya

Summary Detection and separation of the subtle postseismic deformation signals associated with moderate magnitude earthquakes from Interferometric Synthetic Aperture Radar (InSAR) time-series is often challenging. Singular Spectrum Analysis (SSA) is a statistical non-parametric technique used to decompose and reconstruct signals from complex time-series data. We show that the SSA analysis effectively distinguished the postseismic signal associated with the 2019 Mw 6 Mirpur earthquake from periodic and noise components. The SSA derived postseismic deformation signal is smoother and fits better to an exponential model with a decay time of 34 days. The postseismic deformation is confined to the southeast of the rupture area and lasted for ∼90 days following the mainshock. Inversion of the postseismic deformation suggests an afterslip mechanism with a maximum slip of ∼0.07 m on the shallow, up-dip portions of the Main Himalayan Thrust. The 2019 Mirpur earthquake and afterslip together released less than 12 per cent of the accumulated strain energy since the 1555 Kashmir earthquake and implies continued seismic hazard in the future.

Surface Deformation of Xiamen, China Measured by Time-Series InSAR.

Due to its unique geographical location and rapid urbanization, Xiamen is particularly susceptible to geological disasters. This study employs 80 Sentinel-1A SAR images covering Xiamen spanning from May 2017 to December 2023 for comprehensive dynamic monitoring of the land subsidence. PS-InSAR and SBAS-InSAR techniques were utilized to derive the surface deformation field and time series separately, followed by a comparative analysis of their results. SBAS-InSAR was finally chosen in this study for its higher coherence. Based on its results, we conducted cause analysis and obtained the following findings. (1) The most substantial subsidence occurred in Maluan Bay and Dadeng Island, where the maximum subsidence rate was 24 mm/yr and the maximum cumulative subsidence reached 250 mm over the course of the study. Additionally, regions exhibiting subsidence rates ranging from 10 to 30 mm/yr included Yuanhai Terminal, Maluan Bay, Xitang, Guanxun, Jiuxi entrance, Yangtang, the southeastern part of Dadeng Island, and Yundang Lake. (2) Geological structure, groundwater extraction, reclamation and engineering construction all have impacts on land subsidence. The land subsidence of fault belts and seismic focus areas was significant, and the area above the clay layer settled significantly. Both direct and indirect analysis can prove that as the amount of groundwater extraction increases, the amount of land subsidence increases. Significant subsidence is prone to occur after the initial land reclamation, during the consolidation period of the old fill materials, and after land compaction. The construction changes the soil structure, and the appearance of new buildings increases the risk of subsidence.

Non-linear ground deformation detection and monitoring using time series InSAR along the coastal urban areas of Pakistan.

Conventional geodetic methods rely on point measurements, which have drawbacks for detecting and tracking geologic disasters at specific locations. In this study, the time series Interferometric Synthetic Aperture Radar (InSAR) approach was incorporated to estimate non-linear surface deformation caused by tectonic, shoreline reclamation, and other anthropogenic activities in economically important urban regions of Pakistan's southern coast, which possesses around 270km. The shoreline is extended from the low-populated area on the premises of the Hub River in the west to the highly populated Karachi City and Eastern Industrial Zone, where we collected the Sentinel-1A C-band data from 2017 to 2023 to address urban security and threats to human life and property. The main advantage of opting for the non-linear persistent scatterer interferometric SAR (PSInSAR) approach for this study is that it exposes minute movements without any prior consideration of conventional monitoring techniques, making it valid in continuously varying regions. An average vertical displacement range of - 170 to + 82mm per year was found, which was used to investigate the potential correlation with the most effective causative parameters of deformation. The densely populated areas of the study area experience an annual subsidence of 170mm, and the less populated western region experiences an uplift of 82mm annually. Land deformation varies along the coast of the study area, where the eastern region is highly reclaimed and is affected by erosion. Groundwater table-depleting regions experienced high levels of land subsidence, and tectonic activities controlled vertical displacement in the region. Major variation was detected after an earthquake occurred along fault lines. This study was designed because a non-linear approach is required to address ground movement activities acutely, and it will make it possible to plan surface infrastructure and handle issues brought on by subsidence more effectively.

Land surface response to groundwater drawdown and recovery in Taiyuan city, Northern China, analyzed with a long-term elevation change measurements from leveling and multi-sensor InSAR

In recent years, a comprehensive program of groundwater management was adopted in Taiyuan city, Northern China, with the aim to prevent the lowering of groundwater level and manage land subsidence due to excessive groundwater pumping in the past decades. Groundwater recovery has been observed in this city following the termination of groundwater pumping. While the link between land subsidence and groundwater pumping is well documented, how the land surface responds to the recovery of groundwater has not been studied in detail. In this study, we analyzed the leveling data from 1956 to 2000 to investigate changes in ground elevation in response to groundwater drawdown prior to the termination of groundwater pumping and combined them with InSAR (Interferometric Synthetic Aperture Radar) displacement data during 2003–2020 to evaluate the effect of groundwater recharge after the termination of groundwater pumping. A method was proposed to merge InSAR time series of displacement derived from multiple satellites (ENVISAT, COSMO-SkyMed, TerraSAR-X, and Sentinel-1) to achieve a consistent, long-term continuous deformation. We found that previous groundwater depression cones in Taiyuan (Xizhang, Wanbailin, Wujiabao, and Xiayuan) have turned into groundwater recovery and the corresponding subsidence centers have switched to land uplift. The subsidence center of Xizhang, north of Taiyuan city, experienced a total subsidence of 749 mm by Aug 2003; it then turned into uplift with an amount of rebound of 9.2 % of previous subsidence by the end of 2020. In central area of Taiyuan, the cumulated subsidence is 1127 mm in Wanbailin, 1817 mm in Xiayuan, and 3343 mm in Wujiabao. Beginning in Aug 2010, they all turned into land uplift, with a rebound of 7.9 %, 4.8 % and 3.6 % of the previous subsidence in each center, respectively. An analysis of the correlation between groundwater level and ground displacement suggests that the ground uplift is related to the poro-elastic rebound mechanism in the porous aquifer system, which is caused by the rise in pore pressure with groundwater recovery, and the amount of subsidence/uplift is controlled by the stratigraphic properties. The adverse impacts on the built environment due to the rising groundwater level were discussed. The findings improve our understanding of anthropogenic ground deformation in complex urban aquifers influenced by groundwater pumping and recharge and provides essential information that can contribute to future groundwater management and groundwater-related hazard mitigation over the Taiyuan city.

Urban ground subsidence monitoring and prediction using time-series InSAR and machine learning approaches: a case study of Tianjin, China

Urban ground subsidence monitoring and prediction using time-series InSAR and machine learning approaches: a case study of Tianjin, China

Research on automatic identification of coal mining subsidence area based on InSAR and time series classification

Large-scale mining of coal resources has caused surface subsidence, threatening the ecological environment and surface safety and hindering the sustainable development of coal resource-based cities seriously. In order to identify coal mining subsidence area more accurately and quickly, a novel method for automatic identification of coal mining subsidence area was proposed in this study, which is based on InSAR technology and time series classification algorithm. The method comprehensively considered the quality of data acquisition and the degree of automation of algorithm implementation, realizing automatic identification of large-space coal mining subsidence area in the case of small samples. Firstly, the unclassified subsidence dataset is constructed by SAR data around time series combined with InSAR technology. Secondly, the degree of change of time series subsidence is used as the basis of classification, and it is divided into two categories: regular type of coal mining subsidence and irregular type of non-coal mining subsidence. Finally, Dynamic Time Warping (DTW) algorithm is used to calculate the similarity distance between the time series curves of each image element of the subsidence dataset to be tested and the standard coal mining subsidence type curves, which aim at identifying the coal mining subsidence area. Through utilizing the multitude filtering algorithm to filter the recognition results, the automatic recognition results of coal mining subsidence area is finally obtained. The results show that this method can still be used to efficiently identify and circle coal mining subsidence area with only 640 samples, and the average Overall Accuracy (OA) and Kappa Coefficient (KC) of coal mining subsidence area are 0.91 and 0.83, respectively. The method can not only identify the existing and potential subsidence area in the mining area, but provide technical support for ecological environment restoration and early warning of subsidence damage.

Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage.

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.