Multi-temporal InSAR Techniques Research Articles

Three-dimensional deformation monitoring of San Francisco Bay based on GNSS-InSAR data

As a cutting-edge technology in the field of remote sensing, Synthetic Aperture Radar Interferometry (InSAR) plays a significant role in monitoring urban surface deformation. The traditional multi-temporal InSAR technique can only obtain surface deformation in the line-of-sight (LOS) direction, and there are fewer studies on three-dimensional(3D) deformation monitoring in coastal areas. At the same time, there is a lack of research on integrating GNSS-InSAR data to obtain the three-dimensional deformation field in the San Francisco Bay area. Using ascending and descending orbit InSAR images and GNSS data to monitor ground subsidence in the San Francisco Bay area, various schemes of integrating GNSS and InSAR data to invert the three-dimensional deformation field were analyzed and compared. An improved Robust-Helmert estimation method was proposed to calculate the 3D ground deformation velocity. The results showed that the Robust-Helmert estimation method performed best, with Root Mean Square Error (RMSE) values of 3.96 mm, 6.51 mm, and 2.75 mm in the East-West (E), North-South (N), and Up-Down (U) directions, respectively. It also exhibited more stable solution accuracy and good universality.

Leveraging Multi-Temporal InSAR Technique for Long-Term Structural Behaviour Monitoring of High-Speed Railway Bridges

The effective monitoring of railway facilities is crucial for safety and operational efficiency. This study proposes an enhanced remote monitoring technique for railway facilities, specifically bridges, using satellite radar InSAR (Interferometric Synthetic Aperture Radar) technology. Previous studies faced limitations such as insufficient data points and challenges with topographical and structural variations. Our approach addresses these issues by analysing displacements from 30 images captured by the X-band SAR satellite, TerraSAR-X, over two years. We tested each InSAR parameter to develop an optimal set of parameters, applying the technique to a post-tensioned PSC (pre-stressed concrete) box bridge. Our findings revealed a recurring arch-shaped elevation along the bridge, attributed to temporal changes and long-term deformation. Further analysis showed a strong correlation between this deformation pattern and average surrounding temperature. This indicates that our technique can effectively identify micro-displacements due to temperature changes and structural deformation. Thus, the technique provides a theoretical foundation for improved SAR monitoring of large-scale social overhead capital (SOC) facilities, ensuring efficient maintenance and management.

Structural evaluation of urban bridges in Amsterdam through InSAR-based displacement data

Thousands of bridges worldwide face growing risks due to aging materials, increased traffic loads, and climate change-induced weather extremes. Managing these assets is financially demanding, necessitating prioritisation strategies for interventions. Consequently, innovative approaches are urgently required to evaluate the structural conditions of these bridges continuously and regularly. Recent advancements in space-borne Interferometric Synthetic Aperture Radar (InSAR) technology offer cost-effective remote monitoring capabilities, ensuring extensive coverage and high spatial resolution. Multi Temporal (MT) InSAR techniques enable the reconstruction of millimetre-scale deformation measurements for a large number of assets, opening opportunities for long-term regional-scale monitoring of bridge deformations. However, a major challenge in utilising MT-InSAR-based displacement data operationally is that MT-InSAR analysis reconstructs only the projection of displacements along the satellite Line of Sight (LOS) direction. Due to the typical availability of only two satellite viewing geometries, in most cases the three-dimensional displacement field cannot be fully reconstructed. Consequently, without accounting for the anticipated motion of a given structure and its alignment with respect to the satellite flight path, the actual asset movement is likely to be underestimated, leading to erroneous interpretation. In this paper, we propose a method using the bridge typologies and their associated likely failure mechanisms to derive assumptions regarding expected displacement directions. Then, the information on bridge alignments with respect to the satellite flight direction is used to assess the MT-InSAR sensitivity to the expected displacement directions and define ad-hoc damage indicators. We tested the proposed method on urban bridges in Amsterdam, the Netherlands, using deformation measurements derived from TerraSAR-X data spanning 2011 to 2020. Findings have potential to enhance current procedures for the structural evaluation of bridges.

Bayesian Data Fusion for Enhanced Monitoring of Bridge Displacements Using Satellite InSAR and Topographic Techniques

This paper investigates the effectiveness of satellite Interferometric Synthetic Aperture Radar (InSAR) technology to remotely monitor the structural health of road bridges and detect possible unexpected behaviours. The study is based on an Italian multi-span prestressed concrete bridge and a dataset of eight years of X-band SAR images collected by the COSMO-SkyMed satellite constellation. The method implemented for this study is as follows. First, SAR images are analysed using the Multi-Temporal InSAR technique to extract displacement time series of reflective targets on the bridge decks. Then, the extracted displacements are interpreted to validate their consistency with the bridge's expected responses to environmental loads, such as air temperature and river water flow variations, and detect possible unexpected behaviours. Eventually, the correlation with the air temperature is investigated to identify different bridge spans based only on monitoring data. The findings from this study advance the knowledge on using satellite InSAR-based monitoring for early detection of anomalous bridge behaviours on a large scale, providing valuable insights for the maintenance and safety of critical infrastructure.

Elucidating the complex interplay between natural and anthropogenic factors in the deformation of the Muyubao landslide through time-series InSAR analysis

In the Three Gorges Reservoir area, landslide disasters occur frequently, making scientific monitoring and risk prediction crucial for disaster prevention and mitigation. However, most previous studies have been constrained by analysis of singular influencing factors. In this study, we employed multi-temporal InSAR techniques coupled with multivariate geospatial statistical analysis to monitor and analyze the dynamic evolution of the Muyuba landslide in Zigui County, Hubei Province, China from 2016 to 2023. The findings indicate that the Muyuba landslide was predominantly characterized by continuous, gradual subsidence. Key factors inducing deformation included well-developed drainage networks, gentle slopes of 15–30°, and the orientation of rock strata. Deformation rates in residential areas and along roadways exceeded background levels, implicating anthropogenic activities in the heightened landslide risk. A significant correlation was observed between landslide deformation and reservoir water level fluctuations, as opposed to rainfall patterns, highlighting reservoir regulation disturbances as a critical landslide triggering factor.

Investigating kinematics and triggers of slow-moving reservoir landslide using an improved MT-InSAR method

The safety of the Three Gorges Reservoir, the world’s largest hydraulic project, has attracted significant attention. Among the landslides in the area, the Xinpu landslide complex stands out as one of the largest. However, accurately detecting and interpreting the timing and magnitude of its movement remains a challenge. This study introduces a framework using interferometric synthetic aperture radar (InSAR) for investigating the Xinpu landslide complex. In areas with dense vegetation like Xinpu, estimating landslide movement has traditionally been problematic. The proposed method, based on independent component analysis-assisted intermittent multi-temporal InSAR, overcomes this limitation by retrieving complete and reliable time-series deformation without prior deformation models. Through an analysis of Sentinel-1 datasets covering a period of 4 years, our approach significantly enhances deformation density and accuracy in comparison to other multi-temporal InSAR techniques. When contrasted with these methods, our proposed approach exhibits an average RMS improvement of up to 46.2% between GNSS measurements and the InSAR-derived results. The complete time-series deformation reveals diverse triggers at different sections of the Xinpu landslide complex. Precipitation, with a 45-day lag, primarily controls the landslide, while the toe of the landslide shows joint effects from precipitation and water level fluctuations, with a 14-day lag. Cross-correlation analysis estimates lag times for deformation responses to different triggers and enables discussion of the complex’s kinematic process. Overall, this approach enhances InSAR performance in vegetated areas and aids in uncovering landslide triggers for effective hazard mitigation.

Landslide susceptibility assessment in multiple urban slope settings with a landslide inventory augmented by InSAR techniques

Landslide susceptibility assessment (LSA) evaluates the likelihood of landslide occurrences and can help mitigate and prevent landslide risks. Recently, there have been vast applications of data-driven LSA methods owing to the increased availability of high-quality satellite data and landslide inventories. However, two issues remain to be addressed, as follows: (a) Items in a landslide inventory are mainly historical landslides from the interpretation of optical images and site investigation, resulting in predictive models trained with these items being insensitive to undetectable slope movements, such as slow-moving landslides that have not yet occurred; (b) Most study areas contain a variety of landslide-prone geographical settings that a single model can not accommodate well. Considering the complex landslide causes in Hong Kong with a land area of approximately 1108 km2, we proposed the utilization of multi-temporal InSAR techniques to generate weak landslide samples from slopes with ground surface movements for landslide inventory augmentation; and meta-learn intermediate representations for the fast adaptation of LSA models corresponding to different landslide-prone geographical settings. Besides, we performed feature permutation to identify dominant landslide-predisposing factors. The LSA results in Hong Kong revealed that slope deformation in several mountainous areas is closely associated with the occurrence of recorded landslides. By augmenting the landslide inventory using InSAR techniques, the proposed method enhanced the LSA models' capacity to identify slow-moving landslides and achieved better statistical performance. The discussion highlights that slope and stream power index (SPI) are the key landslide-predisposing factors in Hong Kong, but the dominant landslide-predisposing factors will vary under different geographical conditions. By comparison with the methods that treat LSA as a binary classification problem, such as support vector machine, multilayer perceptron, deep belief network, and random forest based LSA methods, the proposed method entails a fast-learning strategy and outperforms these methods in data-driven model evaluation indicators, e.g., by 3–6% in accuracy, 2–6% in precision, 1–2% in recall, 3–5% in F1-score, and approximately 10% in Cohen Kappa. The information about the relative importance of landslide predisposing factors, derived through feature permutation, can foster guidance for targeted landslide prevention schemes, such as constructing and maintaining slope consolidation facilities in areas where slope is the dominant landslide-predisposing factor.

Study of Recent Deformations in the Bogotá Savanna and the City of Bogotá (Colombia) Using Multi-Temporal Satellite Radar Interferometry

Bogotá, the largest urban center and capital city of Colombia, is located within the Bogotá savanna, which originated as a lake in the central part of the Colombian Eastern Cordillera. Over time, the lake transformed into a gently undulating plain with horizontally deposited sediments that formed around five million years ago. Over the last few decades, the region has undergone significant population growth and rapid urban development, largely driven by migration from rural areas. This development has substantially impacted the subsidence observed in the city, primarily due to the extraction of groundwater. A previous study by the Servicio Geológico Colombiano (SGC) utilized data from GNSS stations and synthetic aperture radar interferometry (InSAR) with TerraSAR-X SAR between 2011 and 2017 to identify a subsidence pattern in the central region of Bogotá. The purpose of the study was to evaluate the risks and potential disasters associated with the subsidence phenomenon. Our study investigates both the subsidence in Bogotá, previously studied, as well as the rural savanna area, which is currently undergoing significant residential and industrial development. We utilized multi-temporal InSAR (MT-InSAR) techniques with Sentinel-1 SAR images from 2014 to 2021. The analysis results indicate that the outer regions of the city display the most significant subsidence, extending from the center to the north. The subsidence velocities in these areas reach approximately 5–6 cm/year.

Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology

This paper presents a study on applying satellite Interferometric Synthetic Aperture Radar (InSAR) technology for the remote monitoring of road bridges and interpreting the results from a structural standpoint. The motivation behind this study arises from the widespread deterioration observed in many road bridges worldwide, leading to the need for large-scale, economic, and effective structural health monitoring (SHM) techniques. While traditional contact-type sensors have cost sustainability limitations, remote sensing techniques, including satellite-based InSAR, offer interesting alternative solutions. The objective of this study is three-fold: (i) to process InSAR data specifically for road bridges in operational conditions through the Multi-Temporal InSAR technique and extract displacement time series of reflective targets on their decks; (ii) to interpret the observed millimetric bridge displacements to verify the consistency with expected response to environmental loads and the possibility to detect unexpected behaviours; and (iii) to investigate the correlation between bridge displacements and environmental loads as temperature and river water flow variations. The study focuses on the multi-span prestressed concrete A22 Po River Bridge in Italy, utilising a dataset of X-Band HIMAGE mode Stripmap images acquired over eight years by the satellite constellation COSMO-SkyMed. The study demonstrates the effectiveness of InSAR-based SHM in detecting temperature-induced displacements and identifying different bridge spans simply by studying the sign of the correlation between displacements and temperature variation. It also reveals an unexpected behaviour in a portion of the bridge retrofitted to prevent scour issues a few years before the dataset start date. Furthermore, the correlation between pier displacements and river level variations underscores the importance of considering environmental factors and the geotechnical characteristics of the foundation soils in bridge monitoring. The results obtained from this study are significant with a view to using this satellite InSAR-based monitoring for early detection of anomalous bridge behaviour on a large scale.

Retrieving the grounding lines of the Riiser-Larsen Ice Shelf using Sentinel-1 SAR images

ABSTRACT Accurately locating and studying grounding lines is essential for predicting the response of glaciers to climate change. However, it is challenging to find grounding lines since they are subglacial features. In this study, Sentinel-1 synthetic aperture radar (SAR) data were utilized to derive the grounding lines of the Riiser-Larsen Ice Shelf. A new method with inspiration drawn from multi-temporal baseline InSAR techniques is proposed. It takes advantage of the temporal consistency of the vertical displacement gradients and identifies grounding zones pixel-by-pixel on a stack of double differential interferograms, thereby providing grounding line proxies. As it fully exploits coherent signals in both spatial and temporal domains, the maximum possible number of grounding zone pixels can be obtained. Moreover, due to the introduction of the concept of the temporal consistency, the method can cope with short term grounding line fluctuations to some extent and may mitigate the influences of atmospheric disturbances and residual ice displacements. The resulting grounding lines are compared with the MEaSUREs Antarctic grounding line product. The comparison confirms the effectiveness of the proposed method and corroborates that the Riiser-Larsen Ice Shelf should have not undergone significant changes over the past few decades.

Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)

The construction of tunnels in urban areas can affect the nearby existing infrastructures and buildings, as shallow excavations induce movements up to the ground surface. An important parameter to be monitored during the excavation is the volume loss, which plays a crucial role in determining the ground movements at the surface. InSAR satellite monitoring has the potential to detect ground movements at the millimetric scale on a vast area for tunneling applications. In the present study, the Multi-Temporal InSAR (MT-InSAR) technique, based on the persistent scattering method, is used to retrieve vertical displacements induced by the excavation of twin tunnels of a metro line in the City of Naples (Italy). Here, the volume loss is obtained by fitting a Gaussian curve on the monitored settlement data induced by the excavation of the first tunnel. The latter is then used to predict the settlement of the second excavation about one year later and compared to the MT-InSAR data. These monitored data show the typical shape of the settlement profile, confirming the empirical Gaussian distribution and MT-InSAR capability to detect millimetric displacements. Therefore, MT-InSAR can be used to feed algorithms to improve the prediction of tunneling-induced displacements.

Extending polarimetric optimization of multi-temporal InSAR techniques on dual polarized Sentinel-1 data

The rationale of combining polarimetric SAR data with multi-temporal InSAR techniques, which is based on polarimetric optimization, is to improve their performance in identifying coherent pixels and increasing the accuracy of deformation monitoring. This paper extended the polarimetric optimization of Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques on dual-polarized Sentinel-1 data. For this purpose, 20 dual-polarization (VV/VH) SAR images acquired by the Sentinel-1A sensor were used. The results indicated that by applying polarimetric optimization on dual-polarized data in the SBAS technique, the number of candidate and final coherent pixels increased 2.95 and 1.42 times, respectively. Similarly, the number of candidate and final coherent pixels (CP) increased 3.12 and 1.45 times in the PSI technique, respectively. Also, the effect of the polarimetric optimization on increasing the density of coherent points in different regions was investigated. In this framework, Sentinel-2 time-series images were classified into four classes (bare soil, vegetation, build-up, and others) using Google Earth Engine by applying remote sensing indices. This demonstrated that polarimetric optimization of the SBAS technique was more effective in bare soil and vegetated regions than in urban areas. However, polarimetric optimization of the PSI technique was more successful in non-urban regions than in urban areas. An external comparative analysis was also performed between the deformation results derived by PSI and SBAS techniques and GPS observation. The evaluation demonstrated that polarimetric optimization coupled with multi-temporal InSAR analysis yielded more reliable deformation results than conventional single-polarimetric data.

Land subsidence analysis using synthetic aperture radar data

Land subsidence is considered a threat to developing cities and is triggered by several natural (geological and seismic) and human (mining, groundwater withdrawal, oil and gas extraction, constructions) factors. This research has gathered datasets consisting of 80 Sentinel-1A ascending and descending SLC images from July 2017 to July 2019. This dataset, concerning InSAR and PS-InSAR, is processed with SARPROZ software to determine the land subsidence in Gwadar City, Balochistan, Pakistan. Later, the maps were created with ArcGIS 10.8. Due to InSAR’s limitations in measuring millimeter-scale surface deformation, Multi-Temporal InSAR techniques, like PS-InSAR, are introduced to provide better accuracy, consistency, and fewer errors of deformation analysis. This remote-based SAR technique is helpful in the Gwadar area; for researchers, city mobility is constrained and has become more restricted post-Covid-19. This technique requires multiple images acquired of the same place at different times for estimating surface deformation per year, along with surface uplifting and subsidence. The InSAR results showed maximum deformation in the Koh-i-Mehdi Mountain from 2017 to 2019. The PS-InSAR results showed subsidence up to −92 mm/year in ascending track and −66 mm/year in descending track in the area of Koh-i-Mehdi Mountain, and up to −48 mm/year in ascending track and −32 mm/year in descending track in the area of the deep seaport. From our experimental results, a high subsidence rate has been found in the newly evolving Gwadar City. This city is very beneficial to the country’s economic development because of its deep-sea port, developed by the China-Pakistan Economic Corridor (CPEC). The research is associated with a detailed analysis of Gwadar City, identifying the areas with significant subsidence, and enlisting the possible causes that are needed to be resolved before further developments. Our findings are helpful to urban development and disaster monitoring as the city is being promoted as the next significant deep seaport with the start of CPEC.

A SqueeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques analyze a study area using a set of SAR image data composed of time series, reaching millimeter surface subsidence accuracy. To effectively acquire the subsidence information in low-coherence areas without obvious features in non-urban areas, an MT-InSAR technique, called SqueeSAR, is proposed to improve the density of the subsidence points in the study area by fusing the distributed scatterers (DS). However, SqueeSAR filters the DS points individually during spatial adaptive filtering, which requires significant computer memory, which leads to low processing efficiency, and faces great challenges in large-area InSAR processing. We propose a spatially adaptive filtering parallelization strategy based on the Spark distributed computing engine in a Hadoop distributed cluster environment, which splits the different DS pixel point data into different computing nodes for parallel processing and effectively improves the filtering algorithm’s performance. To evaluate the effectiveness and accuracy of the proposed method, we conducted a performance evaluation and accuracy verification in and around the main city of Kunming with the original Sentinel-1A SLC data provided by ESA. Additionally, parallel calculation was performed in a YARN cluster comprising three computing nodes, which improved the performance of the filtering algorithm by a factor of 2.15, without affecting the filtering accuracy.

InSAR-Based Active Landslide Detection and Characterization Along the Upper Reaches of the Yellow River

Landslides have attracted extensive attention and research worldwide because of their tremendous destructiveness, and many catastrophic landslides have occurred along river corridors and reservoir bank slopes. The detection and detailed monitoring of potential landslides are prerequisites for landslide disaster prevention, with recent advance in spaceborne Interferometric Synthetic Aperture Radar (InSAR) having effectively addressed this challenge. In this study, a wide-area landslide detection and monitoring framework, combining multiple InSAR techniques, is proposed to investigate active landslides along the upper reaches of the Yellow River from Longyang Gorge to Liujia Gorge (UYRLL), north-west China. A total of 597 active landslides have been mapped in this region for the first time. Our analyses suggest that a large percentage of the detected landslides are distributed at an elevation of 2,000-3,000 m with slope angles of 5-30°and their spatial distribution density is correlated with regional tectonic activity. Multi-Temporal InSAR (MTI) techniques have also been adopted to analyze the surface motion characteristics of two typical landslides (i.e., the Lijia landslide and the Xijitan landslide), showing that these two landslides are dominated by linear motion. The landslide movements also contain slight non-linear oscillations, inferred from wavelet analysis to be associated with reservoir water area changes and seasonal rainfall. The proposed framework herein can be extended to the detection and monitoring of active landslides in other areas with similar geomorphic features, and the above findings on landslide characteristics can also contribute to landslide prevention and mitigation in the upper Yellow River area.

Application of Multi-Temporal InSAR (MT-InSAR) for structural monitoring: the case study of Scrovegni Chapel in Padova

In the last years, the use of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique for structural health monitoring (SHM) of buildings and infrastructures has spread. Compared to traditional monitoring methods, MT-InSAR technique involves a number of advantages, e.g. perform back-analysis and extend the monitoring to the surrounding area, that makes it a very cost-effective tool. On the other hand, some drawbacks still have to be fully overcome, such as the issues related to the geocoding of the measurement points and the noisiness in the time series. In this article, the application of MT-InSAR monitoring to a case study is presented. The structure of interest is the Scrovegni Chapel in Padova, recently declared a world heritage site by UNESCO as it contains numerous frescos executed by Giotto. Using images acquired by COSMO-SkyMed constellation, the deformation of the area surrounding the Chapel was estimated, by means of spatial interpolation algorithms. In addition, time series referred to measurement points located on the rooftop were analyzed and compared to the environmental data (temperature and humidity) acquired by on-site physical sensors already installed.

Building risk monitoring and prediction using integrated multi-temporal InSAR and numerical modeling techniques

• Large-scale ground subsidence was measured using an improved MTInSAR method. • Building risk level was assessed at the district scale using InSAR measurements. • The simulated subsidence due to underground construction agreed with InSAR data. • The integrated method achieves building risk monitoring and prediction. Frequent anthropogenic activities associated with rapid urbanization may cause ground subsidence and endanger the adjacent buildings. Interferometric synthetic aperture radar (InSAR) has generated dense measurement points for large-scale deformation monitoring and risk assessment. However, the applications of InSAR for building deformation and risk level prediction are still rare, especially under complex loading scenarios. In this study, we present an integrated method to monitor and predict building deformation and risk levels using multi-temporal InSAR and numerical modeling. Persistent and distributed scatterers were jointly detected to monitor deformation using TerraSAR-X, COSMO-SkyMed, and Sentinel-1 images over the Kowloon district of Hong Kong and the Futian district of Shenzhen. First, the building risk level was assessed at the district scale using ground subsidence and angular distortion to identify potentially damaged buildings. 10 out of 807 buildings in the Kowloon and 2 out of 145 buildings in the Futian were classified as medium risk. Back-analysis was used to retrieve the geological and hydrological conditions of buildings at the medium risk level. Then, time-series subsidence of the Lucky and BMW buildings during 2012 to 2016 and the Jingli building during 2010 to 2014 was simulated by numerical modeling, which agreed well with the InSAR measurements. By defining the groundwater level changes and upper loading, the predicted subsidence of the Lucky, BMW, and Jingli buildings reached 47.9, 62.8, and 39.9 mm by 2021, respectively, with a decelerated trend. Time-series results showed that the Lucky and BMW buildings were still at the negligible-to-medium risk level in 2021, and should be constantly inspected; the risk level of the Jingli building became negligible from 2020. This method can be transferred to other rapidly urbanized areas prone to ground subsidence for geohazard assessment and improve the understanding of the dynamic behaviors of anthropogenic activity-induced ground subsidence.

Deformation monitoring and influence factor analysis of expressway over strong saline soil based on an advanced multi-temporal InSAR technique

Saline soil harbors crystallized salt in the solid phase and salt solution in the liquid phase. By the natural environmental and human activity factors, migration and accumulation of the salt and crystallization process alternated with dissolving, which makes saline soil with significant seasonal differences appear in the deformation and regional scattering characteristic. These phenomena raise the limitation of conventional multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique and consequently high-precision deformation monitoring of strong saline soil subgrade and pavement. To overcome the limitations, this study aimed to propose an advanced MT-InSAR method, which considers the seasonal interferometric coherences caused by precipitation and the temporal physical deformation evolution of the subgrade and pavement over strong saline soil. To present the better performance of the advanced method, a segment of the Qarhan–Golmud Expressway (QGE), which is the first expressway built in the strong saline soil area in China, was selected for this study. Two sets of SAR images acquired from January 2018 to January 2022 from Sentinel-1A ascending and descending orbits over the QGE were utilized, 774 and 928 seasonal filtered interferograms are optimized for deformation monitoring based on the deformation Poisson curve (PC) model. Compared with the method in the previous studies, the accuracy, efficiency, and reliability of the monitoring results increased dramatically. Subsequently, further discussions are conducted in detail on the regularity of expressway deformation over strong saline soil, especially from physical and chemical perspectives. Findings show that the ratio between Cl− and SO42- determines the deformation distribution and morphology. Also, the precipitation and temperature affect the seasonal characteristics. The contributions of this investigation might provide technical references for related expressway management and policy-making departments to ensure the long-term safe operation and stability of the QGE.

Land Subsidence Monitoring and Dynamic Prediction of Reclaimed Islands with Multi-Temporal InSAR Techniques in Xiamen and Zhangzhou Cities, China

Artificial islands and land reclamation are one of the most important ways to expand urban space in coastal cities. Long-term consolidation of reclaimed material and compaction of marine sediments can cause ground subsidence, which may threaten the buildings and infrastructure on the reclaimed lands. Therefore, it is crucial to monitor the land subsidence and predict the future deformation trend to mitigate the damage and take measures for the land reclamation and any infrastructure. In this paper, a total of 125 SAR images acquired by the C-band Sentinel-1A satellite between June 2017 and September 2021 are collected. The small baseline subsets (SBAS) SAR interferometry (InSAR) method is first conducted to detect the land deformation in Xiamen and Zhangzhou cities of Fujian Province, China, and the distributed scatterers (DS)-InSAR method is used to recover the complete deformation history of some typical areas including Xiamen Airport in Dadeng Island and Shuangyu Island. Then, the sequential estimation and the geotechnical model are jointly applied to demonstrate the current and future evolution of land subsidence of the constructed roads on Shuangyu Island. The results show that the maximum cumulative deformation reaches 425 mm of Xiamen Xiang’an Airport and 626 mm of Shuangyu Island, and the maximum deformation is predicted to be as large as 1.1 m by 2026 of Shuangyu Island. This research will provide important guidelines for the design and construction of Xiamen Xiang’an Airport and Shuangyu Island to prevent and control land subsidence.

Deformations monitoring in complicated-surface areas by adaptive distributed Scatterer InSAR combined with land cover: Taking the Jiaju landslide in Danba, China as an example

Multi-temporal InSAR (MT-InSAR) technique provides a powerful tool for measuring large-scale surface displacements at high precision. However, traditional MT-InSAR approaches always employ a single global threshold to select measurement points (MPs), making it difficult to identify enough MPs in complex land cover areas (e.g., mountainous areas). In this study, to increase the number and spatial distribution density of distributed scatterer (DS) detection while maintaining its quality, we proposed a new MT-InSAR method, named Adaptive Distributed Scatterer InSAR Combined with Land Cover (ADSI-CLC). The key ideas of ADSI-CLC include the introduction of land cover maps as the constraint condition for DS adaptive selection and the employment of coherence weighted spatial adaptive filtering method to estimate optimal phase. Of these, DS adaptive selection method as the most pivotal idea realizes the unification of mathematical, physical, and geographical mechanism in the process of statistically homogeneous pixels (SHP) identification and adaptively adjusts the DS selection thresholds in different land cover areas in the process of DS selection. ADSI-CLC significantly increases the number and spatial distribution density of DS by using the adaptive selection method, and purifies the SHPs populations, and improves the efficiency of SHPs identification through introducing land cover maps. To demonstrate the effectiveness of the ADSI-CLC method, a case study of the Jiaju landslide using 19 scenes ALOS-1 PALSAR-1 L-band data (2006–2011) and 31 scenes Sentinel-1 C-band data (2014–2016) was carried out. The comparison between deformation measurements retrieved by ADSI-CLC and classical StaMPS-SBAS method showed that their distribution pattern is similar, but the ADSI-CLC method can provide more deformation details due to much higher (nearly 10 times) DS density. Quantitative comparisons between the ADSI-CLC results from ALOS-1 data and GNSS measurements also showed that the accuracy of this method is about 11.7 mm/yr in terms of root mean square error (RMSE). These results proved the reliability and effectiveness of ADSI-CLC and indicated its great potential for displacements monitoring in complicated-surface areas with large topographic fluctuations (e.g., mountainous areas).