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Due to the physicochemical properties of salt swelling and collapsibility, the potential geological hazards threaten the large-scale infrastructure and residential areas in the saline-soil region. Geodetic imaging and dynamic monitoring of the spatiotemporal evolution process of saline soil are vital to detect the potential geological hazards in a vast area. Therefore, this paper presents a coupled deformation model synthesizing environmental factors and trigonometric function model for estimating the time-series InSAR deformation and interpreting the dynamic evolutionary mechanism of the saline mudflat. To demonstrate the advantages of the proposed model, the sulphate saline mudflat is selected as the study area, which is traversed by the Tehran-Isfahan high-speed railway in Howz-e-Soltan Salt Lake, Qom, Iran. Moreover, 259 interconnecting interferograms were selected for the time-series InSAR estimation, composed of 87 Sentinel-1A images obtained from October 2014 through December 2018. Then, the time-series deformations, including the annual deformation velocity and the accumulated deformation, are extracted by the original estimation, polynomial, trigonometric and environmental models. The comparison results show that the environmental model's RMSEs (root-mean-square errors) are smaller than others. It should be noted that the linear deformation, periodic oscillation, and deformation derived from the environmental factors (i.e., precipitation and temperature) are extracted by the environmental model. Furthermore, the dynamic evolutionary mechanism of the saline mudflat is interpreted according to the physicochemical properties of sulphate/carbonate saline soil. The results derived from the environmental model suggested that the saline-soil deformation is directly related with the environmental changes: rapid collapsibility occurs when the precipitation increases from September to the next May, while uplift due to the salt swelling in the dry and rainless season from May to September every year. Our findings provide an opportunity to detect, prevent and control the potential geological hazards in saline mudflats.

Since 2012, a mega-project, “Mountain Excavation and City Construction” (MECC), has been underway on the Chinese Loess Plateau, aiming to create extra flat ground for urban growth. However, this large-scale land creation has led to dramatic changes in the local geological environment, forming a large number of engineered loess slopes. This study aims to map and assess the boundaries and stability of these potentially unstable engineered slopes (potential landslides). Based on spatio-temporal deformation patterns of landslides prior to failure, a novel interferometric synthetic aperture radar (InSAR)-based procedure for potential landslide mapping and stability evaluation was constructed in this study. The proposed procedure was employed to map and evaluate the potential landslides in the Yan’an New District (YND), the largest land-creation area in the Loess Plateau. First, the spatial locations and boundaries of the potential landslides were determined by combining optical image interpretation with differential deformation maps derived from Sentinel-1 small baseline subset InSAR (SBAS-InSAR) analysis. Then, the kinematic evolution trend of the mapped potential landslide was evaluated by quantitative analysis of the time-series displacements using an exponential model. Finally, the potential influencing factors and failure modes of the potential landslide were examined and discussed. The results indicate that the InSAR-derived differential deformation maps reflect the external boundary of the potential landslides well, and a total of 18 potential landslides are identified and mapped for the first time in the large-scale land-creation area of the YND. Over 82% of the coherent targets (CTs) within these potential landslides showed primary or steady creep during the InSAR observation period; however, tertiary creep was also observed on several slopes. Thus, it is crucial to utilize multitemporal InSAR and/or ground-based equipment for the continuous monitoring of engineered slopes in large-scale land-creation areas. The high correlation between spatiotemporal deformation patterns and environmental factors indicates that the activity of potential landslides is mainly controlled by the thickness of filling loess, while rainfall accelerates the displacement of slopes. These findings can contribute to the development of landslide disaster prevention and mitigation measures in the YND.

There is an urgent demand for continuous detection and monitoring of active slopes in wide reservoir areas, as reservoir impoundments may activate unstable slopes. Although satellite time-series InSAR has been widely used in mapping active landslides, the tedious artificial interpretation of InSAR results limits the efficiency and reliability of landslides detection. We propose a set of procedures for deformation monitoring and continuous automatic landslide identification in wide reservoir areas. The local time window estimation can extract the nonlinear deformation in the time series InSAR signal, thus enhancing the deformation field. Spatial adaptive clustering enables the effective extraction of unstable slopes. The abnormal deformation trends of landslides can be updated through the continuous identification of new results and comparison with historical results. The procedure is used to continuously detect unstable slopes in the Lianghekou reservoir area before and after the impoundment. Combining the ascending and descending SAR data of Sentinel-1, 109 historically active landslides were found and 18 new landslides were activated within one year after the first water storage. The distribution and types of unstable slopes were analyzed, followed by two case studies. The first case shows the different effects of the two-stage water storage on slope deformations, indicating rising water levels' critical role in landslide activation. The second case shows the consolidation settlement of the embankment dam and the influence of water storage on slope deformation monitoring. The results demonstrate the effectiveness of the InSAR automatic landslide identification and this study provides a technical reference for similar reservoir area.

The metro system solves traffic congestion in urban transportation while bringing potential deformation risks, especially in tunnels built in typical soft deposits in coastal cities. Time-series Synthetic Aperture Radar Interferometry (TS-InSAR) is an effective tool for monitoring land subsidence in urban areas. However, the different structures of metro lines, such as elevated tracks, ground lines, and tunnels, were often neglected in previous studies. Here, we introduced a Station Based Monitoring (SBM) method combined with InSAR analyses for metro safety monitoring. TS-InSAR provided the measurements along the elevated rails and ground lines. For tunnels, the TS-InSAR and SBM were jointly performed to deduct partial deformation under tunnels caused by land subsidence. The proposed metro system monitoring strategy was demonstrated and validated over the Shanghai Metro and Tianjin Rail Transit in the coastal cities of China. The results show that most deformed sections are located on the metro network's periphery, with velocities around −5 mm/year in Shanghai and −25 mm/year in Tianjin. The spatial distribution and temporal evolution of deformation characteristics along the two cases were then explored and compared. The proposed hazard matrix combining geological conditions and passenger density provided a more comprehensive assessment of potential hazards. The study highlights that InSAR and SBM analyses could be potentially applied to monitoring metro systems considering different structures.

On 1 May 2020, the Sardoba Reservoir in Uzbekistan breached its western wall, and the uncontrolled release of water caused casualties, environmental damages and economic losses. We investigate the dam failure based on three sets of Earth observation data, including: (i) satellite altimetry products, i.e., ICESat-2 data, with the aim of understanding the topographic features in the study area; (ii) multi-geometry Sentinel-1 SAR data to retrieve the pre-failure deformation along the vertical and horizontal east-west directions between 2017 and 2020; (iii) optical images from Sentinel-2 satellites and Global Precipitation Measurement (GPM) products, which are involved in exploring the environmental status before the failure. We analyse the possible causes of the collapse in terms of both physical and human factors. The differential settlement of ∼60 mm revealed by InSAR at the failure section is a sign of internal erosion through the embankment, which is the physical factor contributing to the failure. The opportunity to prevent the collapse were missed due to the human factor of ignorance brought by limitations of the conventional monitoring methods. Neither ground observations nor satellite-based GPM products show extreme precipitation in the region, ruling out the likelihood of rainfall-induced overtopping. The settlement rate of the embankment shows no decaying trend, indicating that the dam is undergoing the primary consolidation phase of total settlement. Maximum settlement of ∼270 mm (∼0.8% of the dam height) has happened on the north bank since the reservoir impoundment, which should raise concern in future monitoring and surveillance. The results reveal that InSAR can discern the failure precursor by detecting surface motion, and that the deformation signals can help to warn of risks and avoid dam damage. We recommend InSAR deformation monitoring be included in future safety programs, providing detailed deformation and resisting risks of ignorance.

Deformation monitoring and risk identification of sea-crossing bridges are essential to mitigate hazards and prevent loss of human life and property. Satellite-based Synthetic Aperture Radar Interferometry (InSAR) technology can detect millimeter-scale deformation, showing unique advantages in the safety monitoring of sea-crossing bridges. However, the existing InSAR methods only extract point-like targets (PTs) based on the coherent index, but ignores the analysis of multiple SAR incoherent information and the foreground-background scattering characteristics differences of bridges, leading to low-density and low-accuracy of PTs on sea-crossing bridges. Moreover, most InSAR-based studies identified structural risks according to deformation measurements without fully considering the various safe deformation ranges of different structural components, resulting in high false-alarm/miss-detection rates in structural risk identification of sea-crossing bridges. To address these issues, a structure knowledge-InSAR integration approach is developed for high-precision deformation monitoring and reliable risk identification of sea-crossing bridges. Firstly, the SAR incoherent information and foreground-background scattering characteristics of the bridge structure are analyzed and applied to improve the density of extractable PTs and remove the incorrect noise signals. Then, the bridge structural mechanics model is combined with the InSAR time-series displacements to analyze the mechanical property degradation of different bridge components, improving the reliability of InSAR-based structural risk identification. This approach is applied to the Stonecutters Bridge and Tsing Ma Bridge using the TerraSAR-X and COSMO-SkyMed images from 2011 to 2012 and the Sentinel-1A images from 2015 to 2017. The results indicate that the densities of PTs extracted on the two bridges increased by about 40% using the new approach, and incorrect noise signals are removed. Moreover, the mechanical properties of different bridge components can be evaluated through the analysis of their structural stress and time-series displacements, helping to decrease the false-alarm/miss-detection rates of InSAR-based structural risk identification. The bridge deformation is correlated with the temperature variation when the temperature difference is large (≥10 °C), but no longer dominated by thermal dilation when the temperature difference is less than 10 °C due to the influence of environmental effects.

Land subsidence, an insidious and gradual geological phenomenon, presents a latent threat to future urban development and socio-economic progress. Beijing City, renowned for its high population density, has encountered significant challenges associated with land subsidence. In this study, we leverage time-series interferometric synthetic aperture radar (time-series InSAR) method to analyze the spatio-temporal patterns of land subsidence in Beijing. Furthermore, we quantify the contributions of natural and anthropogenic factors to land subsidence. Our findings reveal that land subsidence primarily occurs in the plain area of Beijing, exhibiting an average rate of -5.6 mm/year (Positive values indicate uplift, while negative values indicate subsidence.). Notably, several large-scale subsidence centers are identified, with the maximum subsidence rate reaching an alarming -232.7 mm/year. The assessments indicate that geological factors, specifically fault activity, account for 33 % of the observed land subsidence, while human activities contribute to the remaining 67 %, with groundwater overexploitation playing a prominent role. The insights gained from this study provide a foundation for understanding the causative factors behind urban land subsidence and can aid in the formulation of effective intervention policies targeting this critical issue., Competing Interests: Declaration of competing interest Authors have no conflict of interest to declare., (Copyright © 2024 Elsevier B.V. All rights reserved.)