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High-temporal-frequency monitoring of transport infrastructure is crucial to facilitate maintenance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long inspection times are required for data collection and their implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructure (roads, railways and airfields) with a much higher temporal frequency of investigation and the capability to cover wider areas. Nevertheless, the integration of information from (i) satellite remote sensing and (ii) ground-based NDT methods is a subject that is still to be fully explored in civil engineering. This paper aims to review significant stand-alone and combined applications in these two areas of endeavour for transport infrastructure monitoring. The recent advances, main challenges and future perspectives arising from their mutual integration are also discussed.

Jincheng City’s mining areas have long been plagued by surface subsidence, posing significant threats to local residents’ safety and impacting the region’s economic and social stability. Understanding and effectively monitoring the driving factors and mechanisms of surface subsidence are crucial for devising scientific prevention measures and promoting the sustainable development of mining areas. This article aims to comprehensively reveal the large-scale surface subsidence phenomenon in Jincheng City’s mining clusters by utilizing advanced remote sensing technology and machine learning models, identifying its main driving forces, and predicting future subsidence trends to provide scientific evidence for geological disaster prevention in mining areas. The study employs Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology, using both Permanent Scatterer Interferometric SAR (PS-InSAR) and Small Baseline Subset Interferometric SAR (SBAS-InSAR) techniques for cross-validation, to confirm the existence of surface subsidence. Further, by integrating Variational Mode Decomposition (VMD), Singular Spectrum Analysis (SSA), and Long Short-Term Memory (LSTM) networks, a high-precision time series prediction model (VMD-SSA-LSTM) was developed. The results indicate that from 2018 to 2021, the surface subsidence rates in Jincheng City ranged from − 34 to 34 mm per year, with significant variations in subsidence levels across different areas. Gaoping City exhibited the highest subsidence, with rates ranging from − 34 to 5 mm per year, while Yangcheng County showed the most pronounced subsidence changes. These variations are primarily attributed to mining activities, land use changes, and adverse geological conditions in Jincheng City. This study revealed the large-scale surface subsidence phenomenon in the mining agglomeration area of Jincheng City, and for the first time, a comprehensive ground deformation monitoring and analysis was carried out in the small-scale mining agglomeration area of Jincheng City. The development of a high-precision surface subsidence prediction model provides new insights for scientifically understanding geological disasters in mining areas. These findings are significant for formulating effective geological disaster prevention measures and land management policies.

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets. Amongst others, the Persistent Scatterers Interferometry (PSI) technique has proven to be viable for the long-term evaluation of ground scatterers. However, its effectiveness as a routine tool for certain critical application areas, such as the assessment of millimetre-scale differential displacements in airport runways, is still debated. This research aims to demonstrate the viability of using medium-resolution Copernicus ESA Sentinel-1A (C-Band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, “Runway n.3” of the “Leonardo Da Vinci International Airport” in Fiumicino, Rome, Italy was investigated as an explanatory case study, in view of historical geotechnical settlements affecting the runway area. In this context, a geostatistical study is developed for the exploratory spatial data analysis and the interpolation of the Sentinel-1A SAR data. The geostatistical analysis provided ample information on the spatial continuity of the Sentinel 1 data in comparison with the high-resolution COSMO-SkyMed data and the ground-based topographic levelling data. Furthermore, a comparison between the PSI outcomes from the Sentinel-1A SAR data—interpolated through Ordinary Kriging—and the ground-truth topographic levelling data demonstrated the high accuracy of the Sentinel 1 data. This is proven by the high values of the correlation coefficient (r = 0.94), the multiple R-squared coefficient (R2 = 0.88) and the Slope value (0.96). The results of this study clearly support the effectiveness of using Sentinel-1A SAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways, paving the way for the development of more efficient and sustainable maintenance strategies for inclusion in next generation Airport Pavement Management Systems (APMSs).

Zhengzhou city (China) experienced relatively significant land deformation following the July 20, 2021, extreme rainstorm (7·20 event). This study jointly utilised Multi-temporal synthetic aperture radar interferometry (MT-InSAR), eXtreme Gradient Boosting (XGBoost), and hydrogeological analysis to quantitatively assess the extent and trends, as well as the causes of land deformation before and after the 7·20 event in Zhengzhou city. The findings detected three major subsidence zones and two uplift zones within the city. The most significant subsidence occurred in the northern part of Zhongmu (− 28 mm/year), the northwest of Xingyang (− 16 mm/year), and the western region of Gongyi (− 6 mm/year). Conversely, a notable uplift was observed in the central city district (13 mm/year) and Xinzheng Airport (12 mm/year). The accuracy assessment of in-situ measurements (GNSS and levelling) yielded an overall root-mean-square error (RMSE) of 2.2 mm/year and an R-square of 0.948. Subsequently, the feature evaluation results based on the XGBoost method suggest that road density and precipitation are the dominant factors affecting land deformation in the entire study area or in the subsidence and uplift zones individually. Nevertheless, the other five factors (groundwater storage, soil type, soil thickness, NDVI, and slope) also act on land deformation individually and are intricately intertwined with each other. Furthermore, hydrogeological analysis from six groundwater wells reveals a synchronous relationship between groundwater level decline and land subsidence. The building load analysis shows a significant correlation between build-up density and subsidence rates, especially for those severe subsidence areas, with the maximum correlation coefficient reaching 0.6312. Finally, the geographic patterns analysis of post-event demonstrated a northeastward trend in land deformation, with a gradual reduction of deformation impact from 2018 to 2022.

Permafrost degradation can significantly affect vegetation, infrastructure, and sustainable development on the Qinghai-Tibet Plateau (QTP). The permafrost on the QTP faces a risk of widespread degradation due to climate change and ecosystem disturbances; thus, monitoring its changes is critical. In this study, we conducted a permafrost surface deformation prediction over the Tuotuo River tributary watershed in the southwestern part of the QTP using the Long Short-Term Memory model (LSTM). The LSTM model was applied to the deformation information derived from a time series of Multi-Temporal Interferometry Synthetic Aperture Radar (MT-InSAR). First, we designed a quadtree segmentation-based Small BAseline Subset (SBAS) to monitor the seasonal permafrost deformation from March 2017 to April 2022. Then, the types of frozen soil were classified using the spatio-temporal deformation information and the temperature at the top of the permafrost. Finally, the time-series deformation trends of different types of permafrost were predicted using the LSTM model. The results showed that the deformation rates in the Tuotuo River Basin ranged between −80 to 60 mm/yr. Permafrost, seasonally frozen ground, and potentially degraded permafrost covered 7572.23, 900.87, and 921.70 km2, respectively. The LSTM model achieved high precision for frozen soil deformation prediction at the point scale, with a root mean square error of 4.457 mm and mean absolute error of 3.421 mm. The results demonstrated that deformation monitoring and prediction using MT-InSAR technology integrated with the LSTM model can be used to accurately identify types of permafrost over a large region and quantitatively evaluate its degradation trends.

Rock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in alpine periglacial environments are poorly quantified, especially in the central Himalayas. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been shown to be a useful technique for rock glacier deformation detection. In this study, we developed a multi-baseline persistent scatterer (PS) and distributed scatterer (DS) combined MT-InSAR method to monitor the activity of rock glaciers in the central Himalayas. In periglacial landforms, the application of the PS interferometry (PSI) method is restricted by insufficient PS due to large temporal baseline intervals and temporal decorrelation, which hinder comprehensive measurements of rock glaciers. Thus, we first evaluated the rock glacier interferometric coherence of all possible interferometric combinations and determined a multi-baseline network based on rock glacier coherence; then, we constructed a Delaunay triangulation network (DTN) by exploiting both PS and DS points. To improve the robustness of deformation parameters estimation in the DTN, we combined the Nelder–Mead algorithm with the M-estimator method to estimate the deformation rate variation at the arcs of the DTN and introduced a ridge-estimator-based weighted least square (WLR) method for the inversion of the deformation rate from the deformation rate variation. We applied our method to Sentinel-1A ascending and descending geometry data (May 2018 to January 2019) and obtained measurements of rock glacier deformation for 4327 rock glaciers over the central Himalayas, at least more than 15% detecting with single geometry data. The line-of-sight (LOS) deformation of rock glaciers in the central Himalayas ranged from −150 mm to 150 mm. We classified the active deformation area (ADA) of all individual rock glaciers with the threshold determined by the standard deviation of the deformation map. The results show that 49% of the detected rock glaciers (monitoring rate greater than 30%) are highly active, with an ADA ratio greater than 10%. After projecting the LOS deformation to the steep slope direction and classifying the rock glacier activity following the IPA Action Group guideline, 12% of the identified rock glaciers were classified as active and 86% were classified as transitional. This research is the first multi-baseline, PS, and DS network-based MT-InSAR method applied to detecting large-scale rock glaciers activity.

Land subsidence is a global environmental geological hazard caused by natural or human activities. The high spatial resolution and continuous time coverage of interferometric synthetic aperture radar (InSAR) time series analysis techniques provide data and a basis for the development of methods for the investigation and evolution mechanism study of regional land subsidence. Beijing, the capital city of China, has suffered from land subsidence for decades since it was first recorded in the 1950s. It was reported that uneven ground subsidence and fractures have seriously affected the normal operation of the Beijing Capital International Airport (BCIA) in recent years before the overhaul of the middle runway in April 2017. In this study, InSAR time series analysis was successfully used to detect the uneven local subsidence and ground fissure activity that has been gradually increasing in BCIA since 2010. A multi-temporal InSAR (MT-InSAR) technique was performed on 63 TerraSAR-X/TanDem-X (TSX/TDX) images acquired between 2010 and 2017, then deformation rate maps and time series for the airport area were generated. Comparisons of deformation rate and displacement time series from InSAR and ground-leveling were carried out in order to evaluate the accuracy of the InSAR-derived measurements. After an integrated analysis of the distribution characteristics of land subsidence, previous research results, and geological data was carried out, we found and located an active ground fissure. Then main cause of the ground fissures was preliminarily discussed. Finally, it can be conducted that InSAR technology can be used to identify and monitor geological processes, such as land subsidence and ground fissure activities, and can provide a scientific approach to better explore and study the cause and formation mechanism of regional subsidence and ground fissures.

Interferometric Synthetic Aperture Radar (InSAR) has been increasingly used in landslide detection over wide areas. However, atmospheric delays, phase unwrapping errors, and noise, which behave like deformation signals in InSAR deformation results, pose a challenge for semi-automatic and automatic landslide detection. C-index can assistant in landslide interpretation by assessing the rationality of InSAR deformation and filtering out non-real landslide deformation signals. Yet, few studies have analyzed its applicability in identifying landslide from InSAR results. In this study, we develop a method that uses C-index to assist in landslide detection using InSAR. We validate our method in a selected region of the Jinsha River basin, China. We first using multi-temporal InSAR (MT-InSAR) to obtain the deformation rate of the study area. Sixty-nine and 47 suspicious slope deformation areas are extracted from the ascending and descending deformation results, respectively. Next, C-index is used to automatically exclude 28 and 12 false deformation areas from the ascending and descending InSAR results, respectively. Remarkably, all the excluded false deformation areas do not correspond to landslides, suggesting the reliability of the proposed method. Finally, by visually interpreting the optical images of the remaining deformation results, we successfully detect 54 landslides. Among these, 9 landslides can be detected by both the ascending and descending Sentinel-1 data, and 15 landslides are located on the riverbanks, posing a potential risk of river blockage in the event of failure. The presented landslide detection method effectively filters out false deformation areas, ultimately enhancing the efficiency and accuracy of landslide detection over wide areas using InSAR.

Permafrost is widely distributed in the Tibetan Plateau. Seasonal freeze–thaw cycles of permafrost result in upward and downward surface displacement. Multitemporal interferometric synthetic aperture radar (MT-InSAR) observations provide an effective method for monitoring permafrost displacement under difficult terrain and climatic conditions. In this study, a seasonal sinusoidal model-based new small baselines subset (NSBAS) chain was adopted to obtain a deformation time series. An experimental study was carried out using 33 scenes of Sentinel-1 data (S-1) from 28 November 2017 to 29 December 2018 with frequent revisit (12 days) observations. The spatial and temporal characteristics of the surface displacements variation combined with different types of surface land cover, elevation and surface temperature factors were analyzed. The results revealed that the seasonal changes observed in the time series of ground movements, induced by freeze–thaw cycles were observed on flat surfaces of sedimentary basins and mountainous areas with gentle slopes. The estimated seasonal oscillations ranged from 2 mm to 30 mm, which were smaller in Alpine deserts than in Alpine meadows. In particular, there were significant systematic differences in seasonal surface deformation between areas near mountains and sedimentary basins. It was also found that the time series of deformation was consistent with the variation of surface temperature. Based on soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data, the deformation analysis influenced by soil moisture factors was also carried out. The comprehensive analysis of deformation results and auxiliary data (elevation, soil moisture and surface temperature et al.) provides important insights for the monitoring of the seasonal freeze-thaw cycles in the Tibetan Plateau.

Spatio-temporal ground-movement measurements and mappings have been carried out in the Campine coalfield in Belgian Limburg since the closure of the mines to document post-mining effects. MT-InSAR measurements are compared to groundwater head changes in the overburden and to height data from the closest GNSS stations. Radar interferometry is used to estimate the extension and the velocity of ground movements. In particular, the MT-InSAR technique has been applied to SAR acquisitions of the satellites ERS-1/2 (1991–2005), ENVISAT (2003–2010), COSMO-SkyMed (2011–2014), and Sentinel-1A (2014–2022). The images were processed and used to highlight a switch from subsidence to uplift conditions in the western part of the coal basin, while the eastern part had already been affected by a rebound since the beginning of the ERS-1/2 acquisitions. Following the closure of the last active colliery of Zolder in 1992 and the subsequent cease of mine-water pumping, a recharge of mine-water aquifers occurred in the western part of the basin. This process provoked the change from subsidence to uplift conditions that was recorded during the ENVISAT period. In the center of the coal-mining area, measured uplift velocities reached a maximum of 18 mm/year during the ENVISAT period, while they subsided at −12 mm/year during the ERS-1/2 period. Mean velocities in the western and eastern parts of the coalfield area have decreased since the last MT-InSAR measurements were performed using Sentinel-1A, while the Zolder coal mine continues to rise at a faster-than-average rate of a maximum of 16 mm/year. The eastern part of the coalfield is still uplifting, while its rate has been reduced from 18 mm/year (ERS-1/2) to 9 mm/year (Sentinel-1A) since the beginning of the radar–satellite observations. Time-series data from the two GNSS stations present in the study area were used for a local comparison with the evolution of ground movements observed by MT-InSAR. Two leveling campaigns (2000, 2013) were also used to make comparisons with the MT-InSAR data. The station’s measurements and the leveling data were in line with the MT-InSAR data. Overall, major ground movements are obviously limited to an extension of the actual underground-mining works and rapidly diminish outside of them.