Search Results (88)

Geospatial monitoring is crucial for landslide research and hazard mitigation. This paper provides a comprehensive overview of contemporary landslide monitoring methods and lays the groundwork for a unified monitoring framework. An in-depth bibliometric analysis and critical review of state-of-the-art approaches developed over the past decade are presented. The study proposes a new classification and systematization of geospatial monitoring methods based on dimensionality (1D, 2D, and 3D) and referencing approach (absolute or relative). The reviewed methods include geodetic techniques, photogrammetry, laser scanning, global satellite navigation systems, UAVs, radar interferometry, and various sensors. The operational characteristics, advantages, and limitations of the existing methods are analyzed with respect to monitoring accuracy, spatial coverage, temporal resolution, and applicability to different deformation conditions. A comparative analysis and systematization of monitoring methods according to landslide velocity classes are presented. This framework links achievable observation accuracy and monitoring frequency to landslide dynamics. Based on the analysis, a refined workflow for geospatial landslide monitoring is proposed. The workflow integrates monitoring design, observation network configuration, data integration, statistical analysis, and forecasting stages. The analysis indicates that effective landslide monitoring requires integrated multi-sensor systems. Future developments are expected to focus on geospatial and non-geospatial data integration, monitoring automation, and next-generation monitoring system design. Full article

Monitoring dam deformation is critical for ensuring structural integrity and identifying long-term settlement trends. However, traditional InSAR techniques often face limitations in tropical environments due to severe temporal decorrelation. This study addresses these challenges at Jatiluhur Dam, Indonesia, by implementing an integrated framework using Sentinel-1 InSAR, in situ leveling, GNSS, and reservoir water-level data from 2019 to 2024. To overcome the observation bottlenecks, Tracy–Widom-guided PSI (TW-PSI) was employed and compared against SBAS and conventional PSI. The TW-PSI approach successfully increased on-structure measurement point density by approximately 40%, supporting a first-order ascending–descending decomposition into east–west and quasi-vertical components. The analysis reveals a persistent settlement bowl at the central crest (C7–C12), consistent with long-term leveling observations and supported by regional GNSS trend checking. While the 2022 Mw 5.6 Cianjur earthquake showed no statistically significant co-seismic crest deformation, a strong correlation (r = −0.709) was identified between crest deformation and reservoir water-level variations, suggesting an observational association between reservoir level and crest settlement tendency. Furthermore, the application of the Annual Structural Deformation Tolerance Ratio (ASDTR) identified specific priority monitoring zones. These findings demonstrate that the proposed integrated framework can support operational dam deformation monitoring by linking satellite-derived measurements with in situ observations and engineering-oriented interpretation. Full article

Subsidence is the lowering of the ground surface caused by both natural processes, such as geological and tectonic dynamics, and anthropogenic activities related to land and resource use. Identifying and monitoring this phenomenon is essential for several reasons, including ensuring public safety, supporting the sustainable management of subsurface resources, and mitigating potential economic impacts. This study investigates ground deformation in an underexplored sector of the Calabria Region (Southern Italy), namely the Sant’Eufemia Plain. To this end, long-term Sentinel-1 datasets were processed using multi-temporal Synthetic Aperture Radar Interferometry techniques. Significant subsidence, reaching locally up to −17 mm/yr, was detected in the industrial area of San Pietro Lametino. Historical SAR datasets (ERS, ENVISAT) and optical imagery were used to reconstruct the long-term evolution of deformation since the 1990s. Satellite observations were integrated with rainfall records, piezometric data, and geotechnical modelling. A spatially distributed comparison between groundwater level variations and InSAR-derived deformation, supported by local time-series analysis, highlights weak and inconsistent correlations, indicating that groundwater fluctuations alone do not linearly control subsidence. The results suggest that subsidence is primarily associated with long-term consolidation processes affecting highly compressible Holocene deposits, likely enhanced by anthropogenic loading, while groundwater variations may contribute by modifying effective stress conditions within the subsoil. The relative contribution of these processes remains unquantified, highlighting the need for coupled hydro-mechanical investigations. Full article

Surface deformation is characterized by long-term accumulation and significant spatial differences, commonly inducing ground fissures and structural damage to roads and buildings, and in severe cases, causing collapses and other accidents that directly threaten human life. Reliable deformation monitoring and prediction are of great significance for early warning and infrastructure safety. Synthetic aperture radar interferometry (InSAR) technology can be used to acquire high-spatiotemporal-resolution temporal observations of surface deformation over a large area, but research on surface deformation prediction using InSAR temporal images is still relatively limited. Therefore, in this study, we used Sentinel-1 temporal imagery as the data foundation. Firstly, small baseline subset (SBAS)-InSAR inversion was used to obtain the line-of-sight-oriented cumulative deformation sequence and subsidence rate results. Based on this, the causal multi-trend fusion patch-to-point (CMTF-P2P) surface deformation prediction framework was developed. This framework effectively separates the long-term trends and short-term fluctuations in the deformation sequence through causal decomposition; introduces external drivers such as rainfall and event phase characteristics to enhance the temporal expression; and utilizes patch-to-point fusion of neighborhood spatial information to improve the spatial continuity and the ability to characterize local differences. Experimental results show that the method has an RMSE of 0.43 mm and an R² of 0.92, outperforming time-series deformation prediction models such as LSTM and iTransformer. Compared with traditional models that learn overall change patterns from historical time series, this paper alleviates the confusion between trends and volatility using causal STL decomposition, making the model’s expression of long-term trends and seasonal and short-term fluctuations in volatility clearer; event phase encoding enhances the model’s ability to characterize sudden disturbances and phased responses to events; the patch-to-point structure incorporates spatiotemporal information within the neighborhood, enhancing the model’s ability to apply spatiotemporal information; multi-branch collaborative modeling enhances the model’s ability to characterize multi-scale temporal features; and incremental cumulative consistency constraints enhance the physical consistency of the prediction results. Full article

Monitoring urban subsidence over ultra-long periods using time-series Interferometric synthetic aperture radar (InSAR) technology is critically important. Conventional approaches, however, face two main limitations: significant atmospheric phase residuals in complex urban settings, and discontinuous temporal time-series with short temporal coverage due to single-platform data constraints. To address these limitations, this study presents a new method for estimating ultra-long-term subsidence time series in urban areas, which combines Interferometric Subset Stacking (ISS) with multi-platform data fusion (DF). The methodology firstly processes TerraSAR-X and Sentinel-1A datasets through differential interferometry and applies ISS for atmospheric phase suppression. Next, bilinear interpolation unifies the spatial resolution and aligns the spatial reference frames of the two datasets. Subsequently, joint modeling derives subsidence velocities. Finally, temporal integration via linear interpolation and moving averaging produces a unified spatio-temporal deformation sequence. Applied to the Beijing region, China, this approach generated a 12-year ultra-long-term subsidence time series result (2012–2024), revealing maximum cumulative subsidence of 1100 mm spatially correlated with groundwater extraction patterns. Validation against Global Navigation Satellite System (GNSS) data showed strong agreement (correlation coefficient: 0.94, Root Mean Square Error (RMSE): 6.3 mm). The method achieved substantial atmospheric reduction—67.7% for Sentinel-1A and 24.1% for TerraSAR-X—representing approximately 15–20% accuracy improvement over conventional Generic Atmospheric Correction Online Service (GACOS) for InSAR. By effectively utilizing multi-platform data, this approach makes fuller use of the available phase information and compensates for the temporal gaps inherent in single-satellite datasets. It thus offers a valuable framework for long-term urban deformation monitoring. Full article

Spaceborne Synthetic Aperture Radar (SAR) interferometry provides long-term displacement measurements, but the quality of Persistent Scatterer (PS) time series depends critically on temporal coherence. Low-coherence points often exhibit auto-uncorrelated behaviours, which may be relevant to discriminate fast phenomena. This work introduces a coherence-based framework that identifies the coherence threshold beyond which PS displacement series retain sufficient reliability to support modelling. The threshold is estimated by analysing how data uncertainty, inferred through Sparse Bayesian Learning (SBL) techniques, varies with coherence and by detecting abrupt changes in this relationship. Once the optimal threshold is established, only the most reliable PS are used to train an SBL regression model linking satellite line-of-sight displacement to soil temperature and surface humidity measured by a low-cost ground sensor. PS-Interferometric SAR (PS-InSAR) time series are derived from COSMO-SkyMed raw images. The SBL model employs compressive-sensing principles and latent-parameter dictionaries of basis functions, whose latent parameters are calibrated through a constrained multi-start optimisation of a normalised residual-based objective function, regularised by a sub-validation dataset. In this work, it is shown that the trained model enables temporally denser reconstruction of displacement histories than the satellite revisit cycle allows and enables continuous soil monitoring by comparing model predictions with newly acquired PS-InSAR data. Full article

Within the field of the structural monitoring of bridges, numerous technologies and methodologies have been developed. Among these, methods based on synthetic aperture radar (SAR) which utilise satellite data from missions such as Sentinel-1 (European Space Agency-ESA) and COSMO-SkyMed (Agenzia Spaziale Italiana—ASI) to capture displacements, temperature-related changes, and other geophysical measurements have gained increasing attention. However, SAR has yet to establish its value and potential fully; its broader adoption hinges on consistently demonstrating its robustness through recurrent applications, well-defined use cases, and effective strategies to address its inherent limitations. This study presents a systematic literature review (SLR) conducted in accordance with key stages of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. An initial corpus of 1218 peer-reviewed articles was screened, and a final set of 25 studies was selected for in-depth analysis based on citation impact, keyword recurrence, and thematic relevance from the last five years. The review critically examines SAR-based techniques—including Differential Interferometric SAR (DInSAR), multi-temporal InSAR (MT-InSAR), and Persistent Scatterer Interferometry (PSI), as well as approaches to integrating SAR data with ground-based measurements and complementary digital models. Emphasis is placed on real-world case studies and persistent technical challenges, such as atmospheric artefacts, Line-of-Sight (LOS) geometry constraints, phase noise, ambiguities in displacement interpretation, and the translation of radar-derived deformations into actionable structural insights. The findings underscore SAR’s significant contribution to the structural health monitoring (SHM) of bridges, consistently delivering millimetre-level displacement accuracy and enabling engineering-relevant interpretations. While standalone SAR-based techniques offer wide-area monitoring capabilities, their full potential is realised only when integrated with complementary procedures such as thermal modelling, multi-sensor validation, and structural knowledge. Finally, this document highlights the persistent technical constraints of InSAR in bridge monitoring—including measurement ambiguities, SAR image acquisition limitations, and a lack of standardised, automated workflows—that continue to impede operational adoption but also point toward opportunities for methodological improvement. Full article

The Synthetic Aperture Radar Interferometry (InSAR) technique enables researchers to generate Digital Elevation Models (DEMs) from SAR data, which researchers widely apply in multi-temporal analyses, including ground deformation monitoring, susceptibility mapping, and analysis of spatial changes in erosion basins. In this study, we generated two interferometric DEMs from Sentinel-1 (S1, VV polarization) and TerraSAR-X (TSX, HH polarization, ascending orbit) data, processed in SNAP, over a mountainous sector of the central Andes in Chile. We assessed the accuracy of the DEMs against two reference datasets: the SRTM DEM and a high-resolution LiDAR-derived DEM. We selected 150 randomly distributed points across different slope classes to compute statistical metrics, including RMSE and MedAE. Relative to the LiDAR DEM, both sensors yielded rMSE values of approximately 20 m, increasing to 23–24 m when compared with the SRTM DEM. The MedAE, a metric less sensitive to outliers, was 3.97 m for S1 and 3.26 m for TSX with respect to LiDAR, and 7.07 m for S1 and 7.49 m for TSX relative to SRTM. We observed a clear positive correlation between elevation error and terrain slope. In areas with slopes greater than 45°, the MedAE exceeded 14 m relative to the LiDAR DEM and reached ~15 m relative to the SRTM for both S1 and TSX. Full article

Reliable assessments of dam stability require the continuous acquisition and interpretation of deformation data, as monitoring technologies provide essential information for evaluating structural behavior. Surface displacement measurements are particularly valuable for identifying instability within the dam embankment and adjacent slopes. While terrestrial surveying networks can provide accurate point-based observations, they are often time-consuming and costly to maintain. Satellite radar interferometry (InSAR) offers a complementary, cost-effective means of monitoring surface displacement with wide spatial coverage; however, careful analysis is required to avoid misinterpreting superficial motions of riprap and cover materials as true dam settlement. In this study, we use multi-platform SAR datasets, including Sentinel-1A (2014–2019) and high-resolution TerraSAR-X (2018), to investigate the deformation behavior of the Taleqan Dam. We compare LOS displacement derived from InSAR with independent measurements from a terrestrial surveying network spanning the same period. TerraSAR-X data indicate up to ~20 mm of LOS displacement over three months (May–August 2018), and the displacement pattern is consistent with the Sentinel-1 time series. Despite lower spatial resolutions, Sentinel-1 provided dense, temporally continuous coverage, with LOS velocities reaching ~4 mm/yr on the downstream slope. The combined datasets demonstrate that the observed deformation predominantly reflects the ongoing lateral movement of downstream riprap materials rather than the vertical settlement of the dam’s core. These results highlight both the utility of InSAR for long-term dam monitoring and the importance of integrating multi-sensor observations to ensure accurate interpretations of dam deformation signals. Full article

Coal mines play an important role in the global energy supply. Monitoring the displacement of open-pit mines is crucial to preventing geological disasters, such as landslides and surface displacement, caused by high-intensity mining activities. In recent years, multi-temporal Synthetic Aperture Radar Interferometry (InSAR) technology has advanced and become widely used for monitoring the displacement of open-pit mines. However, the scattering characteristics of surfaces in open-pit mining areas are unstable, resulting in few coherence points with uneven distribution. Small BAseline Subset InSAR (SABS-InSAR) technology struggles to extract high-density points and fails to capture the overall displacement trend of the monitoring area. To address these challenges, this study focused on the Shengli West No. 2 open-pit coal mine in eastern Inner Mongolia, China, using 201 Sentinel-1 images collected from 20 May 2017 to 13 April 2024. We applied both SBAS-InSAR and distributed scatterer InSAR (DS-InSAR) methods to investigate the surface displacement and long-term behavior of the open-pit coal mine over the past seven years. The relationship between this displacement and mining activities was analyzed. The results indicate significant land subsidence was observed in reclaimed areas, with rates exceeding 281.2 mm/y. The compaction process of waste materials was the main contributor to land subsidence. Land uplift or horizontal displacement was observed over the areas near the active working parts of the mines. Compared to SBAS-InSAR, DS-InSAR was shown to more effectively capture the spatiotemporal distribution of surface displacement in open-pit coal mines, offering more intuitive, comprehensive, and high-precision monitoring of open-pit coal mines. Full article

Land subsidence poses a significant risk in built-up environments, particularly in geologically complex and tectonically active regions. In this study, we integrated Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques—Persistent Scatterer Interferometry (PS-InSAR) and Small Baseline Subset (SBAS)—with Global Navigation Satellite System (GNSS) observations to assess ground deformation in the Metamorphosis (MET0) area of Attica, Greece. A Kalman filtering approach was applied to fuse displacement measurements from GNSS, PS-InSAR, and SBAS, reducing noise and improving temporal consistency. Additionally, the PS and SBAS vertical displacement data were fused using Kalman filtering to enhance spatial coverage and refine displacement estimates. The results reveal significant subsidence trends ranging between −10 mm and −24 mm in localized zones, particularly near hydrographic networks and active fault systems. Fault proximity, fluvial processes, and unconsolidated sediments were identified as key drivers of displacement. Random Forest regression analysis, coupled with Partial Dependence analysis, demonstrated that distance to faults, proximity to streams, and the presence of stream drops and debris zones were the most influential factors affecting displacement patterns. This study highlights the effectiveness of integrating multi-sensor remote sensing techniques with data-driven machine learning analysis (Kalman filtering) to improve land subsidence assessment. The findings highlight the necessity of continuous geospatial monitoring for infrastructure resilience and geohazard risk mitigation in the Attica region. Full article

In recent decades, the interferometric synthetic aperture radar (InSAR) technique has emerged as a powerful tool for monitoring ground subsidence and geohazards. Various satellite SAR systems with different modes, such as Sentinel-1 and Lutan-1, have produced abundant SAR datasets with wide coverage and large historical archives, which have significantly influenced long-term deformation monitoring applications. However, large-scale InSAR data have posed significant challenges to conventional InSAR methods. These issues include the computational burden and storage of multi-temporal InSAR (MT-InSAR) methods, as well as temporal decorrelation for coherent scatterers with long temporal baselines. In this study, we propose a stepwise MT-InSAR with a temporal coherent scatterer method to address these problems. First, a batch sequential method is introduced in the algorithm by grouping the SAR dataset in the time domain based on the average coherence distribution and then applying permanent scatterer interferometry to each temporal subset. Second, a multi-layer network is employed to estimate deformation for partially coherent scatterers using small baseline subset interferograms, with permanent scatterer deformation parameters as the reference. Finally, the final deformation rate and displacement time series were obtained by incorporating all the temporal subsets. The proposed method efficiently generates high-density InSAR deformation measurements for long-time series analysis. The proposed method was validated using 9 years of Sentinel-1 data with 229 SAR images from Jakarta, Indonesia. The deformation results were compared with those of conventional methods and global navigation satellite system data to confirm the effectiveness of the proposed method. Full article

Multi-temporal synthetic aperture radar interferometry (MT-InSAR) has evolved from a niche research technique into a powerful global monitoring tool. With the launch of nationwide and continent-wide ground motion services (GMSs), freely available deformation data can now be analyzed on a large scale. However, their applicability for monitoring critical infrastructure, such as dams, has not yet been thoroughly assessed, and several challenges have hindered the integration of MT-InSAR into existing monitoring frameworks. These challenges include technical limitations, difficulties in interpreting deformation results, and the rigidity of existing safety protocols, which often restrict the adoption of remote sensing techniques for operational dam monitoring. This study evaluates the effectiveness of persistent scatterer (PS) data from the German ground motion service (Bodenbewegungsdienst Deutschland, BBD) in complementing time-consuming in situ techniques. By analyzing a gravity dam in Germany, BBD time series were compared with in situ pendulum data. We propose a two-stage assessment procedure: First, we evaluate the dam’s suitability for PS analysis using the CR-Index to identify areas with good radar visibility. Second, we assess the interpretability of BBD data for radial deformations by introducing a novel index that quantifies the radial sensitivity of individual PS points on the dam. This index is universally applicable and can be transferred to other types of infrastructure. The results revealed a fair correlation between PS deformations and pendulum data for many PS points (up to $R^2$ = 0.7). A priori feasibility assessments are essential, as factors such as topography, land cover, and dam type influence the applicability of the PS technique. The dam’s orientation relative to the look direction of the sensor emerged as a key criterion for interpreting radial deformations. For angle differences (${\Delta }_{\mathrm{RAD}}$) of up to 20° between the true north radial angle of a PS point and the satellite’s look direction, the line-of-sight (LOS) sensitivity accounts for approximately 50 to 70% of the true radial deformation, depending on the satellite’s incidence angle. This criterion is best fulfilled by dams aligned in a north–south direction. For the dam investigated in this study, the LOS sensitivity to radial deformations was low due to its east–west orientation, resulting in significantly higher errors (6 mm $\le RMSE\le 43$ mm) compared to in situ pendulum data. Eliminating PS points with an unfavorable alignment with the sensor should be considered before interpreting radial deformations. For implementation into operational monitoring programs, greater effort must be spent on near-real-time updates of BBD datasets. Full article

Deformation monitoring is a critical task for dam operators to guarantee safe operation. Given an increasing number of extreme weather events caused by climate change, the precise prediction of dam deformations has become increasingly important. Traditionally, multiple linear regression models have been employed, utilizing in situ data from pendulum systems or trigonometric measurements. These methods sometimes suffer from sparse data, which typically represent deformations only at specific points on the dam, if such data are available at all. Technical advances in multi-temporal synthetic aperture radar interferometry (MT-InSAR), particularly Persistent Scatterer Interferometry (PSI), address these limitations by enabling monitoring in high spatial and temporal resolution, capturing dam deformations with millimeter precision, and providing extensive spatial coverage. This study advances traditional methods of dam monitoring by employing data-driven techniques and integrating Sentinel-1 C-band Persistent Scatterer (PS) time series alongside in situ data. Through a comprehensive evaluation of advanced data-driven approaches, we demonstrated considerable improvements in predicting dam deformations and evaluating their drivers. The analysis provided evidence for the following insights: First, the accuracy of current modeling approaches can be greatly improved by utilizing advanced feature engineering and data-driven model selection. The prediction performance of the pendulum data was improved by utilizing data-driven algorithms, reducing the mean absolute error from 0.51 mm in the baseline model (R² = 0.92) to as low as 0.05 mm using the full model search space (R² = 0.99). Although the model accuracy for the PS datasets (MAE_max: 0.81 mm) was about one order of magnitude lower than that for pendulum data, the mean absolute errors could be reduced by up to 0.25 mm. Second, by incorporating freely available PS time series into deformation prediction, dams can be monitored in higher spatial resolution, making PSI a valuable tool for dam operators. This requires adequate dataset filtering to eliminate noisy PS points. Third, extended representations of water level and temperature, including interaction effects, can improve model accuracy and reduce prediction errors. With these insights, we recommend incorporating the proposed methodology into the monitoring program of gravity dams to enhance the accuracy in predicting their expected deformations. Full article

The European Ground Motion Service (EGMS) and geospatial data are integrated in this paper to evaluate ground deformation and its effects on critical infrastructures in the Preveza Regional Unit. The EGMS, a new service of the Copernicus Land Monitoring Service, employs information from the C-band Synthetic Aperture Radar (SAR)-equipped Sentinel-1A and Sentinel-1B satellites. This allows for the millimeter-scale measurement of ground motion, which is essential for assessing anthropogenic and natural hazards. The study examines ground displacement from 2018 to 2022 using multi-temporal Synthetic Aperture Radar Interferometry (MTInSAR). The Regional Unit of Preveza was selected for study area. According to the investigation, the area’s East–West Mean Velocity Displacement varies between 22.5 mm/y and −37.7 mm/y, while the Vertical Mean Velocity Displacement ranges from 16 mm/y to −39.3 mm/y. Persistent Scatterers (PSs) and Distributed Scatterers are the sources of these measurements. This research focuses on assessing the impact of ground deformation on 21 school units, 2 health centers, 1 hospital, 4 bridges and 1 dam. The findings provide valuable insights for local authorities and other stakeholders, who will greatly benefit from the information gathered from this study, which will lay the groundwork for wise decision-making and the creation of practical plans to strengthen the resistance of critical infrastructures to ground motion. Full article