Search Results (166)

Landslide susceptibility maps (LSMs) are crucial for risk mitigation, but integrating Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data is often hampered by a lack of physical interpretation. To address this issue, this study proposes an enhanced modeling framework that integrates multi-source monitoring data by coupling dynamic deformation features. Ground deformation velocity is obtained using MT-InSAR and embedded as dynamic physical constraints into the loss function of a Multi-Layer Perceptron (MLP) model. This approach enables the joint optimization of static geological factors and dynamic deformation characteristics in landslide susceptibility prediction. The proposed framework was applied to Zunyi City, Guizhou Province, China, utilizing an inventory of landslide hazard sites and a dataset of 16 susceptibility factors for model training and evaluation. The results demonstrated that the dynamically constrained model significantly improved predictive performance (AUC = 0.976, an increase of 0.032 compared to the baseline model), and enhanced spatial consistency, reflected by an average increase of 0.0184 in predicted susceptibility for inventoried landslide hazard sites. The framework also outperformed other conventional machine learning models across multiple evaluation metrics. Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that slope (18.68%), DEM (13.26%), rainfall (11.57%), and mining activities (8.79%) were the primary contributing factors in high-susceptibility areas. This study offers a physically interpretable and robust methodology that advances landslide risk assessment and contributes to disaster prevention strategies. Full article

Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques have received rapid advance in recent years, and the Multi-temporal InSAR (MT-InSAR) has been widely applied in various earth observations. Distributed scatterer (DS) InSAR is one of the most advanced MT-InSAR methods, and has overcome the limitation of the lack of enough measurement points in the low coherent regions for traditional methods. While the Joint-Scatterer InSAR (JS-InSAR) is the extension of DS InSAR method, which exploited the overall information of Joint Scatterers to carry out DS identification and phase optimization. And it can avoid the inaccuracy caused by the offset errors between scatterers in complex terrain areas. However, the intensive computation and low efficiency have severely restricted the application of JS-InSAR, especially when dealing with massive and long historical SAR images. As the sequential estimator has proven to successfully improve the efficiency of MT-InAR and obtain near-time deformation time series, in this work, we proposed the sequential-based JS-InSAR (S-JSInSAR) method with flexible batches. This method has adaptively divided large single look complex (SLC) stack into different batches with flexible number and certain overlaps. Then, the JS-InSAR processing is performed on each batch, respectively, and these estimated results are integrated into the final deformation time series based on the connection mode. Thus, S-JSInSAR can efficiently process large InSAR dataset, and mitigate the decorrelation effect caused by long temporal baselines. To demonstrate the effectiveness of the S-JSInSAR, a multi-year of 145 Sentinel-1 ascending SAR images in Tangshan, China, were collected to estimate the long deformation time series. And the results compared with other methods have shown the processing time has substantially decreased without the loss of deformation accuracy, and obtain deformation spatial distribution with more details in local regions, which have well validated the efficiency and reliability of the proposed method. Full article

Wearable antennas for wireless sensor network (WSN) applications require circularly polarized (CP) radiation to maintain stable communication link under human body movement and complex environments. However, many existing wearable CP antennas rely on either linearly polarized (LP) or CP radiator with a single axial ratio (AR) mode combined with external polarization conversion structures, which limit the achievable axial ratio bandwidth (ARBW). In this work, an all-textile wideband CP antenna with a square-ring notched slot radiator, a 50 Ω microstrip line, and a 3 × 3 nonuniform metasurface (MTS) is proposed for 5.85 GHz WSN applications. Unlike conventional CP generation approaches, the square-ring notched slot, analyzed using characteristic mode analysis (CMA), directly excites three distinct AR modes, enabling potential wideband CP radiation. The nonuniform MTS further improves IBW performance by exciting additional surface wave resonances. Moreover, the nonuniform MTS further enhances ARBW by redirecting the incident wave into an orthogonal direction with equivalent amplitude and a 90° phase difference at higher frequency region. The proposed antenna is composed of conductive textile and felt substrates, offering flexibility for wearable applications. The proposed antenna is measured in free space, on human bodies, and fresh pork in an anechoic chamber. The measured results show a broad IBW and ARBW of 84.52% and 43.56%, respectively. The measured gain and radiation efficiency are 4.47 dBic and 68%, respectively. The simulated specific absorption rates (SARs) satisfy both US and EU standards. 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

Critical transmission lines frequently traverse geologically complex mountainous regions, where harsh environments and variable climatic conditions pose significant geohazard risks. Utilizing 163 Sentinel-1A scenes (January 2018 to October 2023), we employed Multi-Temporal InSAR (MT-InSAR) to derive the deformation field along the transmission corridor. Time-series analysis of the Lingshao (LS) line towers, interpreted through the principles of mining subsidence, revealed the mechanisms behind their differential tilt. Simultaneously, time-series deformation at the tower footings was input to a deep learning model for 365-day prediction; the accuracy and practical applicability of which were rigorously assessed. The results demonstrate that (1) a unidirectional subsidence funnel within the transmission corridor deformation field, in the absence of zonal settlement features, strongly indicates the presence of a goaf beneath the line; (2) the integrated approach combining time-series InSAR with the settlement trough method proves feasible for monitoring transmission tower tilt, as validated through field verification; (3) the magnitude and direction of tower tilt correlate directly with their position in the mining-induced subsidence basin, showing convergent tilt in tensile zones, divergent tilt in compressive zones, and uniform settlement in neutral zones; (4) for the eight selected typical tower footings, predicted deformation values ranged from −284.6 mm to −186.3 mm, showing excellent agreement with measurements through correlation coefficients of 0.989–0.999 and Root Mean Square Error (RMSE) values of 0.54–2.17 mm. The framework enables proactive hazard avoidance during line routing and provides early warning for tower defects, significantly enhancing power infrastructure resilience in mining-affected regions. Full article

By altering aquifer storage capacity, groundwater level (GWL) plays a critical role in driving surface deformation, including ground subsidence and uplift. Groundwater depletion can induce sinkholes or subsidence, whereas recharge can cause surface uplift. These processes pose significant risks to soft grounds composed of soft alluvial sediments, emphasizing the importance of regular monitoring. In this study, we applied the small baseline subset (SBAS) technique to conduct a time-series analysis of surface deformation in Gimhae City, South Korea, where a continuous GWL increase was observed. Seasonal trend decomposition using the Loess (STL) method was employed to isolate the long-term GWL trend by removing seasonal variability. Multi-frequency synthetic aperture radar datasets, including ALOS PALSAR, COSMO-SkyMed, and Sentinel-1, revealed a cumulative surface uplift of approximately 9.2 cm, primarily concentrated along the deepest GWL contour line and confined between two lineament structures. The decomposed velocities from Sentinel-1 highlighted the predominance of vertical displacement over horizontal movement. Time-series analyses consistently showed uplift patterns, whereas correlation analysis demonstrated a strong relationship (R² > 0.75) between surface deformation and GWL changes from 2013 to 2021. These results suggest a significant link between surface uplift and the rising GWL in Gimhae City, providing insights into the hydrogeological processes that influence ground deformation. Furthermore, a time lag between the GWL changes and surface displacement was identified, providing valuable insights into the dynamics of groundwater-related surface deformation. Full article

Henan Province, characterized by hills and mountains in its western, northern, and southern regions, is a high-risk area for geohazards in China. In this paper, we are the first to investigate the geohazards over Henan using the basic deformation products of LuTan-1, and we provide the minimum detectable deformation gradients of the products. The basic products consist of deformation field products generated by differential interferometric synthetic aperture radar (InSAR, DInSAR) and time-series deformation products derived from multi-temporal InSAR (MT-InSAR). They were produced using the acquisitions from June 2023 to February 2025. We identified 1620 potential geohazards, including 1340 landslides located in western and southern Henan, 139 ground collapses due to underground mining concentrated in the coal-rich central and eastern regions, and 141 cases of ground deformation located mainly in the agricultural areas of central and northern Henan. DInSAR detected 1470 hazards, while MT-InSAR found 150 more. By calculating the deformation between adjacent pixels, we found that the minimum detectable deformation gradients of the 150 geohazards were less than 0.061 mm/m, which is not detectable by DInSAR. The deformation gradients were greater than 0.017 mm/m and were discovered by MT-InSAR. The overall distribution exhibits a certain pattern, offering a basis for geohazard monitoring. Full article

Time-series Interferometric Synthetic Aperture Radar (InSAR) techniques encounter substantial reliability challenges, primarily due to the presence of gross errors arising from phase unwrapping failures. These errors propagate through the processing chain and adversely affect displacement estimation accuracy, particularly in the case of a small number of SAR datasets. This study presents a unified data fusion framework designed to enhance the detection of gross errors in multi-source InSAR observations, incorporating a robust Least Squares Adjustment (LSA) methodology. The proposed framework develops a comprehensive mathematical model that integrates the fusion of multi-source InSAR data with robust LSA analysis, thereby establishing a theoretical foundation for the integration of heterogeneous datasets. Then, a systematic, reliability-driven data fusion workflow with robust LSA is developed, which synergistically combines Multi-Temporal InSAR (MT-InSAR) processing, homonymous Persistent Scatterer (PS) set generation, and iterative Baarda’s data snooping based on statistical hypothesis testing. This workflow facilitates the concurrent localization of gross errors and optimization of displacement parameters within the fusion process. Finally, the framework is rigorously evaluated using datasets from Radarsat-2 and two Sentinel-1 acquisition campaigns over the Tianjin Binhai New Area, China. Experimental results indicate that gross errors were successfully identified and removed from 11.1% of the homonymous PS sets. Following the robust LSA application, vertical displacement estimates exhibited a Root Mean Square Error (RMSE) of 5.7 mm/yr when compared to high-precision leveling data. Furthermore, a localized analysis incorporating both leveling validation and time series comparison was conducted in the Airport Economic Zone, revealing a substantial 42.5% improvement in accuracy compared to traditional Ordinary Least Squares (OLS) methodologies. Reliability assessments further demonstrate that the integration of multiple InSAR datasets significantly enhances both internal and external reliability metrics compared to single-source analyses. This study underscores the efficacy of the proposed framework in mitigating errors induced by phase unwrapping inaccuracies, thereby enhancing the robustness and credibility of InSAR-derived displacement measurements. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) closely interacts with host cellular mechanisms, with mitochondria playing a crucial role in this process. As essential organelles that control cellular energy production, apoptosis, reactive oxygen species (ROS) metabolism, and innate immune responses, mitochondria are vital to the development of COVID-19. However, the exact molecular interactions between mitochondria and SARS-CoV-2 remain under active investigation. Gaining a comprehensive understanding of mitochondrial involvement in SARS-CoV-2 infection is therefore essential for uncovering complex disease mechanisms, identifying prognostic biomarkers, and developing effective treatments. Ultimately, exploring these virus–host interactions may provide new insights into the fundamental and complex aspects of mitochondrial physiology and pathophysiology. Full article

Fibroblast growth factor 21 (FGF21), a pleiotropic hormone, is a significant modulator of energy homeostasis. We evaluated serum FGF21 levels in patients with a deficiency of mitochondrial aminoacyl-tRNA synthetase (mt-aARSs). Six patients with mitochondrial aminoacyl tRNA synthetase deficiency and twelve healthy volunteers were included in this study. Whole-exome sequencing was used for molecular diagnosis. Serum FGF21 levels in the case group and healthy volunteers were analyzed using the enzyme-linked immunosorbent assay. Exome sequencing test revealed nine different pathogenic variants in the AARS2, EARS2, DARS2, SARS2, and WARS2 genes. A statistically significant difference was found between the serum FGF21 levels of the case and control groups: case group (n = 6), 882.49 ± 923.60 pg/mL; control group (n = 12), 20.89 ± 2.63 pg/mL (p < 0.001). The area under the ROC curve for FGF21 in the differential diagnosis of mitochondrial aminoacyl-tRNA synthetase deficiency was 1.000 (0.813–1.000). Sensitivity and specificity were 100%, and positive and negative predictive values were also 100% for an FGF21 cut-off value > 27.4 pg/mL. Assessment of FGF 21 levels as an indicator of mitochondrial damage in mt-aARSs deficiency may provide insight into the level of damage. Investigation of the biochemical mechanisms underlying the different levels of damage caused by different aminoacyl tRNA synthetases will be important in terms of elucidating clinical heterogeneity. Full article

Post-acute sequelae of SARS-CoV-2 infection (long COVID) present with persistent fatigue, cognitive impairment, and autonomic and multisystem dysfunctions that often go unnoticed by standard diagnostic tests. Increasing evidence suggests that mitochondrial dysfunction and oxidative stress are central drivers of these post-viral sequelae. Viral infections, particularly SARS-CoV-2, disrupt mitochondrial bioenergetics by altering membrane integrity, increasing mitochondrial reactive oxygen species (mtROS), and impairing mitophagy, leading to sustained immune activation and metabolic imbalance. This review synthesizes an understanding of how mitochondrial redox signaling and impaired clearance of damaged mitochondria contribute to chronic inflammation and multisystem organ symptoms in both long COVID and post-vaccine injury. We discuss translational biomarkers and non-invasive techniques, exploring therapeutic strategies that include pharmacological, non-pharmacological, and nutritional approaches, as well as imaging modalities aimed at assessing and restoring mitochondrial health. Recognizing long COVID as a mitochondrial disorder that stems from redox imbalance will open new options for personalized treatment and management guided by biomarkers. Future clinical trials are essential to validate these approaches and translate mitochondrial resuscitation into effective care for patients suffering from long COVID and related post-viral syndromes. Full article

Endothelial-to-mesenchymal transition (EndMT) is associated with various pathologies including cardiovascular, inflammatory, and fibrotic diseases or neoplasia. Concerning COVID-19, multiple organ dysfunctions and long COVID syndrome are mediated by microvascular damage and, recently, the role of SARS-CoV-2 peptide fragments in the induction of EndMT was demonstrated. Here, we investigated the immune-mediated effects of Spike S1 of SARS-CoV-2 on EndMT and demonstrated that cytokines secreted by S1-activated macrophages, mainly TNFα + IFNγ, also induce the phenotypical switch in HUVECs. In particular, a loss of the typical cobblestone morphology is observed, along with a huge reduction in endothelial adhesion molecules, such as vWF, CD31, and VE-cadherin, and a concomitant acquisition of mesenchymal markers, such as N-cadherin and FSP1 protein. In addition, the combined use of the drug infliximab, targeting TNFα, and baricitinib, an inhibitor of the JAK-STAT pathway, hinders the phenotypical changes by restoring the proper expression of endothelial markers. The protective effect of these drugs is evident not only when they are added to the culture medium together with the trigger, but also when added later, i.e., once EndMT has been started. These findings reinforce the role of COVID-19-associated cytokine storm in endothelial dysfunction and in the onset of the fibrotic process and sustain the clinical relevance of infliximab and baricitinib for the prevention of vascular damage. Full article

The background for this study is the limitations of the conventional approach of using deforestation area multiplied by biomass densities or emission factors. We demonstrated how TanDEM-X and GEDI data can be combined to estimate forest Above Ground Biomass (AGB) change at the national scale for Tanzania. The results can be further recalculated to estimate CO₂ emissions and removals from the forest. We used repeated short wavelength, InSAR DEMs from TanDEM-X to derive changes in forest canopy height and combined this with GEDI data to convert such height changes to AGB changes. We estimated AGB change during 2012–2019 to be −2.96 ± 2.44 MT per year. This result cannot be validated, because the true value is unknown. However, we corroborated the results by comparing with other approaches, other datasets, and the results of other studies. In conclusion, TanDEM-X and GEDI can be combined to derive reliable temporal change in AGB at large scales such as a country. An important advantage of the method is that it is not required to have a representative field inventory plot network nor a full coverage DTM. A limitation for applying this method now is the lack of frequent and systematic InSAR elevation data. Full article

We inspected a sector of the Apennines (central–southern Italy) in geographic and structural continuity with the Quaternary-active extensional belt but where clear geomorphic and seismological signatures of normal faulting are unexpectedly missing. The evidence of active tectonics in this area, between Abruzzo and Molise, does not align with geodetic deformation data and the seismotectonic setting of the central Apennines. To investigate the apparent disconnection between active deformation and the absence of surface faulting in a sector where high lithologic erodibility and landslide susceptibility may hide its structural evidence, we combined multi-scale and multi-source data analyses encompassing morphometric analysis and remote sensing techniques. We utilised high-resolution topographic data to analyse the topographic pattern and investigate potential imbalances between tectonics and erosion. Additionally, we employed aerial-photo interpretation to examine the spatial distribution of morphological features and slope instabilities which are often linked to active faulting. To discern potential biases arising from non-tectonic (slope-related) signals, we analysed InSAR data in key sectors across the study area, including carbonate ridges and foredeep-derived Molise Units for comparison. The topographic analysis highlighted topographic disequilibrium conditions across the study area, and aerial-image interpretation revealed morphologic features offset by structural lineaments. The interferometric analysis confirmed a significant role of gravitational movements in denudating some fault planes while highlighting a clustered spatial pattern of hillslope instabilities. In this context, these instabilities can be considered a proxy for the control exerted by tectonic structures. All findings converge on the identification of an ~20 km long corridor, the Castel di Sangro–Rionero Sannitico alignment (CaS-RS), which exhibits varied evidence of deformation attributable to active normal faulting. The latter manifests through subtle and diffuse deformation controlled by a thick tectonic nappe made up of poorly cohesive lithologies. Overall, our findings suggest that the CaS-RS bridges the structural gap between the Mt Porrara–Mt Pizzalto–Mt Rotella and North Matese fault systems, potentially accounting for some of the deformation recorded in the sector. Our approach contributes to bridging the information gap in this complex sector of the Apennines, offering original insights for future investigations and seismic hazard assessment in the region. Full article

Monitoring the structural health of bridges in road infrastructure is crucial for ensuring public safety and efficient maintenance. This paper presents a multi-level semi-automatic methodology for bridge monitoring, using Multi-Temporal Differential SAR Interferometry (MT-DInSAR) data. The proposed approach requires a dataset of satellite-derived MT-DInSAR measurements for the Area of Interest. The methodology involves creating a georeferenced database of bridges which allows the filtering of measurement points (generally named Persistent Scatterers—PSs) using spatial queries. Since existing datasets often provide only point geometries for bridge locations, additional data sources such as OpenStreetMaps-derived repositories have been utilized to obtain linear representations of bridges. These linear features are segmented into 20 m sections, which are then converted into polygonal geometries by applying a uniform buffer. Spatial joining between the bridge polygons and PS datasets allows the extraction of key statistics, such as mean displacement velocity, PS density and coherence levels. Based on predefined velocity thresholds, warning flags are triggered, indicating the need for further in-depth analysis. Finally, an upscaling step is performed to provide a practical tool for infrastructure managers, visually categorizing bridges based on the presence of flagged pixels. The proposed approach facilitates large-scale bridge monitoring, supporting the early detection of potential structural issues. Full article