COSMO-SkyMed Images Research Articles

Mapping vertical and horizonal deformation of the newly reclaimed third runway at Hong Kong International Airport with PAZ, COSMO-SkyMed, and Sentinel-1 SAR images

In order to meet long-term air transportation needs, Hong Kong International Airport (HKIA) has been constructing the third runway system (3RS) through land reclamation projects since 2016. However, the third runway and taxiway, which were paved in September 2021, suffered from severe surface settlement. Therefore, we attempted to reveal the settlement pattern within the 3RS and analyze the factors contributing to this displacement. Employing the multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique, we utilized PAZ, COSMO-SkyMed, and Sentinel-1 satellite images to extract ground displacements along the radar line of sight. The results show that, compared with COSMO-SkyMed and Sentinel-1 images, PAZ demonstrates superior performance in the deformation monitoring of the HKIA, with more deformation details and larger displacements. The maximum settlement rate derived by PAZ on the 3RS exceeds 110 mm/year. To ensure the reliability of the derived displacements along the LOS, we validated them through cross-validation, comparing the outcomes from different SAR data with data from GPS stations. In order to gain a detailed deformation information, we derived the vertical and horizontal (ground range direction) displacements by utilizing deformation measurements along the LOS of multiple SAR satellites and the geometric information, and the maximum values recorded for vertical and E-W displacements reached 88.31 mm/y and 41.91 mm/y, respectively. Furthermore, our investigation revealed that the significant local deformation observed on the taxiway and third runway was predominantly attributed to various factors including the creep of soft soil, consolidation of filling materials, characteristics of road surface materials, and distinct stages of construction.

Satellite monitoring of reinforced concrete buildings in areas affected by slow-moving landslides

Slow-moving landslides are very spread phenomena in Italy and worldwide. The study of their impact on the existing structures is of great interest, since they can provoke more or less serious consequences for structures, facilities and human life safety. Traditional monitoring techniques on site are usually adopted for landslides. The innovative Differential Interferometry Synthetic Aperture Radar (DInSAR) techniques, based on the use of data derived from satellite images, constitute an advanced technology that allows to remotely monitor the deformation of large portions of land over long periods of time, with relatively low costs. These techniques are able to well capture displacements slowly evolving in time; for this reason, they fit very well with slow-moving landslides. This work follows a previous study by some of the authors, where the structural health monitoring of a reinforced concrete infilled building within a landslide-affected area was made. A finite element model of the structure was created, assuming a non-linear behavior for the structural elements, focusing on the elastic part up to yielding, while infills were modeled as diagonal struts. DInSAR data derived from the processing of COSMO-SkyMed images in the period 2012-2016 were used in order to investigate the evolution of the infills damage over the years. In this work, the monitoring period has been extended by processing new COSMO-SkyMed images, up to 2022. The prediction of the future expected displacements, shown in the previous study, has been now compared to the real displacements detected. According to them, the analysis of the expected damage on the infills has been checked.

SAR-Transformer-based decomposition and geophysical interpretation of InSAR time-series deformations for the Hong Kong-Zhuhai-Macao Bridge

Time-series interferometric synthetic aperture radar (InSAR) provides a unique tool for measuring large-scale and long-term land surface deformation. Under the assumption of a single linear deformation model in conventional InSAR, it is difficult to quantify and interpret the impacts of multiple environmental factors that presumably induce nonlinear deformations. In this paper, we propose a SAR-Transformer method to decompose InSAR time-series signals into various physics-related components and apply the method to evaluate the deformation of the world's longest cross-sea bridge, the Hong Kong-Zhuhai-Macao Bridge (HZMB). We first developed an improved bridge geometry-based InSAR network to monitor the deformation of the HZMB using Sentinel-1 and COSMO-SkyMed images from 2019 to 2022, which were validated using the leveling and GPS data. The SAR-Transformer model was trained using synthetic InSAR time-series samples and applied to decompose the monitored InSAR measurements. Compared with that of conventional curve-fitting and seasonal-trend decomposition using LOESS, SAR-Transformer reduced the mean absolute error at least by 58.32% and mean absolute percentage error at least by 8.84% for time-series signal reconstruction. We evaluated the decomposed patterns according to the geotechnical, meteorological, and marine processes, and found that: 1) Seasonal thermal expansion owing to temperature changes was significant in all parts of the bridge, and deflection due to concrete shrinkage and creep was observed on cable-stayed bridges. 2) The artificial islands experienced evident ground subsidence with a decelerating trend. In particular, the newly adopted non-dredged reclamation method resulted in a lower decelerated settlement than that of fully-dredged reclamation areas. 3) The seawall showed linear horizontal movement from the outward stretching of the reclaimed soil consolidation and periodic displacement related to sea tidal loading. Furthermore, typhoons and coastal earthquakes had limited effects on the permanent movement of the bridge. These results improve the understanding of the interactions between artificial super-infrastructures and environmental factors, and provide valuable guidelines for the maintenance and management of the HZMB.

A machine learning-based method for multi-satellite SAR data integration

Large- and small-scale subsidence coexist in the world's coastal cities due to extensive land reclamation and fast urbanization. Synthetic aperture radar (SAR) images are typically limited by either low resolution or small coverage, making them ineffective for fully monitoring displacement in coastal areas. In this research, a machine learning-based method is developed to investigate the reclaimed land subsidence based on multi-satellite SAR data integration. The proposed method requires at least a pair of SAR images from complementary tracks. First, the line-of-sight (LOS) displacements are recovered in connection to a series of extremely coherent points based on the differential interferometry synthetic aperture radar (DInSAR). These LOS displacements are then converted into their vertical component, geocoded to a common grid, and simultaneously integrated (i.e., pixel-by-pixel) based on Support Vector Regression (SVR). The proposed methodology does not necessitate the simultaneous processing of huge DInSAR interferogram sequences. The experiments include high-resolution COSMO-SkyMed (CSK) and TerraSAR-X (TSX) images, as well as a small monitoring cycle Sentinel-1 (S1) images of reclaimed territories near Hong Kong Kowloon City. The overall average annual displacement (AAD) ranges from -12.86 to 11.63 mm/year derived from 2008 to 2019. The evaluation metrics including RMSE, MAE, correlation coefficient, and R-squared are used to investigate the impact of SVR in the integration of SAR datasets. Based on these evaluation metrics, SVR is superior in terms of integration performance, accuracy, and generalization ability. Thus, the proposed method has potentially performed multi-satellite SAR data integration.

Evaluation of Post-Tunneling Aging Buildings Using the InSAR Nonuniform Settlement Index

Tunneling work, including the construction of municipal tunnels and metro lines, may disturb the structural health of aging buildings in densely built urban areas. Deformation monitoring and risk assessments of aging buildings are crucial to mitigate incidents and prevent losses of people’s lives and properties. Time-series InSAR reveals spatio-temporal information about observed targets by extracting persistent scatterers of the structures, which can achieve the wide-range monitoring of buildings and infrastructure. However, solely relying on InSAR-derived general parameters (deformation rates and time series of specific points) cannot objectively assess the safety conditions of buildings. To address this issue, this study proposes an InSAR Nonuniform Settlement Index. First, the point targets of buildings are extracted through time-series InSAR processing. Then, using the points as inputs, the Nonuniform Settlement Index calculates the 3D settlement plane and the inclination angle of the plane corresponding to each building. In this way, the proposed Nonuniform Settlement Index acts as a subsequent analysis method of time-series InSAR to characterize the safety statuses of buildings. In our study, 147 scenes of COSMO-SkyMed images from 2013 to 2022 were used to inverse the nine-year deformation evolution of the tested area. After time-series InSAR processing and index analysis based on the above SAR datasets, cross-validation was implemented with static-level and manual investigation data. The approach was to use one aging, collapsed building affected by tunneling work, as well as the eight adjacent aging buildings. The results showed high consistency with the in situ data, which proves the efficiency of the proposed approach.

An Operational Processing Framework for Spaceborne SAR Formations

The paper proposes a flexible and efficient wavenumber domain processing scheme suited for close formations of low earth orbiting (LEO) synthetic aperture radar (SAR) sensors hosted on micro-satellites or CubeSats. Such systems aim to generate a high-resolution image by combining data acquired by each sensor with a low pulse repetition frequency (PRF). This is usually performed by first merging the different channels in the wavenumber domain, followed by bulk focusing. In this paper, we reverse this paradigm by first upsampling and focusing each acquisition and then combining the focused images to form a high-resolution, unambiguous image. Such a procedure is suited to estimate and mitigate artifacts generated by incorrect positioning of the sensors. An efficient wave–number method is proposed to focus data by adequately coping with the orbit curvature. Two implementations are provided with different quality/efficiency. The image quality in phase preservation, resolution, sidelobes, and ambiguities suppression is evaluated by simulating both point and distributed scatterers. Finally, a demonstration of the capability to compensate for ambiguities due to a small across-track baseline between sensors is provided by simulating a realistic X-band multi-sensor acquisition starting from a stack of COSMO-SkyMed images.

Unified Framework for Ship Detection in Multi-Frequency SAR Images: A Demonstration with COSMO-SkyMed, Sentinel-1, and SAOCOM Data

In the framework of maritime surveillance, vessel detection techniques based on spaceborne synthetic aperture radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. This paper deals with this topic, presenting a novel approach that exploits a cascade application of a pre-screening algorithm and a discrimination phase. Pre-screening is based on a constant false alarm rate (CFAR) detector, whereas discrimination exploits sub-look analysis (SLA). For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images, demonstrating a significant reduction of up to 40% in false alarms within highly congested scenarios, along with a notable enhancement of the receiving operating characteristic (ROC) curves. For future synergic exploitation of multiple SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM, and COSMO-SkyMed images, is comprehensive, having images gathered over the same area with a short time lag (below 15 min). Finally, the diversified processing chains and the results for each mission product and scenario are discussed. Being the first dataset of single-look complex (SLC) SAR multi-frequency data, the present work intends to encourage additional investigation in this promising field of research.

Monitoring Displacements and Damage Detection through Satellite MT-InSAR Techniques: A New Methodology and Application to a Case Study in Rome (Italy)

Satellite interferometry has recently developed as a powerful tool for monitoring displacements on structures for structural health monitoring (SHM), as it allows obtaining information on past deformation and performing back analysis on structural behavior. Despite the increasing literature on this subject, the lack of protocols for applying and interpreting interferometric data for structural assessment prevents these techniques from being employed alongside conventional SHM. This paper proposes a methodology for exploiting satellite interferometric data aiming at remotely detecting displacements and buildings’ criticalities at different levels of analysis, i.e., urban scale and single-building scale. Moreover, this research exploits the capability of satellite monitoring for damage diagnosis, comparing the millimeter scale displacements to information derived from on-site inspections. Different data-driven algorithms were applied to detect seasonal and irreversible components of displacements, such as statistical models for damage identification derived from traditional on-site monitoring. Thus, the proposed methodology was applied to a XVI-century case study located in the city center of Rome (Italy), Palazzo Primoli, and two stocks of COSMO-SkyMed (CSK) images processed through the Small BAseline Subset Differential Interferometry (SBAS-DInSAR) technique were used to assess displacements for an eight-year-long (between 2011 and 2019) monitoring period.

Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data

The damage caused by natural disasters in rural areas differs in nature extent, landscape, and structure, from the damage caused in urban environments. Previous and current studies have focused mainly on mapping damaged structures in urban areas after catastrophic events such as earthquakes or tsunamis. However, research focusing on the level of damage or its distribution in rural areas is lacking. This study presents a methodology for mapping, characterizing, and assessing the damage in rural environments following natural disasters, both in built-up and vegetation areas, by combining synthetic-aperture radar (SAR) and optical remote sensing data. As a case study, we applied the methodology to characterize the rural areas affected by the Sulawesi earthquake and the subsequent tsunami event in Indonesia that occurred on 28 September 2018. High-resolution COSMO-SkyMed images obtained pre- and post-event, alongside Sentinel-2 images, were used as inputs. This study's results emphasize that remote sensing data from rural areas must be treated differently from that of urban areas following a disaster. Additionally, the analysis must include the surrounding features, not only the damaged structures. Furthermore, the results highlight the applicability of the methodology for a variety of disaster events, as well as multiple hazards, and can be adapted using a combination of different optical and SAR sensors.

Semi-real time systems for subsidence monitoring in areas affected by underground mining: the example of the Nuraxi-Figus coal district (Sardinia, Italy)

Underground mining can produce subsidence, which can be coincident with mining activities or delayed in response to the time-dependent deformation of the rocks. Therefore, in these cases, it is essential to effectively monitor the soil deformations at different times during and after mining activity. In the present work, an integrated approach based on geotechnical numerical modeling and Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) method has been applied to detect, study and monitor the subsidence related to mining activity in the Nuraxi Figus coal district (Sardinia, Italy). Two datasets of high-resolution COSMO-Skymed images were acquired, respectively in two covering periods: from 2011 and 2014, and from 2013 to 2020. The A-DInSAR results show that the predominant displacement rates are located in correspondence with the panels. The cumulated satellite-based LoS displacements vary in the first period between − 130 and + 28 mm and − 293 and + 28.4 mm, while, during the second period between − 6.9 and + 1.6 mm and − 8.72 and + 4.33 mm in ascending and descending geometries, respectively. The geotechnical numerical model allowed to obtain a value for the maximum expected. By using the vertical and horizontal components it was possible to reconstruct the kinematics of the deformation considering three phases: pre-mining, syn-mining, and post-mining activity. The temporal evolution of displacements started during the mining extraction in 2011, achieved the major values in correspondence of post-mining operations, during the period from 2013 to 2014 and continued slowly until 2020. The near real-time monitoring system applied in this study proved to be very useful for detecting subsidence during the mining activity and the post-mining period.

Assessing the Performance of Multi-Resolution Satellite SAR Images for Post-Earthquake Damage Detection and Mapping Aimed at Emergency Response Management

The increasing availability of satellite Synthetic Aperture Radar (SAR) images is opening new opportunities for operational support to predictive maintenance and emergency actions. With the purpose of investigating the performances of SAR images characterized by different geometric resolutions for post-earthquake damage detection and mapping, we analyzed three SAR image datasets (Sentinel-1, COSMO-SkyMed Spotlight, and COSMO-SkyMed StripMap) available in Norcia (Central Italy) that were severely affected by a strong seismic sequence in 2016. By applying the amplitude and the coherent change detection processing tools, we compared pairs of images with equivalent features collected before and after the main shock on 30 October 2016 (at 06:40, UTC). Results were compared against each other and then measured against the findings of post-earthquake field surveys for damage assessment, performed by the Italian National Fire and Rescue Service (Corpo Nazionale dei Vigili del Fuoco—CNVVF). Thanks to the interesting and very rare opportunity to have pre-event COSMO-SkyMed Spotlight images, we determined that 1 × 1-m nominal geometric resolutions can provide very detailed single-building damage mapping, while COSMO-SkyMed StripMap HIMAGE images at 3 × 3-m resolutions return relatively good detections of damaged buildings; and, the Sentinel-1 images did not allow acquiring information on single buildings—they simply provided approximate identifications of the most severely damaged sectors. The main outcomes of the performance investigation we carried out in this work can be exploited considering the exponentially growing satellite market in terms of revisit time and image resolution.

Multi-Temporal SAR Interferometry for Vertical Displacement Monitoring from Space of Tengiz Oil Reservoir Using SENTINEL-1 and COSMO-SKYMED Satellite Missions

This study focused on the quantitative assessment of the vertical displacement velocities retrieved using Sentinel-1 and Cosmo-SkyMed synthetic aperture radar images for the Tengiz oilfield. Tengiz oilfield was selected as a study area because of its historically reported continuous subsidence and limited up-to-date studies during recent years. The small baseline subset time-series technique was used for the interferometric processing of radar images acquired for the period of 2018–2020. The geospatial and statistical analyses allowed to determine the existing hotspots of the subsidence processes induced by oil extraction in the study area. Ground deformation measurements derived from the Sentinel-1 and COSMO-SkyMed satellite missions showed that the Tengiz oilfield continuously subsided during 2018–2020 with the maximum annual vertical displacement velocity around −77.4 mm/y and −71.5 mm/y, respectively. The vertical displacement velocities derived from the Sentinel-1 and the COSMO-SkyMed images showed a good statistical relationship with R2≥0.73 and RMSE ≤3.68 mm. The cumulative vertical displacement derived from both satellites for the most subsiding location also showed a good statistical relationship with R2 equal to 0.97 and RMSE = ± 4.69. The observed relative differences of measurements by both satellites were acceptable to determine the ongoing vertical surface displacement processes in the study area. These studies demonstrated a practical novelty for the petroleum industry in terms of the comparative assessment of surface displacement measurements using time-series of medium-resolution Sentinel-1 and high-resolution COSMO-SkyMed radar images.

12 Years of Area Variation by the Drygalski Ice Tongue as Measured With COSMO-SkyMed

One important coastal polynya around Antarctica is Terra Nova Bay (TNB) polynya. Its formation and persistence are due to the combined effect of katabatic winds, regional ice conditions, and the Drygalski Ice Tongue (DIT). The combined effect of these elements arranges a delicate balance in Terra Nova Bay. To evaluate the interacting elements, we focused our attention on the Drygalski Ice Tongue, one of the largest ice tongues in Antarctica. The DIT is a 70-kilometre long tongue which juts out like a pier from the icy land into northern McMurdo Sound of Antarctica's Ross Dependency. The analysis presented in this paper was carried out using Cosmo-SkyMed (CSK) images. The methodology developed for the analysis consists of three steps: 1) DIT shape enhancement, 2) fuzzy Bayesian segmentation which associates the analysed set of pixels with a binary label field, and 3) parametric representation of the shape of the DIT to compare the area of the ice tongue as it was 12 years ago and as it is today. The computed mean value of the DIT surface velocity was 703 m y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . To estimate the closeness of the derived parameter to the true value, using a statistical approach, the 95% confidence interval for the true mean value is 703±4.236 m y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . Our analysis confirms a trend of advance. That is, after the significant calving events of the tongue in 2005 and 2006, the DIT has resumed its normal growth, and its length has increased by 12% in the last 12 years.

Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.

A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images

The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon.

A structure knowledge-synthetic aperture radar interferometry integration method for high-precision deformation monitoring and risk identification of sea-crossing bridges

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.

Performance of Speckle Filters for COSMO-SkyMed Images From the Brazilian Amazon

Speckle filtering is an important step for target detection in SAR images since this effect makes it difficult or even impossible to extract information from these images. There are several filters available in the literature although evaluating their performances is not a trivial task since it requires comparing the filtered images with a speckle-free image, which is generally unknown. This evaluation is even more complex when the features in the images are heterogeneous, for example, from tropical forests. The objective of this study is to evaluate the performance of the Lee, deGrandi, GammaMAP, single Anisotropic Nonlinear Diffusion (ANLD), multitemporal ANLD, Fast Adaptive Nonlocal SAR (FANS), and Fast GPU-Based Enhanced Wiener filters to reduce the speckle present in the COSMO-SkyMed Stripmap X-band images from the Brazilian Amazon forest region. The evaluation was conducted qualitatively through the visual inspection of the ratio image and the edge detection in the ratio images and quantitatively through the αβ estimator and other statistical parameters of the filtered images. The GammaMAP filter showed the best performances, both qualitatively and quantitatively, and the FANS filter only qualitative.

Studying postseismic deformation of the 2010–2011 Rigan earthquake sequence in SE Iran using geodetic data

In this paper, we study the spatial distribution and temporal evolution of the postseismic deformation of the 2010–2011 Rigan earthquake sequence which occurred at the southern termination of the East Lut fault system, southeast Iran. One-year GPS measurements after the Rigan earthquake sequence reveals right-lateral postseismic displacement along the East Chahqanbar fault and left-lateral postseismic displacement along the South Chahqanbar fault. To investigate the deformation variations in time and space, InSAR time-series of COSMO-SkyMed images is applied using the Small Baseline Subset algorithm. The results confirm a clear cumulative postseismic signal increasing to 8 mm during the first five months following the first mainshock in the direction of the coseismic displacement. The cumulative postseismic displacements are well correlated with the cumulative number of the aftershocks and their associated moment release. Considering this correlation and the observation of a sharp discontinuity along the coseismic fault in the displacement map, it is concluded that the after-slip mechanism is responsible for the observed postseismic deformation in the study region. This study is the first observation of a short-term postseismic motion in eastern Iran through geodetic data in contrast with long-lasting postseismic displacements following the earthquakes that occurred around Lut block. Modeling of the postseismic displacement results in a distributed slip pattern with a maximum slip of 0.8 m on the fault plane responsible for the 2010 Rigan coseismic deformation. This indicates that the postseismic deformation on barriers remained unbroken during the mainshock.

Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring.

In this study, we monitor pavement and land subsidence in Tabriz city in NW Iran using X-band synthetic aperture radar (SAR) sensor of Cosmo-SkyMed (CSK) satellites (2017–2018). Fifteen CSK images with a revisit interval of ~30 days have been used. Because of traffic jams, usually cars on streets do not allow pure backscattering measurements of pavements. Thus, the major paved areas (e.g., streets, etc.) of the city are extracted from a minimum-based stacking model of high resolution (HR) SAR images. The technique can be used profitably to reduce the negative impacts of the presence of traffic jams and estimate the possible quality of pavement in the HR SAR images in which the results can be compared by in-situ road roughness measurements. In addition, a time series small baseline subset (SBAS) interferometric SAR (InSAR) analysis is applied for the acquired HR CSK images. The SBAS InSAR results show land subsidence in some parts of the city. The mean rate of line-of-sight (LOS) subsidence is 20 mm/year in district two of the city, which was confirmed by field surveying and mean vertical velocity of Sentinel-1 dataset. The SBAS InSAR results also show that 1.4 km2 of buildings and 65 km of pavement are at an immediate risk of land subsidence.

InSAR observations and analysis of the Medicina Geodetic Observatory and CosmoSkyMed images

We have observed some discrepancies between installed geodetic instruments including global positioning systems (GPS) and very long baseline interferometry (VLBI) in the Medicina Geodetic Observatory (MGO). This study overcomes the above-mentioned discrepancies. We analysed several CosmoSkyMed images utilizing the interferometric synthetic aperture radar (InSAR) technique to improve the understanding of 3D surface displacements. The MGO is in a rural area, and the radar coherence is low; therefore, the coverage of permanent scatterers (PS) is limited. In ascending mode, the closest scatterer to the MEDI GPS station shows that the surface displacements are as large as 0.72 ± 1.45 mm/year along the line of sight (LOS) direction. The GPS height projected on the LOS vector shows a rate of 0.92 ± 0.04 mm/year of displacements. In descending mode, the closest scatterer to MEDI shows that the displacement is at a rate of − 0.1 ± 0.51 mm/year along the LOS direction, and the MEDI projected on the LOS vector is at a rate of 0.91 ± 0.002 mm/year. In this study, the deformation rates and their standard deviations stress that the GPS and PS-InSAR have different values. Despite a large (32-m-diameter parabolic antenna) VLBI telescope, we do not observe a single PS on the telescope for ascending and descending passes due to the frequent movements of the VLBI telescope in different directions.