Urban infrastructures have become imperative to human life. Any damage to these infrastructures as a result of detrimental activities would accrue huge economical costs and severe casualties. War in particular is a major anthropogenic calamity with immense collateral effects on the social and economic fabric of human nations. Therefore, damaged buildings assessment plays a prominent role in post-war resettlement and reconstruction of urban infrastructures. The data-analysis process of this assessment is essential to any post-disaster program and can be carried out via different formats. Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) techniques help us to establish a reliable and fast monitoring system for detecting post-war damages in urban areas. Along this thread, the present study aims to investigate the feasibility and mode of implementation of Sentinel-1 SAR data and InSAR techniques to estimate post-war damage in war-affected areas as opposed to using commercial high-resolution optical images. The study is presented in the form of a survey to identify urban areas damaged or destroyed by war (Islamic State of Iraq and the Levant, ISIL, or ISIS occupation) in the city of Mosul, Iraq, using Sentinel-1 (S1) data over the 2014–2017 period. Small BAseline Subset (SBAS), Persistent Scatterer Interferometry (PSI) and coherent-intensity-based analysis were also used to identify war-damaged buildings. Accuracy assessments for the proposed SAR-based mapping approach were conducted by comparing the destruction map to the available post-war destruction map of United Nations Institute for Training and Research (UNITAR); previously developed using optical very high-resolution images, drone imagery, and field visits. As the findings suggest, 40% of the entire city, the western sectors, especially the Old City, were affected most by ISIS war. The findings are also indicative of the efficiency of incorporating Sentinel-1 SAR data and InSAR technique to map post-war urban damages in Mosul. The proposed method could be widely used as a tool in damage assessment procedures in any post-war reconstruction programs.

The Copernicus Sentinel-1 mission provides synthetic aperture radar (SAR) acquisitions over large areas with high temporal and spatial resolution. This new generation of satellites providing open-data products has enhanced the capabilities for continuously studying Earth surface changes. Over the past two decades, several studies have demonstrated the potential of differential synthetic aperture radar interferometry (DInSAR) for detecting and quantifying land surface deformation. DInSAR limitations and challenges are linked to the SAR properties and the field conditions (especially in mountainous environments) leading to spatial and temporal decorrelation of the SAR signal. High temporal decorrelation can be caused by changes in vegetation (particularly in nonurban areas), atmospheric conditions, or high ground surface velocity. In this study, the kinematics of the complex and vegetated Corvara landslide, situated in Val Badia (South Tyrol, Italy), are monitored by a network of three permanent and 13 monthly measured benchmark points measured with the differential global navigation satellite system (DGNSS) technique. The slope displacement rates are found to be highly unsteady and reach several meters a year. This paper focuses firstly on evaluating the performance of DInSAR changing unwrapping and coherence parameters with Sentinel-1 imagery, and secondly, on applying DInSAR with DGNSS measurements to monitor an active and complex landslide. To this end, 41 particular SAR images, coherence thresholds, and 2D and 3D unwrapping processes give various results in terms of reliability and accuracy, supporting the understanding of the landslide velocity field. Evolutions of phase changes are analysed according to the coherence, the changing field conditions, and the monitored ground-based displacements.

This paper presents an analysis of the integration between interferometric and intensity-offset tracking-based SAR remote sensing for landslide hazard mitigation in the Italian Alps. Despite the advantages of Synthetic Aperture Radar Interferometry (InSAR) methods for quantifying landslide deformation, some limitations remain. The temporal decorrelation, the 1-D Line Of Sight (LOS) observation restriction, the high velocity rate and the multi-directional movement properties make it difficult to monitor accurately complex landslides in areas covered by vegetation. Therefore, complementary and integrated approaches, such as offset tracking-based techniques, are needed to overcome these InSAR limitations for monitoring ground surface deformations. As sub-pixel offset tracking is highly sensitive to data spatial resolution, the latest generations of SAR sensors, such as TerraSAR-X and COSMO-SkyMed, open interesting perspective for a more accurate hazard assessment. In this paper, we consider high-resolution X-band data acquired by the COSMO-SkyMed (CSK) constellation for Permanent Scatterers Interferometry (PSI), Multi-Aperture Interferometry (MAI) and offset tracking processing. We analyze the offset tracking techniques considering area and feature-based matching algorithms to evaluate their applicability to CSK data by improving sub-pixel offset estimations. To this end, PSI and MAI are used for extracting LOS and azimuthal displacement components. Then, four well-known area-based and five feature-based matching algorithms (taken from computer vision) are applied to 16 X-band corner reflectors. Results show that offset estimation accuracy can be considerably improved up to less than 3% of the pixel size using the combination of the different feature-based detectors and descriptors. A sensitivity analysis of these techniques applied to CSK data to monitor complex landslides in the Italian Alps provides indications on advantages and disadvantages of each of them.

Changes in subsurface water resources might alter the surrounding ground by generating subsidence or uplift, depending on geological and hydrogeological site characteristics. Improved understanding of the relationships between surface water storage and ground deformation is important for design and maintenance of hydraulic facilities and ground stability. Here, we construct one of the longest series of Interferometric Synthetic Aperture Radar (InSAR) to date, over twenty-five years, to study the relationships between water level changes and ground surface deformation in the surroundings of Lake Mead, United States, and at the site of the Hoover Dam. We use the Small Baseline Subset (SBAS) and Permanent scatterer interferometry (PSI) techniques over 177 SAR data, encompassing different SAR sensors including ERS1/2, Envisat, ALOS (PALSAR), and Sentinel-1(S1). We perform a cross-sensor examination of the relationship between water level changes and ground displacement. We found a negative relationship between water level change and ground deformation around the reservoir that was consistent across all sensors. The negative relationship was evident from the long-term changes in water level and deformation occurring from 1995 to 2014, and also from the intra-annual oscillations of the later period, 2014 to 2019, both around the reservoir and at the dam. These results suggest an elastic response of the ground surface to changes in water storage in the reservoir, both at the dam site and around the reservoir. Our study illustrates how InSAR-derived ground deformations can be consistent in time across sensors, showing the potential of detecting longer time-series of ground deformation.

Phase delay caused by atmospheric effects due to spatial and temporal variations of pressure, temperature, and water vapor content is one of the major error sources in estimation of ground deformation by interferometric synthetic aperture radar (InSAR). Therefore, accuracy of ground deformation measurement is highly contingent on the robustness of the atmospheric correction techniques. These techniques rely either on auxiliary data such as numerical weather models or on the analysis of the interferometric phase itself. The accuracy in phase delays estimation of mixing effects of turbulent delay in atmosphere and stratified delay in lower troposphere is a key factor in determination of performance of each technique. Hence, the performance evaluation of the techniques is required in order to assess their potentials, robustness and limitations. This paper analyzes and evaluates the performance of four numerical weather models (i.e., ERA-Interim, ERA5, MERRA2 and WRF) and two phase-based techniques (i.e., linear and power law) to estimate phase delay using Sentinel-1A/B data over the Corvara landslide located in the Alps. The GPS data and GACOS product were used to validate the results. We generally found that ERA5 outperformed among other weather models with a phase standard deviation reduction of 77.7% (with respect to the InSAR phase), a correlation coefficient of 0.86 (between InSAR phase and estimated tropospheric delay) and a less significant error in the velocity estimation of the landslide.

Man-made reservoirs and lakes are key elements in the terrestrial water system. The increased concern about the impact of anthropogenic interventions on and the dynamics of these water resources has given rise to various approaches for representing human-water interactions in land surface models. Synthetic aperture radar interferometry (InSAR) has become a powerful geodetic tool for this purpose, by evidencing changes of ground and water surfaces across time and space. In this research, the Lake Mead and associated Hoover Dam are studied using Small Baseline Subset (SBAS) technique. Lake Mead is the largest reservoir in the United States, in terms of water capacity, supplies water and hydropower for millions of people in Las Vegas, Los Angeles and southwestern part of the USA. In recent years, rising temperature, increasing evaporation and decreasing precipitation have decreased water levels substantially, and probably modified its surrounding groundwater and surface as well.This study aims to identify a hydrology-induced ground deformation around the lake Mead and a probable Hoover dam movement displacement. For the reservoir, we used the SBAS technique using 138 SAR data, including ERS1/2, Envisat, ALOS PALSAR and Sentinel-1, covering a time-spam between 1995 and 2019. For the analysis on the dam, we used the SBAS technique from 2014 to 2019 with descending and ascending modes of Sentinel-1A/B imageries. We found two main deformation patterns around the lake associated with the water level changes. Firstly, ERS and Sentinel-1 data evidenced a ground deformation that manifested itself as as a subsidence pattern in 1995 that has gradually changed into an uplift up to 2019. Secondly, the correlation trend between the deformation and water level changes has changed from negative to positive, with a transition point around March 2008. A possible interpretation for this is that the ground has initially reacted to the water fluctuations in the reservoir before March 2008 but after no longer plays a dominant role in the deformation occurring around the lake. The findings will help us to have a better understanding over the changes happened around the lake due to the water level changes and provide the valuable information for more effective management and maintenance of hydraulic structures and facilities near by the lake and water control in the future.