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.

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.

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.