Disaster Early Warning, Detection, Monitoring and Management with DInSAR and MT-DInSAR Technologies

Abstract

Today, numerous scientific studies are conducted for purposes such as prevent disasters or respond to them quickly. With the advent of the 21st century, the use of data obtained from remote sensing technologies in these studies has become indispensable. Especially in the last decade, the availability of free data from satellites with high resolution and wide coverage capabilities, along with advancements in unmanned aerial vehicle technology (UAV), have enhanced the effectiveness of remote sensing in disaster early warning, detection, monitoring, and management. Synthetic aperture radar (SAR) represents a leading technology in the field of remote sensing. Interferometric SAR (InSAR) is distinguished among SAR technology methods for its ability to measure phase differences with high precision using multiple images, enabling the generation of high-quality three-dimensional (3D) cartographic representations, digital surface models (DSM), and millimeter-scale deformation maps of the target area. InSAR provides the capability to rapidly obtain critical metrics such as the epicenter, impact magnitude, and spread area of disasters. In disaster detection, monitoring, and management studies, differential InSAR (DInSAR) and multi-temporal InSAR (MT-DInSAR) methods are utilized. Among the most commonly used MT-DInSAR methods are the Short Baseline Subset (SBAS), Persistent Scatterer Interferometry (PSI), and SAR Tomography (TomoSAR). This study reviews InSAR techniques for disaster monitoring, highlighting key literature on landslides, tsunamis, earthquakes, volcanic activity, and floods, while discussing SAR data sources, preferred InSAR methods, encountered challenges, solutions, and achieved results.