Monitoring of ground deformation before and after an earthquake using interferometric SAR

Ground deformation can be detected by processing SAR (Synthetic Aperture Radar) phase data acquired in different periods. However, due to the characteristics of SAR, it is difficult to determine the direction of ground deformation as the distance change between the satellite and the ground surface is observed. Therefore, on-site field observation is required since SAR observation results differ from the actual amount of ground deformation. This study aims to estimate ground deformation over a wide area using satellite SAR data, understand the disaster situation quickly, and reduce secondary damage risks caused by on-site field observation. In this paper, Interferometric SAR (InSAR) analysis is applied to estimate ground deformation caused by Kumamoto earthquake in 2016 from C-band SAR data on Sentinel-1 satellite. 2.5-dimensional analysis is conducted by combining the InSAR analysis results of the ascending and descending orbits, and the direction of ground deformation caused by earthquake is visualized using displacement vectors. Furthermore, changes in land cover classification, which classifies land based on surface vegetation and geology is performed by using time-series analysis based on machine learning techniques from optical sensor images obtained from Sentinel-2. The results show that the accurate understanding of the damage situation over a wide area is very effective in terms of estimating landslides and speeding up disaster response, such as evacuation.