Do We Need a Higher Resolution? Case Study: Sentinel-1-Based Change Detection of the 2018 Hokkaido Landslides, Japan

Since 2014, Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data have become an important source in the field of displacement detection thanks to regular acquisitions and 7.5 years temporal coverage at global level. Despite increasing number publications on role S1 landslide detection, there is still a need for research further clarify capabilities sensor applicable image analysis techniques. Previous studies successfully exploited high-resolution ALOS-PALSAR image-based intensity coherence 2018 Hokkaido landslides. Nevertheless, they expressed clear analyse other sensors (such as S1). This raises question: Do we SAR imagery with higher spatial resolution ALOS-PALSAR) or are freely available also suitable rapid detection? The images could provide material comparative answer aforementioned question. Therefore, 17 ascending 19 descending were analysed test accuracy detection. Multitemporal analyses both performed along differences, multitemporal features (MTF) MTF differences images. In addition, classification results was evaluated highlight potential analysis. found limitations site, single provided low-quality results. However, significantly improved by calculating almost reached success rate ALOS-PALSAR-based analysis, even though improvement not possible. Half false positives identified 30–45-m buffer zone agreement, underlining that appropriate accurate Only approximation landslide-affected area can be given considerable overestimation. Due inclusion post-event images, perfectly purposes here.