To detect landslide hazards for a wide region, remotely sensed data has been applied due to its efficiency and low cost. However, the cloudy condition during a typhoon may limit the application of optical data. For an emergent monitoring task, Synthetic Aperture Radar (SAR) is, therefore, a suitable tool for detecting disasters, such as landslides and debris flows in cloudy and rainy weather. This study aims to evaluate the capability of applying radar imagery to monitor river morphological changes, for a long-term, continuous hillslope monitoring task. Elevation measurements before and after typhoon seasons will be generated by using InSAR (Interferometric Synthetic Aperture Radar) technique to detect the changes of bare areas of a selected river section. Results obtained by our 2021 SWCB project show that the measurement error is between 2.37 m and -6.47 m, so it is suggested to be suitable for monitoring significant morphological changes (>10 m), such as debris flows and dam break events. This study focuses on advancing the accuracies of elevation measurement by analyzing possible error sources using the machine learning method and applying SAR data with different wavelengths, including TerraSAR-X/TenDEM, Sentinel-1, and ALOS-2/PALSAR-2. Overall, the proposed method is expected to serve as a part of a rapid response system of hazard monitoring when optical data is not available.
|Effective start/end date||18/01/22 → 31/12/22|
- River morphological monitoring
- Synthetic Aperture Radar
- Elevation measurement
- Machine learning
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