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 landslides in cloudy and rainy weather. Our previous study (2018 MOST project) has proposed a change detection method with applying images from multiple observation modes for landslide mapping. In this study, we will further eliminate the effects of terrain over mountainous areas to produce better synthetic images for the analysis . This study, therefore, aims to develop an effective landslide detection method, applying an algorithm that is expected to combine different observation modes to correct terrain effect. In addition, multi-temporal Sentinel-1 C-band SAR images (VV and VH) will be used to construct the temporal signatures of landslide. We expect the method proposed in this study could serve as a part of a rapid response system of hazard monitoring when optical data is not available.
|Effective start/end date||1/08/20 → 31/07/21|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- Landslide detection
- multi-temporal SAR imagery
- polarization mode
- observation mode
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