2:30 PM - 2:45 PM
[STT39-10] Assessing the subsidence disaster risk in megacities using the InSAR time series analysis
Keywords:Subsidence, Disaster risk, InSAR
As cities develop rapidly throughout the world, ground subsidence will cause serious damages in megacities, through tilting or deformation of buildings and infrastructure (like pipes, roads), and increasing the potential risk of inundation. Subsidence proceeds very slowly compared to other disaster (like earthquake, tsunami, eruption). Therefore, it is not easy for us to recognize its potential risk for social activities. To mitigate the potential disaster risk caused by subsidence, it is important to monitor the current state of subsidence and assess the associated disaster risk before damaging. Therefore, the goal of this research is detecting subsidence in megacities by using the satellite geodetic technique, and investigating the way to assess disaster risk caused by subsidence which can be applicable to any cities in globe. The concept “Risk” can be modeled as the multiplication of “Hazard”, “Exposure”, and “Vulnerability” (Cardona et al., 2012). In this research, we estimated exposures to subsidence in 5 megacities, Tokyo, Nagoya, Osaka, Jakarta and Maynila.
In this research, we investigated the subsidence distribution by InSAR (Interferometric Synthetic Aperture Radar) time series analysis. InSAR time series analysis can detect surface deformation accurately in whole target area (not points), regardless weather conditions. We used “LiCSBAS (morishita et al., 2020)” software, which is an open-source InSAR time series analysis package that utilizes “LiCSAR” products. LiCSAR is the automated Sentinel-1 InSAR processor. We used Sentinel-1 InSAR images from LiCSAR products. Derived InSAR time series data were converted to quasi-vertical displacements by 2.5-dimensional analysis (Fujiwara et al., 2000).
In this research, we used cumulative subsidence (mm) and angular distortion (degree) as the “Hazard”. We set 50 mm and 92.5 mm as lower and middle limits, respectively, as the thresholds of subsidence existence. In terms of angular distortion, thresholds are 0.23° (lower limit) and 0.40° (middle limit). After mapping Hazard according to those thresholds, we counted number of population and the extent of the building area located within the Hazard area to estimate exposures. We used gridded population datasets produced by WorldPop and World Settlement Footprint (WSF) 2019 data produced by German Aerospace Center (DLR).
In Japanese megacity, we could only detect small subsidence especially near large rivers or basins. On the other hand, in Jakarta and Maynila, large amplitude subsidences (more than 30 mm/year) were detected. The higher subsidence rate is, the higher the angular distortion tends to be.
Assuming subsidence will continue at a constant rate for subsequent decades, we also estimated potential future exposures at next 1, 5, 10, 30, 50 years. We could see the difference among Japan and south-east Asian countries. In Tokyo, Aichi and Osaka prefecture, not more than 1.6% of population and building area are assessed as Exposure within next 50 years. On the other hands, about 35% of them will become Exposure in Special Capital Region of Jakarta, and 15% in Metro Maynila.
In this research, we investigated the subsidence distribution by InSAR (Interferometric Synthetic Aperture Radar) time series analysis. InSAR time series analysis can detect surface deformation accurately in whole target area (not points), regardless weather conditions. We used “LiCSBAS (morishita et al., 2020)” software, which is an open-source InSAR time series analysis package that utilizes “LiCSAR” products. LiCSAR is the automated Sentinel-1 InSAR processor. We used Sentinel-1 InSAR images from LiCSAR products. Derived InSAR time series data were converted to quasi-vertical displacements by 2.5-dimensional analysis (Fujiwara et al., 2000).
In this research, we used cumulative subsidence (mm) and angular distortion (degree) as the “Hazard”. We set 50 mm and 92.5 mm as lower and middle limits, respectively, as the thresholds of subsidence existence. In terms of angular distortion, thresholds are 0.23° (lower limit) and 0.40° (middle limit). After mapping Hazard according to those thresholds, we counted number of population and the extent of the building area located within the Hazard area to estimate exposures. We used gridded population datasets produced by WorldPop and World Settlement Footprint (WSF) 2019 data produced by German Aerospace Center (DLR).
In Japanese megacity, we could only detect small subsidence especially near large rivers or basins. On the other hand, in Jakarta and Maynila, large amplitude subsidences (more than 30 mm/year) were detected. The higher subsidence rate is, the higher the angular distortion tends to be.
Assuming subsidence will continue at a constant rate for subsequent decades, we also estimated potential future exposures at next 1, 5, 10, 30, 50 years. We could see the difference among Japan and south-east Asian countries. In Tokyo, Aichi and Osaka prefecture, not more than 1.6% of population and building area are assessed as Exposure within next 50 years. On the other hands, about 35% of them will become Exposure in Special Capital Region of Jakarta, and 15% in Metro Maynila.