4:15 PM - 4:30 PM
[STT40-10] Observation of Land Subsidence in Yashio City Using Time-Series Interferometric SAR
Keywords:Time-Series InSAR, Yashio City, land subsidence, ALOS-2/PALSAR-2
On January 28, Reiwa 7, at approximately 10:00 AM, a road collapse occurred near Chuo 1-chome, Yashio City, Saitama Prefecture, which is believed to have been caused by the failure of a sewer pipe in the Nakagawa Basin Sewerage System. This collapse involved a truck, and the rescue of the driver remains difficult. This study aims to quantitatively analyze the relationship between this road collapse and land subsidence by utilizing past years' land deformation data to clarify their causal relationship.
Generally, land subsidence is observed using leveling surveys; however, this method has several drawbacks. Leveling surveys require the establishment of observation points and involve significant time and cost. Additionally, on-site visits are necessary for measurements, making this method unsuitable for large-scale observations. In contrast, a newly emerging technology for observing land deformation is Synthetic Aperture Radar (SAR). SAR allows for extensive and area-wide observations from satellites, significantly reducing time and costs compared to traditional leveling surveys. Additionally, SAR is all-weather capable, ensuring stable observations regardless of weather conditions.
In this study, we conducted time-series interferometric analysis using SAR data to evaluate the relationship between land subsidence and road collapse. Specifically, we used ALOS-2/PALSAR-2 data and performed time-series interferometric SAR analysis. The analysis software used was Sigma-SAR, which reconstructed all observation data as if they were observed from a zero-baseline reference orbit before conducting time-series interferometric processing.
The research procedure involved acquiring data, generating SLC data, performing interference processing, unwrapping processing, and time-series interferometric processing, followed by ionospheric and GNSS corrections. The study area focused on the vicinity of Yashio City, Saitama Prefecture, analyzing 21 ascending scenes from 2019 to 2024. Interference analysis was performed using 20 ascending pairs; however, due to ionospheric effects, the summation process did not function correctly. Therefore, ionospheric correction was applied to eliminate these effects, improving the accuracy of the interferometric images and enabling a more precise evaluation of land deformation.
The analysis results confirmed a trend of subsidence around Yashio City, Saitama Prefecture. In particular, at the location where the road collapse occurred, subsidence of approximately 1.92 cm over six years from 2019 to 2024 was observed. The direct cause of the road collapse, identified as sewer pipe failure, is likely to have been influenced, at least to some extent, by long-term land subsidence.
Based on these results, this study quantitatively demonstrated the correlation between road collapse and land subsidence using time-series interferometric SAR analysis. The findings of this study are valuable for infrastructure management and urban planning, providing a key indicator for preventing similar incidents by monitoring the progression of land subsidence. In particular, the utilization of SAR technology enables extensive and highly accurate monitoring of land deformation, which is expected to play a crucial role in future disaster prevention measures.
Generally, land subsidence is observed using leveling surveys; however, this method has several drawbacks. Leveling surveys require the establishment of observation points and involve significant time and cost. Additionally, on-site visits are necessary for measurements, making this method unsuitable for large-scale observations. In contrast, a newly emerging technology for observing land deformation is Synthetic Aperture Radar (SAR). SAR allows for extensive and area-wide observations from satellites, significantly reducing time and costs compared to traditional leveling surveys. Additionally, SAR is all-weather capable, ensuring stable observations regardless of weather conditions.
In this study, we conducted time-series interferometric analysis using SAR data to evaluate the relationship between land subsidence and road collapse. Specifically, we used ALOS-2/PALSAR-2 data and performed time-series interferometric SAR analysis. The analysis software used was Sigma-SAR, which reconstructed all observation data as if they were observed from a zero-baseline reference orbit before conducting time-series interferometric processing.
The research procedure involved acquiring data, generating SLC data, performing interference processing, unwrapping processing, and time-series interferometric processing, followed by ionospheric and GNSS corrections. The study area focused on the vicinity of Yashio City, Saitama Prefecture, analyzing 21 ascending scenes from 2019 to 2024. Interference analysis was performed using 20 ascending pairs; however, due to ionospheric effects, the summation process did not function correctly. Therefore, ionospheric correction was applied to eliminate these effects, improving the accuracy of the interferometric images and enabling a more precise evaluation of land deformation.
The analysis results confirmed a trend of subsidence around Yashio City, Saitama Prefecture. In particular, at the location where the road collapse occurred, subsidence of approximately 1.92 cm over six years from 2019 to 2024 was observed. The direct cause of the road collapse, identified as sewer pipe failure, is likely to have been influenced, at least to some extent, by long-term land subsidence.
Based on these results, this study quantitatively demonstrated the correlation between road collapse and land subsidence using time-series interferometric SAR analysis. The findings of this study are valuable for infrastructure management and urban planning, providing a key indicator for preventing similar incidents by monitoring the progression of land subsidence. In particular, the utilization of SAR technology enables extensive and highly accurate monitoring of land deformation, which is expected to play a crucial role in future disaster prevention measures.