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[STT40-03] Analysis of Surface Displacement by InSAR Time Series Method in Danba County, China
Keywords:InSAR, Landslide
Landslide is a very important type of geological hazard, which causes substantial economic losses and casualties every year. The Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) technique can provide wide-area coverage (thousands of km²) and high-precision (millimeter to centimeter-level) ground deformation observation. By selecting interferograms with small temporal and spatial baselines, SBAS effectively reduces atmospheric phase delays and provides valuable information of surface deformation time series. However, how to better integrate the SBAS method with landslide research and achieve accurate and real-time monitoring of landslide motion remains an area of exploration. This study focuses on the heavy rainfall event that occurred in Danba County in June 2020. This rainfall event induced multiple landslides and geological hazards across the Danba County region. This study aims to investigate the relationship between precipitation and ground deformation.
The SBAS method was used to invert the ground deformation over two periods, 2019–2020 and 2022–2023. For the descending orbit, 40 interferograms were selected for the 2019–2020 period and 41 interferograms for the 2022–2023 period. SBAS processing was performed using MintPy. Based on SBAS results, four slopes within the landslide area were selected to investigate their respective temporal deformation characteristics. Precipitation data for the study area from 2018 to 2023 were obtained from ERA5 hourly precipitation datasets.
The 2019–2020 SBAS results indicate that the displacement patterns in the Gaoding landslide area exhibited significant differences compared to those of the surrounding slopes. The displacement velocity within the Gaoding landslide area ranged from 10 mm/year to 29 mm/year. The upper sections of the Jiaju and Niela slopes also exhibit significant displacement signals, with areas where the annual deformation rate exceeds 10 mm/year. One month before the landslide, the SBAS results had already shown significant anomalous displacements. The monthly average precipitation in the month before the landslide was 424 mm, compared to 387 mm in 2019 and 366 mm in 2018. Typically, when a landslide occurs, precipitation during that period tends to show a significant increase compared to the same period in previous years. However, in this case, no significant differences were observed, which indicates that, in addition to precipitation, other critical factors may have played a role in triggering multiple landslides over a wide area in this event.
The SBAS method was used to invert the ground deformation over two periods, 2019–2020 and 2022–2023. For the descending orbit, 40 interferograms were selected for the 2019–2020 period and 41 interferograms for the 2022–2023 period. SBAS processing was performed using MintPy. Based on SBAS results, four slopes within the landslide area were selected to investigate their respective temporal deformation characteristics. Precipitation data for the study area from 2018 to 2023 were obtained from ERA5 hourly precipitation datasets.
The 2019–2020 SBAS results indicate that the displacement patterns in the Gaoding landslide area exhibited significant differences compared to those of the surrounding slopes. The displacement velocity within the Gaoding landslide area ranged from 10 mm/year to 29 mm/year. The upper sections of the Jiaju and Niela slopes also exhibit significant displacement signals, with areas where the annual deformation rate exceeds 10 mm/year. One month before the landslide, the SBAS results had already shown significant anomalous displacements. The monthly average precipitation in the month before the landslide was 424 mm, compared to 387 mm in 2019 and 366 mm in 2018. Typically, when a landslide occurs, precipitation during that period tends to show a significant increase compared to the same period in previous years. However, in this case, no significant differences were observed, which indicates that, in addition to precipitation, other critical factors may have played a role in triggering multiple landslides over a wide area in this event.