In this study, we investigate the occurrence of seasonal ground displacement in the coastal area of the Albegna river plain (central Italy) for the past four years (2017 to 2020) by using the Persistent Scatterer Interferometry (PSI) technique. Land use/cover in the study area is characterized primarily by agriculture, urban towns, and industrial clusters. By some years, damages to buildings and infrastructure have been reported to be connected to ground displacement which may be related to groundwater exploitation. However, the extent and amount of ground displacements are only qualitatively known in the area. So, as a first step, we applied the single master PSI approach from October 2017 to September 2020 using Sentinel-1 SAR data to map the spatial and temporal extent of the displacements by the Stanford Method for Persistent Scatterers (StaMPS). The mean velocity data estimated is a long-term linear trend of the time-displacement function; hence they did not allowed to characterize the nonlinear seasonal and the local behavior of the process. Moreover, the density of the PS around the buildings where damages have been reported are deficient due to the loss of coherence related to long temporal baselines. To overcome these limitations, we implemented the single master PSI approach splitting the Sentinel-1 data into two seasonal subsets per year. Each seasonal subset is composed of images according to the winter (“wet” conditions) and summer (“dry” conditions) seasons and co-registered to a unique master image (individual for each seasonal dataset) to minimize the dispersion of geometrical and temporal baseline values. A highly accurate Lidar DSM was used for the interferogram formation between all SAR images and the master. Finally, the PSI technique was performed using the StaMPS and mean velocity for each season was estimated. The estimated velocity was evaluated as a linear trend, so representing measured displacements into six months. The displacement maps for each season allowed us to highlight the seasonal ground displacement trends with maximum magnitude in the order of 20 mm in six months, but most of the PS was observed to move less than 5 mm. High-accuracy levelling data, coeval with SAR acquisitions, were acquired within the study area, providing the vertical ground displacement values measured similar to those obtained by the StaMPS PSI. While the seasonal approach decreased the temporal decorrelation thus improving the coherence in the area, the density of the PS is not consistent among seasons. Also, the low magnitude of seasonal displacements with higher standard deviation, does not allow us to spatially map the displacement field, even though local significant displacement values are recognized. We analyse both the groundwater level change data and the geotechnical properties of sub-surface soils in the study area, and we infer that local soil swelling-shrinkage processes may be regarded as possible causes of the seasonal ground displacements detected by the PSInSAR.

Venkatadripathi Pattela, T., Disperati, L., Virdis, S.G.P., Pignatiello, M., Fantozzi, P.L., Morelli, A. (2021). Investigation of Seasonal Ground displacement in the coastal Albegna river plain (central Italy) by using InSAR Time Series.

Investigation of Seasonal Ground displacement in the coastal Albegna river plain (central Italy) by using InSAR Time Series

Disperati, Leonardo
Membro del Collaboration Group
;
Virdis, Salvatore G. P.;Fantozzi, Pier Lorenzo
Membro del Collaboration Group
;
2021-01-01

Abstract

In this study, we investigate the occurrence of seasonal ground displacement in the coastal area of the Albegna river plain (central Italy) for the past four years (2017 to 2020) by using the Persistent Scatterer Interferometry (PSI) technique. Land use/cover in the study area is characterized primarily by agriculture, urban towns, and industrial clusters. By some years, damages to buildings and infrastructure have been reported to be connected to ground displacement which may be related to groundwater exploitation. However, the extent and amount of ground displacements are only qualitatively known in the area. So, as a first step, we applied the single master PSI approach from October 2017 to September 2020 using Sentinel-1 SAR data to map the spatial and temporal extent of the displacements by the Stanford Method for Persistent Scatterers (StaMPS). The mean velocity data estimated is a long-term linear trend of the time-displacement function; hence they did not allowed to characterize the nonlinear seasonal and the local behavior of the process. Moreover, the density of the PS around the buildings where damages have been reported are deficient due to the loss of coherence related to long temporal baselines. To overcome these limitations, we implemented the single master PSI approach splitting the Sentinel-1 data into two seasonal subsets per year. Each seasonal subset is composed of images according to the winter (“wet” conditions) and summer (“dry” conditions) seasons and co-registered to a unique master image (individual for each seasonal dataset) to minimize the dispersion of geometrical and temporal baseline values. A highly accurate Lidar DSM was used for the interferogram formation between all SAR images and the master. Finally, the PSI technique was performed using the StaMPS and mean velocity for each season was estimated. The estimated velocity was evaluated as a linear trend, so representing measured displacements into six months. The displacement maps for each season allowed us to highlight the seasonal ground displacement trends with maximum magnitude in the order of 20 mm in six months, but most of the PS was observed to move less than 5 mm. High-accuracy levelling data, coeval with SAR acquisitions, were acquired within the study area, providing the vertical ground displacement values measured similar to those obtained by the StaMPS PSI. While the seasonal approach decreased the temporal decorrelation thus improving the coherence in the area, the density of the PS is not consistent among seasons. Also, the low magnitude of seasonal displacements with higher standard deviation, does not allow us to spatially map the displacement field, even though local significant displacement values are recognized. We analyse both the groundwater level change data and the geotechnical properties of sub-surface soils in the study area, and we infer that local soil swelling-shrinkage processes may be regarded as possible causes of the seasonal ground displacements detected by the PSInSAR.
Venkatadripathi Pattela, T., Disperati, L., Virdis, S.G.P., Pignatiello, M., Fantozzi, P.L., Morelli, A. (2021). Investigation of Seasonal Ground displacement in the coastal Albegna river plain (central Italy) by using InSAR Time Series.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1188490