Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced technique enabling effective ground deformation monitoring. In this study, PS-InSAR time series of Sentinel-1 ascending and descending orbits for period 2015–2022 are utilized for an industrial zone in Sacco River Valley, Central Italy. The Sequential Turning Point Detection (STPD) is applied to estimate the trend turning points and their directions in PS-InSAR time series. In addition, river flow and climate time series for Sacco River near the industrial zone are analyzed using the coefficient of determination and the Least-Squares Cross-Wavelet Analysis (LSCWA) to investigate their potential impact on ground deformation. A significant land subsidence was observed prior to Fall 2016 likely as the result of drought and excessive water extraction followed by land uplift after Fall 2017 likely due to groundwater rebound. The LSCWA showed statistically significant seasonal coherency between precipitation and streamflow in 2018 when relatively much higher precipitation and streamflow were observed in this year compared to 2016 and 2017, potentially contributing to the land uplift in the study zone during 2018. These results not only highlight the capabilities of STPD for detecting trend turning points and LSCWA for analyzing streamflow and precipitation time series but also can help policymakers and stakeholders for developing a sustainable city and environment.

Ground deformation monitoring via PS-InSAR time series. An industrial zone in Sacco River Valley, central Italy / Ghaderpour, Ebrahim; Mazzanti, Paolo; Bozzano, Francesca; Scarascia Mugnozza, Gabriele. - In: REMOTE SENSING APPLICATIONS. - ISSN 2352-9385. - 34:(2024), pp. 1-14. [10.1016/j.rsase.2024.101191]

Ground deformation monitoring via PS-InSAR time series. An industrial zone in Sacco River Valley, central Italy

Ghaderpour, Ebrahim
Primo
;
Mazzanti, Paolo;Bozzano, Francesca;Scarascia Mugnozza, Gabriele
Ultimo
2024

Abstract

Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced technique enabling effective ground deformation monitoring. In this study, PS-InSAR time series of Sentinel-1 ascending and descending orbits for period 2015–2022 are utilized for an industrial zone in Sacco River Valley, Central Italy. The Sequential Turning Point Detection (STPD) is applied to estimate the trend turning points and their directions in PS-InSAR time series. In addition, river flow and climate time series for Sacco River near the industrial zone are analyzed using the coefficient of determination and the Least-Squares Cross-Wavelet Analysis (LSCWA) to investigate their potential impact on ground deformation. A significant land subsidence was observed prior to Fall 2016 likely as the result of drought and excessive water extraction followed by land uplift after Fall 2017 likely due to groundwater rebound. The LSCWA showed statistically significant seasonal coherency between precipitation and streamflow in 2018 when relatively much higher precipitation and streamflow were observed in this year compared to 2016 and 2017, potentially contributing to the land uplift in the study zone during 2018. These results not only highlight the capabilities of STPD for detecting trend turning points and LSCWA for analyzing streamflow and precipitation time series but also can help policymakers and stakeholders for developing a sustainable city and environment.
2024
Climate change; Rainfall/runoff; Remote and satellite sensing; Sacco river valley; Subsidence; Turning point detection; Wavelet analysis
01 Pubblicazione su rivista::01a Articolo in rivista
Ground deformation monitoring via PS-InSAR time series. An industrial zone in Sacco River Valley, central Italy / Ghaderpour, Ebrahim; Mazzanti, Paolo; Bozzano, Francesca; Scarascia Mugnozza, Gabriele. - In: REMOTE SENSING APPLICATIONS. - ISSN 2352-9385. - 34:(2024), pp. 1-14. [10.1016/j.rsase.2024.101191]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1707539
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