Abstract
The main aim of this study is to compare the two commonly used multi-temporal interferometric synthetic aperture radar (InSAR) techniques, i.e. permanent scatterers (PS) and small baseline subset (SBAS), in monitoring shallow landslides. PS and SBAS techniques have been applied to ascending and descending Sentinel-1 SAR data to measure the rate of surface deformation and the displacement time series in the Rovegliana area (NE Italian pre-Alps) from 2014 to 2019. As expected, PS results cover only urban areas, while those obtained by SBAS cover up to the 85% of the investigated area. Velocity maps obtained by the two techniques show that some sectors of the investigated slope are affected by active shallow landslides which threaten the stability of buildings, walls and road network. The comparison between ascending and descending velocity maps along the satellite line of sight reveals the presence of a horizontal component in the east–west direction which is consistent with the landslide kinematic. The analysis of the displacement time series shows that, in the case of linear deformation trends, PS and SBAS results are similar, whereas, in the case of high oscillations and non-linear behavior, SBAS technique can provide a better estimation of the displacements. Besides, SBAS provides smoother and less noisy displacement time series. However, both the techniques showed their high capability in monitoring the evolution of the landslides, which is crucial for the implementation of effective risk prevention and mitigation strategies. To deep investigate the differences between the two techniques, other geomatic methodologies, based on global navigation satellite system and terrestrial laser scanning, should be used.
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Chen, X., Tessari, G., Fabris, M., Achilli, V., Floris, M. (2021). Comparison Between PS and SBAS InSAR Techniques in Monitoring Shallow Landslides. In: Casagli, N., Tofani, V., Sassa, K., Bobrowsky, P.T., Takara, K. (eds) Understanding and Reducing Landslide Disaster Risk. WLF 2020. ICL Contribution to Landslide Disaster Risk Reduction. Springer, Cham. https://doi.org/10.1007/978-3-030-60311-3_17
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