Quantitative Assessment of Interferometric Synthetic Aperture Radar (INSAR) for Landslide Monitoring and Mitigation

Abstract This work focuses on understanding to what degree the remote sensing tool of Interferometric Synthetic Aperture Radar (InSAR) can be used to track sub-surface ground motion in deep-seated landslides. We also consider the uncertainties that may arise out of using a remote sensing tool to track ground motion, as opposed to traditional boreholes, and how InSAR can be used to understand this uncertainty. The landslide case study of interest in this work is the El-Forn landslide in Canilllo, Andorra. These objectives of this work will be completed by focusing on the utilization of available Sentinel-1 data. Sentinel-1 data was processed and were used to generate a stack of Sentinel-1 images using small temporal and spatial baseline subsets (SBAS), which relies on many SAR acquisitions and implements a combination of the multi-look interferograms computed from the original SAR acquisitions, generating mean deformation velocity maps and time series (Berardino et al., IEEE Trans Geosci Remote Sens 40:2375–2383, 2002; Handwerger et al., Sci Rep 9: 1–12, 2019; Yunjun et al., Comput Geosci 133:104–331, 2019). From there, the interferogram pairs were inverted with the Miami Insar Time series software in Python (MintPy), InSAR Scientific Computing Environment (ISCE), and Hyp3 toolboxes/open-repositories to create displacement time series (Yunjun et al., Comput Geosci 133:104–331, 2019) over the main scarp. The displacement time series from InSAR was compared to in-situ ground motion measurements, suggesting that InSAR-based displacement data can be used to track sub-surface ground motion trends. Similarly, InSAR was found to indicate extreme sub-surface events in ground motion time seriry kriging was used as a method of geospatial interpolation over the landslide in order to visualize the quantity of remote observations necessary to minimize error in scarp reconstruction, suggesting that around 20 total observations lowers the normalized root mean squared error for the geospatially interpolated reconstruciton of the El Forn landslide surface.