Integration of Digital Image Correlation of Sentinel-2 Data and Continuous GNSS for Long-Term Slope Movements Monitoring in Moderately Rapid Landslides

This work explores the advantages and drawbacks of the application of Digital Image Correlation (DIC) to Sentinel-2 Multi Spectral Instrument (MSI) data in conjunction with continuous Global Navigation Satellite System (GNSS) monitoring. The goal is to retrieve a spatially distributed and long-term time-series of slope movements in large-scale moderately rapid landslides. The short revisit time of Sentinel-2 satellites (5 days since March 2017 and 10 days before) increases the availability of cloud and snow free satellite acquisitions of the area of interest, which is a prerequisite for the extrapolation of slope movement time-series using DIC techniques. Despite the Sentinel-2 limited spatial resolution, the derived long time-series can be integrated with-and validated by-continuous GNSS monitoring data. This allows to effectively monitor landslide movements that are too fast for the application of interferometric approaches. In this study, we used the Normalized Cross Correlation (NCC) digital image correlation technique by 51 Sentinel-2 MSI scenes (band 4 with 10 m spatial resolution), acquired between 19 February 2016 and 16 July 2019, to derive the slope movement time-series of the Ca' Lita earthslide-earthflow in the northern Apennines (Italy). During the period considered, the landslide experienced two to three months-long phases of moderately rapid velocity (around 10 m/month) and, in between, prolonged periods of slow movements (approx. 10 cm/month). NCC results have been integrated with, and are compared to, time series from three continuous GNSS devices located in different geomorphic zones of the landslide. On this basis, the errors and limitations associated to NCC time series are analysed and discussed together with their advantages and potentialities for assessing the spatial distribution and monitoring slope movements during moderately rapid reactivation events.