Assessment of Landslide Pre-Failure Monitoring and Forecasting Using Satellite SAR Interferometry

Abstract

Abstract:In this work, the ability of advanced satellite interferometry to monitor pre-failure landslide behaviours and the potential application of this technique to Failure Forecasting Methods (FFMs) are analysed. Several limits affect the ability of the technique to monitor a landslide process, especially during the pre-failure phase (tertiary creep). In this study, two of the major limitations affecting the technique have been explored: (1) the low data sampling frequency and (2) the phase ambiguity constraints. We explored the time series of displacements for 56 monitored landslides inferred from the scientific literature and from different in situ and remote monitoring instruments (i.e., extensometers, inclinometers, distometers, Ground Base InSAR, and total station). Furthermore, four different forecasting techniques have been applied to the monitoring data of the selected landslides. To analyse the reliability of the FFMs based on the InSAR satellite data, the 56 time series have been sampled based on different satellite features, simulating the satellite revisit time and the phase ambiguity constraints. Our analysis shows that the satellite InSAR technique could be successful in monitoring the landslide's tertiary creep phase and, in some cases, for forecasting the corresponding time of failure using FFMs. However, the low data sampling frequency of the present satellite systems do not capture the necessary detail for the application of FFMs in actual risk management problems or for early warning purposes.

Monitoring Surface Deformation over a Failing Rock Slope with the ESA Sentinels: Insights from Moosfluh Instability, Swiss Alps

“…As far as Sentinel-1 data are concerned, Intrieri et al [26] have used DInSAR time series after the occurrence of the Maoxian landslide event (Sichuan province, China), suggesting that acquisitions with a short revisiting time could be used not only as a tool for mapping unstable areas, but also for systematic landslide monitoring. In addition, Moretto et al [43] have explored the possibility to use DInSAR time series to monitor landslide in pre-failure stages by simulating accelerated slope deformation over more than 50 landslide events as they would be acquired from past, actual, and future constellations of SAR satellites, including the Sentinel-1. They concluded that, based on Sentinel-1 acquisition parameters, phase ambiguity and revisit time limitations would have allowed to follow the evolution towards failure only for a minor portion of the analyzed dataset.…”

Observing Geohazards from Space

“…With a specific look at landslide phenomena, on the other hand, Moretto et al [6] explore the potential of satellite InSAR methods to sense pre-failure landslide movements and to feed into failure forecasting methods. For 56 landslide sites with monitoring data available in the scientific literature, the authors collate pre-failure deformation information from on site and remote monitoring instruments, such as inclinometers, ground-based InSAR, and total station, and resample the frequency of these records to simulate satellite acquisition parameters (e.g., revisit times and phase ambiguity).…”

Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia revealed by advanced InSAR

“…Because displacement typically accelerates in each of the 25 aforementioned failure types (Fig. 10C inset), it provides an opportunity for failure forecasting (Voight, 1989;Crosta and Agliardi, 2003;Eberhardt, 2012;Baron and Supper, 2013;Hermanns et al, 2013a;Federico et al, 2015;Moretto et al, 2017).…”

“…Possible acceleration in the days or weeks prior to the 2011 landslide cannot be evaluated with the temporal resolution of the displacement histories used here. RADARSAT-2's 24-day revisit period provides low sampling frequency compared to 5 some other RADAR satellites (Moretto et al, 2017) and especially to in-situ sensors or ground-based remote sensors (e.g. Kalaugher et al, 2000;Crosta and Agliardi, 2003;Gischig et al, 2011;Eberhardt, 2012;Federico et al, 2015;Confuorto et al, 2017;Carlà et al, 2018).…”

“…Petley et al, 2002;Colesanti and Wasowski, 2006). Continued technological advancements and data availability offer the potential for further improvements to the characterization of slope behaviour, including prediction of future failure (Moretto et al, 2017). Improvement of RADAR systems increases ground resolution and phase quality, whereas the establishment of satellite constellations is improving temporal resolution and coherence.…”