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.

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The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides’ pre-failure behaviour.

Montescaglioso village is located in southern Italy (Matera, Basilicata region), on a hill top, at about 350 m a.s.l., along the left bank of the Bradano River. Several landslides involved this area, some of them classified as relict; the latest one occurred on December 3rd, 2013 on the south-western slope of Montescaglioso hill. A review of the geological setting of this slope is presented, aimed at defining the failure mechanism of the slope. Sub-pixel cross-correlation analysis based on SAR images was performed to infer the co-failure displacement pattern and A-DInSAR was carried out to detect the spatial-temporal deformational pattern before and after the failure. The field surveys confirmed the main role played by geological setting in structurally constraining the landslide mechanism and its complex kinematic, featured by three main distinct "kinematic blocks" with different direction of movement. The 3rd December landslide has been recognized as a partial reactivation along a slope affected by a long-lasting sequence of landslides, the last one triggered by a transient action.

The paper 'Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses' by T. Carlà, E. Intrieri, F. Di Traglia, T. Nolesini, G. Gigli and N. Casagli deals with a sensitive topic for landslide risk management. Exploring the pre-failure behaviour of four different case histories, the authors proposed standard procedures for the application of the inverse velocity method (INV, Fukuzono 1985). Specifically, they suggested guidelines for the filtering of velocity data and an original and simple approach to automatically set the first and the second alarm thresholds using the inverse velocity method. The present discussion addresses three different topics: (1) data filter selection according to the features of monitoring instrument; (2) the importance of data sampling frequency for the forecasting analysis and (3) the influence of the starting point (SP in this discussion) for the application of INV analysis. Moreover, based on this matter, a new method is proposed to update the INV analysis on an ongoing basis.

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Satellite Advanced Differential Synthetic Aperture Radar Interferometry (A-DInSAR) is becaming a key-technique for monitoring ground deformations. The potential of A-DInSAR for settlement monitoring is exploited in this paper through the investigation of a reclaimed land in Dubai (UAE). Time histories of displacements were obtained from COSMO-SkyMed satellite images over the period between 2011 to 2016, allowing to derive the long-term deformation of the entire artificial island. Special attention was paid on long-term settlement of the hydraulically-placed rockfill of the peripheral rubble-mound revetments. The A-DInSAR results have been compared with results derived from numerical analyses and with field surveys, proving the relation between observed and modeled displacements. The study has also revealed that rockfill long-term settlement (creep) rate is significantly dependent on the aging (time since placement). In the analyzed time-frame (2011–2016) it has been observed that recently placed rockfill experienced creep rate up to ten times higher than the creep rate measured for similar rockfill structures placed 30 years earlier. Furthermore, it has been demonstrated that static compression by preloading and dynamic or impact densification induced by wave forces proved to have also a significant impact on reducing the creep rate of the rockfill.