The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings

Abstract

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.

Investigating slow-moving shallow soil landslides using Sentinel-1 InSAR data in Gisborne, New Zealand

“…The continuous linear deformation trends and identification of precursory slope movements by InSAR are similar to other published studies (e.g. Dong et al 2017;Handwerger et al 2019;Moretto et al 2021). In particular, the study by Dong et al (2017) on the Xinmo landslide using Sentinel-1 data showed an acceleration of surface displacement, similar to P1 in Fig.…”

Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China

“…DEM data were utilized to eliminate the flat land phase and topographic effect in order to generate the time series interference phase. The interference phase of the master and slave image [ 40 , 41 , 42 ] was as follows: where is the terrain phase, is the deformation phase, is the atmospheric delay phase, is the flat phase, and is the noise phase. The effective deformation data was extracted using phase unwrapping, and the deformation rate was inversed using singular value decomposition (SVD).…”

On mitigation of earthquake and landslide hazards in the eastern Himalayan region

“…• In recent years, the use of Synthetic Aperture Radar (SAR), particularly interferometry SAR (InSAR), has emerged as a well-established technique in landslide monitoring, identification of precursory signatures for catastrophic slope failures, forecasting and early warning (Carlà et al 2019;Chae et al 2017). InSAR's ability to day-and-night observation, remote area detection, cloud-free and wide-swath datasets makes it a potential and powerful tool for early detection of landslide-critical conditions (Moretto et al 2021). However, there are still limitations and challenges, both technical and operational, which need to be overcome to implement landslide forecasting (Moretto et al 2021).…”

“…InSAR's ability to day-and-night observation, remote area detection, cloud-free and wide-swath datasets makes it a potential and powerful tool for early detection of landslide-critical conditions (Moretto et al 2021). However, there are still limitations and challenges, both technical and operational, which need to be overcome to implement landslide forecasting (Moretto et al 2021). • The seismic signals also play an important role in monitoring the landslides and help progress towards potential early warning for catastrophic landslides.…”