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

Serena Moretto,Francesca Bozzano,Paolo Mazzanti

doi:10.3390/rs13183735

Serena Moretto, Francesca Bozzano + Show 1 more

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https://doi.org/10.3390/rs13183735

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Journal: Remote Sensing	Publication Date: Sep 17, 2021
Citations: 34	License type: CC BY 4.0

Affiliation: Sapienza University of Rome

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.

Highlights

The main factors that contributed to the spread and development of satellite SAR interferometry can be summarized as follows: The launch of several satellite missions belonging to different national and international space agencies, the improvement of satellite mission technologies, the enhancement of computing capabilities and the improvement of processing algorithms
The landslide mass was mainly constituted of debris originated by previous landslides, which involved the Argille Subappenine and the Irsina formations (Supplementary Materials Figure S2)
According to the results obtained in the five case studies presented, we propose a novel classification regarding the predictability of landslides from satellite InSAR, differentiating among five classes of predictability:

Summary

Introduction

The availability of SAR satellite images since 1992 has allowed the development of A-DInSAR (advanced differential synthetic aperture radar interferometry) processing methods since the early 2000s [1,2,3]. The main factors that contributed to the spread and development of satellite SAR interferometry can be summarized as follows: The launch of several satellite missions belonging to different national and international space agencies, the improvement of satellite mission technologies, the enhancement of computing capabilities and the improvement of processing algorithms. Satellite SAR interferometry is considered an effective technique for land, structure and infrastructure monitoring, finding several applications in landslide risk mitigation strategies at different stages and scales [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]

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