Abstract
Landslides are geomorphological processes that shape the landscapes of all continents, dismantling mountains and contributing sediments to the river networks. Caused by geophysical and meteorological triggers, including intense or prolonged rainfall, seismic shaking, volcanic activity, and rapid snow melting, landslides pose a serious threat to people, property, and the environment in many areas. Given their abundance and relevance, investigators have long experimented with techniques and tools for landslide detection and mapping using primarily aerial and satellite optical imagery interpreted visually, or processed by semi-automatic or automatic procedures or algorithms. Optical (passive) sensors have known limitations due to their inability to capture Earth surface images through the clouds and to work in the absence of daylight. The alternatives are active, “all-weather” and “day-and-night”, microwave radar sensors capable of seeing through the clouds and working in presence and absence of daylight. We review the literature on the use of Synthetic Aperture Radar (SAR) imagery to detect and map landslide failures – i.e., the single most significant movement episodes in the history of a landslide – and of landslide failure events – i.e., populations of landslides in areas ranging from a few to several thousand square kilometres caused by a single trigger. We examine 54 articles published in representative journals presenting 147 case studies in 32 nations, in all continents, except Antarctica. Analysis of the geographical location of 70 study areas shows that SAR imagery was used to detect and map landslides in most morphological, geological, seismic, meteorological, climate, and land cover settings. The time history of the case studies reveals the increasing interest of the investigators in the use of SAR imagery for landslide detection and mapping, with less than one article per year from 1995 to 2011, rising to about 5 articles per year between 2012 and 2020, and an average period of about 4.2 years between the launch of a satellite and the publication of an article using imagery taken by the satellite. To detect and map landslides, investigators use a common framework that exploits the phase and the amplitude of the electromagnetic return signal recorded in the SAR images, to measure terrain surface properties and their changes. To discriminate landslides from the surrounding stable terrain, a classification of the ground properties is executed by expert visual (heuristic) interpretation, or through numerical (statistical) modelling approaches. Despite undisputed progress over the last 26 years, challenges remain to be faced for the effective use of SAR imagery for landslide detection and mapping. In the article, we examine the theoretical, research, and operational frameworks for the exploitation of SAR images for landslide detection and mapping, and we provide a perspective for future applications considering the existing and the planned SAR satellite missions.
Highlights
We review the literature on the use of Synthetic Aperture Radar (SAR) imagery to detect and map landslide failures – i.e., the single most significant movement episodes in the history of a landslide – and of landslide failure events – i.e., populations of landslides in areas ranging from a few to several thousand square kilometres caused by a single trigger
We first examine if the SAR imagery commonly used for landslide failure detection and mapping (Table 1) are theoretically adequate for the scope, and we present a general framework for the detection and mapping of landslide failures and of populations of event failures using SAR imagery (Fig. 10)
We encourage the broad community of research investigators, practitioners, engineers, planners, rescue operators, risk and disaster managers, and decision makers, who exploit – routinely or occasionally – landslide data and information, to take advantage of the new oppor tunities offered by remote sensing technologies, and of SAR imagery and related processing techniques, for enhanced landslide detection and mapping
Summary
In all continents landslides contribute to shape landscapes (Den smore et al, 1997; Lave and Burbank, 2004; Malamud et al, 2004b; Chang et al, 2014), and in many areas they pose a serious threat to people, properties, and the environment (Brabb and Harrod, 1989; Dowling and Santi, 2013; Guthrie, 2013; Nadim et al, 2013; Petley, 2012; Badoux et al, 2016; Grahn and Jaldell, 2017; Froude and Petley, 2018; Herrera et al, 2018; Salvati et al, 2018; Rossi et al, 2019). Multitemporal DInSAR techniques (e.g., Ferretti et al, 2001; Berardino et al, 2002; Lanari et al, 2004; Ferretti et al, 2011) have been used extensively to detect and measure ground surface displacements caused by slow moving landslides – in the range from mm to cm per year – to update geomorphological and multi-temporal landslide inventory maps (Guzzetti et al, 2012), and to determine and rank the degree of activity (UNESCO Working Party on World Landslide Inventory, 1993) of single or multiple landslides (Bovenga et al, 2006; Farina et al, 2006; Lauknes et al, 2010; Notti et al, 2010; Righini et al, 2012; Bianchini et al, 2012; Ciampalini et al, 2012; Cigna et al, 2013; Bardi et al, 2014; Raspini et al, 2015; Michoud et al, 2016; Solari et al, 2019; Lu et al, 2019a). We conclude (Section 9) by summaris ing the main lessons learnt
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