Abstract
Abstract. Landslides leave discernible signs on the land surface, most of which can be captured in remote sensing images. Trained geomorphologists analyse remote sensing images and map landslides through heuristic interpretation of photographic and morphological characteristics. Despite a wide use of remote sensing images for landslide mapping, no attempt to evaluate how the image characteristics influence landslide identification and mapping exists. This paper presents an experiment to determine the effects of optical image characteristics, such as spatial resolution, spectral content and image type (monoscopic or stereoscopic), on landslide mapping. We considered eight maps of the same landslide in central Italy: (i) six maps obtained through expert heuristic visual interpretation of remote sensing images, (ii) one map through a reconnaissance field survey, and (iii) one map obtained through a real-time kinematic (RTK) differential global positioning system (dGPS) survey, which served as a benchmark. The eight maps were compared pairwise and to a benchmark. The mismatch between each map pair was quantified by the error index, E. Results show that the map closest to the benchmark delineation of the landslide was obtained using the higher resolution image, where the landslide signature was primarily photographical (in the landslide source and transport area). Conversely, where the landslide signature was mainly morphological (in the landslide deposit) the best mapping result was obtained using the stereoscopic images. Albeit conducted on a single landslide, the experiment results are general, and provide useful information to decide on the optimal imagery for the production of event, seasonal and multi-temporal landslide inventory maps.
Highlights
This paper presents an experiment to determine the effects of optical image characteristics, such as spatial resolution, spectral content and image type, on landslide mapping
We considered eight maps of the same landslide in central Italy: (i) six maps obtained through expert heuristic visual interpretation of remote sensing images, (ii) one map through a reconnaissance field survey, and (iii) one map obtained through a real-time kinematic (RTK) differential global positioning system survey, which served as a benchmark
The largest differences were observed for the landslide maps obtained through the reconnaissance field survey (Map B), and the visual interpretation of the monoscopic satellite images (Map C and Map D)
Summary
Accurate detection of individual landslides has different scopes, including landslide mapping (Di Maio and Vassallo, 2011; Manconi et al, 2014; Plank et al, 2016), landslide hazard analysis and risk assessment (Allasia et al, 2013), to support the installation of landslide monitoring systems (Tarchi et al, 2003; Teza et al, 2007; Monserrat and Crosetto, 2008; Giordan et al, 2013), and for landslide geotechnical characterization and modelling (Gokceoglu et al, 2005; Rosi et al, 2013). Mapping of individual landslides can be executed using the same techniques and tools commonly used by geomorphologists to prepare landslide inventory maps Such techniques and tools include (a) field survey (Santangelo et al, 2010), (b) heuristic visual interpretation of monoscopic or stereoscopic aerial or satellite images (Brardinoni et al, 2003; Fiorucci et al, 2011; Ardizzone et al, 2013), (c) lidar-derived images (Ardizzone et al, 2007; Van Den Eeckhaut et al, 2007; Haneberg et al, 2009; Giordan et al, 2013; Razak et al, 2013; Niculita et al, 2016, Petschko et al, 2016), (d) ultra-high-resolution images acquired by unmanned aerial vehicles (UAVs; Niethammer et al, 2010; Giordan et al, 2015a, b; Torrero et al, 2015; Turner et al, 2015). Fiorucci et al.: Optical images to map event landslides b b a c c d
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