Criteria for the optimal selection of remote sensing images to map event landslides

Abstract

Abstract. We executed an experiment to determine the effects of image characteristics on event landslide mapping. In the experiment, we compared eight maps of the same landslide, the Assignano landslide, in Umbria, central Italy. Six maps were obtained through the expert visual interpretation of monoscopic and pseudo-stereoscopic (2.5D), ultra-resolution (3 × 3 cm) images taken on 14 April 2014 by a Canon EOS M photographic camera flown by an CarbonCore 950 hexacopter over the landslide, and of monoscopic and stereoscopic, true-colour and false-colour-composite, 1.84 × 1.84 m resolution images taken by the WorldView-2 satellite also on 14 April 2014. The seventh map was prepared through a reconnaissance field survey aided by a pre-event satellite image taken on 8 July 2013, available on Goggle EarthTM, and by colour photographs taken in the field with a hand-held camera. The images were interpreted visually by an expert geomorphologist using the StereoMirrorTM hardware technology combined with the ERDAS IMAGINE® and Leica Photogrammetry Suite (LPS) software. The eighth map, which we considered our reference showing the ground truth, was obtained through a Real Time Kinematic differential GPS survey conducted by walking a GPS receiver along the landslide perimeter to capture geographic coordinates every about 5 m, with centimetre accuracy. The eight maps of the Assignano landslide were stored in a GIS, and compared adopting a pairwise approach. Results of the comparisons, quantified by the error index E, revealed that where the landslide signature was primarily photographical (in the landslide source and transport area) the best mapping results were obtained using the higher resolution images, and where the landslide signature was mainly morphometric (in the landslide deposit) the best results were obtained using the stereoscopic images. The ultra-resolution image proved very effective to map the landslide, with results comparable to those obtained using the stereoscopic satellite image. Conversely, the field-based reconnaissance mapping provided the poorest results, measured by large mapping errors, and confirmed the difficulty in preparing accurate landslide maps in the field. Albeit conducted on a single landslide, we maintain that our 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.

Automatic landslide mapping from satellite imagery with a topography-driven thresholding algorithm

“…We report results of the three different approximations (also reported in Alvioli et al (2018)), expressed in terms of an error index E I , first introduced in Carrara (1993) and recently employed as a benchmark for selecting optimal requirements of images from remote sensing for landslide mapping (Fiorucci et al, 2018b). Results for "grid" and "SU" are obtained with a number of polygons that minimizes E I in both cases.…”

Answer to Reviewer 2

“…In this scenario, the use of the mini-micro RPAS is practical for small areas and optimal for landslides that often cover an area that range from less than one square kilometres up to few square kilometres. Ultra-high resolution images acquired by RPAS can support the definition not only of the identification of studied landslide limit, but also the identification and mapping of main geomorphological features (Fiorucci et al, 2017). Furthermore, a sequence of RPAS acquisitions over the time can provide useful support for the study of the gravitational process evolution.…”

“…Niethammer et al (2009) showed how RPAS could be considered a good solution for the acquisition of ultra-high resolution images with low-cost systems. Fiorucci et al (2017) compared the results of the landslide limit mapped using different techniques and found that satellite images can be considered a good solution for the identification and map of landslides over large areas. On the contrary, if the target of the study is the definition of landslide's morphological features, the use of more detailed RPAS images seemed to be the better solution.…”

Low cost, multiscale and multi-sensor application for flooded areas mapping

“…Also previous experiences of CNR IRPI and Civil Protection Agency in the use of small RPAS for the study of geo-hydrological processes (Giordan et al, 2015;250 Boccardo et al 2015;Fiorucci et al, 2017;Giordan et al, 2017) were useful for the definition of a correct use of these systems.…”