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https://doi.org/10.5194/nhess-2020-55
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-55
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 26 Feb 2020

Submitted as: research article | 26 Feb 2020

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This preprint is currently under review for the journal NHESS.

A semi-automatic procedure to support the detection of rapid-moving landslides using spaceborne SAR imagery

Giuseppe Esposito1, Ivan Marchesini2, Alessandro Cesare Mondini2, Paola Reichenbach2, Mauro Rossi2, and Simone Sterlacchini3 Giuseppe Esposito et al.
  • 1National Research Council, Research Institute for Geo-Hydrological Protection (CNR-IRPI), Rende (CS), 87036, Italy
  • 2National Research Council, Research Institute for Geo-Hydrological Protection (CNR-IRPI), Perugia, 06128, Italy
  • 3National Research Council, Research Institute for the Dynamics of Environmental Processes (CNR-IDPA), Milano, 20126, Italy

Abstract. The increasing availability of free-access satellite data represents a relevant opportunity for the analysis and assessment of natural hazards. The systematic acquisition of spaceborne imagery allows monitoring areas prone to geo-hydrological disasters, providing relevant information for risk evaluation and management. In case of major landslide events, for example, spaceborne radar data can provide an innovative solution for the detection of slope failures, even in case of persistent cloud cover. The information about extension and location of the landslide-affected areas may support decision-making processes during the emergency responses.

In this paper, we present a semi-automatic procedure, based on Sentinel-1 Synthetic Aperture Radar (SAR) images, aimed to facilitate the detection of rapid-moving landslides. The procedure evaluates changes of radar backscattered signals associated to land cover modifications, that may be also caused by mass movements. The procedure requires an initial manual selection of some parameters, and is able to execute automatically the download and pre-processing of images, the detection of SAR amplitude changes, and the identification of areas potentially affected by landslides, which are then displayed in a geo-referenced map. This map should help decision-makers and emergency managers to organize field investigations. The processes automatization is implemented with specific scripts running on a GNU/Linux operating system and exploiting modules of Open Source software.

We tested the processing chain, in back analysis, on an area of about 3000 km2 in central Papua New Guinea that in February/March 2018 was struck by a severe seismic sequence that triggered numerous widespread landslides. In the area, we simulated a periodic survey of about seven months, from 12 November 2017 to 6 June 2018, downloading 36 Sentinel-1 images and performing 17 change detection analyses automatically. The procedure resulted in statistical and graphical evidences of widespread land cover changes occurred just after the most severe seismic events. Most of them can be interpreted as mass movements triggered by the main seismic shocks.

Giuseppe Esposito et al.

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Short summary
In landslide disaster responses, prompt information about extension and location of landslide-affected areas are important to manage emergency operations. This paper presents a semi-automatic procedure aimed to support the detection of rapid-moving landslides on vast mountainous areas. The performance of the implemented procedure was tested, in back analysis, on an area of about 3000 km2 in Papua New Guinea, where two consecutive earthquakes triggered widespread slope failures.
In landslide disaster responses, prompt information about extension and location of...
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