Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives
AbstractLandslides represent major natural hazards, which cause every year significant loss of lives and damages to buildings, properties and lifelines. In the last decades, a significant increase in landslide frequency took place, in concomitance to climate change and the expansion of urbanized areas. Remote sensing techniques represent a powerful tool for landslide investigation: applications are traditionally divided into three main classes, although this subdivision has some limitations and borders are sometimes fuzzy. The first class comprehends techniques for landslide recognition, i.e., the mapping of past or active slope failures. The second regards landslide monitoring, which entails both ground deformation measurement and the analysis of any other changes along time (e.g., land use, vegetation cover). The third class groups methods for landslide hazard analysis and forecasting. The aim of this paper is to give an overview on the applications of remote-sensing techniques for the three categories of landslide investigations, focusing on the achievements of the last decade, being that previous studies have already been exhaustively reviewed in the existing literature. At the end of the paper, a new classification of remote-sensing techniques that may be pertinently adopted for investigating specific typologies of soil and rock slope failures is proposed. View Full-Text
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Scaioni, M.; Longoni, L.; Melillo, V.; Papini, M. Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives. Remote Sens. 2014, 6, 9600-9652.
Scaioni M, Longoni L, Melillo V, Papini M. Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives. Remote Sensing. 2014; 6(10):9600-9652.Chicago/Turabian Style
Scaioni, Marco; Longoni, Laura; Melillo, Valentina; Papini, Monica. 2014. "Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives." Remote Sens. 6, no. 10: 9600-9652.