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Terrestrial laser scanning survey in support of unstable slopes analysis: the case of Vulcano Island (Italy)

Author

Listed:
  • Maria Marsella
  • Peppe D’Aranno
  • Silvia Scifoni
  • Alberico Sonnessa
  • Marco Corsetti

Abstract

The capability to measure at distance dense cloud of 3D point has improved the relevance of geomatic techniques to support risk assessment analysis related to slope instability. This work focuses on quantitative analyses carried out to evaluate the effects of potential failures in the Vulcano Island (Italy). Terrestrial laser scanning was adopted to reconstruct the geometry of investigated slopes that is required for the implementation of numerical modeling adopted to simulate runout areas. Structural and morphological elements, which influenced past instabilities or may be linked to new events, were identified on surface models based on ground surveying. Terrestrial laser scanning was adopted to generate detailed 3D models of subvertical slopes allowing to characterize the distribution and orientation of the rock discontinuities that affect instability mechanism caused by critical geometry. Methods for obtaining and analyzing 3D topographic data and to implement simulation analyses contributing to hazard and risk assessment are discussed for two case studies (Forgia Vecchia slope and Lentia rock walls). Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Maria Marsella & Peppe D’Aranno & Silvia Scifoni & Alberico Sonnessa & Marco Corsetti, 2015. "Terrestrial laser scanning survey in support of unstable slopes analysis: the case of Vulcano Island (Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(1), pages 443-459, August.
    • Handle: RePEc:spr:nathaz:v:78:y:2015:i:1:p:443-459
    DOI: 10.1007/s11069-015-1729-3
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    Cited by:

    1. Leilei Liu & Guoyan Zhao & Weizhang Liang, 2023. "Slope Stability Prediction Using k -NN-Based Optimum-Path Forest Approach," Mathematics, MDPI, vol. 11(14), pages 1-31, July.

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