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Landslide inventory map of the upper Sinni River valley, Southern Italy

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  • Michele Santangelo
  • Dario Gioia
  • Mauro Cardinali
  • Fausto Guzzetti
  • Marcello Schiattarella

Abstract

In this paper, we present a geomorphological landslide inventory map for an intermountain catchment in the southern Italian Apennines. The study area is seismically active, and it is characterized by high uplift rates produced by Quaternary tectonics. A total of 531 landslides of different types, relative age, and sizes, including some kilometer-scale relict landslides were mapped through the visual interpretation of 1:33,000-scale stereoscopic aerial photographs, and dedicated field surveys. Analysis of the inventory map revealed that recent landslides consist chiefly of reactivations of older landslides, and of the new landslides formed in pre-existing landslide deposits, triggered primarily by intense rainfall events, or prolonged rainfall periods. We expect that the inventory will be used for the evaluation of landslide susceptibility and hazard in the area, and to investigate the long-term geomorphological evolution of a portion of the southern Apennines.

Suggested Citation

  • Michele Santangelo & Dario Gioia & Mauro Cardinali & Fausto Guzzetti & Marcello Schiattarella, 2015. "Landslide inventory map of the upper Sinni River valley, Southern Italy," Journal of Maps, Taylor & Francis Journals, vol. 11(3), pages 444-453, May.
    • Handle: RePEc:taf:tjomxx:v:11:y:2015:i:3:p:444-453
    DOI: 10.1080/17445647.2014.949313
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    Cited by:

    1. Derly Gómez & Edwin F. García & Edier Aristizábal, 2023. "Spatial and temporal landslide distributions using global and open landslide databases," 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. 117(1), pages 25-55, May.
    2. Jingjing Sun & Chong Xu & Liye Feng & Lei Li & Xuewei Zhang & Wentao Yang, 2024. "The Yinshan Mountains Record over 10,000 Landslides," Data, MDPI, vol. 9(2), pages 1-14, February.
    3. Somnath Bera & Vaibhav Kumar Upadhyay & Balamurugan Guru & Thomas Oommen, 2021. "Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India," 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. 108(1), pages 1257-1289, August.

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