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Landslide inventory of the Basilicata region (Southern Italy)

Author

Listed:
  • Maurizio Lazzari
  • Dario Gioia
  • Bernardino Anzidei

Abstract

A 1:10,000 scale landslide inventory map has been prepared for the Basilicata region (southern Italy) through extensive geomorphological analyses based on aerial photo-interpretations and field surveys. The study area (about 8000 km2) includes different morpho-structural domains of the southern Italian Apennines, and our results suggest that mass movement processes are mainly related to the intrinsic fragility of the landscape, which is featured by high relief and widespread outcrops of clay-rich deposits. As a matter of fact, the landslides of the inventory map cover about 4.8% of the surface area of the Basilicata region, whereas the total percentage of the area covered by the landslides reaches a value of 7.7%, if one considers also the landslides surveyed in previous works.

Suggested Citation

  • Maurizio Lazzari & Dario Gioia & Bernardino Anzidei, 2018. "Landslide inventory of the Basilicata region (Southern Italy)," Journal of Maps, Taylor & Francis Journals, vol. 14(2), pages 348-356, November.
    • Handle: RePEc:taf:tjomxx:v:14:y:2018:i:2:p:348-356
    DOI: 10.1080/17445647.2018.1475309
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    Citations

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    Cited by:

    1. Xiaoyi Shao & Siyuan Ma & Chong Xu & Lingling Shen & Yongkun Lu, 2020. "Inventory, Distribution and Geometric Characteristics of Landslides in Baoshan City, Yunnan Province, China," Sustainability, MDPI, vol. 12(6), pages 1-23, March.
    2. Luigi Spalluto & Antonio Fiore & Maria Nilla Miccoli & Mario Parise, 2021. "Activity maps of multi-source mudslides from the Daunia Apennines (Apulia, southern 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. 106(1), pages 277-301, March.
    3. 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.
    4. Sheng Ma & Jian Chen & Saier Wu & Yurou Li, 2023. "Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi," Sustainability, MDPI, vol. 15(22), pages 1-26, November.
    5. Bilquis Shah & M. Sultan Bhat & Akhtar Alam & Hilal Ahmad Sheikh & Noureen Ali, 2022. "Developing landslide hazard scenario using the historical events for the Kashmir Himalaya," 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. 114(3), pages 3763-3785, December.
    6. Guilherme Garcia Oliveira & Luis Fernando Chimelo Ruiz & Laurindo Antonio Guasselli & Claus Haetinger, 2019. "Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil," 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. 99(2), pages 1049-1073, November.

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