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Mapping Yucca gloriosa in coastal dunes: evaluating the cost and time efficiency of photointerpretation, machine learning and field detection approaches

  • Elena Cini
  • Flavio Marzialetti
  • Marco Paterni
  • Andrea Berton
  • Alicia Teresa Rosario Acosta
  • Daniela Ciccarelli

Biological invasions threaten biodiversity and cause significant economic and ecological costs. Effective management of invasive species is crucial, as highlighted by the European Community's Regulation 1143/2014 on Invasive Alien Species (IAS). This study focuses on coastal dune ecosystems, particularly assessing the time and cost-effectiveness of three monitoring methods for detecting and mapping alien plants: photointerpretation, machine learning classification, and field monitoring. Yucca gloriosa L., an invasive species in Regional Park of Migliarino-San Rossore-Massaciuccoli (Tuscany, Italy), served as the target species. Using RGB DJI Phantom 4 Pro v. 2.0 and DJI P4 Multispectral drones, images were analyzed via photointerpretation and machine learning. Photointerpretation, though precise, was time-consuming and subjective. Machine learning minimized human effort but required extensive computing. Field monitoring produced accurate maps but was labor-intensive and limited by accessibility issues. This study concludes that UAV-based monitoring of Y. gloriosa is optimal for balancing cost and time efficiency in coastal dune ecosystems.

  • Keywords:
  • Alien plants,
  • Drones,
  • Monitoring,
  • RGB and multispectral,
  • Mapping,
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Elena Cini

Roma Tre University, Italy - ORCID: 0009-0003-9396-1352

Flavio Marzialetti

University of Sassari, Italy - ORCID: 0000-0001-5661-4683

Marco Paterni

Clinical Physiology Institute CNR, Italy - ORCID: 0000-0002-9799-7059

Andrea Berton

Clinical Physiology Institute CNR, Italy - ORCID: 0000-0002-8798-9469

Alicia Teresa Rosario Acosta

Roma Tre University, Italy - ORCID: 0000-0001-6572-3187

Daniela Ciccarelli

University of Pisa, Italy - ORCID: 0000-0001-9715-9779

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  • Publication Year: 2024
  • Pages: 168-178

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  • Publication Year: 2024

Chapter Information

Chapter Title

Mapping Yucca gloriosa in coastal dunes: evaluating the cost and time efficiency of photointerpretation, machine learning and field detection approaches

Authors

Elena Cini, Flavio Marzialetti, Marco Paterni, Andrea Berton, Alicia Teresa Rosario Acosta, Daniela Ciccarelli

Language

Italian

DOI

10.36253/979-12-215-0556-6.14

Peer Reviewed

Publication Year

2024

Copyright Information

© 2024 Author(s)

Content License

CC BY-NC-SA 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

Tenth International Symposium Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques

Book Subtitle

Livorno (Italy) 11th-13th June 2024

Editors

Laura Bonora, Marcantonio Catelani, Matteo De Vincenzi, Giorgio Matteucci

Peer Reviewed

Publication Year

2024

Copyright Information

© 2024 Author(s)

Content License

CC BY-NC-SA 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0556-6

eISBN (pdf)

979-12-215-0556-6

eISBN (xml)

979-12-215-0557-3

Series Title

Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques

Series E-ISSN

2975-0288

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