Book chapter

A drone’s eye view: A preliminary assessment of the efficiency of drones in mapping shallow-water benthic assemblages

Andrea Francesca Bellia
University of Malta, Malta - ORCID: 0000-0001-7426-6141

Julian Evans
University of Malta, Malta - ORCID: 0000-0001-7837-5927

Sandro Lanfranco
University of Malta, Malta - ORCID: 0000-0002-0360-7065


ABOUT THIS CHAPTER

The study assesses consumer drone efficiency for surveying shallow-water benthic cover. We hypothesised that using a drone would reduce duration, and manpower requirements, while increasing accuracy, relative to manual surveys. Results obtained during this study clearly indicated that automated drone surveys were faster and more accurate than manual survey methods under most circumstances. This result has important implications for the scientific and economic aspects of the process and would have a multiplicative effect in monitoring programs that require regular surveys.
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Keywords: Drones, Drone survey , Benthic mapping, Aerial imagery, Image analysis, k-clustering

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Pages: 501-509

Published by: Firenze University Press

Publication year: 2020

DOI: 10.36253/978-88-5518-147-1.50

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© 2020 Author(s)
Content licence CC BY 4.0
Metadata licence CC0 1.0

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Publication year: 2020

DOI: 10.36253/978-88-5518-147-1.50

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© 2020 Author(s)
Content licence CC BY 4.0
Metadata licence CC0 1.0

References

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  2. Blondeau-Patissier, D., Gower, J. F., Dekker, A. G., Phinn, S. R., & Brando, V. E. (2014). A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans 10.1016/j.pocean.2013.12.008
  3. Ng, H. P., Ong, S. H., Foong, K. W. C., Goh, P. S., & Nowinski, W. L. (2006). Medical image segmentation using k-means clustering and improved watershed algorithm. In 2006 IEEE southwest symposium on image analysis and interpretation (pp. 61-65). IEEE. 10.1109/SSIAI.2006.1633722
  4. Orlando-Bonaca, M., Lipej, L., & Orfanidis, S. (2008). Benthic macrophytes as a tool for delineating, monitoring and assessing ecological status: the case of Slovenian coastal waters. Marine pollution bulletin, 56(4), 666-676. 10.1016/j.marpolbul.2007.12.018
  5. Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature methods, 9(7), 671. 10.1038/nmeth.2089
  6. Spellerberg, I. F., & Fedor, P. J. (2003). A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘Shannon–Wiener’ Index. Global Ecology and Biogeography, 12(3), 177-179. 10.1046/j.1466-822X.2003.00015.x
  7. Ventura, D., Bonifazi, A., Gravina, M. F., Belluscio, A., & Ardizzone, G. (2018). Mapping and Classification of Ecologically Sensitive Marine Habitats Using Unmanned Aerial Vehicle (UAV) Imagery and Object-Based Image Analysis (OBIA). Remote Sensing, 10(9 10.3390/rs10091331

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Bellia, A.; Evans, J.; Lanfranco, S.; 2020; A drone’s eye view: A preliminary assessment of the efficiency of drones in mapping shallow-water benthic assemblages. Firenze, Firenze University Press.


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