Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/103475
Title: Beached and Floating Litter Surveys by Unmanned Aerial Vehicles: Operational Analogies and Differences
Authors: Andriolo, Umberto 
Garcia-Garin, Odei
Vighi, Morgana
Borrell, Asunción
Gonçalves, Gil 
Keywords: Plastics; Environmental Monitoring; Beach Pollution; Ocean Pollution; Machine Learning; Drone; Coastal Monitoring
Issue Date: 2022
Publisher: MDPI
Project: UIDB 00308/2020 
PTDC/EAM-REM/30324/2017 
1MED15_3.2_M12_334; European Union- European Regional Development Fund- Interreg MED 
metadata.degois.publication.title: Remote Sensing
metadata.degois.publication.volume: 14
metadata.degois.publication.issue: 6
Abstract: The abundance of litter pollution in the marine environment has been increasing globally. Remote sensing techniques are valuable tools to advance knowledge on litter abundance, distribution and dynamics. Images collected by Unmanned Aerial Vehicles (UAV, aka drones) are highly efficient to map and monitor local beached (BL) and floating (FL) marine litter items. In this work, the operational insights to carry out both BL and FL surveys using UAVs are detailly described. In particular, flight planning and deployment, along with image products processing and analysis, are reported and compared. Furthermore, analogies and differences between UAV-based BL and FL mapping are discussed, with focus on the challenges related to BL and FL item detection and recognition. Given the efficiency of UAV to map BL and FL, this remote sensing technique can replace traditional methods for litter monitoring, further improving the knowledge of marine litter dynamics in the marine environment. This communication aims at helping researchers in planning and performing optimized drone-based BL and FL surveys.
URI: https://hdl.handle.net/10316/103475
ISSN: 2072-4292
DOI: 10.3390/rs14061336
Rights: openAccess
Appears in Collections:I&D INESCC - Artigos em Revistas Internacionais
FCTUC Matemática - Artigos em Revistas Internacionais

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