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Sci Total Environ. 2020 Mar 1;706:135742. doi: 10.1016/j.scitotenv.2019.135742. Epub 2019 Nov 25.

Mapping marine litter using UAS on a beach-dune system: a multidisciplinary approach.

Author information

1
Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), University of Coimbra, Coimbra, Portugal; Department of Mathematics, University of Coimbra, Coimbra, Portugal. Electronic address: gil@mat.uc.pt.
2
Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), University of Coimbra, Coimbra, Portugal. Electronic address: uandriolo@mat.uc.pt.
3
CMUC, Department of Mathematics, University of Coimbra, Coimbra, Portugal. Electronic address: luisp@mat.uc.pt.
4
MARE - Marine and Environmental Sciences Centre, Department of Life Sciences, Faculty of Science and Technology, University of Coimbra, Coimbra, Portugal.

Abstract

The amount of marine litter, mainly composed by plastic materials, has become a global environmental issue in coastal environments. Traditional monitoring programs are based on in-situ visual census, which require human effort and are time-demanding. Therefore, it is crucial to implement innovative mapping strategies to improve the environmental monitoring of marine litter on the coast. This work presents a procedure for an automated Unmanned Aerial System (UAS)-based marine litter mapping on a beach-dune system. A multidisciplinary framework, which comprises photogrammetry, geomorphology, machine learning and hydrodynamic modelling, was developed to process a block of UAS images. The work shows how each of these scientific methodologies can be complementary to improve and making more efficient the mapping of marine litter items with UAS on coastal environment. The very high-resolution orthophoto produced from UAS images was automatically screened by random forest machine learning method, in order to characterize the marine litter load on beach and dune areas, distinctively. The marine litter objects were identified with a F-test score of 75% when compared to manual procedure. The location of major marine litter loads within the monitored area was found related to beach slope and water level dynamics on the beach profiles, suggesting that UAS flight deployment and post-processing for beach litter mapping can be optimized based on these environmental parameters. The described UAS-based marine litter detection framework is intended to support scientists, engineers and decision makers aiming at monitoring marine and coastal pollution, with the additional aim of optimizing and automating beach clean-up operations.

KEYWORDS:

Coastal pollution; Drones; Machine learning; Plastic; Wave runup

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