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Open AccessArticle

Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods

1
Department of Mathematics, University of Coimbra, 3001-501 Coimbra, Portugal
2
INESC-Coimbra, Department of Electrical and Computer Engineering, 3030-290 Coimbra, Portugal
3
Polytechnic Institute of Leiria, ESTG, Campus 2–Morro do Lena–Alto Vieiro, 2411–901 Leiria, Portugal
4
MARE-Marine and Environmental Sciences Centre, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Campus da Caparica, 2829-516 Caparica, Portugal
5
MARE-Marine and Environmental Sciences Centre, Department of Life Sciences, University of Coimbra, 3000-456 Coimbra, Portugal
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(16), 2599; https://doi.org/10.3390/rs12162599
Received: 16 July 2020 / Revised: 9 August 2020 / Accepted: 10 August 2020 / Published: 12 August 2020
(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Anthropogenic Debris)
Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments. View Full-Text
Keywords: drone; anthropogenic debris; OBIA; random forest; support vector machine; k-nearest neighbor drone; anthropogenic debris; OBIA; random forest; support vector machine; k-nearest neighbor
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MDPI and ACS Style

Gonçalves, G.; Andriolo, U.; Gonçalves, L.; Sobral, P.; Bessa, F. Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods. Remote Sens. 2020, 12, 2599. https://doi.org/10.3390/rs12162599

AMA Style

Gonçalves G, Andriolo U, Gonçalves L, Sobral P, Bessa F. Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods. Remote Sensing. 2020; 12(16):2599. https://doi.org/10.3390/rs12162599

Chicago/Turabian Style

Gonçalves, Gil; Andriolo, Umberto; Gonçalves, Luísa; Sobral, Paula; Bessa, Filipa. 2020. "Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods" Remote Sens. 12, no. 16: 2599. https://doi.org/10.3390/rs12162599

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