Please use this identifier to cite or link to this item:
Title: Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods
Authors: Gonçalves, Gil 
Andriolo, Umberto 
Gonçalves, Luísa 
Sobral, Paula 
Bessa, Filipa 
Keywords: drone; anthropogenic debris; OBIA; random forest; support vector machine; k-nearest neighbor
Issue Date: 2020
Publisher: MDPI
Project: UIDB 00308/2020 
UAS4Litter (PTDC/EAM-REM/30324/2017) 
University of Coimbra through contract IT057-18-7252 
Serial title, monograph or event: Remote Sensing
Volume: 12
Issue: 16
Abstract: 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.
ISSN: 2072-4292
DOI: 10.3390/rs12162599
Rights: openAccess
Appears in Collections:FCTUC Matemática - Artigos em Revistas Internacionais
I&D MARE - Artigos em Revistas Internacionais
I&D INESCC - Artigos em Revistas Internacionais

Show full item record


checked on Apr 2, 2024

Page view(s)

checked on Apr 16, 2024


checked on Apr 16, 2024

Google ScholarTM




This item is licensed under a Creative Commons License Creative Commons