Abstract
A global beach litter assessment is challenged by use of low-efficiency methodologies and incomparable protocols that impede data integration and acquisition at a national scale. The implementation of an objective, reproducible and efficient approach is therefore required. Here we show the application of a remote sensing based methodology using a test beach located on the Saudi Arabian Red Sea coastline. Litter was recorded via image acquisition from an Unmanned Aerial Vehicle, while an automatic processing of the high volume of imagery was developed through machine learning, employed for debris detection and classification in three categories. Application of the method resulted in an almost 40 times faster beach coverage when compared to a standard visual-census approach. While the machine learning tool faced some challenges in correctly detecting objects of interest, first classification results are promising and motivate efforts to further develop the technique and implement it at much larger scales.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 662-673 |
| Number of pages | 12 |
| Journal | Marine Pollution Bulletin |
| Volume | 131 |
| DOIs | |
| State | Published - May 5 2018 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): 2639
Acknowledgements: This work was supported and funded by King Abdullah University of Science and Technology (KAUST) through the baseline funding of CMD and by KAUST Office of Sponsored Research (OSR) under Award No. 2639. We thank the crew of R/V Thuwal, the Coastal and Marine Resources Core Lab and Red Sea Research Center colleagues for field assistance. We particularly thank Mohammed Magbool Aljahdali, Núria Marbà and Dorte Krause-Jensen for support during field-work.