Litter on the beaches of the Red Sea of Yemen

Al-Shwafi, Nabil A.; Ahmed, Abdulhakim Mohsen

doi:10.1134/s1070363211130135

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Hence, visual censuses are subjective, time and labor consuming, and the area covered is in most cases only a sub-sample of the targeted beach. The median area surveyed across a number of studies [25,31,32,35,36,42,44,45] was 1.162 ha, ranging from 0.131 to 15.915 ha. Therefore, efficient acquisition of estimates of beach litter remains a major bottle neck to produce the data necessary to assess global distribution patterns, stocks, and contribution to regional and global marine litter mass balances.…”

Section: Introductionmentioning

confidence: 99%

Use of Unmanned Aerial Vehicles for efficient beach litter monitoring

Martin¹,

Parkes²,

Zhang³

et al. 2018

Preprint

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A global beach litter assessment is challenged by use of low-efficiency methodologies and incomparable pro- tocols 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.

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