Mucilage Problem in the Sea of Marmara

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DOI :10.26650/B/LS32.2023.003.11   IUP :10.26650/B/LS32.2023.003.11    Full Text (PDF)

Detection and Monitoring of Mucilage Formations Using Pixel Based Convolutional Neural Networks: The Case Study of Izmit Gulf, Turkey

Taşkın Kavzoğlu, Elif Özlem Yılmaz, İsmail Çölkesen, Umut Güneş Sefercik, Cem Gazioğlu

The rapid increase in urbanization and industrialization has brought about a boosting effect in environmental pollution, causing a decrease in biological diversity and a deterioration in the ecological balance. As a result, the number of environmental disasters drastically increases every year. One of the most significant of these disasters is recent marine mucilage cases that occurred in the Sea of Marmara, Turkey. Detecting, monitoring, and predicting the extent of mucilage formations is essential for decision-makers to understand the extent of the disaster and develop contingency strategies for mitigation and clean-up activities. Within this scope, remote sensing technologies offer periodical monitoring of the mucilage formations at low cost in large coverage areas. Deep learning models, in particular Convolutional Neural Networks (CNN), have recently delivered state-of-the-art results by developing complex representations while taking into account advanced feature representation strategies. In this study, cloudless Sentinel-2A images of Izmit Gulf acquired on 14, 19, and 24 May 2021 were utilized to identify mucilage formations by applying pixel-based CNN models. Overall classification accuracies of 99.49, 99.06, and 98.70% were achieved with the constructed CNN models for the three dates, respectively. The mucilage-covered areas increased from 7.75 km2 to 18.51 km² in the first 5-day period and then slightly increased to 21.79 km2 on May 24, corresponding to 7.26% of Izmit Gulf. Produced thematic maps revealed that the mucilage formations gradually spread from the shoreline of Izmit to the Izmit Gulf and the mucilage-covered areas increased significantly over 10-day period in the study area. Movements and shapes of the mucilage aggregates were highly correlated to prevailing wind and currents existing in the Sea of Marmara. 


Keywords: Convolutional Neural Network, Deep Learning, Marine Mucilage, Sentinel-2A

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