Ecological Changes in the Sea of Marmara

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DOI :10.26650/B/LSB21LSB37.2024.023.014   IUP :10.26650/B/LSB21LSB37.2024.023.014    Tam Metin (PDF)

Detection and Tracking of Mucilage Phenomenon in the Sea of Marmara by Remote Sensing Images

Sefa Küçük, Bahri Abacı, Murat Dede, Seniha Esen Yüksel, Mete Yılmaz

 Marine mucilage is a collection of mucus-like organic matter released by marine microorganisms. Intense mucilage formation in the sea prevents fisheries, maritime, and tourism activities, reduces oxygen levels, and adversely affects biodiversity. The traditional method of detecting mucilage involves taking samples from the sea and analyzing them in a laboratory. However, detecting mucilage with these standard methods is laborious since it can spread over kilometers. On the other hand, several satellites in orbit regularly collect data from the Earth’s surface, making it possible to monitor the presence of mucilage through satellite data analysis. Therefore, using both traditional and deep learning algorithms, we utilized PRISMA hyperspectral and Sentinel-2 multispectral data to detect mucilage in its early stages. Sentinel-2A has four 10m fine bands and six 20m coarse bands. To benefit from all bands of Sentinel 2A, the spectral bands must have the same spatial resolution. Although the Sentinel-2A does not have a panchromatic band, the spatial resolution of the 20m bands has been increased to 10m employing its four fine bands as a panchromatic band. We aim to identify or construct a suitable panchromatic band for coarse bands using seven of the existing pansharpening techniques to enhance the spatial resolution of 20m bands to 10m. After preprocessing, we comprehensively compare four different methods, namely Linear regression, Random forest, U-Net, and Vescovi index, on two datasets. On the multispectral dataset, we correctly detect 87.8% of the mucilage formations with the U-Net model and achieve the area under the curve (AUC) score of 0.977. However, the Random forest model has outperformed the other methods, identifying 89.8% of the mucilage formations on the hyperspectral dataset. Experimental results on satellite data with multiple resolutions, bands, different days, and times indicate that detecting mucilage from satellite data with high accuracy and without massive effort is possible.


Anahtar Kelimeler: Hyperspectral, Mucilage, Multispectral, PRISMA, Sentinel-2

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