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    Analysis and Detection of Mucilage Bloom from Multispectral Satellite Images
    (Ieee, 2022) Kucuk, Sefa; Abaci, Bahri; Dede, Murat; Yuksel, Seniha Esen; Yilmaz, Mete
    In this paper, we aim to detect and observe the mucilage formations in the Sea of Marmara by means of Sentinel-2A satellite data. For this purpose, we produce mucilage index maps by utilizing the relationship between the spectral bands of Sentinel-2A. Sentinel-2A has four 10m fine bands and six 20m coarse bands. To compute the mucilage index, 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 can be increased to 10m thanks to its four fine bands. Based on the results of this analysis, we utilize seven of the existing image fusion approaches to enhance the spatial resolution of 20m bands to 10m. We monitor changes in mucilage formations over time with the mucilage maps acquired by using fused images through the mucilage index.
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    DETECTION AND TRACKING OF MUCILAGE PHENOMENON IN THE SEA OF MARMARA BY REMOTE SENSING IMAGES
    (Istanbul Univ Press, Istanbul Univ Rectorate, 2025) Kucuk, Sefa; Abaci, Bahri; Dede, Murat; Yuksel, Seniha Esen; Yilmaz, Mete
    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.
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    Mucilage Detection from Hyperspectral and Multispectral Satellite Data
    (Spie-Int Soc Optical Engineering, 2022) Abaci, Bahri; Dede, Murat; Yuksel, Seniha Esen; Yilmaz, Mete
    Mucilage also called sea snot or sea saliva is a collection of mucus-like organic matter found in the sea. Although not harmful in the beginning, when mucilage increases over time, it covers the sea creatures and forms thick layers in the sea. Its existence and long duration change the oxygen balance in the seas, reduce biodiversity, fisheries, and tourism. Since April 2021, mucilage has emerged as both an ecological and economical problem in Turkey, spreading over an area of kilometers, clogging the fishing nets, causing problems in marine vessels, and disrupting the industry. These findings indicate that mucilage monitoring, early detection, and intervention before the economic and ecological damages grow out of proportion is quite necessary. Through the analysis of satellite data, it is possible to observe the existence of mucilage as thin, extended layers of white substance. Therefore, in this work, we analyze the Sentinel-2 multispectral data and PRISMA hyperspectral data to detect the mucilage in the early stages through the use of both traditional as well as deep learning algorithms. Our results indicate that it is possible to detect mucilage from satellite data with high accuracy, saving time and money for the cleaning efforts.