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Convolution Neural Network for the Prediction of Cochlodinium polykrikoides Bloom in the South Sea of Korea

by Youngjin Choi, Youngmin Park, Weol-Ae Lim, Seung-Hwan Min and Joon-Soo Lee

J. Mar. Sci. Eng. 2022, 10(1), 31; https://doi.org/10.3390/jmse10010031 - 29 Dec 2021

Cited by 2 | Viewed by 2425

In this study, the occurrence of Cochlodinium polykrikoides bloom was predicted based on spatial information. The South Sea of Korea (SSK), where C. polykrikoides bloom occurs every year, was divided into three concentrated areas. For each domain, the optimal model configuration was determined by designing a verification experiment with 1–3 convolutional neural network (CNN) layers and 50–300 training times. Finally, we predicted the occurrence of C. polykrikoides bloom based on 3 CNN layers and 300 training times that showed the best results. The experimental results for the three areas showed that the average pixel accuracy was 96.22%, mean accuracy was 91.55%, mean IU was 81.5%, and frequency weighted IU was 84.57%, all of which showed above 80% prediction accuracy, indicating the achievement of appropriate performance. Our results show that the occurrence of C. polykrikoides bloom can be derived from atmosphere and ocean forecast information. Full article

(This article belongs to the Special Issue “Coastal Dynamics, Hazards, and Numerical Modelling” in Memory of Prof. Byung Ho Choi)

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21 pages, 9504 KiB

Open AccessFeature PaperArticle

Synergistic Effect of Multi-Sensor Data on the Detection of Margalefidinium polykrikoides in the South Sea of Korea

by Jisun Shin, Keunyong Kim, Young Baek Son and Joo-Hyung Ryu

Remote Sens. 2019, 11(1), 36; https://doi.org/10.3390/rs11010036 - 26 Dec 2018

Cited by 12 | Viewed by 4579

Abstract

Since 1995, Margalefidinium polykrikoides blooms have occurred frequently in the waters around the Korean peninsula. In the South Sea of Korea (SSK), large-scale M. polykrikoides blooms form offshore and are often transported to the coast, where they gradually accumulate. The objective of this study was to investigate the synergistic effect of multi-sensor data for identifying M. polykrikoides blooms in the SSK from July 2018 to August 2018. We found that the Spectral Shape values calculated from in situ spectra and M. polykrikoides cell abundances in the SSK were highly correlated. Comparing red tide spectra from near-coincident multi-sensor data, remote-sensing reflectance (R_rs) spectra were similar to the spectra of in situ measurements from blue to green wavelengths. R_rs true-color composite images and Spectral Shape images of each sensor showed a clear pattern of M. polykrikoides patches, although there were some limitations for detecting red tide patches in coastal areas. We confirmed the complementarity of red tide data extracted from each sensor using an integrated red tide map. Statistical assessment showed that the sensitivity of red tide detection increased when multi-sensor data were used rather than single-sensor data. These results provide useful information for the application of multi-sensor for red tide detection. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2018”)

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