Cochlodinium polykrikoides red tide detection in the South Sea of Korea using spectral classification of MODIS data

2011 ◽  
Vol 46 (4) ◽  
pp. 239-263 ◽  
Author(s):  
Young Baek Son ◽  
Joji Ishizaka ◽  
Jong-Chul Jeong ◽  
Hyun-Choel Kim ◽  
Taehee Lee
ALGAE ◽  
2017 ◽  
Vol 32 (3) ◽  
pp. 199-222 ◽  
Author(s):  
An Suk Lim ◽  
Hae Jin Jeong ◽  
Kyeong Ah Seong ◽  
Moo Joon Lee ◽  
Nam Seon Kang ◽  
...  

ALGAE ◽  
2017 ◽  
Vol 32 (4) ◽  
pp. 285-308 ◽  
Author(s):  
Moo Joon Lee ◽  
Hae Jin Jeong ◽  
Jae Seong Kim ◽  
Keon Kang Jang ◽  
Nam Seon Kang ◽  
...  

Desalination ◽  
2009 ◽  
Vol 249 (3) ◽  
pp. 1171-1179 ◽  
Author(s):  
Yongmin Kim ◽  
Younggi Byun ◽  
Yongil Kim ◽  
Yangdam Eo

Harmful Algae ◽  
2015 ◽  
Vol 45 ◽  
pp. 26-32 ◽  
Author(s):  
An Suk Lim ◽  
Hae Jin Jeong ◽  
Tae Young Jang ◽  
Nam Seon Kang ◽  
Se Hyeon Jang ◽  
...  

2021 ◽  
Vol 10 (1) ◽  
pp. 31
Author(s):  
Youngjin Choi ◽  
Youngmin Park ◽  
Weol-Ae Lim ◽  
Seung-Hwan Min ◽  
Joon-Soo Lee

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.


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