Estimation of Phytoplankton Size Classes in the Littoral Sea of Korea Using a New Algorithm Based on Deep Learning
The size of phytoplankton (a key primary producer in marine ecosystems) is known to influence the contribution of primary productivity and the upper trophic level of the food web. Therefore, it is essential to identify the dominant sizes of phytoplankton while inferring the responses of marine ecosy...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/10/10/1450 |
| _version_ | 1797472413952245760 |
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| author | Jae Joong Kang Hyun Ju Oh Seok-Hyun Youn Youngmin Park Euihyun Kim Hui Tae Joo Jae Dong Hwang |
| author_facet | Jae Joong Kang Hyun Ju Oh Seok-Hyun Youn Youngmin Park Euihyun Kim Hui Tae Joo Jae Dong Hwang |
| author_sort | Jae Joong Kang |
| collection | DOAJ |
| description | The size of phytoplankton (a key primary producer in marine ecosystems) is known to influence the contribution of primary productivity and the upper trophic level of the food web. Therefore, it is essential to identify the dominant sizes of phytoplankton while inferring the responses of marine ecosystems to change in the marine environment. However, there are few studies on the spatio-temporal variations in the dominant sizes of phytoplankton in the littoral sea of Korea. This study utilized a deep learning model as a classification algorithm to identify the dominance of different phytoplankton sizes. To train the deep learning model, we used field measurements of turbidity, water temperature, and phytoplankton size composition (chlorophyll-a) in the littoral sea of Korea, from 2018 to 2020. The new classification algorithm from the deep learning model yielded an accuracy of 70%, indicating an improvement compared with the existing classification algorithms. The developed classification algorithm could be substituted in satellite ocean color data. This enabled us to identify spatio-temporal variation in phytoplankton size composition in the littoral sea of Korea. We consider this to be highly effective as fundamental data for identifying the spatio-temporal variation in marine ecosystems in the littoral sea of Korea. |
| first_indexed | 2024-03-09T20:01:26Z |
| format | Article |
| id | doaj.art-ea3e0fe23dd345e187b4e69b3a051f5d |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T20:01:26Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-ea3e0fe23dd345e187b4e69b3a051f5d2023-11-24T00:44:23ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-10-011010145010.3390/jmse10101450Estimation of Phytoplankton Size Classes in the Littoral Sea of Korea Using a New Algorithm Based on Deep LearningJae Joong Kang0Hyun Ju Oh1Seok-Hyun Youn2Youngmin Park3Euihyun Kim4Hui Tae Joo5Jae Dong Hwang6National Institute of Fisheries and Sciences, Gijan Haean-ro, Gijang Gun, Busan 15807, KoreaNational Institute of Fisheries and Sciences, Gijan Haean-ro, Gijang Gun, Busan 15807, KoreaNational Institute of Fisheries and Sciences, Gijan Haean-ro, Gijang Gun, Busan 15807, KoreaGeosystem Research Corporation, Department of Marine Forecast, 306, 172 LS-ro, Gunpo-si 15807, KoreaGeosystem Research Corporation, Department of Marine Forecast, 306, 172 LS-ro, Gunpo-si 15807, KoreaNational Institute of Fisheries and Sciences, Gijan Haean-ro, Gijang Gun, Busan 15807, KoreaNational Institute of Fisheries and Sciences, Gijan Haean-ro, Gijang Gun, Busan 15807, KoreaThe size of phytoplankton (a key primary producer in marine ecosystems) is known to influence the contribution of primary productivity and the upper trophic level of the food web. Therefore, it is essential to identify the dominant sizes of phytoplankton while inferring the responses of marine ecosystems to change in the marine environment. However, there are few studies on the spatio-temporal variations in the dominant sizes of phytoplankton in the littoral sea of Korea. This study utilized a deep learning model as a classification algorithm to identify the dominance of different phytoplankton sizes. To train the deep learning model, we used field measurements of turbidity, water temperature, and phytoplankton size composition (chlorophyll-a) in the littoral sea of Korea, from 2018 to 2020. The new classification algorithm from the deep learning model yielded an accuracy of 70%, indicating an improvement compared with the existing classification algorithms. The developed classification algorithm could be substituted in satellite ocean color data. This enabled us to identify spatio-temporal variation in phytoplankton size composition in the littoral sea of Korea. We consider this to be highly effective as fundamental data for identifying the spatio-temporal variation in marine ecosystems in the littoral sea of Korea.https://www.mdpi.com/2077-1312/10/10/1450phytoplanktonphytoplankton size classes (PSCs)ocean colordeep neural network (DNN) |
| spellingShingle | Jae Joong Kang Hyun Ju Oh Seok-Hyun Youn Youngmin Park Euihyun Kim Hui Tae Joo Jae Dong Hwang Estimation of Phytoplankton Size Classes in the Littoral Sea of Korea Using a New Algorithm Based on Deep Learning Journal of Marine Science and Engineering phytoplankton phytoplankton size classes (PSCs) ocean color deep neural network (DNN) |
| title | Estimation of Phytoplankton Size Classes in the Littoral Sea of Korea Using a New Algorithm Based on Deep Learning |
| title_full | Estimation of Phytoplankton Size Classes in the Littoral Sea of Korea Using a New Algorithm Based on Deep Learning |
| title_fullStr | Estimation of Phytoplankton Size Classes in the Littoral Sea of Korea Using a New Algorithm Based on Deep Learning |
| title_full_unstemmed | Estimation of Phytoplankton Size Classes in the Littoral Sea of Korea Using a New Algorithm Based on Deep Learning |
| title_short | Estimation of Phytoplankton Size Classes in the Littoral Sea of Korea Using a New Algorithm Based on Deep Learning |
| title_sort | estimation of phytoplankton size classes in the littoral sea of korea using a new algorithm based on deep learning |
| topic | phytoplankton phytoplankton size classes (PSCs) ocean color deep neural network (DNN) |
| url | https://www.mdpi.com/2077-1312/10/10/1450 |
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