High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope Imagery
Red tides caused by <i>Margalefidinium polykrikoides</i> occur continuously along the southern coast of Korea, where there are many aquaculture cages, and therefore, prompt monitoring of bloom water is required to prevent considerable damage. Satellite-based ocean-color sensors are widel...
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/13/4447 |
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| author | Jisun Shin Young-Heon Jo Joo-Hyung Ryu Boo-Keun Khim Soo Mee Kim |
| author_facet | Jisun Shin Young-Heon Jo Joo-Hyung Ryu Boo-Keun Khim Soo Mee Kim |
| author_sort | Jisun Shin |
| collection | DOAJ |
| description | Red tides caused by <i>Margalefidinium polykrikoides</i> occur continuously along the southern coast of Korea, where there are many aquaculture cages, and therefore, prompt monitoring of bloom water is required to prevent considerable damage. Satellite-based ocean-color sensors are widely used for detecting red tide blooms, but their low spatial resolution restricts coastal observations. Contrarily, terrestrial sensors with a high spatial resolution are good candidate sensors, despite the lack of spectral resolution and bands for red tide detection. In this study, we developed a U-Net deep learning model for detecting <i>M. polykrikoides</i> blooms along the southern coast of Korea from PlanetScope imagery with a high spatial resolution of 3 m. The U-Net model was trained with four different datasets that were constructed with randomly or non-randomly chosen patches consisting of different ratios of red tide and non-red tide pixels. The qualitative and quantitative assessments of the conventional red tide index (RTI) and four U-Net models suggest that the U-Net model, which was trained with a dataset of non-randomly chosen patches including non-red tide patches, outperformed RTI in terms of sensitivity, precision, and F-measure level, accounting for an increase of 19.84%, 44.84%, and 28.52%, respectively. The <i>M. polykrikoides</i> map derived from U-Net provides the most reasonable red tide patterns in all water areas. Combining high spatial resolution images and deep learning approaches represents a good solution for the monitoring of red tides over coastal regions. |
| first_indexed | 2024-03-09T04:44:59Z |
| format | Article |
| id | doaj.art-751a5cfeb7d44994a7c88082675007fc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:44:59Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-751a5cfeb7d44994a7c88082675007fc2023-12-03T13:16:34ZengMDPI AGSensors1424-82202021-06-012113444710.3390/s21134447High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope ImageryJisun Shin0Young-Heon Jo1Joo-Hyung Ryu2Boo-Keun Khim3Soo Mee Kim4BK21 School of Earth and Environmental Systems, Pusan National University, Busan 46241, KoreaBK21 School of Earth and Environmental Systems, Pusan National University, Busan 46241, KoreaKorea Ocean Satellite Center, Korea Institute of Ocean Science and Technology (KIOST), Busan 49111, KoreaBK21 School of Earth and Environmental Systems, Pusan National University, Busan 46241, KoreaMaritime ICT R&D Center, Korea Institute of Ocean Science and Technology (KIOST), Busan 49111, KoreaRed tides caused by <i>Margalefidinium polykrikoides</i> occur continuously along the southern coast of Korea, where there are many aquaculture cages, and therefore, prompt monitoring of bloom water is required to prevent considerable damage. Satellite-based ocean-color sensors are widely used for detecting red tide blooms, but their low spatial resolution restricts coastal observations. Contrarily, terrestrial sensors with a high spatial resolution are good candidate sensors, despite the lack of spectral resolution and bands for red tide detection. In this study, we developed a U-Net deep learning model for detecting <i>M. polykrikoides</i> blooms along the southern coast of Korea from PlanetScope imagery with a high spatial resolution of 3 m. The U-Net model was trained with four different datasets that were constructed with randomly or non-randomly chosen patches consisting of different ratios of red tide and non-red tide pixels. The qualitative and quantitative assessments of the conventional red tide index (RTI) and four U-Net models suggest that the U-Net model, which was trained with a dataset of non-randomly chosen patches including non-red tide patches, outperformed RTI in terms of sensitivity, precision, and F-measure level, accounting for an increase of 19.84%, 44.84%, and 28.52%, respectively. The <i>M. polykrikoides</i> map derived from U-Net provides the most reasonable red tide patterns in all water areas. Combining high spatial resolution images and deep learning approaches represents a good solution for the monitoring of red tides over coastal regions.https://www.mdpi.com/1424-8220/21/13/4447<i>Margalefidinium polykrikoides</i>PlanetScopesouthern coast of Koreaconvolutional neural networkU-Net |
| spellingShingle | Jisun Shin Young-Heon Jo Joo-Hyung Ryu Boo-Keun Khim Soo Mee Kim High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope Imagery Sensors <i>Margalefidinium polykrikoides</i> PlanetScope southern coast of Korea convolutional neural network U-Net |
| title | High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope Imagery |
| title_full | High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope Imagery |
| title_fullStr | High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope Imagery |
| title_full_unstemmed | High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope Imagery |
| title_short | High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope Imagery |
| title_sort | high spatial resolution red tide detection in the southern coast of korea using u net from planetscope imagery |
| topic | <i>Margalefidinium polykrikoides</i> PlanetScope southern coast of Korea convolutional neural network U-Net |
| url | https://www.mdpi.com/1424-8220/21/13/4447 |
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