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<p>According to the water characteristics of the coastal waters of Hebei Province, this paper selects the data of the Marine Environmental Quality Bulletin of Hebei Province from 2009 to 2018 published on the website of the Department of Natural Resources of Hebei Province, and proposes a red tide monitoring method based on decision tree classification for the pre-processed MODIS 1B image data. The most important thing in the construction of decision tree is the determination of threshold, and this process is finally determined according to the value of Entropy. In this paper, the newly constructed red tide monitoring method is used to extract the occurrence area of red tide and count the red tide area. Finally, the decision tree classification method is compared with other typical red tide monitoring methods. The experimental results show that the red tide occurrence area and statistical area extracted by the red tide monitoring method based on decision tree classification are closer to the data displayed in the Ocean Bulletin, which demonstrates that this method is suitable for red tide monitoring in the coastal waters of Hebei Province.</p>
</abstract>

According to the water characteristics of the coastal waters of Hebei Province, this paper selects the data of the Marine Environmental Quality Bulletin of Hebei Province from 2009 to 2018 published on the website of the Department of Natural Resources of Hebei Province, and proposes a red tide monitoring method based on decision tree classification for the pre-processed MODIS 1B image data. The most important thing in the construction of decision tree is the determination of threshold, and this process is finally determined according to the value of Entropy. In this paper, the newly constructed red tide monitoring method is used to extract the occurrence area of red tide and count the red tide area. Finally, the decision tree classification method is compared with other typical red tide monitoring methods. The experimental results show that the red tide occurrence area and statistical area extracted by the red tide monitoring method based on decision tree classification are closer to the data displayed in the Ocean Bulletin, which demonstrates that this method is suitable for red tide monitoring in the coastal waters of Hebei Province.

Red tide monitoring method in coastal waters of Hebei Province based on decision tree classification

School of Mathematics and Information Science & Technology

Hebei Normal University of Science & Technology

Department of Civil and Architectural Engineering, College of Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

According to the water characteristics of the coastal waters of Hebei Province, this paper selects the data of the Marine Environmental Quality Bulletin of...

Red tide monitoring method in coastal waters of Hebei Province based on decision tree classification

Published Online: Jun 24, 2022

Page range: 43 - 60

Received: Oct 29, 2021

Accepted: May 15, 2022

DOI: https://doi.org/10.2478/amns.2022.1.00051

Keywords
MODIS Image, Red tide monitoring, Decision tree classification, Pre-process, Coastal water

© 2022 Kang Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Red tide monitoring method in coastal waters of Hebei Province based on decision tree classification

Published Online: Jun 24, 2022

Page range: 43 - 60

Received: Oct 29, 2021

Accepted: May 15, 2022

DOI: https://doi.org/10.2478/amns.2022.1.00051

KeywordsMODIS Image, Red tide monitoring, Decision tree classification, Pre-process, Coastal water

© 2022 Kang Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
MODIS Image, Red tide monitoring, Decision tree classification, Pre-process, Coastal water