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Remote Sensing
Volume 16
Issue 24
10.3390/rs16244794
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Article

Identification of Green Tide Decomposition Regions in the Yellow Sea, China: Based on Time-Series Remote Sensing Data

by
Guangzong Zhang
1,2,
Yufang He
1,2,
Lifeng Niu
1,2,
Mengquan Wu
3,
Hermann Kaufmann
4,
Jian Liu
1,2,
Tong Liu
1,2,
Qinglei Kong
1,2 and
Bo Chen
1,2,*
1
School of Aerospace, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
2
Key Laboratory of Aerospace RS Big-Data Intelligent Processing and Application of Guangdong Higher Education Institutes, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
3
School of Resources and Environment Engineering, Ludong University, Yantai 264025, China
4
School of Space and Physics, Shandong University, Weihai 264209, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(24), 4794; https://doi.org/10.3390/rs16244794
Submission received: 11 November 2024 / Revised: 19 December 2024 / Accepted: 20 December 2024 / Published: 23 December 2024
(This article belongs to the Section Ocean Remote Sensing)
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Abstract

Approximately 1 million tons of green tides decompose naturally in the Yellow Sea of China every year, releasing large quantities of nutrients that disrupt the marine ecological balance and cause significant environmental consequences. Currently, the identification of areas affected by green tides primarily relies on certain methods, such as ground sampling and biochemical analysis, which limit the ability to quickly and dynamically identify decomposition regions at large spatial and temporal scales. While multi-source remote sensing data can monitor the extent of green tides, accurately identifying areas of algal decomposition remains a challenge. Therefore, satellite data were integrated with key biochemical parameters, such as the carbon-to-nitrogen ratio (C/N), to develop a method for identifying green tide decomposition regions (DRIM). The DRIM shows a high accuracy in identifying green tide decomposition areas, validated through regional repetition rates and UAV measurements. Results indicate that the annual C/N threshold for green tide decomposition regions is 1.2. The method identified the primary decomposition areas in the Yellow Sea from 2015 to 2020, concentrated mainly in the southeastern region of the Shandong Peninsula, covering an area of approximately 1909.4 km2. In 2015, 2016, and 2017, the decomposition areas were the largest, with an average annual duration of approximately 35 days. Our method provides a more detailed classification of the dissipation phase, offering reliable scientific support for accurate and detailed monitoring and management of green tide disasters.
Keywords: green tides; decomposition regions; Yellow Sea; carbon-to-nitrogen ratio green tides; decomposition regions; Yellow Sea; carbon-to-nitrogen ratio

Share and Cite

MDPI and ACS Style

Zhang, G.; He, Y.; Niu, L.; Wu, M.; Kaufmann, H.; Liu, J.; Liu, T.; Kong, Q.; Chen, B. Identification of Green Tide Decomposition Regions in the Yellow Sea, China: Based on Time-Series Remote Sensing Data. Remote Sens. 2024, 16, 4794. https://doi.org/10.3390/rs16244794

AMA Style

Zhang G, He Y, Niu L, Wu M, Kaufmann H, Liu J, Liu T, Kong Q, Chen B. Identification of Green Tide Decomposition Regions in the Yellow Sea, China: Based on Time-Series Remote Sensing Data. Remote Sensing. 2024; 16(24):4794. https://doi.org/10.3390/rs16244794

Chicago/Turabian Style

Zhang, Guangzong, Yufang He, Lifeng Niu, Mengquan Wu, Hermann Kaufmann, Jian Liu, Tong Liu, Qinglei Kong, and Bo Chen. 2024. "Identification of Green Tide Decomposition Regions in the Yellow Sea, China: Based on Time-Series Remote Sensing Data" Remote Sensing 16, no. 24: 4794. https://doi.org/10.3390/rs16244794

APA Style

Zhang, G., He, Y., Niu, L., Wu, M., Kaufmann, H., Liu, J., Liu, T., Kong, Q., & Chen, B. (2024). Identification of Green Tide Decomposition Regions in the Yellow Sea, China: Based on Time-Series Remote Sensing Data. Remote Sensing, 16(24), 4794. https://doi.org/10.3390/rs16244794

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