The Dynamics of Floating Macroalgae in the East China Sea and Its Vicinity Waters: A Comparison between 2017 and 2023

Author:

Yu Dingfeng1234ORCID,Li Jinming1234,Xing Qianguo567ORCID,An Deyu1234,Li Jinghu567ORCID

Affiliation:

1. Institute of Oceanograhic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266100, China

2. Shandong Provincial Key Laboratory of Marine Monitoring Instrument Equipment Technology, Qingdao 266100, China

3. National Engineering and Technological Research Center of Marine Monitoring Equipment, Qingdao 266100, China

4. School of Ocean Technology Sciences, Qilu University of Technology, Qingdao 266100, China

5. CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

6. Shandong Key Laboratory of Coastal Environmental Processes, Yantai 264003, China

7. University of Chinese Academy of Sciences, Beijing 100049, China

Abstract

Ulva prolifera and Sargassum are two common floating macroalgae in China’s coastal algal bloom events. Ulva prolifera frequently emerges concomitantly with Sargassum outbreaks, thereby presenting challenges to the monitoring of algal blooms, thereby presenting challenges to the monitoring of algae. To tackle the challenge of differentiating between Ulva prolifera and Sargassum, this study employs Sentinel-2 MSI data for spectral analysis. Notably, significant disparities in the Remote Top of Atmosphere Reflectance (Rtoa) between Ulva prolifera and Sargassum are observed. This study proposes a random forest-based algorithm for discriminating between Ulva prolifera and Sargassum in the regions of the Yellow Sea and East China Sea. The algorithm introduced in this study attains remarkable accuracy in distinguishing Ulva prolifera and Sargassum within Sentinel-2 MSI data, achieving identical F1 scores of 99.1% for both. Moreover, when tested with GF-1 WFV data, the algorithm showcases outstanding performance; this demonstrates the algorithm’s robustness and its ability to mitigate the uncertainty linked to threshold selection. Simultaneously, a comparative analysis of algae distribution was conducted for both 2017 and the period from January to May 2023. Experimental results indicate that the algorithm exhibits high accuracy in distinguishing between Ulva prolifera and Sargassum. This capability will significantly enhance the monitoring of large algae in maritime regions; this holds crucial theoretical significance and offers substantial practical value in the realm of marine ecological conservation.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Natural Science Foundation of Qingdao

Qingdao Marine Science and Technology Innovation Project

Project Plan of Pilot Project of Integration of Science, Education and Industry

University-Industry Collaborative Education Program

Publisher

MDPI AG

Subject

Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry

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