

FOLLOWUS
1.CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
2.Shandong Key Laboratory of Coastal Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
zqgao@yic.ac.cn
收稿:2023-06-12,
网络首发:2023-11-07,
纸质出版:2024-07-01
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Estimation and verification of green tide biomass based on UAV remote sensing[J]. 海洋湖沼学报(英文), 2024,42(4):1216-1226.
JIANG Xiaopeng,GAO Zhiqiang,WANG Zhicheng.Estimation and verification of green tide biomass based on UAV remote sensing[J].Journal of Oceanology and Limnology,2024,42(04):1216-1226.
Estimation and verification of green tide biomass based on UAV remote sensing[J]. 海洋湖沼学报(英文), 2024,42(4):1216-1226. DOI:
JIANG Xiaopeng,GAO Zhiqiang,WANG Zhicheng.Estimation and verification of green tide biomass based on UAV remote sensing[J].Journal of Oceanology and Limnology,2024,42(04):1216-1226. DOI:
Since 2007
the Yellow Sea green tide has broken out every summer
causing great harm to the environment and society. Although satellite remote sensing (RS) has been used in biomass research
there are several shortcomings
such as mixed pixels
atmospheric interference
and difficult field validation. The biomass of green tide has been lacking a high-precision estimation method. In this study
high-resolution unmanned aerial vehicle (UAV) RS was used to quantitatively map the biomass of green tides. By utilizing experimental data from previous studies
a robust relationship was established to link biomass to the red-green-blue floating algae index (RGB-FAI). Then
the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements. Results show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas. The study provided an effective complement to the traditional satellite RS
as well as high-precision quantitative techniques for decision-making in disaster management.
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