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Estimation and analysis of the green-tide drift velocity using ship-borne UAV
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Xiaopeng Jiang1, 2, 3, Zhiqiang Gao1, 3, *, Xiaoqing Wu1, 3, Yueqi Wang1, 3, Jicai Ning1, 3
Haiyang Xuebao | 2021, 43(4) : 96 - 105
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Haiyang Xuebao | 2021, 43(4): 96-105
• Article •
Estimation and analysis of the green-tide drift velocity using ship-borne UAV
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Xiaopeng Jiang1, 2, 3, Zhiqiang Gao1, 3, *, Xiaoqing Wu1, 3, Yueqi Wang1, 3, Jicai Ning1, 3
Affiliations
  • 1Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Chinese Academy of Sciences, Yantai 264003, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Shandong Key Laboratory of Coastal Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
Published: 2021-04-25 doi: 10.12284/hyxb2021054
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Unmanned aerial vehicle (UAV) remote sensing has distinct advantages of flexible use, no cloud interference, and high spatial-temporal resolution. Aim to explore UAV’s utilization potential in marine disaster monitoring, research ship was used as the UAV landing pad, and for the first time, based on the bi-temporal orthophotos acquired by the ship-borne UAV, the drift velocity of green-tide in the Yellow Sea was estimated. In addition, the velocity result extracted from satellite images was compared, and the influences of wind and tidal currents on green-tide drift were analyzed. The results show that: (1) the red-green-blue floating algae index (RGB-FAI) can extract green-tide patches from UAV-based RGB orthophotos with a high-accuracy (kappa coefficient=0.95); (2) the green-tidal speed of three sites estimated by UAV remote sensing are 0.26−0.44 m/s, and the drift direction changed significantly throughout the day; (3) the short-term drift of green-tide is forced by the wind and tidal current. The drift direction of the green-tide is basically consistent with the tidal current of M2, at 1°−62° to the right of wind direction. The ability to estimate green-tidal velocity accurately from the ship-borne UAV images is expected to provide technical support for the precise prediction, warning and control of green-tide disaster.

UAV remote sensing  /  Yellow Sea  /  green tides  /  drift velocity  /  RGB-FAI
Xiaopeng Jiang, Zhiqiang Gao, Xiaoqing Wu, Yueqi Wang, Jicai Ning. Estimation and analysis of the green-tide drift velocity using ship-borne UAV[J]. Haiyang Xuebao, 2021 , 43 (4) : 96 -105 . DOI: 10.12284/hyxb2021054
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Year 2021 volume 43 Issue 4
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Article Info
doi: 10.12284/hyxb2021054
  • Receive Date：2020-07-27
  • Online Date：2026-02-26
  • Published：2021-04-25
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  • Received：2020-07-27
  • Revised：2020-12-08
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Affiliations
    1Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Chinese Academy of Sciences, Yantai 264003, China
    2University of Chinese Academy of Sciences, Beijing 100049, China
    3Shandong Key Laboratory of Coastal Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
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