基于船载无人机的绿潮漂移速度估算与分析

无人机遥感具有应用灵活、不受云层干扰以及时空分辨率高的显著优势。为探索无人机在海洋灾害监测中的应用，本文以科考船为起降平台，首次基于无人机获取的双时相绿潮正射影像，开展了黄海绿潮漂移速度的估算研究。同时对比了卫星影像提取的速度结果，并探讨了风与潮流对海上绿潮漂移的驱动。研究发现:(1)可见光波段的漂浮藻类指数能高精度地提取无人机可见光影像中的绿潮(kappa 系数=0.95)；(2)无人机遥感估算3个站位的绿潮漂移速率为0.26～0.44 m/s，漂移方向在1天之内变化明显；(3)绿潮短时间内的漂移受到风与潮流的共同影响，漂移方向与M₂分潮的潮流方向基本一致，位于风向右侧1°～62°。基于船载的无人机航测，能高精度地估算绿潮漂移速度，为精细化的绿潮灾害预警与防控提供技术支撑。

Estimation and analysis of the green-tide drift velocity using ship-borne UAV

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.