摘要
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M_(2)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。
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.
作者
姜晓鹏
高志强
吴晓青
王跃启
宁吉才
Jiang Xiaopeng;Gao Zhiqiang;Wu Xiaoqing;Wang Yueqi;Ning Jicai(Key Laboratory of Coastal Environmental Processes and Ecological Remediation,Chinese Academy of Sciences,Yantai 264003,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shandong Key Laboratory of Coastal Environmental Processes,Yantai Institute of Coastal Zone Research,Chinese Academy of Sciences,Yantai 264003,China)
出处
《海洋学报》
CAS
CSCD
北大核心
2021年第4期96-105,共10页
基金
国家自然科学基金(41876107)
国家重点研发计划“蓝色粮仓科技创新”项目(2019YFD0900705)
山东省联合基金(U1706219)
中国科学院海洋大科学研究中心重点部署项目(COMS2019J02)
中国科学院前沿科学重点研究计划(ZDBS-LY-7010)
中国科学院海洋生态与环境科学重点实验室开放基金(KLMEES202005)
山东省海岸带环境过程重点实验室(中国科学院烟台海岸带研究所)开放基金(2019SDHADKFJJ07)。
