基于船载无人机的绿潮漂移速度估算与分析

姜晓鹏; 高志强; 吴晓青; 王跃启; 宁吉才

doi:10.12284/hyxb2021054

基于船载无人机的绿潮漂移速度估算与分析

doi: 10.12284/hyxb2021054

姜晓鹏^{1, 2, 3,},
高志强^{1, 3, ,},
吴晓青^{1, 3},
王跃启^{1, 3},
宁吉才^{1, 3}

1.
中国科学院海岸带环境过程与生态修复重点实验室，山东烟台 264003
2.
中国科学院大学，北京 100049
3.
中国科学院烟台海岸带研究所山东省海岸带环境过程重点实验室，山东烟台 264003

基金项目: 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）。

详细信息

作者简介:
姜晓鹏（1986-），男，山东省威海市人，博士研究生，研究方向为环境遥感与GIS开发。E-mail：xpjiang@yic.ac.cn

通讯作者:
高志强，博士生导师，研究员，研究方向为海岸带信息集成及定量遥感。E-mail：zqgao@yic.ac.cn

中图分类号: X87
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- 被引次数: 0
出版历程
- 收稿日期: 2020-07-27
- 修回日期: 2020-12-08
- 网络出版日期: 2021-03-24
- 刊出日期: 2021-04-01

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

Jiang Xiaopeng^{1, 2, 3
,},
Gao Zhiqiang^{1, 3
, ,},
Wu Xiaoqing^{1, 3},
Wang Yueqi^{1, 3},
Ning Jicai^{1, 3}

1.
Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Chinese Academy of Sciences, Yantai 264003, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Shandong Key Laboratory of Coastal Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

摘要

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。
- 无人机遥感 /
- 黄海 /
- 绿潮 /
- 漂移速度 /
- 漂浮藻类指数
Abstract: 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

HTML全文

图 1 航拍站点、验潮站及研究区示意图

Fig. 1 Map of aerial photography sites, tide station and study area

下载: 全尺寸图片幻灯片

图 2 船载无人机获取绿潮正射影像示意图

Fig. 2 Green-tidal orthophoto taken by ship-borne unmanned aerial vehicle (UAV)

下载: 全尺寸图片幻灯片

图 3 3个站点作业期间的1 min平均风速矢量

2条红色竖线间为选取的有效风时段，2个绿色标签点之间表示该站点航拍的绿潮漂移时段

Fig. 3 The 1-minute average wind data of three sites during operation

The selected effective wind period is between two red vertical lines, and two green label points represent the green-tide drift period photographed by UAV

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图 4 绿潮提取步骤

a. 生成数字正射影像；b. 计算RGB-FAI；c. 应用阈值分割法提取绿潮，并使用随机点进行精度评价

Fig. 4 Steps of extracting green-tide

a. Generating digital orthophoto image; b. calculating RGB-FAI; c. extracting green-tide by threshold segmentation, and using the random points for accuracy evaluation

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图 5 3个站点的绿潮斑块漂移图

点状符号表示各子斑块的中心点

Fig. 5 The green-tide drifting map of three sites

The dot symbols in the graph represent the center of different sub patches

下载: 全尺寸图片幻灯片

图 6 3个站点航拍作业时的潮位

Fig. 6 Tidal height during aerial photography of three sites

下载: 全尺寸图片幻灯片

图 7 绿潮、风、潮流的移动矢量图

a、c、e分别为S1、S2、S3站点风与绿潮漂移的矢量图，红色箭头为风矢量，绿色箭头为绿潮漂移矢量；b、d分别为南黄海6月表层低潮时、落潮中间时的流场分布（改绘自文献[29]），红点标识了3个站点的位置

Fig. 7 Motion vector of green-tide, wind and tidal current

a, c, e are vector diagrams of wind and green-tide drift at sites S1, S2 and S3, respectively, with red arrow as the wind vector and green arrow as the green-tide drifting vector; b, d are the tidal current field of the low tide and the middle of ebb in surface of the southern Yellow Sea in June (modified from reference [29]), where the red dots indicate the positions of three sites

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图 8 基于GOCI影像的绿潮逐时漂移图

Fig. 8 The one-hour drifting map of green-tide from GOCI images

下载: 全尺寸图片幻灯片

表 1 绿潮提取误差矩阵表

Tab. 1 Error matrix of green-tide extraction

	海水	绿藻	行总和	用户精度/%
海水	692	17	709	97.60
绿藻	4	286	290	98.62
列总和	696	303	999
生产者精度/%	99.43	94.39

下载: 导出CSV

表 2 绿潮斑块漂移速度与风速的统计

Tab. 2 Statistics of green-tide drifting velocity and the corresponding wind velocity

站点	站点坐标	航拍日期	航拍时段	漂移速率/(m·s⁻¹)	漂移方向*/(°)	有效风时段	风速均值/(m·s⁻¹)	风向均值*/(°)
S1	36°N, 121°E	2019年6月19日	13:04−13:12	0.257	17.1	13:03−13:13	3.17	139.7
S2	36°N, 121.5°E	2019年6月19日	09:36−10:06	0.439	69.0	09:29−10:01	3.23	186.9
S3	35°N, 121°E	2019年6月16日	07:55−08:03	0.256	136.5	07:48−07:58	1.75	315.7
注：*方向以正北向为0°，顺时针为正。

下载: 导出CSV

表 3 基于UAV和GOCI影像的绿潮漂移速度提取结果的对比

Tab. 3 Comparison of green-tide drifting velocity derived from UAV and GOCI images

	无人机遥感	GOCI卫星遥感	偏差*
影像分辨率/m	0.14	500
漂移方向（北偏东）/(°)	69.0	58.4	2.9%
漂移速率/(m·s⁻¹)	0.439	0.547	0.108
注：*方向偏差=∣测定值−基准值∣/360°×100%；速率偏差=∣测定值−基准值∣。

下载: 导出CSV

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