Unmanned aerial system (UAS) current mapping in Bear Cut, Florida from 2018-02-19 to 2018-02-23

This study developed a novel approach to unmanned aerial system (UAS) current mapping based on optical video data of the sea surface. Three-dimensional fast Fourier transform and least-squares fitting were used to measure surface waves’ phase velocities and thereby derive currents via the linear dispersion relationship. The UAS used was a low-cost consumer-grade quadcopter whose camera position and attitude measurements may cause spurious currents as large as the signal. A novel wave-based UAS heading and position correction technique was developed, improving the image rectification accuracy by a factor of ~3.5 and the current measurements’ temporal repeatability by a factor of 1.8 to 4.8. This technique was validated using the data included herein. The UAS mapped the currents across the ~700 m wide tidally dominated Bear Cut channel in Miami, Florida over several days in February 2018. The UAS currents were validated by flotsam tracks, obtained through automated UAS video object detection and tracking, drifter tracks, and Acoustic Doppler Current Profiler (ADCP) measurements. All of these data sources are included here in this dataset, and the video files used are also available in GRIIDC under Unique Dataset Identifier (UDI) R6.x806.000:0012 (DOI: 10.7266/n7-11te-m018). This dataset supports the publication: Lund, B., Carrasco, R., Dai, H., Graber, H. C., Guigand, C. M., Haus, B. K., Horstmann, J., Lodise, J. A., Novelli G, Özgökmen T., Rebozo M. A., Ryab E. H. and Streßer, M. (2021). UAS current mapping: A wave-based heading and position correction. Journal of Atmospheric and Oceanic Technology. doi:10.1175/jtech-d-20-0123.1.本研究提出了一种基于海面光学视频数据的无人航空系统（Unmanned Aerial System, UAS）海流测绘新方法。本研究采用三维快速傅里叶变换与最小二乘拟合方法，测量海面波浪的相速度，并通过线性色散关系推导海流流速。本次研究使用的无人航空系统为一款低成本消费级四旋翼无人机，其相机位置与姿态测量误差可能产生与信号量级相当的虚假海流。本研究提出了一种基于波浪的无人航空系统航向与位置校正新方法，将图像校正精度提升约3.5倍，海流测量的时间重复性提升1.8至4.8倍。本研究使用本数据集包含的数据对该方法进行了验证。2018年2月，研究团队使用该无人航空系统对佛罗里达州迈阿密市约700米宽、受潮汐主导的贝尔卡特水道（Bear Cut Channel）的海流进行了为期数天的测绘。无人航空系统测得的海流通过三种方式进行了验证：通过无人航空系统视频自动目标检测与跟踪得到的漂浮物轨迹、漂流浮标轨迹，以及声学多普勒流速剖面仪（Acoustic Doppler Current Profiler, ADCP）测量数据。本数据集包含上述所有数据源，所用的视频文件也可在GRIIDC中获取，其唯一数据集标识符（Unique Dataset Identifier, UDI）为R6.x806.000:0012，DOI为10.7266/n7-11te-m018。本数据集支撑以下已发表论文：Lund, B.、Carrasco, R.、Dai, H.、Graber, H. C.、Guigand, C. M.、Haus, B. K.、Horstmann, J.、Lodise, J. A.、Novelli G.、Özgökmen T.、Rebozo M. A.、Ryab E. H. 与 Streßer, M.（2021）：《无人航空系统海流测绘：基于波浪的航向与位置校正方法》，《大气与海洋技术期刊（Journal of Atmospheric and Oceanic Technology）》，DOI: 10.1175/jtech-d-20-0123.1。