Jiang, Xiaopeng1,2,3,4; Gao, Meng1,2,4
; Gao, Zhiqiang1,2,4发表期刊ESTUARINE COASTAL AND SHELF SCIENCE
ISSN0272-7714
2020-10-30
卷号245页码:8
关键词Unmanned aerial vehicle (UAV)RGB-FAIRemote sensingGreen tideDrift velocity estimation
DOI10.1016/j.ecss.2020.106943
通讯作者Gao, Zhiqiang(zqgao@yic.ac.cn)
英文摘要Unmanned aerial vehicles (UAV) equipped with high-resolution camera have been increasingly applied in environment monitoring as an important complement to traditional satellite remote sensing. An accurate extraction of marine green-tide regions still faces many technological challenges, such as the absence of centimeter-level orthophoto maps and a dedicated green-tide index based on red-green-blue (RGB) bands. In this study, a new green-tide index, namely, the red-green-blue floating algae index (RGB-FAI) using RGB images captured by ship-borne UAV, is developed for green-tide detection in the Yellow Sea, China. Specifically, RGB-FAI is defined to measure the green-reflectance height by using the red and blue bands as the baselines. Our results show that the RGB-FAI performs well in the detection of green-tide and the accuracy is satisfactory (kappa = 0.95). It is worthy to note that RGB-FAI has the highest extraction accuracy among these competing indices for green-tide in the declining phase under a hazy atmospheric condition. In addition, by combining the bi-temporal UAV images with RGB-FAI, the drift velocity of green-tide has also been estimated as 0.26 m/s in a 17.1 degrees east by north during aerial photography. In conclusion, the proposed RGB-FAI is effective for green-tide detection and has more potential usage in marine environment monitoring.
资助机构NSFC fund project; NSFC-Shandong joint fund project; National Key R&D Program of China; Key Deployment Project of Center for Ocean Mega-Science, Chinese Academy of Sciences; Key Research Program of Frontier Science, Chinese Academy of Sciences
收录类别SCI
语种英语
关键词[WOS]LARGEST MACROALGAL BLOOM; ULVA-PROLIFERA BLOOMS; YELLOW SEA; VEGETATION INDEXES; ALGAE; AQUACULTURE
研究领域[WOS]Marine & Freshwater Biology; Oceanography
WOS记录号WOS:000582677500017
引用统计被引频次：3[WOS][WOS记录][WOS相关记录]
文献类型期刊论文
条目标识符http://ir.yic.ac.cnhttp://ir.yic.ac.cn/handle/133337/28339
专题中科院海岸带环境过程与生态修复重点实验室
中科院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
通讯作者Gao, Zhiqiang作者单位1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Shandong, Peoples R China
2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Shandong Key Lab Coastal Environm Proc, Yantai 264003, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266400, Shandong, Peoples R China
推荐引用方式
GB/T 7714Jiang, Xiaopeng,Gao, Meng,Gao, Zhiqiang. A novel index to detect green-tide using UAV-based RGB imagery[J]. ESTUARINE COASTAL AND SHELF SCIENCE,2020,245:8.
APAJiang, Xiaopeng,Gao, Meng,&Gao, Zhiqiang.(2020).A novel index to detect green-tide using UAV-based RGB imagery.ESTUARINE COASTAL AND SHELF SCIENCE,245,8.
MLAJiang, Xiaopeng,et al."A novel index to detect green-tide using UAV-based RGB imagery".ESTUARINE COASTAL AND SHELF SCIENCE 245(2020):8.
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