A novel index to detect green-tide using UAV-based RGB imagery

doi:10.1016/j.ecss.2020.106943

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室

	A novel index to detect green-tide using UAV-based RGB imagery
	Jiang, Xiaopeng 1,2,3,4; Gao, Meng 2,3,4; Gao, Zhiqiang 2,3,4
刊名	ESTUARINE COASTAL AND SHELF SCIENCE
	2020-10-30
卷号	245 页码:8
关键词	Unmanned aerial vehicle (UAV) RGB-FAI Remote sensing Green tide Drift velocity estimation
ISSN号	0272-7714
DOI	10.1016/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.
资助项目	NSFC fund project[41876107] ; NSFC-Shandong joint fund project[U1706219] ; National Key R&D Program of China[2019YFD0900705] ; Key Deployment Project of Center for Ocean Mega-Science, Chinese Academy of Sciences[COMS2019J02] ; Key Research Program of Frontier Science, Chinese Academy of Sciences[ZDBS-LY-7010]
WOS关键词	LARGEST MACROALGAL BLOOM ; ULVA-PROLIFERA BLOOMS ; YELLOW SEA ; VEGETATION INDEXES ; ALGAE ; AQUACULTURE
WOS研究方向	Marine & Freshwater Biology ; Oceanography
语种	英语
WOS记录号	WOS:000582677500017
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
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/28338]
专题	烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室烟台海岸带研究所_海岸带信息集成与综合管理实验室
通讯作者	Gao, Zhiqiang
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Shandong, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266400, Shandong, Peoples R China 4.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Shandong Key Lab Coastal Environm Proc, Yantai 264003, Peoples R China
推荐引用方式 GB/T 7714	Jiang, 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.
APA	Jiang, 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.
MLA	Jiang, Xiaopeng,et al."A novel index to detect green-tide using UAV-based RGB imagery".ESTUARINE COASTAL AND SHELF SCIENCE 245(2020):8.