Institutional Repository of Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences (KLCEP)
| A novel index to detect green-tide using UAV-based RGB imagery | |
Jiang, Xiaopeng1,2,3,4; Gao, Meng1,2,4 ; Gao, Zhiqiang1,2,4
| |
| 发表期刊 | ESTUARINE COASTAL AND SHELF SCIENCE
 |
| ISSN | 0272-7714 |
| 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 |
| 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 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.yic.ac.cn/handle/133337/28339 |
| 专题 | 中国科学院海岸带环境过程与生态修复重点实验室 中国科学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 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. |
| 条目包含的文件 | 下载所有文件 | |||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| A novel index to det（6267KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论