Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China

doi:10.1007/s11356-017-9544-x

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 大数据挖掘及应用中心

	Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China
	Zhou, Botian1 ; Shang, Mingsheng1 ; Wang, Guoyin 1; Feng, Li 2; Shan, Kun1 ; Liu, Xiangnan 3; Wu, Ling 3; Zhang, Xuerui1
刊名	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
	2017-08-01
卷号	24 期号:23 页码:19044-19056
关键词	Remote sensing Cyanobacterial blooms Water optical classification Density peaks Risky grade index Coverage area index Three Gorges Reservoir
ISSN号	0944-1344
DOI	10.1007/s11356-017-9544-x
通讯作者	Zhang, XR (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China.
英文摘要	Harmful cyanobacterial blooms are exemplified as a major environmental concern due to producing toxin, and have generated a serious threat to public health. Knowledge on the spatial-temporal distribution of cyanobacterial blooms is therefore crucial for public health organizations and environmental agencies. In this study, field data and charge coupled device (CCD) image were collected in Lakes Gaoyang and Hanfeng of the Three Gorges Reservoir (TGR), China. We conducted the risky grade index (RGI) and coverage area index to develop a feasible estimation framework of cyanobacterial blooms. First, the close relationships between CCD reflectance spectral indices and water quality parameters were constructed based on water optical classification. Then, a regional algorithm for the RGI classification was established by density peaks. Finally, our proposed algorithm was applied to investigate dynamics of cyanobacterial blooms in the two lakes from 6-year series of CCD images. Encouraging results demonstrated that satellite remote sensing in conjunction with field observation can aid in the estimation of cyanobacterial blooms in the TGR.
资助项目	National Science and Technology Major Project[2014ZX07104-006] ; Chongqing Science and Technology Innovation Special Project for Social Livelihood[Y61Z030A10] ; National Natural Science Foundation of China[51609229]
WOS研究方向	Environmental Sciences & Ecology
语种	英语
出版者	SPRINGER HEIDELBERG
WOS记录号	WOS:000407723100027
内容类型	期刊论文
源URL	[http://172.16.51.4:88/handle/2HOD01W0/182]
专题	大数据挖掘及应用中心
通讯作者	Zhang, Xuerui
作者单位	1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 2.Chongqing Acad Environm Sci, Chongqing Collaborat Innovat Ctr Big Data Applica, Chongqing 401147, Peoples R China 3.China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Botian,Shang, Mingsheng,Wang, Guoyin,et al. Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China[J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,2017,24(23):19044-19056.
APA	Zhou, Botian.,Shang, Mingsheng.,Wang, Guoyin.,Feng, Li.,Shan, Kun.,...&Zhang, Xuerui.(2017).Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,24(23),19044-19056.
MLA	Zhou, Botian,et al."Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 24.23(2017):19044-19056.