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Long-term remote tracking the dynamics of surface water turbidity using a density peaks -based classification: A case study in the Three Gorges Reservoir, China

文献类型：期刊论文

作者Zhou, Botian3,4; Shang, Mingsheng3,4; Feng, Li2; Shan, Kun3,4; Feng, Lei4; Ma, Jianrong1; Liu, Xiangnan5; Wu, Ling5
刊名ECOLOGICAL INDICATORS
出版日期2020-09-01
卷号116页码:10
ISSN号1470-160X
关键词Surface water turbidity Remote sensing Density peaks Water optical classification Long-term trend Three Gorges Reservoir
DOI10.1016/j.ecolind.2020.106539
通讯作者Zhou, Botian(zhoubotian@cigit.ac.cn)
英文摘要Surface water turbidity (SWT), as a low-cost proxy of surface suspended sediment, is important for characterizing the hydro-ecological process and light availability in the lake or reservoir ecosystem. In this study, we proposed the combined use of HJ-1 charge-coupled device imaging and field observation to track the long-term SWT dynamics with environmental changes in Lakes Gaoyang, Hanfeng, and Changshou of the Three Gorges Reservoir, China. In situ remote sensing reflectance spectra were utilized to develop the characteristic spectral indexes for the SWT estimation in different water optical classes separated by a density peaks-based classification. Significant correlations were found between the red-, four-band, band ratio spectral indexes and SWT (determination coefficient >0.71 and root-mean-square error <8.32 nephelometric turbidity unit), suggesting a crucial role of the class-specific retrieval models for the SWT estimation in optically complex waters. The proposed method was further used to monitor the spatio-temporal SWT dynamics over the three lakes from 2008 to 2019, demonstrating that the significant SWT decline in Lakes Gaoyang and Hanfeng and the relatively stable trend in Lake Changshou during the 11-year period. Specifically, the SWT decreasing trends may be attributed to the water level linkage mechanism of Three Gorges and Wuyang Dams. In addition, analyses with simultaneous environmental factors showed that the seasonal and inter-annual variations of SWT appear to be closely correlated with water level and rainfall. Long-term remote tracking of the SWT dynamics presented in this study could provide new insight and reference for reservoir management in the post-Three Gorges Project Era.
资助项目National Natural Science Foundation of China[41901366] ; National Science and Technology Major Project[2014ZX07104-006] ; Chongqing Science and Technology Innovation Special Project for Social Livelihood[Y61Z030A10]
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
语种英语
出版者ELSEVIER
WOS记录号WOS:000540278400034
源URL[http://119.78.100.138/handle/2HOD01W0/11455]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Zhou, Botian
作者单位1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Key Lab Reservoir Aquat Environm, Chongqing 400714, Peoples R China
2.Chongqing Acad Ecol & Environm Sci, Chongqing Collaborat Innovat Ctr Big Data Applica, Chongqing 401147, Peoples R China
3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
4.Chinese Acad Sci, Ecol & Environm Online Monitoring Ctr Three Gorge, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
5.China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Botian,Shang, Mingsheng,Feng, Li,et al. Long-term remote tracking the dynamics of surface water turbidity using a density peaks -based classification: A case study in the Three Gorges Reservoir, China[J]. ECOLOGICAL INDICATORS,2020,116:10.
APA Zhou, Botian.,Shang, Mingsheng.,Feng, Li.,Shan, Kun.,Feng, Lei.,...&Wu, Ling.(2020).Long-term remote tracking the dynamics of surface water turbidity using a density peaks -based classification: A case study in the Three Gorges Reservoir, China.ECOLOGICAL INDICATORS,116,10.
MLA Zhou, Botian,et al."Long-term remote tracking the dynamics of surface water turbidity using a density peaks -based classification: A case study in the Three Gorges Reservoir, China".ECOLOGICAL INDICATORS 116(2020):10.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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