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题名:
Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water
作者: Li, Dong; Tang, Cheng; Xia, Chunlei; Zhang, Hua
刊名: ESTUARINE COASTAL AND SHELF SCIENCE
ISSN号: 0272-7714
出版日期: 2017-02-05
卷号: 185, 页码:11-21
关键词: Artificial reef ; Acoustic mapping ; Automated classification ; Multibeam echosounder
DOI: 10.1016/j.ecss.2016.12.001
产权排序: [Li, Dong; Tang, Cheng; Xia, Chunlei; Zhang, Hua] Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Zone Environm Proc & Ecol Remedia, Yantai, Shandong, Peoples R China; [Li, Dong] Univ Chinese Acad Sci, Beijing, Peoples R China
通讯作者: Tang, C
作者部门: 中科院海岸带环境过程与生态修复重点实验室
英文摘要: Artificial reefs (ARs) are effective means to maintain fishery resources and to restore ecological environment in coastal waters. ARs have been, widely constructed along the Chinese coast. However, understanding of benthic habitats in the vicinity of ARs is limited, hindering effective fisheries and aquacultural management. Multibeam echosounder (MBES) is an advanced acoustic instrument capable of efficiently generating large-scale maps of benthic environments at fine resolutions. The objective of this study is to develop a technical approach to characterize, classify, and map shallow coastal areas with ARs using an MBES. An automated classification method is designed and tested to process bathymetric and backscatter data from MBES and transform the variables into simple, easily visualized maps. To reduce the redundancy in acoustic variables, a principal component analysis (PCA) is used to condense the highly collinear dataset. An acoustic benthic map of bottom sediments is classified using an iterative self-organizing data analysis technique (ISODATA). The approach is tested with MBES surveys in a 1.15 km(2) fish farm with a high density of ARs off the Yantai coast in northern China. Using this method, 3 basic benthic habitats (sandy bottom, muddy sediments, and ARs) are distinguished. The results of the classification are validated using sediment samples and underwater surveys. Our study shows that the use of MBES is an effective method for acoustic mapping and classification of ARs. (C) 2016 Elsevier Ltd. All rights reserved.
研究领域[WOS]: Marine & Freshwater Biology ; Oceanography
项目资助者: National Key Basic Research Program of China (973)(2015CB453301) ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDA11020305) ; Key Deployment Project of Chinese Academy of Sciences(KZZD-EW-14)
关键词[WOS]: MARINE PROTECTED AREAS ; MULTIBEAM ECHOSOUNDER ; FISH ASSEMBLAGES ; SEA ; BACKSCATTER ; FISHERIES ; DISCRIMINATION ; STATISTICS ; MANAGEMENT ; DEPLOYMENT
文章类型: Article
收录类别: SCI
语种: 英语
WOS记录号: WOS:000393628700002
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.yic.ac.cn/handle/133337/21941
Appears in Collections:中科院海岸带环境过程与生态修复重点实验室_期刊论文

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作者单位: 1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Zone Environm Proc & Ecol Remedia, Yantai, Shandong, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China

Recommended Citation:
Li, Dong,Tang, Cheng,Xia, Chunlei,et al. Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water[J]. ESTUARINE COASTAL AND SHELF SCIENCE,2017,185:11-21.
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文件名: Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water.pdf
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