Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water

doi:10.1016/j.ecss.2016.12.001

	Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water
	Li, Dong ; Tang, Cheng; Xia, Chunlei; Zhang, Hua; Tang, C
发表期刊	ESTUARINE COASTAL AND SHELF SCIENCE
ISSN	0272-7714
	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
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.
文章类型	Article
资助机构	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)
收录类别	SCI
语种	英语
关键词[WOS]	MARINE PROTECTED AREAS ; MULTIBEAM ECHOSOUNDER ; FISH ASSEMBLAGES ; SEA ; BACKSCATTER ; FISHERIES ; DISCRIMINATION ; STATISTICS ; MANAGEMENT ; DEPLOYMENT
研究领域[WOS]	Marine & Freshwater Biology ; Oceanography
WOS记录号	WOS:000393628700002
引用统计	被引频次：23[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/21941
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_海岸带环境过程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	Tang, C
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
推荐引用方式 GB/T 7714	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.
APA	Li, Dong,Tang, Cheng,Xia, Chunlei,Zhang, Hua,&Tang, C.(2017).Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water.ESTUARINE COASTAL AND SHELF SCIENCE,185,11-21.
MLA	Li, Dong,et al."Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water".ESTUARINE COASTAL AND SHELF SCIENCE 185(2017):11-21.

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