Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water
文献类型：期刊论文
| 作者 | Li, Dong; Tang, Cheng ; Xia, Chunlei; Zhang, Hua; Tang, C
|
| 刊名 | ESTUARINE COASTAL AND SHELF SCIENCE
 |
| 出版日期 | 2017-02-05 |
| 卷号 | 185页码:11-21 |
| ISSN号 | 0272-7714 |
| 关键词 | Artificial reef Acoustic mapping Automated classification Multibeam echosounder |
| 通讯作者 | Tang, C |
| 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 |
| 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 |
| 关键词[WOS] | MARINE PROTECTED AREAS ; MULTIBEAM ECHOSOUNDER ; FISH ASSEMBLAGES ; SEA ; BACKSCATTER ; FISHERIES ; DISCRIMINATION ; STATISTICS ; MANAGEMENT ; DEPLOYMENT |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000393628700002 |
| 源URL | [http://ir.yic.ac.cn/handle/133337/21941]  |
| 专题 | 烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室 烟台海岸带研究所_近岸生态与环境实验室 烟台海岸带研究所_海岸带信息集成与综合管理实验室 |
| 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. |
入库方式： OAI收割
来源：烟台海岸带研究所
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