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.
WOS Subject Extended:
Marine & Freshwater Biology
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)
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
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.