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Open AccessArticle

Real-Time Classification of Seagrass Meadows on Flat Bottom with Bathymetric Data Measured by a Narrow Multibeam Sonar System

Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-8564, Japan
Japan Science and Technology Agency, CREST, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
Author to whom correspondence should be addressed.
Academic Editors: Nicholas Makris, Xiaofeng Li and Prasad S. Thenkabail
Remote Sens. 2016, 8(2), 96;
Received: 19 October 2015 / Revised: 23 December 2015 / Accepted: 18 January 2016 / Published: 27 January 2016
(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)
Seagrass meadows, one of the most important habitats for many marine species, provide essential ecological services. Thus, society must conserve seagrass beds as part of their sustainable development efforts. Conserving these ecosystems requires information on seagrass distribution and relative abundance, and an efficient, accurate monitoring system. Although narrow multibeam sonar systems (NMBSs) are highly effective in resolving seagrass beds, post-processing methods are required to extract key data. The purpose of this study was to develop a simple method capable of detecting seagrass meadows and estimating their relative abundance in real time using an NMBS. Because most seagrass meadows grow on sandy seafloors, we proposed a way of discriminating seagrass meadows from the sand bed. We classify meadows into three categories of relative seagrass abundance using the 95% confidence level of beam depths and the depth range of the beam depth. These are respectively two times the standard deviation of beam depths, and the difference between the shallowest and the deepest depths in a 0.5 × 0.5 m grid cell sampled with several narrow beams. We examined Zostera caulescens Miki, but this simple NMBS method of seagrass classification can potentially be used to map seagrass meadows with longer shoots of other species, such as Posidonia, as both have gas filled cavities. View Full-Text
Keywords: acoustics; narrow multibeam sonar; real-time classification; seagrass acoustics; narrow multibeam sonar; real-time classification; seagrass
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MDPI and ACS Style

Hamana, M.; Komatsu, T. Real-Time Classification of Seagrass Meadows on Flat Bottom with Bathymetric Data Measured by a Narrow Multibeam Sonar System. Remote Sens. 2016, 8, 96.

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