a quantitative, non-destructive methodology for habitat characterisation and benthic monitoring at offshore renewable energy developments量化,非破坏性方法栖息地的描述和底栖生物监测海洋可再生能源的发展.pdfVIP
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a quantitative, non-destructive methodology for habitat characterisation and benthic monitoring at offshore renewable energy developments量化,非破坏性方法栖息地的描述和底栖生物监测海洋可再生能源的发展
A Quantitative, Non-Destructive Methodology for
Habitat Characterisation and Benthic Monitoring at
Offshore Renewable Energy Developments
1 2 1
Emma V. Sheehan , Timothy F. Stevens , Martin J. Attrill *
1 Peninsula Research Institute for Marine Renewable Energy (PRIMaRE), Marine Institute, University of Plymouth, Plymouth, United Kingdom, 2 Griffith School of
Environment and Australian Rivers Institute - Coasts and Estuaries, Griffith University, Gold Coast, Queensland, Australia
Abstract
Following governments’ policies to tackle global climate change, the development of offshore renewable energy sites is
likely to increase substantially over coming years. All such developments interact with the seabed to some degree and so a
key need exists for suitable methodology to monitor the impacts of large-scale Marine Renewable Energy Installations
(MREIs). Many of these will be situated on mixed or rocky substrata, where conventional methods to characterise the habitat
are unsuitable. Traditional destructive sampling is also inappropriate in conservation terms, particularly as safety zones
around (MREIs) could function as Marine Protected Areas, with positive benefits for biodiversity. Here we describe a
technique developed to effectively monitor the impact of MREIs and report the results of its field testing, enabling large
areas to be surveyed accurately and cost-effectively. The methodology is based on a high-definition video camera, plus LED
lights and laser scale markers, mounted on a ‘‘flying array’’ that maintains itself above the seabed grounded by a length of
chain, thus causing minimal damage. Samples are taken by slow-speed tows of the gear behind a boat (200 m transects).
The HD video and randomly selected frame grabs are analysed to quan
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