An automatic and quantitative approach to the detection and tracking of acoustic scattering layers. (3rd November 2014)

• Record Type: Journal Article
• Authors: Cade, David E.; Benoit‐Bird, Kelly J.
• Is Part Of: Limnology and oceanography, methods. Volume 12:Number 11(2014:Nov.)
• Page Start: 742
• Page End: 756
• Publication Date: 2014-11-03
• Subjects: Limnology -- Methodology -- Periodicals; Oceanography -- Methodology -- Periodicals; 551.48
• DOI: 10.4319/lom.2014.12.742
• Languages: English
• ISSNs: 1541-5856

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Acoustic scattering layers are ubiquitous, horizontally extensive aggregations of both vertebrate and invertebrate organisms that play key roles in oceanic ecosystems. However, currently there are no conventions or widely adaptable automatic methods for identifying these often dynamic, spatially complex features, so it is difficult to consistently and efficiently describe and compare results. We developed an automatic scattering layer detection method that can be used to monitor changes in layer depth, width, and internal structure over time. Extensive, contiguous regions of the water column that have echo strengths above a threshold were identified as "background layers." They correspond to regions of the water column that contain scattering from diffusely distributed organisms. Often, background layers contained contiguous, horizontally extensive features of concentrated acoustic scattering we identified as "strata." These features were identified by fitting Gaussian curves to the echo envelope of each vertical profile of scattering, and their boundaries were identified as the endpoints of the region containing 95% of the area under the fitted curves. These endpoints were linked horizontally to make continuous tracks. Bottom and top tracks were paired to identify features that sometimes extended horizontally for tens of kilometers. This approach was effective in three disparate ecosystems (the Gulf of California, Monterey Bay, and the Bering Sea), and a sensitivity analysis showed its robustness to changes in input parameters. By allowing a comparable, automated approach to be used across environments, this method promotes the improved classification and characterization of acoustic scattering layers necessary for examining their role in oceanic ecosystems.</p> </abstract>