Acoustic tracking of migrating salmon

Kupilik, Matthew; Petersen, Todd

doi:10.1121/1.4894796

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…For example, the raw data of DIDSON is a 2‐dimensional acoustic array that has to be converted to the polar coordinates (Jing et al, 2019; Zang et al, 2021). As the data are dispersed, interpolation is often used between beams to produce a smoother image (Jing et al, 2019; Kupilik & Petersen, 2014a; Yu et al, 2016).…”

Section: Sonar Data Processing For Fish Detection Tracking and Countingmentioning

confidence: 99%

“…Fish may disappear in several frames when the swimming direction is parallel to the beam, or the signal is overwhelmed by stronger echoes from the background. Fish tracks generated by the tracking algorithm compensate for this kind of missing detection (Kupilik & Petersen, 2014a). In addition, fish tracks are also used for counting and behaviour metrics extraction in abundance estimation and behaviour analysis.…”

Section: Fish Trackingmentioning

confidence: 99%

“…Tracking error occurred when the target accelerated suddenly and changed direction Kupilik and Petersen (2014a) the milling behaviour makes overcounting more serious (Hughes & Hightower, 2015). Martignac et al (2021) discovered that multiple counting of the same fish was the major source of counting error, which accounted for 24% of the detected targets.…”

Section: Gaussian Mixture Probability Hypothesis Densitymentioning

confidence: 99%

“…As the edge extracted are commonly broken and fine, morphological operations such as filling and erosion are needed in the subsequent steps (Jing et al, 2017; Shahrestani et al, 2017). To improve the robustness to noise, the Gaussian mixture model and optical flow were also utilized for fish segmentation successfully (Kupilik & Petersen, 2014a; Perivolioti et al, 2021). In some complex scenarios, the foreground objects also contain non‐fish objects.…”

Section: Sonar Data Processing For Fish Detection Tracking and Countingmentioning

confidence: 99%

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Monitoring fish using imaging sonar: Capacity, challenges and future perspective

Wei

Duan

2022

Fish and Fisheries

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The demand for fish products, which provide crucial protein for humans, is rising as the global population grows. In contrast, fish stock is declining due to human activity, environmental changes and overfishing. Fish monitoring provides valuable support data for effective fishery management and ecosystem conservation. The common monitoring methods are based on manual sampling, which is time-consuming, laborious and intrusive. Imaging sonar is a hydroacoustic system that produces acoustic images similar to optical images by transmitting and receiving sound waves, allowing for in situ monitoring of fish non-intrusively in the dark and turbid water environments where optical cameras are limited. In the last decade, imaging sonar, especially high frequency multibeam forward-looking sonar and side-scan sonar, has been widely used in fish monitoring. We reviewed the literature from the previous decade on the use of these two types of imaging sonar in fish species identification, abundance estimation, length measurement and behaviour analysis, as well as the sonar imagery processing concerning fish. The review results show that these imaging sonars are efficient and effective tools for fish monitoring in complex environments. The challenges include(1) the recognition of small fish forming dense aggregations; (2) species identification, which limits their use in species-specific studies; (3) time-consuming massive data processing. Therefore, advanced algorithms for sonar imagery processing and integrations with other sampling technologies are needed for future development.

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Section: Sonar Data Processing For Fish Detection Tracking and Countingmentioning

confidence: 99%

Section: Fish Trackingmentioning

confidence: 99%

Section: Gaussian Mixture Probability Hypothesis Densitymentioning

confidence: 99%

Section: Sonar Data Processing For Fish Detection Tracking and Countingmentioning

confidence: 99%

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Monitoring fish using imaging sonar: Capacity, challenges and future perspective

Wei

Duan

2022

Fish and Fisheries

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“…Acoustic fish tracking provides an individual-based, objective sampling of position allowing for high spatial and temporal resolution (Føre et al, 2017). Although previous studies have evaluated the feasibility of acoustic telemetry to assess individual fish movement (e.g., Juell and Westerberg, 1993;Cubitt et al, 2003;Clark et al, 2010;Broell et al, 2013;Thorstad et al, 2013;Kupilik and Petersen, 2014;Føre et al, 2017) not much attention has been paid to applying acoustic telemetry in aquaculture compared to ecological studies (e.g., Childs et al, 2008;Crossin et al, 2017;Haulsee et al, 2018;Kraus et al, 2018;Rothermel et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Determining the Effects of Environmental Events on Cultured Atlantic Salmon Behaviour Using 3-Dimensional Acoustic Telemetry

Stockwell¹,

Filgueira²,

Grant³

2021

Front. Anim. Sci.

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The health and welfare of farmed fish are highly dependent on environmental conditions. Under suboptimal conditions, the negative impact on welfare can cause changes in fish behaviour. Acoustic tags can provide high resolution and high frequency data to monitor fish positioning within the cage, which can be used to infer swimming behaviour. In this study, implanted acoustic tags were used to monitor the three-dimensional positioning of Atlantic salmon (Salmo salar) at a commercial farm in Nova Scotia, Canada. The one-month study period allowed the characterisation of background behaviour and changes in behaviour in relation to different environmental conditions, namely, water characteristics in terms of dissolved oxygen and temperature caused by the fall overturn, storm conditions, and feeding activity. The three-dimensional position of 15 fish was recorded using high temporal resolution (3 s). Fish movement was characterised by calculating four fish variables: distance from the centre of the cage [m], depth [m], velocity [ms−1], and turning angle [°]. The population swam in a counterclockwise swimming direction around 4 ± 2 m depth at an average speed of 0.61 ± 0.38 ms−1. After the fall overturn, the population moved significantly towards cage centre while decreasing velocity, and non-significant differences in depth and turning angle were observed. During feeding periods, a significant increase in depth and velocity, as well as a reduction in turning angle were observed. The storm event did not cause any significant change in the four fish variables. While some of the behavioural changes were difficult to assess with respect to causation, the high resolution, high frequency data provide unique detailed positioning information to further our understanding of the swimming behaviour of farmed fish.

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Measuring Tailbeat Frequencies of Three Fish Species from Adaptive Resolution Imaging Sonar Data

2021

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Imaging sonars, such as the Adaptive Resolution Imaging Sonar (ARIS), provide high-resolution sonar data that are used in fisheries research and management. While sonar methods have enormous potential for making population estimates, species identification via sonar remains an unresolved challenge. One method that may overcome this challenge involves measuring tailbeat frequencies to guide species differentiation. The tailbeat frequencies of three commonly sympatric anadromous fish species of eastern North America, Atlantic Salmon Salmo salar, Striped Bass Morone saxatilis, and American Shad Alosa sapidissima, were measured from imaging sonar data collected in an experimental low-flow, short-range setup. The frequencies were significantly different between the species (mean ± 1 SD beats/s: 0.6 ± 0.3 [Atlantic Salmon], 0.9 ± 0.2 [Striped Bass], and 1.4 ± 0.3 [American Shad]) when measured using a previously established manual method. Building on this, an automated method was developed and tested, and the method showed promising results. However, when compared to manually identified number of beats the error was large (on average, 1.1 [Atlantic Salmon], 4.8 [Striped Bass], and −0.4 [American Shad] beats in a fish track), especially in high fish densities. Despite the limitations, the automated method has utility in fisheries management when high-quality data can be collected for species with differing tailbeat frequencies.

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Acoustic tracking of migrating salmon

Cited by 10 publications

References 15 publications

Monitoring fish using imaging sonar: Capacity, challenges and future perspective

Monitoring fish using imaging sonar: Capacity, challenges and future perspective

Determining the Effects of Environmental Events on Cultured Atlantic Salmon Behaviour Using 3-Dimensional Acoustic Telemetry

Measuring Tailbeat Frequencies of Three Fish Species from Adaptive Resolution Imaging Sonar Data

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