首页> 外文期刊>Journal of Fish Biology >Length measurement accuracy of adaptive resolution imaging sonar and a predictive model to assess adult Atlantic salmon (Salmo salar) into two size categories with long-range data in a river
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Length measurement accuracy of adaptive resolution imaging sonar and a predictive model to assess adult Atlantic salmon (Salmo salar) into two size categories with long-range data in a river

机译:自适应分辨率成像声纳的长度测量精度和预测模型,以评估成人大西洋三文鱼(沙摩酱)分为两种大小类别,在河流中的远程数据

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摘要

Imaging sonars are used around the world for fish population monitoring. The accuracy of the length measurements has been reported in multiple studies for relatively short (<15 m) ranges and high image resolution. However, imaging sonars are often used at longer ranges (i.e., >15 m) where the images produced from sonar returns become less detailed. The accuracy of the length measurements from the Adaptive Resolution Imaging Sonar (ARIS) was tested by releasingn= 69 known-sized adult Atlantic salmon (Salmo salar) directly into the sonar field at ranges between 15 and 29 m, and measuring their echoes manually by four users and semi-automatically using a computer workflow in Echoview software. Overall, the length measurements were very variable: compared to true (fork) lengths, the mean of differences varied between -9.9 cm and 7.8 cm in the human-generated datasets, and between -42.8 cm and -20 cm in the computer-generated dataset. In addition, the length measurements in different datasets were only in poor or moderate agreement with each other (intraclass correlation <0.61). Contrary to our expectations, the distance from the transducer or the subjectively assessed echo quality did not have an effect on the measurement accuracy in most of the datasets and when it did, the effect was not systematic between the datasets. Therefore, a size class and length prediction model was implemented in a Bayesian framework to group salmon into two size categories: One-Sea-Winter (<63 cm) and Multi-Sea-Winter (>= 63 cm) groups. The model correctly predicted the size category in 83% of the fish in the computer-generated dataset and ranged from 68% to 74% in the human-generated datasets. We conclude that fish length measurements derived from long-range imaging sonar data should be used with caution, but post-processing can improve the usefulness of the data for specific purposes, such as adult Atlantic salmon population monitoring.
机译:成像声纳在世界各地用于鱼类人口监测。在多种研究中报道了长度测量的精度,用于相对短(<15米)的范围和高图像分辨率。然而,成像声纳通常以更长的范围(即,> 15米)使用,其中由声纳返回产生的图像变得更少。自适应分辨率成像声纳(ARIS)的长度测量的准确性通过RELEASINGN = 69个已知的成人大西洋鲑鱼(Salmo Salar)直接进入15至29米的索纳尔场,并通过手动测量它们的回声四个用户和半自动使用EchoView软件中的计算机工作流程。总的来说,长度测量非常变化:与真(叉)长度相比,人生成的数据集中的差异在-9.9cm和7.8cm之间变化,计算机产生的-42.8cm和-20cm之间。数据集。此外,不同数据集中的长度测量仅彼此差或中等协议(脑内相关<0.61)。与我们的期望相反,换能器或主观评估的回声质量的距离对大多数数据集中的测量精度没有影响,并且在数据集之间没有系统化。因此,在贝叶斯框架中实施了尺寸类和长度预测模型,将鲑鱼分为两种大小类别:单海冬(<63厘米)和多海冬(> = 63厘米)组。该模型正确地预测了计算机生成的数据集中的83%的鱼类中的大小类别,并在人生成的数据集中的68%至74%。我们得出结论,来自远程成像声纳数据的鱼类长度测量应谨慎使用,但后处理可以提高数据的有用性,以获得特定目的,例如成年大西洋鲑鱼类群体监测。

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