利用机器学习实现对浅珊瑚礁鱼类和底栖生物群落的同时地理参考调查

IF 2.1 3区 地球科学 Q2 LIMNOLOGY
Scott D. Miller, Alexandra K. Dubel, Thomas C. Adam, Dana T. Cook, Sally J. Holbrook, Russell J. Schmitt, Andrew Rassweiler
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引用次数: 3

摘要

调查沿海系统以估计鱼类和底栖生物的分布和丰度是劳动密集型的,通常导致数据空间有限,难以扩展到整个珊瑚礁或岛屿。我们开发了一种方法,利用机器学习平台CoralNet的自动化,使单个观察者能够高效、经济地同时生成浅海岸环境中大面积鱼类和底栖生物丰度的地理参考数据。简单地说,一名研究人员在水面浮潜时进行鱼类调查,并拖曳一个装有手持GPS和面向下的GoPro的浮子,被动地~ 每米海底生物10张照片。照片和调查稍后会进行地理参考,照片会由CoralNet自动注释。我们发现,这种方法为普通鱼类提供了与固定样带上传统的水肺鱼类计数相似的生物量和密度值,其优点是覆盖了更大的区域。我们的CoralNet验证确定,虽然CoralNet自动注释的照片在单个图像水平上不如人类注释的照片准确,但自动方法在一分钟的调查水平上提供了对海底基质覆盖率的可比或更好的估计(~ 50 m2的珊瑚礁),这是由于可以自动注释的照片数量,提供了更大的场地空间覆盖范围。这种方法可以用于各种浅层系统,并且在需要空间显式数据或大空间范围的调查时特别有利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using machine learning to achieve simultaneous, georeferenced surveys of fish and benthic communities on shallow coral reefs

Surveying coastal systems to estimate distribution and abundance of fish and benthic organisms is labor-intensive, often resulting in spatially limited data that are difficult to scale up to an entire reef or island. We developed a method that leverages the automation of a machine learning platform, CoralNet, to efficiently and cost-effectively allow a single observer to simultaneously generate georeferenced data on abundances of fish and benthic taxa over large areas in shallow coastal environments. Briefly, a researcher conducts a fish survey while snorkeling on the surface and towing a float equipped with a handheld GPS and a downward-facing GoPro, passively taking ~ 10 photographs per meter of benthos. Photographs and surveys are later georeferenced and photographs are automatically annotated by CoralNet. We found that this method provides similar biomass and density values for common fishes as traditional scuba-based fish counts on fixed transects, with the advantage of covering a larger area. Our CoralNet validation determined that while photographs automatically annotated by CoralNet are less accurate than photographs annotated by humans at the level of a single image, the automated approach provides comparable or better estimations of the percent cover of the benthic substrates at the level of a minute of survey (~ 50 m2 of reef) due to the volume of photographs that can be automatically annotated, providing greater spatial coverage of the site. This method can be used in a variety of shallow systems and is particularly advantageous when spatially explicit data or surveys of large spatial extents are necessary.

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来源期刊
CiteScore
4.80
自引率
3.70%
发文量
56
审稿时长
3 months
期刊介绍: Limnology and Oceanography: Methods (ISSN 1541-5856) is a companion to ASLO''s top-rated journal Limnology and Oceanography, and articles are held to the same high standards. In order to provide the most rapid publication consistent with high standards, Limnology and Oceanography: Methods appears in electronic format only, and the entire submission and review system is online. Articles are posted as soon as they are accepted and formatted for publication. Limnology and Oceanography: Methods will consider manuscripts whose primary focus is methodological, and that deal with problems in the aquatic sciences. Manuscripts may present new measurement equipment, techniques for analyzing observations or samples, methods for understanding and interpreting information, analyses of metadata to examine the effectiveness of approaches, invited and contributed reviews and syntheses, and techniques for communicating and teaching in the aquatic sciences.
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