Highly precise community science annotations of video camera‐trapped fauna in challenging environments

IF 3.9 2区 环境科学与生态学 Q1 ECOLOGY Remote Sensing in Ecology and Conservation Pub Date : 2024-06-25 DOI:10.1002/rse2.402
Mimi Arandjelovic, Colleen R. Stephens, Paula Dieguez, Nuria Maldonado, Gaëlle Bocksberger, Marie‐Lyne Després‐Einspenner, Benjamin Debetencourt, Vittoria Estienne, Ammie K. Kalan, Maureen S. McCarthy, Anne‐Céline Granjon, Veronika Städele, Briana Harder, Lucia Hacker, Anja Landsmann, Laura K. Lynn, Heidi Pfund, Zuzana Ročkaiová, Kristeena Sigler, Jane Widness, Heike Wilken, Antonio Buzharevski, Adeelia S. Goffe, Kristin Havercamp, Lydia L. Luncz, Giulia Sirianni, Erin G. Wessling, Roman M. Wittig, Christophe Boesch, Hjalmar S. Kühl
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Abstract

As camera trapping grows in popularity and application, some analytical limitations persist including processing time and accuracy of data annotation. Typically images are recorded by camera traps although videos are becoming increasingly collected even though they require much more time for annotation. To overcome limitations with image annotation, camera trap studies are increasingly linked to community science (CS) platforms. Here, we extend previous work on CS image annotations to camera trap videos from a challenging environment; a dense tropical forest with low visibility and high occlusion due to thick canopy cover and bushy undergrowth at the camera level. Using the CS platform Chimp&See, established for classification of 599 956 video clips from tropical Africa, we assess annotation precision and accuracy by comparing classification of 13 531 1‐min video clips by a professional ecologist (PE) with output from 1744 registered, as well as unregistered, Chimp&See community scientists. We considered 29 classification categories, including 17 species and 12 higher‐level categories, in which phenotypically similar species were grouped. Overall, annotation precision was 95.4%, which increased to 98.2% when aggregating similar species groups together. Our findings demonstrate the competence of community scientists working with camera trap videos from even challenging environments and hold great promise for future studies on animal behaviour, species interaction dynamics and population monitoring.
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在充满挑战的环境中对摄像捕获的动物群落进行高度精确的群落科学注释
随着相机诱捕技术的普及和应用,一些分析方面的限制因素依然存在,包括处理时间和数据标注的准确性。通常情况下,照相机诱捕器记录的是图像,尽管视频的收集也越来越多,但它们需要更多的注释时间。为了克服图像标注的局限性,相机陷阱研究越来越多地与社区科学(CS)平台联系起来。在这里,我们将以前的 CS 图像注释工作扩展到了具有挑战性的环境中的相机捕捉器视频上;这是一片茂密的热带森林,由于树冠覆盖厚实,相机水平上灌木丛生,能见度低,遮蔽率高。利用为热带非洲 599 956 个视频片段分类而建立的 CS 平台 Chimp&See,我们通过比较专业生态学家(PE)对 13 531 个 1 分钟视频片段的分类与 1744 名注册和未注册 Chimp&See 社区科学家的输出结果,评估了注释的精确度和准确性。我们考虑了 29 个分类类别,包括 17 个物种和 12 个更高层次的类别,其中表型相似的物种被归为一类。总体而言,注释精确度为 95.4%,将相似物种分组汇总后,精确度提高到 98.2%。我们的研究结果表明,社区科学家即使在具有挑战性的环境中也有能力使用相机陷阱视频,这为未来的动物行为、物种相互作用动态和种群监测研究带来了巨大希望。
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来源期刊
Remote Sensing in Ecology and Conservation
Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
9.80
自引率
5.50%
发文量
69
审稿时长
18 weeks
期刊介绍: emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.
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