Efficacy of machine learning image classification for automated occupancy‐based monitoring

IF 3.9 2区环境科学与生态学 Q1 ECOLOGY

Remote Sensing in Ecology and Conservation Pub Date : 2023-07-10 DOI:10.1002/rse2.356

Robert C. Lonsinger, Marlin M. Dart, R. Larsen, Robert N. Knight

引用次数: 0

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机器学习图像分类在基于占用率的自动监控中的有效性

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来源期刊

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

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.