Abstract
Invasive species greatly disrupt island ecosystems, risk assessment and the conservation of native ecosystems have therefore become pressing concerns. However, the cost of monitoring invasive species by humans is often high. In this study, we developed a system to detect an invasive lizard species, Anolis carolinensis, that threatens the native insect ecosystem of the Ogasawara Islands in Japan. Surveying these forest lizards requires specialized field observers, a challenge that prevents the government of Japan from efficient conservation and management of this ecosystem. The proposed system detects these lizards in drone images using a type of machine learning called deep neural network. Data were collected using a drone on Ani-jima in the Ogasawara Islands, and the trained network shows approximately 70% precision of detecting A. carolinensis. This study shows the combination of remote sensing and machine learning have the potential to contribute to an efficient and effective approach to conserving ecosystems.
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This study was supported by nature restoration projects of The Ministry of the Environment.
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Aota, T., Ashizawa, K., Mori, H. et al. Detection of Anolis carolinensis using drone images and a deep neural network: an effective tool for controlling invasive species. Biol Invasions 23, 1321–1327 (2021). https://doi.org/10.1007/s10530-020-02434-y
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DOI: https://doi.org/10.1007/s10530-020-02434-y