한국환경복원기술학회지 (Journal of the Korean Society of Environmental Restoration Technology)

  • 제26권6호
  • /
  • Pages.85-96
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  • 2023
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  • 1229-3032(pISSN)
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  • 2733-5011(eISSN)
한국환경복원기술학회 (The Korea Society of Environmental Restoration Technology)
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딥러닝 기반의 식생 모니터링 가능성 평가

Evaluation of the Feasibility of Deep Learning for Vegetation Monitoring

  • 투고 : 2023.10.27
  • 심사 : 2023.11.27
  • 발행 : 2023.12.30
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초록

This study proposes a method for forest vegetation monitoring using high-resolution aerial imagery captured by unmanned aerial vehicles(UAV) and deep learning technology. The research site was selected in the forested area of Mountain Dogo, Asan City, Chungcheongnam-do, and the target species for monitoring included Pinus densiflora, Quercus mongolica, and Quercus acutissima. To classify vegetation species at the pixel level in UAV imagery based on characteristics such as leaf shape, size, and color, the study employed the semantic segmentation method using the prominent U-net deep learning model. The research results indicated that it was possible to visually distinguish Pinus densiflora Siebold & Zucc, Quercus mongolica Fisch. ex Ledeb, and Quercus acutissima Carruth in 135 aerial images captured by UAV. Out of these, 104 images were used as training data for the deep learning model, while 31 images were used for inference. The optimization of the deep learning model resulted in an overall average pixel accuracy of 92.60, with mIoU at 0.80 and FIoU at 0.82, demonstrating the successful construction of a reliable deep learning model. This study is significant as a pilot case for the application of UAV and deep learning to monitor and manage representative species among climate-vulnerable vegetation, including Pinus densiflora, Quercus mongolica, and Quercus acutissima. It is expected that in the future, UAV and deep learning models can be applied to a variety of vegetation species to better address forest management.

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과제정보

본 논문은 환경부의 환경기술개발사업(과제번호: 2021003360001)의 지원을 받아 한국환경연구원이 수행한 "ICT 기반 생태계 모니터링 기술 및 동식물 탐지 AI 알고리즘 개발(2023-021R)" 사업의 연구결과로 작성되었으며, 일부 재인용이 되었음을 알립니다.

참고문헌

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