講演詳細

経営部門[Forest Management]

日付	2025年3月21日
開始時刻	ポスター発表
会場名	学術交流会館（ロビー）
講演番号	PD-23 （学生ポスター賞審査対象）
発表題目	Individual tree detection in a complex mixed conifer-broadleaf forest using UAV RGB and multispectral imagery Individual tree detection in a complex mixed conifer-broadleaf forest using UAV RGB and multispectral imagery
所属	Graduate School of Agricultural and Life Sciences, The University of Tokyo
要旨本文	Accurate forest inventory is essential for effective forest management. However, field-based individual tree assessments are labor-intensive, time-consuming and costly. Individual tree detection (ITD) serves as a critical component of forest inventory processes. Traditional methods relying on low-resolution satellite imagery frequently struggle to accurately detect trees. In contrast, UAV photogrammetry providing very-high-resolution imagery, overcomes many of these limitations. Despite high ITD accuracy achieved in structurally simple forests, detecting individual trees in complex mixed forests remains challenging due to overlapping, clustered and multi-layered tree crowns. This study utilized UAV-derived canopy height model with RGB and multispectral orthomosaics using ITD methods to enhance detection accuracy in a complex mixed conifer-broadleaf forest. The findings underscored the importance of integrating multispectral UAV imagery with robust methods to improve ITD accuracy.
著者氏名	○Jeyavanan Karthigesu1,2 ・ Toshiaki Owari3 ・ Satoshi Tsuyuki1 ・ Takuya Hiroshima1
著者所属	1The University of Tokyo ・ 2Department of Agronomy, Faculty of Agriculture, University of Jaffna ・ 3The University of Tokyo
キーワード	Tree crown detection, Complex forest, Unmanned aerial vehicle, Forest monitoring, Photogrammetry
Key word	Tree crown detection, Complex forest, Unmanned aerial vehicle, Forest monitoring, Photogrammetry