Japan Geoscience Union Meeting 2022

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[J] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-CG Complex & General

[A-CG41] Aircraft and UAV Observations for Earth-planetary sciences

Mon. May 23, 2022 3:30 PM - 5:00 PM 104 (International Conference Hall, Makuhari Messe)

convener:Nobuhiro Takahashi(Institute for Space-Earth Environmental Research, Nagoya University), convener:Makoto Koike(Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo), Toshinobu Machida(National Institute for Environmental Studies), convener:Taro Shinoda(Institute for Space-Earth Environmental Research, Nagoya University), Chairperson:Nobuhiro Takahashi(Institute for Space-Earth Environmental Research, Nagoya University)

4:15 PM - 4:30 PM

[ACG41-04] Trunk section-based tree detection method in dense plantation forest using drone LiDAR data.

*Zhang Yupan1, Yuichi ONDA1, Asahi HASHIMOTO1, Chen-Wei CHIU2, Takashi Gomi2, Shodai INOKOSHI2 (1.Center for Research on Isotopes and Environmental Dynamics, University of Tsukuba, 2.Department of International Environmental and Agricultural Science, Tokyo University of Agriculture and Technology)

Keywords:Drone LiDAR, Understory structure, Plantation forest, Tree detection

Single tree detection is one of the main research topic in order to quantify the structural properties of the forest. Drone LiDAR systems (DLS) and terrestrial laser scanning (TLS) systems produce high-density point clouds that offer a lot of promise for forest inventories in limited areas. However, most researches have concentrated on the upper canopy layer and only a few have attempted to the lower forest structure. This paper described a basic tree detection method using drone LiDAR data that from a new perspective of understory structure. This method relied on trunk point clouds, with trunk sections ranging in height from 1 to 7 m being processed and compared to determine a suitable height threshold then used to detect trees. We test our method in a dense cedar plantation forest in Japan Aichi prefecture, which have a stem density of 1140 stems/ha and an average age of 42 years. The dense point cloud data was generated from drone LiDAR system and TLS with an average point density of 3100 and 5500 points/m2, respectively. Tree detection was achieved by drawing point cloud section projections of tree trunks at different heights and calculating the center coordinates. The results show that this trunk section-based method greatly reduced the difficulty of tree detection in dense plantation forest and has a high accuracy. The root mean square error was 10% after selecting section of suitable height as parameter. This method can be extended for different forest scenarios by changing the section parameter.