Rapid identification and analysis of vegetation information based on onboard LIDAR point cloud data

Mingzhe Fu; Yuanmao Zheng; Changzhao Qian; Qiuhua He; Chenyan Wei

doi:10.1117/12.3007539

30 August 2023 Rapid identification and analysis of vegetation information based on onboard LIDAR point cloud data

Mingzhe Fu, Yuanmao Zheng, Changzhao Qian, Qiuhua He, Chenyan Wei

Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 127970G (2023) https://doi.org/10.1117/12.3007539
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China

Abstract

The vegetation information system is a key component of the international ecosystem assessment system. It is of great value to construct an environmental assessment system and ecological governance through obtaining plant information from airborne LiDAR data. Focusing on Xiamen University of Technology, this study collected plant point cloud data within the research area using UAV radar technology. It then used the inverse distance weighting interpolation method to construct a plant canopy height model to obtain the appropriate tree height. Meanwhile, the estimated values were compared with actual data points to explore the adaptability of different interpolation calculation methods to extract plant information in the research area.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingzhe Fu, Yuanmao Zheng, Changzhao Qian, Qiuhua He, and Chenyan Wei "Rapid identification and analysis of vegetation information based on onboard LIDAR point cloud data", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 127970G (30 August 2023); https://doi.org/10.1117/12.3007539

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