Research on Individual Tree Canopy Segmentation of Camellia oleifera Based on a UAV-LiDAR System

Liwan Wang, Ruirui Zhang, Linhuan Zhang (), Tongchuan Yi, Danzhu Zhang and Aobin Zhu
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Liwan Wang: School of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Ruirui Zhang: Research Center of Intelligent Equipment, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Linhuan Zhang: School of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Tongchuan Yi: Research Center of Intelligent Equipment, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Danzhu Zhang: Research Center of Intelligent Equipment, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Aobin Zhu: Research Center of Intelligent Equipment, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China

Agriculture, 2024, vol. 14, issue 3, 1-16

Abstract: In consideration of the limited accuracy of individual tree canopy segmentation algorithms due to the diverse canopy structure and complex environments in mountainous and hilly areas, this study optimized the segmentation parameters of three algorithms for individual tree canopy segmentation of Camellia oleifera in such environments by analyzing their respective parameters. Utilizing an Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system, we obtained Canopy Height Models (CHM) of Camellia oleifera canopies based on Digital Elevation Models (DEM) and Digital Surface Models (DSM). Subsequently, we investigated the effects of CHM segmentation, point cloud clustering segmentation, and layer stacking fitting segmentation on Camellia oleifera canopies across different research areas. Additionally, combining ground survey data from forest lands with visual interpretation of UAV orthophoto images, we evaluated the performance of these three segmentation algorithms in terms of the F-score as an evaluation indicator for individual tree canopy segmentation accuracy. Combined with the Cloth Simulation Filter (CSF) filtering algorithm after removing the ground point cloud, our findings indicate that among different camellia densities and terrain environments, the point cloud clustering segmentation algorithm achieved the highest segmentation accuracy at 93%, followed by CHM segmentation at 88% and the layer stacking fitting segmentation method at 84%. By analyzing the data from UAV-LiDAR technology involving various land and Camellia oleifera planting types, we verified the applicability of these three segmentation algorithms for extracting camellia canopies. In conclusion, this study holds significant importance for accurately delineating camellia canopies within mountainous hilly environments while providing valuable insights for further research in related fields.

Keywords: Camellia oleifera; UAV; point cloud data; Canopy Height Models; parameter optimization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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