Research on volume prediction of single tree canopy based on three-dimensional (3D) LiDAR and clustering segmentation

ABSTRACT

Canopy volume information of fruit trees is a very important biological parameter, which is of great significance to predict the yield of fruit trees, estimate the application amount of pesticides and fertilizers. This study proposes a novel volume prediction method of single tree canopy based on the three-dimensional (3D) Light Detection and Ranging (LiDAR) point cloud. The method involves several steps, mainly including point cloud pre-processing, spatial clustering segmentation based on K-dimensional tree (KD tree), acquisition of single tree structural parameters, calculation of tree canopy volume based on multiple regression analysis. This study tests the performance of the proposed method with a collected data set of Begonia forest. The average error and standard deviation between the predicted and manually measured heights to the canopy are 0.038 m and 0.030 m, respectively. As to the diameter of the trunk, the average error and standard deviation are 0.013 m and 0.008 m, respectively. The coefficient of determination (R²) of the proposed canopy volume prediction method is 0.8610, and the F test result is significant. High correlation is found between the predicted canopy volumes and the R² value is 0.8223. The experimental results verify the validity of the proposed method. The research can provide a stable and accurate technical reference for the statistics on forest biomass.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (31601217); the National Natural Science Foundation of China under Grant [31601217].