In order to explore the selection and number of modeling variables in biomass models and their impact on the final fitting accuracy, providing methodological references for the establishment of biomass models.

Taking Eucalyptus plantations as the research object, CHM was constructed using unmanned aerial vehicle (UAV) airborne LiDAR point cloud data. Gaussian low-pass filtering and enhanced Frost filtering were applied to calculate the arithmetic mean height of the sample plots. Using variable projection importance method and VSURF package to screen variables to compare the differences in biomass fitting effects between multiple regression models and machine learning models, and select the optimal model for subsequent research.

1) The arithmetic mean of the sample plots after enhanced Frost filtering and Gaussian low-pass filtering is higher, and its relative error is lower than the arithmetic mean of the sample plots directly extracted on CHM. Among them, the extraction effect of enhanced Frost filtering is slightly better than that of Gaussian low-pass filtering; 2) The fitting accuracy of multiple regression models increases with the increase of variables, and nonlinear models generally outperform linear models. In machine learning, the random forest model performs the best, with R² of 0.88, R_MSE of 16.15 t/hm², and M_AE of 12.17 t/hm², and the fitting effect is better than that of multiple regression models; 3) The variables filtered using the VSURF package have a better modeling effect compared to directly using all variables. After variable screening, it was found that the height feature variables of point clouds have a higher importance on biomass compared to density and intensity variables, indicating that height variables have a stronger explanatory power on forest biomass.

Using airborne LiDAR point cloud data, enhancing Frost filtering to smooth images can significantly reduce the error in extracting tree height, and the extraction effect is slightly better than Gaussian low-pass filtering. Using the VSURF package to filter variables can improve the accuracy of the model, and the random forest model performs best in estimating the biomass of eucalyptus trees in artificial forests.