In order to improve the accuracy and efficiency of forest single-tree volume estimation,the typical Pinus massoniana forest in the suburban area of Zhijin County,Guizhou Province was selected as the research object.Based on airborne LiDAR point cloud and sample survey data,a single-tree structure parameter estimation model for P.massoniana based on airborne LiDAR point cloud data was constructed using extracted tree height,crown width,crown projection area,and crown volume as variables.The research results indicate that:1)There was a good correlation between the height and crown width factors of P.massoniana single-tree extracted from point cloud data and actual survey data,with a determination coefficient of R² above 0.7 and relatively high accuracy,which could be used to construct a single-tree volume model of P.massoniana.2)On the basis of the classic nonlinear CAR model,an enumeration method was used to construct 11 models that combined four variables:tree height,crown width,crown projected area,and crown volume.Among them,the model containing three stand factors,tree height,crown width,and crown volume,performed the best,with an R² of 0.774 1.3)Tree height,crown width and crown volume were identified as key factors in estimating the individual volume of P.massoniana,with tree height contributing the most and showing a highly significant positive correlation with individual volume (P<0.001).It is feasible to extract single tree structural parameters using airborne LiDAR point cloud data and construct a single tree volume model based on nonlinear CAR model to estimate the single tree volume of P.massoniana.This method not only meets the accuracy requirements of forest resource surveys but also effectively improves survey efficiency.