To analyze the main variables affecting crown biomass, we measured crown biomass and a number of variables describing tree size, such as stem diameter at breast height (D), tree height (H), crown width (Cw) and crown length (Cl), from 754 sample trees of four pine species in China, specifically, Pinus massoniana, P. yunnanensis, P. tabulaeformis and P. elliottii. In addition, we developed individual tree crown biomass equations and compatible branch and needle biomass equations based on one-, two- and multi-variable models using nonlinear mixed-effects modeling and error-in-variable simultaneous equations. The results showed that: (i) crown biomass was strongly correlated to D and H, and the new combined variables (D⁴/H) and (D²/H) were suitable for modeling two- and multi-variable crown biomass equations, respectively; (ii) the four species were ranked as P. yunnanensis, P. massoniana, P. elliottii and P. tabulaeformis from small to large based on estimates from crown biomass equations, and the crown biomass estimate of P. tabulaeformis was about 60% greater than that of P. yunnanensis; (iii) the ratio of branch to crown biomass increased with increasing D, and the four species were ranked as P. elliottii, P. tabulaeformis, P. massoniana and P. yunnanensis from small to large by the proportion of the branch to crown biomass; (iv) the mean prediction errors (MPE’s) of the crown biomass equations for the four species were less than 10%, which could meet precision needs for biomass estimation.

This study aimed to develop simultaneous models with universal applicability for the estimation of the main factors of forest stands based on airborne LiDAR data and to provide a reference for standardizing the approach and evaluation indices of main forest factor modeling. Using airborne LiDAR and field survey data from 190 sample plots in spruce (Picea spp.), fir (Abies spp.), and spruce–fir mixed forests in Northeast China, the simultaneous models for estimating the main factors of forest stands were developed. To develop the models, the relationships between mean tree height, stand basal area, stand volume, and the main metrics of the LiDAR data and the correlations between eight quantitative factors of forest stands were considered, and the error-in-variable simultaneous equations approach was employed to fit the models. The results showed that the mean prediction errors (MPEs) of eight forest stand factors estimated by the simultaneous models were mostly within 5%, and only the MPE of the number of trees per hectare exceeded 5%. The mean percentage standard errors (MPSEs) of the estimates, including the mean diameter at the breast height (DBH), mean tree height, and mean dominant tree height, were within 15%; the MPSEs of the estimates of the stand basal area, volume, biomass, and carbon stock per hectare were within 25%; and only the MPSE of the estimated number of trees per hectare exceeded 30%. The coefficients of determination (R²) of the core prediction models for the volume, biomass, and carbon storage were all greater than 0.7. It can be concluded that estimating the main factors of forest stands based on the combination of LiDAR and field survey data is technically feasible, and the simultaneous models developed in this study for the estimation of the eight main stand factors of spruce–fir forests can meet the precision requirements of forest resource inventory, except for the number of trees, indicating that the models can be applied in practice.