Forests, being the largest and most intricate terrestrial ecosystems, play an indispensable role in sustaining ecological balance. To effectively monitor forest productivity, it is imperative to accurately extract structural parameters such as the tree height and diameter at breast height (DBH). Airborne LiDAR technology, which possesses the capability to penetrate canopies, has demonstrated remarkable efficacy in extracting these forest structural parameters. However, current research rarely models different tree species separately, particularly lacking comparative evaluations of tree height-DBH models for diverse tree species. In this study, we chose sample plots within the Bila River basin, nestled in the Greater Hinggan Mountains of the Inner Mongolia Autonomous Region, as the research area. Utilizing both airborne LiDAR and field survey data, individual tree positions and heights were extracted based on the canopy height model (CHM) and normalized point cloud (NPC). Six tree height-DBH models were selected for fitting and validation, tailored to the dominant tree species within the sample plots. The results revealed that the CHM-based method achieved a lower RMSE of 1.97 m, compared to 2.27 m with the NPC-based method. Both methods exhibited a commendable performance in plots with lower average tree heights. However, the NPC-based method showed a more pronounced deficiency in capturing individual tree information. The precision of grid interpolation and the point cloud density emerged as pivotal factors influencing the accuracy of both methods. Among the six tree height-DBH models, a multiexponential model demonstrated a superior performance for both oak and ”birch–poplar” trees, with R² values of 0.479 and 0.341, respectively. This study furnishes a scientific foundation for extracting forest structural parameters in boreal forest ecosystems.

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.

The study aims to analyze comprehensive effects of site quality class (SQC), stand density index (SDI), and species composition (SC) on biomass growth. Based on 5872 observations from 2040 permanent sample plots of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantations from successive national forest resource inventories, five classical growth equations were employed and nonlinear regression and dummy variables were used for modeling. A dominant height (DH) growth model was first developed to determine SQC, followed by a series of stand biomass (SB) growth models incorporating SQC, SDI, and SC (pure vs. mixed stands). Growth differences among different classes or categories were analyzed using inflection age and optimal rotation age. The results show that Korf equation performed best for both DH and SB growth models; SDI contributed the most to SB growth, followed by SQC, with their interaction accounting for over half of the total contribution. Mixed stands grew faster than pure stands; higher SQC was associated with faster growth and earlier attainment of inflection age and optimal rotation age. The productivity increased with rising SDI, but the rate of increase gradually diminished. Different optimal rotation ages should be determined for pure and mixed stands across different SQCs. Reasonable adjustment of harvesting age and control of stand density represent the greatest potential for improving forest productivity.