This study aimed to investigate the effects of different pruning intensities on knot size in young Larix olgensis plantations.

Thirty young L. olgensis trees from the Mengjiagang forest farm in Heilongjiang Province were selected as sample trees and subjected to four pruning intensities (0%, 20%, 30%, and 40%). Knot dissection data were collected, and mixed-effect models for knot diameter (KD), sound knot length (SKL), and loose knot length (LKL) were constructed with pruning intensity as a fixed effect. These models were compared with baseline models to evaluate the effects of pruning on knot attributes.

Compared with baseline models, the mixed-effect models performed better in terms of RMSE and MAE, with R² values of 0.724, 0.826, and 0.308 for KD, SKL, and LKL, respectively, prediction accuracy exceeded 94%, indicating that mixed-effect models provided reliable predictions of knot properties. Artificial pruning significantly reduced knot influence in L. olgensis. Under moderate pruning (30%), SKL increased by 0.46 cm compared with unpruned trees, while KD and LKL were reduced by 0.14 cm and 0.31 cm, respectively. This suggests that moderate pruning promotes healthy branch growth while effectively reducing the formation of loose knots.

This study confirmed the reliability of mixed-effect models in predicting knot attributes, demonstrating their ability to simulate the effects of pruning intensity on knot development. Among different pruning intensities, moderate pruning (30%) showed the best performance in controlling knot size, reducing LKL while maintaining stem stability. Under the study conditions, it represents a reasonable pruning strategy.

To investigate the effects of stand conditions and climatie factors on branch growth, as well as the dynamic patterns of branch growth over time.

This study utilized branch attribute data from 584 branch samples of 45 sampled artificial Larix olgensis trees in Mengjiagang forest farm, Heilongjiang Province, with 20 years of climatic data from the same region. By reparameterizing the base model and conducting model validation, an optimal mixed-effects model for annual branch growth increment was constructed.

The results demonstrated that incorporating branch age, branch insertion depth, tree diameter at breast height (DBH), stand basal area per hectare, summer average temperature, and summer precipitation significantly improved the model's predictive capability. The findings revealed a complex mechanism by which stand conditions and climatic factors influence branch growth. Within a certain range, increases in summer average temperature and summer precipitation promoted branch growth, indicating that warm and humid climatic conditions are conducive to branch development. However, increases in stand density-related indicators significantly suppressed annual branch growth increment, reflecting the negative impact of intensified resource competition on individual growth. Branch growth exhibited significant temporal heterogeneity in response to environmental and stand conditions. Under the same climatic conditions, the annual branch growth increment peaked in the second year, then gradually declined, and stabilized after 16 years. This pattern suggests that early-stage branch growth is highly sensitive to hydrothermal conditions, while growth potential gradually diminishes with branch age due to physiological aging mechanisms and the overall resource-carrying capacity of the stand.

The effects of summer average temperature and precipitation on branch growth followed similar trends. The constructed mixed-effects model significantly enhances the predictive capability for branch growth. The results not only deepen the understanding of the dynamic patterns of branch growth but also provide a theoretical foundation for formulating scientific forest management strategies in the context of global climate change.

To explore the extraction accuracy of ground-based laser radar (TLS) for each relative high diameter of Larix olgensis, and the relationship between stump diameter and wood volume of Larix olgensis under different stand conditions in southern Heilongjiang Province, verify the effect of different modeling methods in the prediction of wood volume, and provide a scientific basis for predicting single wood volume when only stumpwere left.

Eight samples of Larix olgensis plantation plots were used as the research objects, and stump diameter (d_st) and diameter at breast height (DBH) extracted by TLS were substituted into One variable volume formula to obtain volume (V). The basic model of stump diameter and volume was constructed by nonlinear least square method, and the generalized model was constructed by adding trees per hectare (N) and tree AGE (AGE). Then, the sample plot was added as a random effect to construct a mixed effect model, and the prediction effects of different models were compared. Finally, the model was tested by the method of diameter order comparison.

The extraction accuracy of TLS at stump diameter is 93.42%, which is 5.56% lower than that of DBH (96.37%). There is a high correlation between the analytical wood volume (V_解) of 40 trees and the calculated wood volume (V) of One variable volume formula (R²=0.986 2), and the average relative error (RE) is 9.83%. Among the basic models, the Berkhart model has the smallest stump mean square error (RMSE=0.194 9) and the best fitting effect (R²=0.845 2, R_a²=0.844 8). In the correlation analysis, V is positively correlated with d_st and AGE, and negatively correlated with N. Among them, the influence of AGE on V is slightly larger than that of N. The R_a² of the generalized model is 7.98% higher than that of the basic model, and the RMSE is reduced from 0.195 5 to 0.194 9. In the mixed effects model, random effects performed best in b₁ and b₂ positions, R_a² increased by about 1.66% compared with the generalized model, and RMSE decreased from 0.195 5 to 0.194 2. When 11 cm ≤ d_st < 43 cm, the Mean absolute error (MAE) of the Berkhart model, the generalized model and the mixed effects model increased with the increase of the stump diameter (d_st) in the diameter order test. And the growth rate of MAE is from large to small: basic model > generalized model > mixed effect model.

Stump diameter (d_st) and DBH extracted by TLS can be used as modeling data. The mean value of RE with V is within the qualified range (±10%), and V can be used as modeling data. The goodness of fit of the basic model from large to small is: Berkhart model > Krenn model > Meyer model > Gehrardt model. The addition of N and AGE in the generalized model significantly improved the prediction accuracy of the model. The mixed effects model performed better than the generalized model in terms of evaluation metrics R_a² and RMSE.

Forests are one of the most important natural resources. Understanding the impact of various factors on forest biomass is crucial for future forest spatial structure and management. Constructing biomass models for different forest types can provide scientific basis for the restoration and conservation of forest ecosystems.

This study focuses on seven typical forest types in the Daxing'anling region of Heilongjiang Province, using data from 1 157 monitoring plots in 2015. Sentinel-2 satellite images and digital elevation model (DEM) data provided by the European Space Agency were used to calculate vegetation indices, texture features, slope, and other variables. By integrating remote sensing data with field survey data and climate data, we established generalized least squares (GLS) biomass models and generalized additive models (GAM) for biomass. Ten-fold cross-validation was used, and the models were evaluated using root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Additionally, 328 plots resurveyed in 2020 were used for model validation.

The Generalized additive models (GAM) performed better than the Generalized Least Squares (GLS) models across the seven typical forest types. Specifically, the mean absolute error (MAE) of the GAM was reduced by 1.99% to 27.48% compared to the GLS models, the root mean square error (RMSE) was reduced by 4.29% to 20.87%, and the mean square error (MSE) was reduced by 6.72% to 35.43%. Secondary validation results showed that the prediction accuracy of the generalized additive model (GAM) for each forest type is above 80%.

Generalized additive models are a non-parametric method for constructing biomass models and are suitable for predicting biomass across different forest types in the Daxing'anling region.

Taking the data of trunk resolution and branch resolution of three typical coniferous species (Pinus koraiensis, Larix olgensis, Pinus sylvestris var. mongolica) in Mengjiagang forest farm in Heilongjiang province as an example, the models suitable for the base diameter and branch length of the primary branches were constructed by the dummy variable model method and the nonlinear mixed effect model method. The growth characteristics and differences of the branch base diameter and branch length of different tree species were explored and the branch modeling work was simplified.

In order to improve the predictive ability of the models, the dummy variable models of branch base diameter and branch length with tree species as the dummy variables were constructed based on the re-parameterized model. The nonlinear mixed effect models of branch base diameter and branch length with tree species as the random effect were constructed. Then, using four indicators ( $R_{\text{adj}}^{\text{2}}$, RMSE, AIC, BIC), the predictive ability of the models was evaluated. Finally, according to the evaluation results, the growth differences of three tree species were analyzed based on the model with a better fitting effect.

1) The base diameter and length of the primary branches of the same tree species were significantly different under different grades. The higher the tree grade, the greater the base diameter and length. Based on the same grade of wood, Larix olgensis was significantly different from the other two tree species in terms of branch diameter and base length. 2) The $R_{\text{adj}}^{\text{2}}$ value of the base diameter prediction model for the primary branches based on the dummy variable model method and the nonlinear mixed effect model method reached 0.616 7 and 0.603 6, respectively. The $R_{\text{adj}}^{\text{2}}$ value of the length prediction model for the primary branches based on the dummy variable model method and the nonlinear mixed effect model method reached 0.679 4 and 0.671 2, respectively. The prediction effects of the dummy variable models were better than the nonlinear mixed effect models. However, according to the fitting process of the models, the suitable branch attribute prediction model should be selected according to the sample size when predicting the base diameter and length of primary branches. 3) According to the fitting results of the dummy variable models, there were differences in the base diameter and length of the primary branches of the three tree species, and the range of variation was different. From the perspective of tree species, the order of the base diameter and length of the primary branches of the three tree species was Pinus sylvestris var. mongolica > Pinus koraiensis > Larix olgensis.

Both the dummy variable models and the nonlinear mixed effect models are useful attempts to simplify the modeling of branch size.

This study aimed to investigate the accuracy of crown factor extraction from point cloud data by Terrestrial laser scanning (TLS), and the error of tridimensional green biomass calculated by the voxel simulation method, to verify the feasibility of using ground-based lidar data combined with the voxel simulation method to calculate tridimensional green biomass, and construct Larix gmelinii tridimensional green biomass models.

In this paper, 42 Larix gmelinii in the sample plots in Mengjiagang in Jiamusi City, Heilongjiang Province were studied, and the sample plot area was 0.06-0.12 hm², with 3 366 trees. Four Larix gmelinii sample plots (208 trees) were selected, and the single tree factor parameters could be obtained by TLS scanning and LiDAR360 software processing. The “Voxel-based simulation method” was used to realize the calculation of the tridimensional green biomass of individual trees in the four Larix gmelinii sample plots (208 trees) by Matlab programming, and then the precision of tridimensional green biomass calculated by the “voxel simulation method” and “crown formula method” was analyzed. The tridimensional green biomass model of individual Larix gmelinii was constructed based on the crown factors extracted from the TLS and the tridimensional green biomass calculated by Matlab. The tridimensional green biomass model of single wood was combined with the data of each wood survey in 42 sample plots to calculate the tridimensional green biomass of each wood. The tridimensional green biomass of each wood in the sample plots could be accumulated to obtain the tridimensional green biomass of the sample plots, which was converted into the corresponding tridimensional green biomass of the stand according to the area of the sample plots. The tridimensional green biomass model of stands was constructed by combining stand factor data of the 42 plots.

The crown factors obtained by TLS met the accuracy requirements, among which the extraction accuracy of diameter at breast height was the best (P=97.14%), followed by tree height (P=90.96%), crown width (P=89.25%) and crown length (P=77.92%). The “voxel simulation method” was the most stable to calculate the tridimensional green biomass value using Matlab with the step size k=0.1 m. The fitting and testing effect of the three-factor tridimensional green biomass model of individual trees was the best (R²=0.846, R_MSE= 0.250, M_AE=0.210, M_APE=0.099, P=97.314%). The result of the two-factor tridimensional green biomass model of stands was the best (R²=0.706, R_MSE= 0.138, M_AE=0.120, MAPE=0.013, P=99.142%).

The accuracy of crown factors obtained by TLS has high accuracy, and the “voxel simulation method” is more accurate to calculate the three-dimensional green value. The individual-tree model and stand model constructed by TLS data improve the accuracy of tridimensional green biomass estimation in large-scale areas, providing a new method for the tridimensional green biomass estimation in large-scale areas.

This study took 49 artificial Pinus koraiensis with a total of 1 207 knot data from 29 standard plots in the Linkou Forestry Bureau and the Dongjingcheng Forestry Bureau in Heilongjiang province as the research object, and a knot volume prediction model was constructed based on their relevant data. Through analyzing the distribution law of the internal knot volume of the artificial Korean pine trunks, it achieved non-destructive evaluation of wood quality and provided a reference for the scientific and rational utilization of wood.

Firstly, we collected relevant data through analysis and reviewed relevant literature to establish a basic model. When constructing a mixed model, introduced random effects at the sample tree and sample plot levels, and selected the best fitting mixed model by comparing evaluation metrics such as AIC and BIC; When constructing a two-level nested mixed model, sample trees were nested at the sample plot level, and the optimal two-level nested mixed model was obtained based on the fitting evaluation index.

Among the three models, the fitting accuracy of the single-level mixed model and the two-level nested mixed model was higher than that of the basic model. The AIC, BIC, and other evaluation indicators showed that the two-level nested mixed model had a better fitting effect on the attributes of knots than the single-level mixed effect model. The test results showed that the prediction accuracy of the basic model was greater than 90%, and the prediction accuracy of both hybrid models was above 98%, indicating that the constructed model could provide good predictions for nodule volume.

Based on the two-level nested mixed model, not only can non-destructive estimation of knot volume be achieved, but also the distribution law of knot volume inside the artificial P. koraiensis trunk can be more accurately reflected. Realize non-destructive evaluation of wood quality, provide reference for scientific and rational utilization of wood, and thus improve economic benefits.