Ecoregion-based height-diameter models were developed in the present study for Scots pine (Pinus sylvestris L.) stands in Turkiye and included several ecological factors derived from a pre-existing ecoregional classification system. The data were obtained from 2831 sample trees in 292 sample plots. Ten generalized height–diameter models were developed, and the best model (HD10) was selected according to statistical criteria. Then, nonlinear mixed-effects modeling was applied to the best model. The R² for the generalized height‒diameter model (Richards function) modified by Sharma and Parton is 0.951, and the final model included number of trees, dominant height, and diameter at breast height, with a random parameter associated with each ecoregion attached to the inverse of the mean basal area. The full model predictions using the nonlinear mixed-effects model and the reduced model (HD10) predictions were compared using the nonlinear sum of extra squares test, which revealed significant differences between ecoregions; ecoregion-based height–diameter models were thus found to be suitable to use. In addition, using these models in appropriate ecoregions was very important for achieving reliable predictions with low prediction errors.

Stem taper models are essential tools in forestry, allowing for the estimation of stem diameter at any height, as well as the calculation of merchantable and total stem volumes and wood assortments along the tree bole. Therefore, accurate taper prediction is crucial for sustainable forest resource assessment. This study developed stem taper models for estimating tree diameter using both traditional regression and machine learning (ML) approaches, using Pinus brutia Ten. as a model species. The research focused on two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to predict stem taper in comparison to traditional taper models. A total of 121 destructively sampled trees were measured for stem diameter at multiple heights, and various taper models were evaluated for their accuracy. The results show that the XGBoost model outperforms all other approaches, demonstrating superior predictive accuracy with minimal error, as indicated by lower root mean square error (RMSE), mean absolute error (MAE), and bias values. While RF also performed well, XGBoost was selected for this study due to its better predictive performance and the more consistent error distributions between the training and test datasets. This research highlights the potential of ML techniques in forest modeling, offering enhanced accuracy and efficiency for forest inventory and management applications.

In this study, it was aimed to develop height (H)-diameter (D) models for pure Pinus brutia Ten. stands distributed in the Adana-Karaisalı region in Türkiye. Diameter at breast height and tree height data were obtained from 1081 sample trees in 82 sample plots. Additionally, measurements were made regarding various stand variables. Based on the data obtained, parameters were estimated for four base H-D models, which provide estimates based only on diameter at breast height, and five generalized H-D models, which incorporate stand variables in addition to diameter at breast height. According to the results of nonlinear regression analysis, models with all parameters being statistically significant were evaluated based on six statistical criteria. The ranking results showed that the Mirkovich (1958)’s generalized H-D model achieved the best predictive results, with an coefficient of determination (R²) value of 0.941, indicating that the model accounts for 94.1% of the variation in tree height and can be considered a reliable tool for estimating tree height in the studied stands. As a result of statistical and graphical evaluations, it can be concluded that generalized H-D models perform better and yield more unbiased predictions.

The objective of this study is to develop tree volume equations for Scots pine trees in the Kastamonu region and to compare the prediction capability of these equations with existing tree volume equations. For this purpose, stump diameter, diameter at breast height, diameters in each meter up to the top of the tree, and tree height measurements were measured on 127 sample trees from the Kastamonu Forest Enterprise in the Kastamonu Regional Directorate of Forestry. Subsequently, tree volumes were calculated based on these measurements and using the sectional method. This method entailed volumizing the trees in three sections: the stump section, the top section and the one-meter sections between the stump and the top sections. To develop single- and double-entry tree volume equations, the parameters of eight volume equations were estimated. Four statistical criteria were employed to identify the best predictive models. The coefficients of determination for the best predictive single- and double-entry models were found to be 0.972 and 0.975, respectively. Additionally, the results of these models were compared with the volume predictions derived from four models presented in the literature. The results of this study will enhance the accuracy of tree volume estimation for Scots pine stands in the Kastamonu region.