Crown size estimations are of vital importance in forest management practice. This paper presents nonlinear models that were developed for crown width prediction of Black pine (Pinus nigra Arn.) natural, pure, even-aged stands in Olympus Mountain, central Greece. Using a number of measured characteristics at tree and plot level from 66 sample plots as independent variables, an attempt was made to predict crown width accurately, initially based on Least Square Analysis. At the second stage, nonlinear mixed effect modeling was performed in order to increase the fitting ability of the proposed models and to deal with the lack of between observations independence error assumption. Based on the same form, a generalized crown width model was developed by including six main regressors, such as the diameter at breast height, the total height, the canopy base height, the basal area, the relative spacing index and the diameter to quadratic mean diameter ratio, while at the final stage, the same model was expanded to mixed-effect. The proposed models were evaluated against independent crown width sample observations that were also obtained from the study area. The results showed that the two types of mixed-effect models performed equally well and, therefore, we propose those for use in forestry practice. Furthermore, the exact contribution of each inherent variable in crown width allometry was evaluated, thus providing a framework to facilitate field measurements for forest management predictions.

A survey was carried out during a workshop of the Greek National Forest Governance Council to identify the strengths, weaknesses, opportunities and threats for sustainable forest management assessment in Greece. The National Forest Governance Council was established within the frame of the INFORM project financed through LIFE. Its main role is to support the development process of a knowledge base for development of national forest policy on sustainable forest management assessment and monitoring. This paper presents the important internal and external factors for sustainable forest management assessment identified in the four groups: strengths, weaknesses, opportunities and threats, for the forests of Eastern Macedonia and Thrace region, in Northern Greece. It also discusses the strategies for this area resulting in a good fit between the internal and external factors. SWOT provides a basic frame within which an analysis of the strategic forest planning environment towards sustainable forest management assessment can be performed.

In the current study, a novel approach combining quantile regression with nonlinear mixed-effects (QR-NLME) modeling was applied to predict the maximum crown width (cw_max) of three economically important forest species—the European beech (Fagus sylvatica L.), the black pine (Pinus nigra Arn.), and the Calabrian pine (Pinus brutia Ten.) at tree level. A power QR-NLME model was fitted first to a dataset including 1414 European beech trees obtained from 29 randomly distributed sample plots, 770 black pine trees from 25 sample plots, and 1880 Calabrian pine trees from 41 sample plots in Greece, to predict the cw_max at tree level. Additionally, a nonlinear mixed-effects model (NLME) was fitted to the same dataset to predict the average crown width at tree level for all species. In the second stage, the crown competition factor (CCF) was estimated based on the population average response of the cw_max predictions. The proposed approach presented sound results when compared with the outcomes of relevant models from other regions fitted to open-grown tree data, and therefore, it can be well implemented on clustered data structures, in cases of absence of open-grown tree data.

Total height is one of the basic morphometric tree variables included in all forest management inventories, because it is connected with several forest processes and functions related to the estimation of the woody tree volume and sustainable forest yield. The current research, based on a total sample of 1059 measured Black pine (Pinus nigra Arn.) trees from Cyprus, is an attempt to explore the biological processes related to the tree height allometry of this species and develop a generalized mixed-effects model for tree height prediction. The proposed model, with three additional basic covariates and two random parameters, explained almost 96% of the height variance. The model results showed that although competition and site-connected variables affected the total height, it was the crown base height that explained, to a large degree, the height expression. Through the mixed-effects modeling approach it was possible to explore the complex biological processes related to the tree allometry of Black pine and depict those within a mathematical formulation. The proposed generalized model decreased the error significantly, and therefore it can be used for operational forest management purposes. However, for marginal predictions, use of only the fixed part of the basic model could suffice, since this also provided unbiased parameter estimates.

In forest science and practice, the total tree height is one of the basic morphometric attributes at the tree level and it has been closely linked with important stand attributes. In the current research, sixteen nonlinear functions for height prediction were tested in terms of their fitting ability against samples of Abies borisii regis and Pinus sylvestris trees from mountainous forests in central Greece. The fitting procedure was based on generalized nonlinear weighted regression. At the final stage, a five-quantile nonlinear height-diameter model was developed for both species through a quantile regression approach, to estimate the entire conditional distribution of tree height, enabling the evaluation of the diameter impact at various quantiles and providing a comprehensive understanding of the proposed relationship across the distribution. The results clearly showed that employing the diameter as the sole independent variable, the 3-parameter Hossfeld function and the 2-parameter Näslund function managed to explain approximately 84.0% and 81.7% of the total height variance in the case of King Boris fir and Scots pine species, respectively. Furthermore, the models exhibited low levels of error in both cases (2.310m for the fir and 3.004m for the pine), yielding unbiased predictions for both fir (−0.002m) and pine (−0.004m). Notably, all the required assumptions for homogeneity and normality of the associated residuals were achieved through the weighting procedure, while the quantile regression approach provided additional insights into the height-diameter allometry of the specific species. The proposed models can turn into valuable tools for operational forest management planning, particularly for wood production and conservation of mountainous forest ecosystems.