Keywords
modeling
random parameter
calibration
How to Cite
Abstract
Statistical nonlinear mixed effect models were used to predict relationships between the total height and diameter at breast height of individual trees in Oriental beech (Fagus orientalis Lipsky) stands in Kestel, Bursa, Northwestern Turkey. 124 sample plots were selected to represent various stand conditions such as site quality, age, and stand density. Nine generalized nonlinear height–diameter models were fitted and evaluated based on Akaike’s information criterion, Schwarz’s Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), Absolute Bias and Adjusted Coefficient of Determination (R2adj). The nonlinear Schnute’s model was selected as the best predictive model. The height–diameter model based on the nonlinear mixed effect modeling approach accounted for 90.6 % of the total variance in height–diameter relationships and root mean square error (RMSE) values of 1.48 m. Various sampling scenarios that differed in sampling design and size of the selected sub-sample trees from the validation data set revealed that four randomly selected sub-sample trees in a given plot produced the best predictive results (43.3 % reduction of the sum of square errors, 98.4 % reduction of absolute bias, and 36.9 % reduction of the RMSE) in relation to the fixed effect predictions.
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