Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models

Artificial neural network models offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which are very helpful in tree height modeling. Back-propagation artificial neural network models were produced for individual-tree height estimation and the results were compared with the most used tree height estimation methods. Height diameter (h-d) measurements of 1163 Crimean juniper trees in 63 sample plots from southwestern region of Turkey were used. A calibrated basic h-d mixed model, a generalized h-d model and back-propagation artificial neural network h-d models were constructed and compared. When the variability of the h-d relationship fronl. ss stand can be incorporated into the model, then both mixed-effects nonlinear regression and back propagation neural network modeling approaches can produce accurate results, reducing the root mean squared error by more than 20% as compared to a basic nonlinear regression model. The use of a generalized h-d model also showed reliable results (reduction of 13% in root mean squared error as compared to a nonlinear regression model). The back-propagation artificial neural network model seems a reliable alternative to the other methods examined possessing the best generalization ability. Further, from a practical point of view it has the advantage that no height measurements are needed for its implementation. On the contrary prior information is required for the mixed-effects model calibration which is a limiting factor according to its use. (C) 2013 Elsevier B.V. All rights reserved.