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

Ozcelik R., Diamantopoulou M. J., CRECENTE-CAMPO F., ELER U.

FOREST ECOLOGY AND MANAGEMENT, vol.306, pp.52-60, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 306
  • Publication Date: 2013
  • Doi Number: 10.1016/j.foreco.2013.06.009
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.52-60
  • Süleyman Demirel University Affiliated: Yes


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.