Total tree height predictions via parametric and artificial neural network modeling approaches

KARATEPE Y., Diamantopoulou M. J., ÖZÇELİK R., Sürücü Z.

IForest, vol.15, no.2, pp.95-105, 2022 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.3832/ifor3990-015
  • Journal Name: IForest
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.95-105
  • Keywords: Generalized Models, Levenberg-Marquardt Algorithm, Mixed-Effects Models, Resilient Propagation, Tree Height Model Prediction
  • Isparta University of Applied Sciences Affiliated: Yes

Abstract

Height-diameter relationships are of critical importance in tree and stand volume estimation. Stand description, site quality determination and appropriate forest management decisions originate from reliable stem height predictions. In this work, the predictive performances of height-diameter models devel-oped for Taurus cedar (Cedrus libani A. Rich.) plantations in the Western Mediterranean Region of Turkey were investigated. Parametric modeling methods such as fixed-effects, calibrated fixed-effects, and calibrated mixed-effects were evaluated. Furthermore, in an effort to come up with more reliable stem-height prediction models, artificial neural networks were employed using two different modeling algorithms: the Levenberg-Marquardt and the resilient back-propagation. Considering the prediction behavior of each respective modeling strategy, while using a new validation data set, the mixed-effects model with calibration using 3 trees for each plot appeared to be a reliable alternative to other standard modeling approaches based on evaluation statistics regarding the predictions of tree heights. Regarding the results for the remain-ing models, the resilient propagation algorithm provided more accurate predictions of tree stem height and thus it is proposed as a reliable alternative to pre-existing modeling methodologies.