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iForest - Biogeosciences and Forestry

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

Yasin Karatepe (1), Maria J Diamantopoulou (2)   , Ramazan Özçelik (1), Zerrin Sürücü (3)

iForest - Biogeosciences and Forestry, Volume 15, Issue 2, Pages 95-105 (2022)
doi: https://doi.org/10.3832/ifor3990-015
Published: Mar 21, 2022 - Copyright © 2022 SISEF

Research Articles


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 developed 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 remaining 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.

  Keywords


Tree Height Model Prediction, Generalized Models, Mixed-Effects Models, Levenberg-Marquardt Algorithm, Resilient Propagation

Authors’ address

(1)
Yasin Karatepe 0000-0002-8795-3829
Ramazan Özçelik 0000-0003-2132-2589
Faculty of Forestry, Isparta University of Applied Sciences, East Campus, 32260 Isparta (Turkey)
(2)
Maria J Diamantopoulou 0000-0002-6003-1285
Faculty of Agriculture, Forestry and Natural Environment, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, GR-54124 Thessaloniki (Greece)
(3)
Zerrin Sürücü
Ministry of Agriculture and Forestry, VI Regional Directorate, 27002 Burdur (Turkey)

Corresponding author

 
Maria J Diamantopoulou
mardi.diamant@gmail.com

Citation

Karatepe Y, Diamantopoulou MJ, Özçelik R, Sürücü Z (2022). Total tree height predictions via parametric and artificial neural network modeling approaches. iForest 15: 95-105. - doi: 10.3832/ifor3990-015

Academic Editor

Angelo Rita

Paper history

Received: Oct 04, 2021
Accepted: Jan 11, 2022

First online: Mar 21, 2022
Publication Date: Apr 30, 2022
Publication Time: 2.30 months

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