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

iForest: Biogeosciences and Forestry

article

Yasin Karatepe¹  Maria J Diamantopoulou²  Ramazan Özçelik¹  Zerrin Sürücü³ 

[1] Faculty of Forestry, Isparta University of Applied Sciences, East Campus;Faculty of Agriculture, Forestry and Natural Environment, School of Forestry and Natural Environment, Aristotle University of Thessaloniki;Ministry of Agriculture and Forestry

关键词: Tree Height Model Prediction;    Generalized Models;    Mixed-Effects Models;    Levenberg-Marquardt Algorithm;    Resilient Propagation;   

DOI  :  10.3832/ifor3990-015

学科分类：社会科学、人文和艺术（综合）

来源: Societa Italiana di Selvicoltura ed Ecologia Forestale (S I S E F)

【 摘 要 】

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.

【 授权许可】

Unknown