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

By Ramazan Özçelik, M. Diamantopoulou, F. crecente-campo, Ü. Eler. 2013

115 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Mechanical Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/d7868a76e89cdd5c492795dc4feb20a1bf28d91f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. Marcos Barrio-Anta
2. F. crecente-campo
3. Guillermo Trincado
4. Q. V. Cao
5. Ramazan Özçelik
6. Nilsun Göçer

In Mechanical Engineering

1. Ramazan Özçelik
2. Guillermo Trincado
3. Nilsun Göçer
4. Q. V. Cao
5. Marcos Barrio-Anta
6. F. crecente-campo

In Nature and Landscape Conservation

1. Q. V. Cao
2. Ramazan Özçelik
3. Guillermo Trincado
4. Nilsun Göçer
5. Marcos Barrio-Anta
6. F. crecente-campo
7. J. Obeso

Citations in the corpus

• Predicting tree height from tree diameter and dominant height using mixed-effects and quantile regression models for two species in Turkey (2018, methodology): “Many studies have used mixed-models to describe h-d relationship (e.g., Lappi, 1997; Calama and Montero, 2004; Trincado et al., 2007; Sharma and Parton, 2007; Huang et al., 2009; Crecente-Campo et al., 2010; Özçelik et al., 2013; VanderSchaaf, 2014; Gómez-García et al., 2015; Saud et al., 2016; Zang”
• Tree height prediction approaches for uneven-aged beech forests in northwestern Spain (2013, methodology): “However, to our knowledge, only two studies have applied this methodology to h–d relationships (Diamantopoulou and Özçelik, 2012; Özçelik et al., 2013), specifically in even-aged stands of Brutian pine, Cedar of Lebanon, Cilica fir and Crimean juniper in Turkey.”