Interregional nonlinear height-diameter model with random coefficients for stone pine in Spain

By Rafael Calama, G. Montero. 2004

324 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a4402d605e0dcaa73c38f0ecf6d949770fc3fde1

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In Environmental Engineering

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Height–diameter equations for boreal tree species in Ontario using a mixed-effects modeling approach (2007, background): “Details on nonlinear mixed effects modeling for height–diameter relationships can be found in the study by Calama and Montero (2004).”
Forest Growth and Yield Modeling (2011, methodology): “…(Larsen and Hann, 1987; Huang et al., 1992; Hanus et al., 1999a; 1999b; Huang et al., 2000); (4)mixed effects linear regression (Lappi, 1991a, 1997; Robinson andWykoff, 2004); and (5) mixed effects nonlinear regression (Calama and Montero, 2004; Dorado et al., 2006; Temesgen et al., 2008).”
Height–diameter equations for larch plantations in northern and northeastern China: a comparison of the mixed-effects, quantile regression and generalized additive models (2016, background): “D modelling (Lappi, 1997; Calama and Montero, 2004; Huang et al., 2009a; Rijal et al., 2012; de-Miguel et al., 2013; Sharma and Breidenbach, 2015).”
Generalized height-diameter and crown diameter prediction models for cork oak forests in Spain (2007, methodology): “This formulation has already been proposed in heightdiameter models by Krumland and Wensel (1988), Tomé (1989), Cañadas (2000), Calama and Montero (2004) and Diéguez-Aranda et al. (2005), and guarantees that the asymptote is near to the dominant height and that the height growth rate is smaller for…”
Generic biomass functions for Common beech (Fagus sylvatica) in Central Europe: predictions and components of uncertainty (2008, methodology): “…(Pinheiro and Bates 2000) that have been successfully applied in forestry studies for trunk circumference (Lindstrom and Bates 1990), tree height (Calama and Montero 2004, Calegario et al. 2005, Fang and Bailey 2001, Hall and Bailey 2001), stand and bole volume (Fang et al. 2001, Gregoire and…”
A Nonlinear Mixed-Effects Height-to-Diameter Ratio Model for Several Tree Species Based on Czech National Forest Inventory Data (2019, methodology): “nonlinear mixed-effects model (Equation (3)) with respect to its fixed-effect parameter (b1) to which random effect (ui) has been added [48,53,58,91].”
Dominant height growth equations including site attributes in the generalized algebraic difference approach (2008, background): “modelling local variability (Lappi and Bailey 1988; Fang et al. 2001; Calama and Montero 2004).”
Using quadratic mean diameter and relative spacing index to enhance height-diameter and crown ratio models fitted to longitudinal data (2016, background): “…the height–d.b.h. relationship can vary over time due to differences in stand age, productivity and competition (Lappi, 1997; Peng et al., 2001; Sharma and Parton, 2007; Budhathoki et al., 2008) and also differences in a geographical region (Calama and Montero, 2004; Arcangeli et al., 2014).”
A non-linear hierarchical mixed model to describe tree height growth (2006, background): “According to Fang and Bailey Robert (2001) and Calama and Montero (2004), it is necessary to resolve the following issues while constructing the final model:”
Predicting tree height from tree diameter and dominant height using mixed-effects and quantile regression models for two species in Turkey (2018, methodology): “Stand variables that have been added to the base model include stand-density measures (Temesgen and Gadow, 2004), stand density, dominant height, and diameter distribution percentiles (Calama and Montero, 2004; Dorado et al., 2006; Temesgen et al., 2008), stand density and relative position (Temesge”
Modeling height-diameter curves for prediction (2015, methodology): “Especially in situations where sample trees are available from the plot in question, the mixed-effects models have proven highly useful, since they allow for developing plot-specific H–D curves with minimal sample size per plot (e.g., Lappi 1991, 1997; Mehtätalo 2004, 2005; Calama and Montero 2004; ”