A generalized height–diameter model including random components for radiata pine plantations in northwestern Spain

By F. C. Dorado, Ulises Diéguez-Aranda, M. B. Anta, Marina Rodríguez, Klaus von Gadow. 2006

187 citations recorded by the source corpus.

Subfields: Environmental Engineering, Mechanical Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/d8d2407444aa59ec539742a837e883c6cc20f822

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In Environmental Engineering

In Mechanical Engineering

In Nature and Landscape Conservation

Citations in the corpus

Height-diameter models with random coefficients and site variables for tree species of Central Maine (2008, methodology): “these models should include a wider range of diameters and larger numbers of trees than that recommended by Calama and Montero [4] or Castedo Dorado et al. [ 6 ].”
A spatially explicit height–diameter model for Scots pine in Estonia (2011, methodology): “To deal with this problem, the extension of ordinary regression to mixed model theory has been proposed in forest research (Gregoire 1987; Lappi 1991; Calama and Montero 2004; Mehta ¨talo 2004; Nothdurft et al. 2006).”
A generalized height–diameter model with random coefficients for uneven-aged stands in El Salto, Durango (Mexico) (2009, methodology): “because the original Akaike’s information criterion is based on a relation between the relative Kullback – Lieber distance and the maximized loglikelihood ( De Leeuw, 1992 ). All h d pairs of data were used to fi t the models (i.e. a portion of the data was not reserved for model validation) as ther”
Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests (2008, methodology): “The LMEM and NMEM approaches also account for the correlation structure of the data and provide realistic variance estimates for stochastic simulation and for modelling natural variability (e.g., Lappi 1997; Castedo et al. 2006).”
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”
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): “AGE was regarded as a good indicator of the mean size of the individual trees (Dorado et al., 2006), and it affected the H–”
Modelling tree height–diameter relationships in multi-species and multi-layered forests: A large observational study from Northeast China (2014, methodology): “Recognizing this lack of independence, several studies have explored the use of linear mixed effects models (LMEM) (Lappi, 1991, 1997; Monleon, 2003; Mehtätalo, 2004; Eerikäinen, 2009), and non nonlinear mixed models (NMEM) (Calama and Montero, 2004; Hall and Clutter, 2004; Castedo et al., 2006; Tem”
Nonlinear fixed and random generalized height–diameter models for Portuguese cork oak stands (2011, methodology): “For that situation, in accordance with the results obtained by other authors (e.g., Dorado et al., 2006; Temesgen et al., 2008), it was found that the populationspecific response of the nonlinear mixed effects model is more accurate then the predictions of the nonlinear fixed effects model, and sh”