Comparative evaluation of five height–diameter models developed for black spruce and jack pine stand-types in terms of goodness-of-fit, lack-of-fit and predictive ability

By P. Newton, I. Amponsah. 2007

77 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/aa62c2d868967f1c76989c984967753231013d63

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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 larch plantations in northern and northeastern China: a comparison of the mixed-effects, quantile regression and generalized additive models (2016, background): “The latter is known as composite (Newton and Amponsah, 2007), regional (Soares and Tomé, 2002), expanded (Huang et al., 2009a) or generalized model (Temesgen and von Gadow, 2004).”
Tree height prediction approaches for uneven-aged beech forests in northwestern Spain (2013, background): “The use of generalized models reduces bias and increases precision with respect to local models, because it include the dynamics of each stand in the prediction model (Soares and Tomé, 2002; Newton and Amponsah, 2007).”
Non-parametric prediction of diameter distributions using airborne laser scanner data (2009, methodology): “The error indices proposed by Reynolds et al. (1988), later applied by numerous other authors (e.g. Gobakken & Næsset, 2005; Palahı́ et al., 2006; Kangas et al., 2007; Newton & Amponsah, 2007; Packalén & Maltamo, 2008), were also used.”
Predicting tree height from tree diameter and dominant height using mixed-effects and quantile regression models for two species in Turkey (2018, background): “, 2014), and product recovery and carbon budgeting models (Newton and Amponsah, 2007).”
A generalized nonlinear mixed-effects height-diameter model for Eucalyptus globulus L. in northwestern Spain. (2010, background): “In general, the inclusion of stand variables in a local h–d model reduces bias and increases precision (e.g., Soares and Tomé, 2002; Calama and Montero, 2004; Newton and Amponsah, 2007).”
Forest Growth and Yield Modeling (2011, background): “breast height) when DBH is equal to zero, but this constraint may cause poorer model performance across the full range of DBH (Newton and Amponsah, 2007).”