A Generalized Approach for Modeling and Localizing Stem Profile Curves

By Guillermo Trincado, Harold E. Burkhart. Forestry sciences, 2006

151 citations recorded by the source corpus.

The Max and Burkhart segmented taper equation was fitted using nonlinear mixed-effects modeling techniques to account for within- and between-individual variation in loblolly pine (Pinus taeda L.) stem profiles. Within- and between-tree residual variances and spatial autocorrelation between residuals were incorporated in the model with an error variance function and a continuous autocorrelation structure, respectively. However, most of the residual autocorrelation was accounted for by including random effects. Upper stem diameter measurements were used to estimate random effects parameters using an approximate Bayesian estimator, which localized stem profile curves for individual trees. The procedure was tested with an independent data set. Measures of precision and bias showed that upper stem diameter measurements and subsequent estimates of random effects improved the predictive capability of the taper equation mainly in the lower portion of the bole. The method can localize stem curves for trees growing under different site and management conditions. It also represents a general framework that can be applied to other taper equation forms, increasing their flexibility and efficiency in prediction for local conditions.

Subfields: Building and Construction, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/4af03de969e478772cfd7727c6341853f684df0c

How researchers see this paper

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

In Building and Construction

1. Jing Wang
2. Q. V. Cao
3. Guillermo Trincado
4. Mahadev Sharma
5. Yuqing Yang
6. S. Meng
7. S. Huang

In Industrial and Manufacturing Engineering

1. Jing Wang
2. Q. V. Cao
3. Mahadev Sharma
4. Yuqing Yang
5. S. Meng
6. S. Huang
7. Jerome K. Vanclay
8. Aaron Weiskittel

Citations in the corpus

• A multilevel individual tree basal area increment model for aspen in boreal mixedwood stands (2009, background): “Nonlinear mixed models provide a powerful way of analyzing correlated hierarchical data (Vonesh and Chinchilli 1997) and have been increasingly applied in growth and yield modeling over the past decade (Gregoire and Schabenberger 1996; Trincado and Burkhart 2006; Temesgen et al. 2008).”
• Height–diameter equations for boreal tree species in Ontario using a mixed-effects modeling approach (2007, background): “Details on the prediction of random effects parameters in the forestry context can be found in the study by Calama and Montero (2004) and Trincado and Burkhart (2006).”
• Forest Growth and Yield Modeling (2011, background): “…improvement to the application of taper equations is the ability to localize them with additional upper-stem diameter measurements, either by direct calibration (Cao, 2009), or estimating random effects when the parameters are determined using a mixed-effects approach (Trincado and Burkhart, 2006).”
• Nonlinear mixed-effects modeling of variable-exponent taper equations for lodgepole pine in Alberta, Canada (2009, background): “However, others observed that although autocorrelation can be reduced through random-effects parameters, it cannot be completely eliminated (Garber and Maguire 2003; Trincado and Burkhart 2006).”
• Calibrating fixed- and mixed-effects taper equations (2011, methodology): “…mixedeffects models have been applied to predict tree taper because they take into account the correlation among multiple diameter measurements on an individual stem (Garber and Maguire, 2003; Leites and Robinson, 2004; Trincado and Burkhart, 2006; Lejeune et al., 2009; Yang et al., 2009).”