Regional mixed-effects height–diameter models for loblolly pine (Pinus taeda L.) plantations

Virginia Polytechnic Institute and State University, Блэксбург, Virginia, United States
European Journal of Forest Research (Impact Factor: 2.1). 04/2007; 126(2):253-262. DOI: 10.1007/s10342-006-0141-7


A height–diameter mixed-effects model was developed for loblolly pine (Pinus taeda L.) plantations in the southeastern US. Data were obtained from a region-wide thinning study established by the Loblolly
Pine Growth and Yield Research Cooperative at Virginia Tech. The height–diameter model was based on an allometric function,
which was linearized to include both fixed- and random-effects parameters. A test of regional-specific fixed-effects parameters
indicated that separate equations were needed to estimate total tree heights in the Piedmont and Coastal Plain physiographic
regions. The effect of sample size on the ability to estimate random-effects parameters in a new plot was analyzed. For both
regions, an increase in the number of sample trees decreased the bias when the equation was applied to independent data. This
investigation showed that the use of a calibrated response using one sample tree per plot makes the inclusion of additional
predictor variables (e.g., stand density) unnecessary. A numerical example demonstrates the methodology used to predict random
effects parameters, and thus, to estimate plot specific height–diameter relationships.

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Available from: Harold E. Burkhart, May 17, 2014
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    • "In a mixed-effects model, both fixed and random parameters are simultaneously estimated, which allows variability for given phenomena among various factor levels to be modeled (Lindstrom and Bates 1990). This characteristic makes mixed-effects models more efficient when prediction for a new individual is required and prior information is available (Lappi and Bailey 1988, Gregoire et al. 1995, Garber and Maguire 2003, Leites and Robinson 2004, Trincado et al. 2007). "
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    • "To account for growing condition differences among stands for the same species, in addition to D, these models contain measures of site quality and/or stand density, among others. Studies have shown that a calibrated mixedeffects H–D model often produces better predictions than a region-wide H–D model containing stand-level regressors (Trincado et al. 2007, Temesgen et al. 2008, Huang et al. 2009). Mixed-effects models fit using plot-level data can also be calibrated at the plot level during operational inventories, providing more localised H–D relationships within a stand. "
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    • "With increasing forest complexity (i.e., NS, PS, and PS-T), the utility of L-band radar backscatter to predict BA and AGB decrease as reported in previous studies (Kasischke et al., 1995). Additionally, variability was likely introduced from modeled values of AGB that were a function of the strength of the individual tree height versus DBH relationship established byTrincado et al. (2006). The sensitivity of radar has been shown to decrease due to speckle noise (Saatchi et al., 2011). "
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