Site-specific height-diameter and stem volume equations for Lebombo-ironwood

Abstract

T.M. MagalhãesMagalhães T.M., 2017. Site-specific height-diameter and stem volume equations for Lebombo-ironwood. Ann. For. Res. 60(2): 297-312.Abstract. Height-diameter (H-D) and stem volume equations are indispensable tools for forest management and have a wide use in forestry, however they are lacking for Lebombo-ironwood. Based on a dataset of 1144 Lebombo-ironwood trees destructively measured for height and stem volume, H-D and stem volume models were fitted using mixed-effects and dummy variables models. Random-effects and dummy variables were the different growing sites, as they affect H-D relationships and therefore stem volume models. Different model forms were compared to each other with regard to the sources of errors. The error due to uncertainty in the model parameter estimates was insignificant for mixed-effects models, whilst the error due to model misspecification was relatively larger for dummy variables H-D functions when compared to the mixed-effects ones. However, both mixed-effects and dummy variables models were similar in terms of error due to residual variability around model prediction. Overall, mixed-effects and dummy variables models did not differ in terms of predictive ability and accuracy.

Site Specific Stem Volume Models for Pinus patula and Pinus oocarpa

“…The general nonlinear forms used in this study have adequate mathematical properties and have performed satisfactorily in previous studies [7,[18][19][20].…”

“…Parameters of the "best" model were estimated with and without the presence of random parameter(s). Nonlinear mixed-effects modelling approach is well explained by other researchers [13,18,20,24].…”

Least squares-based biomass conversion and expansion factors best estimate biomass than ratio-based ones: Statistical evidences based on tropical timber species

“…Error due to model misspecification is here expressed by Akaike Information Criterion (AIC) [11], as it is a measure of a relative quality of statistical models for a given set of data. The error due to uncertainty in the model parameter estimates is expressed by the standard errors of the regression parameters [12]: standard error of the BCEFs, in this case. In turn, the error due to residual variability around model prediction is here expressed by coefficient of variation of the residuals (CVr) and Furnivaĺs index of fit (FI) [[2], [12]].…”

Spatial and Temporal Variations of Predicting Fuel Load in Temperate Forests of Northeastern Mexico

“…Forest fires affect approximately 34 million ha/year worldwide [3]. Fuel load can be estimated from direct measurements [4], by allometric equations [5][6][7] and by expansion factors, which transform the volume of forest per tree or per unit area into fuel load density in m 3 /ha [8][9][10]. Point estimates are useful for predicting fuel load in areas where measurements are lacking [11].…”