Table 1.
Number and size measures of trees that were used in the destructive measurements categorized per wood density (WD) class.
Table 2.
Model description of above ground biomass (AGB) estimates for the FG aggregated model.
Table 3.
Model description of above ground biomass (AGB) estimates of four functional groups.
Table 4.
Paired t-tests for the mean differences between observed and predicted above ground biomass (AGB) of destructively sampled trees.
Table 5.
Model description for aboveground biomass (AGB) estimates.
Fig 1.
The predicted above ground biomass (AGB) of the sample trees (kg tree-1) made by our FG-aggregated model and by a number of regional and pan-tropical models.
Model used were Brown [1], Ketterings et al. [16], Chave et al. [3], and Kenzo et al. [8], as a function of DBH. The parameters in the brackets indicate the predictors that were used in the model.
Fig 2.
Boxplots showing the mean predicted AGB (Mg ha-1) of six plots.
AGB was predicted by Brown [1], Chave et al. [3], Ketterings et al. [16], Chave et al. [3], our models (both FG-aggregated and FG-specific models) and Kenzo et al. [8]. The parameters in the brackets indicate the predictors that were used in the model.
Table 6.
Model description of root biomass (RB) estimates.
Fig 3.
Root biomass (RB) of sample trees.
Root biomass (kg tree -1) was predicted by the model of this study and the models of Niiyama et al. [20], Lima et al. [2] and Kenzo et al. [8]. The parameters in the brackets indicate the predictors that were used in the model.
Fig 4.
Boxplots showing the value of root biomass in six plots.
Root biomass (ton ha-1) was predicted by Niiyama et al. [20], this study, Lima et al. [2] and Kenzo et al. [8]. The parameters in the brackets indicate the number of predictors in the model.
Fig 5.
The average standard error (S%) in the estimate of total AGB of 30 selected trees.
Each square was the average standard error (S%) of the estimate (Y axis) given by the model that was developed by a certain number of destructive trees i.e., 20, 35, 60, … and 300 trees, respectively (X axis).