Optimal tree sampling for ecosystem-specific biomass allometry modeling in Congo Basin forests

Allometric equations are fundamental for estimating biomass in forests and their accuracy depends heavily on the quality and representativeness of the data used to construct them. This study aimed to benchmark tree sampling techniques and determine the optimal number of sample trees for constructing allometric equations. Ten sampling strategies consisting of the combination of two allometric models and five sampling techniques were evaluated. Random sampling techniques and four sampling techniques with eight diameter size-classes based on cumulative frequency distribution were compared. A wide range of sample data was simulated using a parametric resampling method to ensure unbiased sampling and a representative spread of observations. Data were derived from 15 inventory plots in three Congo Basin forest reserves. Results showed that uncertainty due to differences in size class distribution was minimized by a sampling technique, which effectively represents large trees. High sample sizes were required for precision in the absence of large trees. Sample sizes uncertainty was influenced by stand characteristics, mainly the shape of the inventory plot and data distribution. This study reveals that the biomass prediction uncertainty depends on the population’s specific characteristics, the type of allometric model used, and the representativeness of large trees in the sample.