Individual tree biomass models to estimate forest biomass for large spatial regions developed using four pine species in China

Abstract

The estimation of forest biomass for large spatial regions is key to national carbon stocks, but few models have been developed at the regional level. Based on mensuration data from large samples (755 trees for aboveground and 253 for belowground biomass) of four major pine species in China, we developed compatible individual tree models for above- and belowground biomass, the biomass conversion factor (BCF), and the root-to-shoot ratio, using the indicator variable approach and nonlinear errors-in-variables simultaneous equations. The results indicated the following: there was no significant difference among the power parameters in the biomass models for the four pine species; the four species can be ranked in terms of biomass productivity from Yunnan pine (lowest), slash pine, Masson pine, to Chinese red pine (highest), and in terms of BCF from Yunnan pine (lowest), Masson pine, Chinese red pine, to slash pine (highest); and mean prediction errors of aboveground biomass models for the species were less than 5%, except for Yunnan pine, whereas errors of belowground biomass equations were between 7 and 15%. The modeling technique in this study can be used for individual tree biomass estimation, and the models developed provide new tools for estimating forest biomass and carbon storage.