Improvement of the quality and precision of biomass and carbon equations: case study of mixed-deciduous degraded forest of Thailand

Abstract

Environmental pressures brought about by climate change have increased the urgency for biomass assessment to measure the potential of forests to be carbon sinks and carbon sources. The researcher suggests that improving the quality and precision of models used for measuring carbon stock in forests is thus important. This study aims to investigate the relationships between independent factors, such as dry weight biomass (B), and dependent factors, such as diameter at breast height (D), height (H), and wood specific gravity (ρ), to formulate biomass equations for four common tree species: Sterculia pexa; Millettia brandisiana; Grewia eriocarpa; and Bridelia ovata. Regression models, each with different independent variables (D, ρD, D2H, and ρD2H), were studied. The results showed a strong correlation between B, D, and H, but not ρ. However, ρ showed a significant variation between the four species which indicated that proper species identification is required for accurate modelling. The best regression models for estimating biomass had two forms: ln(B) = c + αln(D) and ln(B) = c + αln(ρD2H). The dry weight of individual trees using the regression model with ρD2H had an average estimated error of 0.09–2.66%. The dry weight using D had an average estimated error of 0.28– 1.77%. Thus, it was most appropriate to use ρD2H as the independent variable in the model. Furthermore, linear regression indicated a significant statistical difference between the four species. In conclusion, the researcher found that formulating species-specific regression models is essential in assessing biomass and carbon, particularly for the mixed-deciduous degraded forest areas in this study.