Publication: Improvement of the quality and precision of biomass and carbon equations: case study of mixed-deciduous degraded forest of Thailand
2
Issued Date
2011-08
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eng
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Mahidol University
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Faculty of Environment and Resource Studies. Mahidol University
Bibliographic Citation
Environment and Natural Resources Journal. Vol.9, No.2 (2011), 39-47
Suggested Citation
Patthanan Porkar, Wimon Sonchaem, Raywadee Roachanakanan, Rungjarat Hutacharoen Improvement of the quality and precision of biomass and carbon equations: case study of mixed-deciduous degraded forest of Thailand. Environment and Natural Resources Journal. Vol.9, No.2 (2011), 39-47. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/3179
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Title
Improvement of the quality and precision of biomass and carbon equations: case study of mixed-deciduous degraded forest of Thailand
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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.