TITLE:
Stem and Total Above-Ground Biomass Models for the Tree Species of Freshwater Wetlands Forest, Coastal Areas and Dry Areas of Bangladesh: Using a Non-Destructive Approach
AUTHORS:
Mahmood Hossain, Chameli Saha, Rakhi Dhali, Srabony Saha, Mohammad Raqibul Hasan Siddique, S. M. Rubaiot Abdullah, S. M. Zahirul Islam
KEYWORDS:
Allometry, Biomass, Freshwater Wetlands, Coastal Areas, Dry Areas
JOURNAL NAME:
Open Journal of Forestry,
Vol.11 No.2,
April
2,
2021
ABSTRACT: Biomass and carbon stock in a forested areas are now prime important
indicators of forest management and climate change mitigation measures. But the
accurate estimation of biomass and carbon in trees of forests is now a
challenging issue. In most cases, pantropical and regional biomass models are
used frequently to estimate biomass and carbon stock in trees, but these
estimations have some uncertainty compared to the species-specific allometric
biomass model. Acacia nilotica, Casuarina equisetifolia and Melia azedarach have been planted
in different areas of Bangladesh considering the species-specific site
requirements. While Barringtonia acutangula and Pongamia pinnata are the dominant tree species of the freshwater
swamp forest of Bangladesh. This study was aimed to develop species-specific
allometric biomass models for estimating stem and above ground biomass (TAGB)
of these species using the non-destructive method and to compare the efficiency
of the derived biomass models with the frequently used regional and pantropical
biomass models. Four Ln-based models with diameter at breast height (DBH) and
total height (H) were tested to derive the best fit allometric model. Among the
tested models, Ln (biomass) = a + b Ln (D) + c Ln (H) was the best-fit model
for A. nilotica, M. azedarach, B. acutangula and P. pinnata and Ln (biomass) = a + b Ln (D2H)
was best-fit for C. equisetifolia. Finally, the derived best-fit species-specific TAGB models have shown superiority
over the other frequently used pantropical and regional biomass models in
relation to model efficiency and model prediction error.