Modelling above-ground live trees biomass and carbon stock estimation of tropical lowland Dipterocarp forest: integration of field-based and remotely sensed estimates

Abstract

ABSTRACTCarbon exists as carbon dioxide (CO2) which is one of the greenhouse gases (GHG) in the atmosphere that has an enormous influence on the impact of climate change. Therefore, the forest plays an undeniably pivotal role as a carbon sink, which absorbs carbon dioxide from the atmosphere. This research aims to develop allometric equation for above-ground live tree biomass (AGB) by combining field-based, combination of field data observation and technology (WV-3 and light detection and ranging (lidar)) and by using only technology derivation. The independent predictor was induced based on the literature review and theories, and an ordinary least square (OLS) estimator will be used to develop multiple linear regression models. During model selection, the best model fit was selected by calculating statistical parameters such as residual of the coefficient of determination (R2) selection methods, adjusted coefficient of determination (R2adj), root mean square error, graphical analysis of the residuals, standard error (Syx), and Akaike information criterion. An allometric equation of this research was developed using carbon stocks as dependent variables, and four of the predictor’s variables: diameter at breast height (DBH); total height observed at field (hF); total height derived from airborne lidar (hL); and morphometric variables of the crown projection area (CPA). Based on the statistic indicators, the most suitable model is Model 1, ln (Sc) = – β0 + β1 ln (hL) + β2 ln (DBH) + β3 ln (CPA) for the combination of remote sensing and field observation; ln (Sc) = – β0 + β1 ln (hF) + β2 ln (DBH) for field inventory only; and ln (Sc) = – β0 + β1 ln (hL) + β2 ln (CPA) for remote sensing only. This model is reliable in forest management to estimate the AGB and carbon stock estimation using a selection of variable sources.