MODELLING INDIVIDUAL TREE ABOVEGROUND BIOMASS USING DISCRETE RETURN LIDAR IN LOWLAND DIPTEROCARP FOREST OF MALAYSIA

Abstract

Light Detection and Ranging (LiDAR) has become a common means for predicting key forest structural attributes. The aim of this study was to explore the relationship between individual tree LiDAR-based metrics and field data on tree attributes from a tropical rainforest in Peninsular Malaysia, to assess the correlation between LiDAR and field data at the individual-tree level for aboveground biomass (AGB) estimates. The model was developed using multiple regression analysis, with a non-linear power model being used to fit the predictive models. The AGB model was developed based on estimated AGB at the field site and LiDAR data using the following methodology; (1) pooling of both field sample and LiDAR data, using ANCOVA to justify this approach, (2) selection of independent variables, (3) regression model development and (4) model assessment and validation. LiDAR height percentile (h80) and crown width (CW) measurement were found to best fit the data as evidenced by Adj-R² value of 0.63, root mean square error (RMSE) of the model
of 14.8% and analysis of the residuals. This study provides an analytic framework for developing a predictive LiDAR-AGB model at tree level as LiDAR derived information helps natural resource managers to provide details of forest that could be derived from the biomass assessment to improve management decisions.