ESTIMATING STAND-LEVEL STRUCTURAL AND BIOPHYSICAL VARIABLES OF LOWLAND DIPTEROCARP FOREST USING AIRBORNE LIDAR DATA

Abstract

Light Detection and Ranging (LiDAR) has been used in a wide range of applications including forestry. This study aims to investigate the potential use of airborne lidar scanning (ALS) data in estimating standlevel structural and biophysical variables of lowland dipterocarp forest. Five forest variables, namely mean height (Hm), basal area (BA), square mean diameter (Dg), stand density (S) and above ground biomass (AGB), were tested based on 40 field plots. A total of 34 ALS metrics were generated and tested for model development. A multiple linear regression approach was performed to generate the best model for estimating the variables. Models for BA and AGB gave strong precisions, with an adjusted-R² of 0.77 and 0.82 and RMSE of 5.45 m² ha^-1 and 71.12 Mg ha^-1. The Hm and Dg gave moderate precisions, with R² of 0.61 and 0.44 and RMSE of 2.35 m and 6.07 cm, respectively, while S gave the lowest precision with an adjusted-R² of 0.27 and RMSE of 149.48 stem ha^-1. This study demonstrated that ALS data performs better in estimating stand-level structural and biophysical parameters of tropical forest, which is important for forest managers towards better monitoring, planning and managing their forests by using this technology