Non-Destructive Tree Volume Estimation using Terrestrial Lidar Data in Teak Dominated Central Indian Forests

Abstract

Tree structure parameters like diameter at breast height (DBH), height and crown descriptors are essential to evaluate tree volume and biomass in the forest ecosystem. Destructive measurement is the most accurate method to estimate tree height and volume. However, in the recent past, advancements in LiDAR technology have enabled us to look at trees with precise 3D description. The main aim of this study is to extract different tree parameters (viz., DBH, height and tree volume) from 3D terrestrial LiDAR (TLS) data and in turn evaluate its accuracy in terms of manual destructive measurements. About five trees are scanned in multi-scan mode and destructive sampling measurements are carried out. Fitting methods such as Random Sample Consensus (RANSAC) based on circle and Quantitative Structure Model (QSM) based on the cylinder is used to extract the DBH and volume respectively from 3D point clouds. The results show that TLS based variables such as DBH (R2=0.995), height (R2=0.998) and volume (R2=0.958) are in good accordance with manual measurements. On average, tree volume bias for five trees is about 5.13 % from the manual measurements. The results of the study indicate that the TLS based measurement is one of the handy methods to obtain tree parameters without destructing the tree, with equal precision.