Chiang Mai Journal of Science

     Above-ground carbon at the stand level is often estimated from the combination of destructive and non-destructive methods using allometric equations. Today, terrestrial laser scanners provide three-dimensional (3D) data for forest structural assessments. We utilized the point clouds generated from light detection and ranging (LiDAR) to compute vegetation area index (VAI) and leaf area index (LAI), providing predictor variables besides the traditional above-ground carbon assessment. The stand above-ground carbon values of five forest plots were better using the VAI (R² = 0.66) than LAI (R² = 0.51) with the linear model fitting. However, the P-values are more than 0.05, which stands for no statistical significance. This study suggests scaling up to a large sample size and area, or downing the scale into individual trees that will promote better estimation of above-ground carbon in different forest types and conditions.