Estimation of Individual Tree Biomass from Airborne Lidar Data using Tree Height and Crown Diameter

Carbon dioxide is one of main causes of global warming. Forests are important parts of terrestrial ecosystems and are considered major carbon sinks because the biomass in a forest is a measure of the amount of carbon dioxide storage. However, the common technique for deriving forest biomass is unproductive, costly and time-consuming. Recently, the use of airborne LiDAR data to estimate forest biomass has been rapidly increasing. In this study, we estimate individual tree biomass from airborne LiDAR data based on individual tree attributes. The local maxima and minima of CHM generated from LiDAR data were extracted as tree tops and crown boundaries, respectively. DBH was predicted from the relationship between DBH and crown diameter based on field measurements. Observed biomass was calculated from field data and predicted biomass was calculated using the information estimated from LiDAR data. Estimated tree height and individual tree biomass was compared with field measurements. Although errors occurred in tree height and DBH estimation, the results demonstrated high performance with an unmanaged forest. For individual tree biomass, the result showed that the regression line for predicted biomass was fitted to a proportional relationship and all R 2 of biomass components are above 0.6. A formula relating individual tree biomass, tree height and crown diameter is derived.