Tree height derived from point clouds of UAV compared to airborne laser scanning and its effect on estimating biomass and carbon stock in tropical rain forest of Malaysia

Forests occupy about one-third of the land area of the earth and have been playing crucial role in regulating the adverse effect of increased emission of greenhouse gasses. Tropical rain forests have higher capacity to sequester carbon dioxide and hence play a role in stabilization of the concentration of greenhouse gasses in the atmosphere. Forest inventory parameters require accurate information for biomass and carbon stock estimation. However, acquiring of forest inventory parameters data especially tree height for estimation of biomass and carbon stock is often a major challenge in tropical forest. A conventional method that is data acquisition using handless tool is tiresome, labor intensive, not applicable in large area and cumbersome approach due to the complexity of tropical forest. On the other hand, data collection using LiDAR technology, is expensive and therefore not readily available. However, rapid advancement in photogrammetry technology in both hardware (i.e., Unmanned Aerial Vehicle) and software (i.e., image matching algorisms) led on data acquisition of fine spatial resolution imagery of less than a meter with notably improved revisit time at affordable cost. Therefore, this study aimed to assess the accuracy of measuring tree height using drone in comparison to that of Airborne LiDAR and assessing its effect on estimating forest biomass and carbon stock.