Biomass estimation using LiDAR data

Abstract

Forest ecosystems play a very important role in carbon cycle because they suppose one of the biggest carbon reservoirs and sinks. Estimating the aboveground forest biomass is critical to understand the global carbon storage process. Different models to estimate aboveground biomass in the Pinus radiata specie in a specific region of Spain have been developed, using, exclusively, public and accessible data with low point density gathered periodically from Light Detection and Ranging (LiDAR) flights. The point clouds data were processed to obtain metrics considered as predictive variables and afterwards, the multiple regression technique has been applied to generate the biomass estimation models. The best models explain 76% of its variability with a standard error of 0.26 ton/ha in logarithmic units. The methodology can be considered as highly automated and extensible to other territories with similar characteristics. Our results support the use of this approach for more sustainable management of forest areas.