Estimation of aboveground carbon stocks in Eucalyptus plantations using LIDAR

In the context of global climate change, the quantification of carbon stocks in forests is essential, mainly because forests play a key role in balancing global carbon cycles. Existing methodologies for measuring carbon stocks in forests are constrained by budgetary issues and time, making it difficult to deliver large scale full inventories in short periods of time. ALS (Airborne Laser Scanning) technologies have been used as an efficient and flexible alternative to estimate carbon stocks in forests, due to its accuracy and efficiency when compared to conventional methods. This study evaluates the use of LIDAR (Ligth Detection and Ranging) ALS to estimate the amount of carbon in aboveground biomass of Eucalyptus plantations. We have used a multiple linear regression model and a suite of 68 predictor variables derived from discrete-return LIDAR data to create the carbon stock model. Six variables related of height and intensity from LiDAR cloud points were selected to build the final model (R²=0.93, Pearson´s correlation r= 0.97 and RMSE = 1.93 m³).