Mapping aboveground carbon stocks using LiDAR data in Eucalyptus spp. plantations in the state of Sao Paulo, Brazil

Abstract

Fast growing plantation forests provide a low-cost means to sequester carbon for greenhouse gas abatement. The aim of this study was to evaluate airborne LiDAR (Light Detection And Ranging) to predict aboveground carbon (AGC) stocks in Eucalyptus spp. plantations. Biometric parameters (tree height (Ht) and diameter at breast height (DBH)) were collected from conventional forest inventory sample plots. Regression models predicting total aboveground carbon (AGCt), aboveground carbon in commercial logs (AGCc), and aboveground carbon in harvest residuals (AGCr) from LiDAR-derived canopy structure metrics were developed and evaluated for predictive power and parsimony. The best models from a family of six models were selected based on corrected Akaike Information Criterion (AICc) and assessed by the root mean square error (RMSE) and coefficient of determination (R²-adj). The best three models to estimate AGC stocks were AGCt: R²-adj = 0.81, RMSE = 7.70 Mg.ha-1; AGCc: R²-adj = 0.83, RMSE = 5.26 Mg.ha-1; AGCr: R²-adj = 0.71, RMSE = 2.67 Mg.ha-1. This study showed that LiDAR canopy structure metrics can be used to predict AGC stocks in Eucalyptus spp. plantations in Brazil with high accuracy. We conclude that there is good potential to monitor growth and carbon sequestration in Eucalyptus spp. plantations using LiDAR.