A principal component approach for predicting the stem volume in Eucalyptus plantations in Brazil using airborne LiDAR data

Abstract

Improving management practices in industrial forest plantations may increase production efficiencies, thereby reducing pressures on native tropical forests for meeting global pulp needs. This study aims to predict stem volume (V) in plantations of fast-growing Eucalyptus hybrid clones located in southeast Brazil using field plot and airborne Light Detection and Ranging (LiDAR) data. Forest inventory attributes and LiDAR-derived metrics were calculated at 108 sample plots. The best LiDAR-based predictors of V were identified based on loadings calculated from a principal component analysis (PCA). After selecting these best predictors using PCA, we developed multiple regression models predicting V from selected LiDAR metrics. Metrics related to tree height and canopy depth were most effective for V prediction, with an overall model coefficient of determination (adj. R2) of 0.87, and a root mean squared error (RMSE) of 27.60 m3 ha-1 (i.e. relative RMSE = 9.99 per cent). We used this model to map stem V of Eucalyptus hybrid clones across the full LiDAR data extent. The accuracy and precision of our results show that LiDAR-derived V is appropriate for updating Eucalyptus forest base maps and registries in the paper and pulp supply chain. However, further studies are necessary to evaluate and compare the cost of acquisition and processing of LiDAR data against conventional V inventory in this system.