Predição da biomassa aérea em plantações de Pinus taeda L. por meio de dados LiDAR aerotransportado

Abstract

The aim of this study was to predict aboveground biomass (AGB) from Pinus taeda L. plantations, located in South of Brazil, using LiDAR data, in-situ measurements and Random Forests (RF) modeling. Fifty regular sample plots were used, in which the diameter at the breast height (DBH) for all trees and about 15% of the heights were measured. Afterwards. forest stem volume was predicted using a fifth degree polynomial model, and used to calculate the field AGB values. To create the RF model we selected the H99TH, HCV and HSKEW LiDAR metrics, because they were not highly correlated to each other and presented the higher calculated value of Model Improvement Ratio (MIR). The estimative model of AGB presented a coefficient of determination (Adj.R2) of 0.98 and RMSE of 5.98%, while for the validation these values were 0.93 and 12.64%, respectively. It was possible to conclude that the RF and LiDAR-derived metrics were able to predict precisely the values of AGB in P. taeda plantation, therefore, it can be used as a helpful tool to forest management.