Above-ground biomass estimation from LiDAR data using random forest algorithms - Torre-Tojal, Leyre; Bastarrika, Aitor; Boyano, Ana; Lopez-Guede, Jose Manuel; Graña, Manuel (Journal of computational science, 2022)
•The paper explores Random Forest models to estimate the biomass.•In the paper real data of Pinus Radiata species in a specific area of the Basque Country (Spain) were used.•The LiDAR data used to create the models are of low-density•The dataset used to create the models has a very small size.•Two models were obtained showing an R2 higher than 0.7 and a prediction between 16–18 % higher than those given by the Basque Government. Random forest (RF) models were developed to estimate the biomass for the Pinus radiata species in a region of the Basque Autonomous Community where this species has high cover, using the National Forest Inventory, allometric equations and low-density discrete LiDAR data. This article explores the tuning for RF hyperparameters, obtaining two models with an R2 higher than 0.7 using 2-fold cross-validation. The models selected were applied in Orozko, a municipality with more than 5000 ha of this species, where the model predicts a biomass of 1.06–1.08 Mton, which is between 16–18 % higher than the biomass predicted by the Basque Government.