Estimation of forest biomass from light detection and ranging data by using machine learning

The use of data driven predictive systems is becoming widespread as innovations in machine learning techniques have allowed the training of increasingly sophisticated models via the available data. The light detection and ranging (LiDAR) remote sensing technique is being increasingly applied to obtain informative terrain maps, due to its ability to collect large amounts of data with satisfactory accuracy. This paper focuses on the application of machine-learning-based predictive systems for the extraction of biomass information from LiDAR data. Biomass information has inmense ecological and economical value. We demonstrate the estimation of the Pinus radiata biomass in the Arratia-Nervion region (Spain). Biomass estimation is considered a regression problem in which the ground truth for some specific sample sites is available. The promising results obtained in this study indicate that LiDAR data can be used to carry out detailed biomass mappings by the extrapolation of the models trained in this study.