A preliminary study of the suitability of deep learning to improve LiDAR-derived biomass estimation

Abstract

Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information about forest structure. Biophysical models have taken advantage of the use of LiDAR-derived information to improve their accuracy. Multiple Linear Regression (MLR) is the most common method in the literature regarding biomass estimation to define the relation between the set of field measurements and the statistics extracted from a LiDAR flight. Unfortunately, there exist open issues regarding the generalization of models from one area to another due to the lack of knowledge about noise distribution, relationship between statistical features and risk of overfitting. Autoencoders (a type of deep neural network) has been applied to improve the results of machine learning techniques in recent times by undoing possible data corruption process and improving feature selection. This paper presents a preliminary comparison between the use of MLR with and without preprocessing by autoencoders on real LiDAR data from two areas in the province of Lugo (Galizia, Spain). The results show that autoencoders statistically increased the quality of MLR estimations by around 15–30%.