Title: Gaussian process regression for forest attribute estimation from airborne laser scanning data,
Abstract: While the analysis of airborne laser scanning (ALS) data often provides
reliable estimates for certain forest stand attributes -- such as total volume
or basal area -- there is still room for improvement, especially in estimating
species-specific attributes. Moreover, while information on the estimate
uncertainty would be useful in various economic and environmental analyses on
forests, a computationally feasible framework for uncertainty quantifying in
ALS is still missing. In this article, the species-specific stand attribute
estimation and uncertainty quantification (UQ) is approached using Gaussian
process regression (GPR), which is a nonlinear and nonparametric machine
learning method. Multiple species-specific stand attributes are estimated
simultaneously: tree height, stem diameter, stem number, basal area, and stem
volume. The cross-validation results show that GPR yields on average an
improvement of 4.6\% in estimate RMSE over a state-of-the-art k-nearest
neighbors (kNN) implementation, negligible bias and well performing UQ
(credible intervals), while being computationally fast. The performance
advantage over kNN and the feasibility of credible intervals persists even when
smaller training sets are used.