Gaussian process regression for forest attribute estimation from airborne laser scanning data

TL;DR

This paper demonstrates that Gaussian process regression improves the accuracy and provides reliable uncertainty quantification for estimating multiple forest attributes from airborne laser scanning data, outperforming traditional methods.

Contribution

It introduces a GPR-based framework for simultaneous estimation of forest attributes and uncertainty quantification, with better accuracy and computational efficiency than existing methods.

Findings

GPR improves RMSE by 4.6% over kNN

GPR provides well-calibrated credible intervals

Framework remains effective with smaller training sets

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.