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Gaussian Process Regression for Forest Attribute Estimation From Airborne Laser Scanning Data

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to)3361-3369
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number6
Early online date2018
Publication statusPublished - Jun 2019
Publication typeA1 Journal article-refereed


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 the 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 paper, 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 root mean square error over a state-of-the-art k-nearest neighbors (kNNs) 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.


  • Area-based approach (ABA), forest inventory, Gaussian process (GP), light detection and ranging (LiDAR), machine learning.

Publication forum classification

Field of science, Statistics Finland