期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 57, 期 6, 页码 3361-3369出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2018.2883495
关键词
Area-based approach (ABA); forest inventory; Gaussian process (GP); light detection and ranging (LiDAR); machine learning
类别
资金
- Finnish Cultural Foundation
- North Savo Regional Fund
- Academy of Finland through the Finnish Centre of Excellence of Inverse Modelling and Imaging [270174, 295341, 295489, 303801]
- Strategic Research Council through the FORBIO Project [293380]
- Academy of Finland (AKA) [295489, 295341, 295489, 295341] Funding Source: Academy of Finland (AKA)
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.
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