¶ principal component analysis (PCA)
| Authors | Ahlinder et al. |
| Country | Skogforsk (Research note) |
| Paper (PDF) | View paper |
| Category | Nursery & Establishment › Remote sensing & mapping |
genetic data analysis to select candidates for breeding and seed orchards
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¶ Overview
Principal component analysis (PCA) is a widely used multivariate statistical technique that reduces the dimensionality of complex datasets by transforming correlated variables into a smaller set of uncorrelated components — called principal components — that capture the maximum variance in the data. Each principal component is a linear combination of the original variables, ordered so that the first component explains the greatest variance, the second explains the next greatest (orthogonal to the first), and so on. PCA is a foundational tool in data exploration, pattern recognition, and the preprocessing of high-dimensional datasets, and it has broad applications across forestry, remote sensing, and wood science. In forestry remote sensing, PCA is commonly applied to hyperspectral or multispectral imagery to reduce data volume, suppress noise, and highlight meaningful spectral variation — for example, separating vegetation health signals from soil background effects, or distinguishing tree species based on their spectral reflectance profiles. It is also routinely used in near-infrared (NIR) spectroscopy workflows to explore the structure of spectral datasets before building predictive calibration models using techniques such as partial least squares (PLS) regression. In wood science, PCA helps researchers understand relationships between wood anatomical traits, chemical composition, and mechanical properties across large sample sets. In the context of LiDAR-based forest inventories, PCA can be applied to structural metrics derived from point clouds to classify stand types or predict forest attributes. Australian forestry researchers at institutions including the University of Melbourne, the ANU Fenner School, and CSIRO have employed PCA extensively in studies of plantation wood quality, forest species discrimination from aerial imagery, and the analysis of forest disturbance and recovery patterns from time-series remote sensing data.
¶ Details
| Field | Value |
|---|---|
| Company | Ahlinder et al. |
| Country | Skogforsk (Research note) |
| Type | Paper |
| Year | 2025 |
| FWPA RD&E | 9.0 |