摘要： Near-infrared (NIR) spectra or NIR-hyperspectral images obtained from radial strips or wood discs provide a cost-effective methodology for examining wood property variation within trees. The calibration used for wood property prediction is critical and can be obtained using two fundamentally different approaches. One involves using a spatial-specific model where wood property data and corresponding spectral data are measured at the same resolution for calibration and prediction, e.g. 10-mm radial increments. The other provides a spatial-interpolated model and involves measuring a property on a broad-scale, e.g. whole-tree, calibrating this data against NIR spectra representing the equivalent scale and then using the calibration to predict the property at higher resolution. To understand the impact of these approaches on subsequent patterns of within-tree variation, whole-tree air-dry density (ADD) and coarseness maps, based on data obtained using the two different approaches, were compared. Patterns of ADD and coarseness variation were comparable indicating that both approaches can be utilized to examine within-tree variation. Spatial-interpolated models have a distinct advantage; being based on whole-tree (or disc) samples, they greatly reduce the cost of wood property analysis and allow the development of maps for properties that are costly and difficult to measure, for example, pulp yield.