Comparison of whole-tree wood property maps based on near-infrared spectroscopic calibrations utilizing data at different spatial resolutions

Abstract

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.

Mapping and modeling within-tree variation for loblolly pine pulp yield and lignin content

“…The within-tree variation of pulp yield and lignin content in loblolly pine trees aged 13 and 22 years was examined utilizing wood property data obtained using the spatialinterpolated approach described in Schimleck et al [56]. This methodology used whole-tree wood property and NIR spectral data (originally collected in 10-mm increments at multiple radial positions and heights but basal area weighted to provide a single spectrum per tree) to successfully generate calibrations for pulp yield and lignin content.…”

“…"spatial-interpolated"). The approach is described in Schimleck et al [56] whereby the objective is to take the individual 10-mm radial spectra (2,569 in total) and weight them by their basal area in order to obtain one spectrum that represents the individual tree (36 in total). The representative spectra for each tree are then used to developed calibration models for the properties measured at the whole tree level.…”

“…We utilized the spatial-interpolated approach described in Schimleck et al [56] to generate NIR-predicted wood property data that was subsequently used to examine within-tree variation of pulp yield and lignin content in loblolly pine trees aged 13 and 22 years. This approach relies on utilizing wood property data collected on wholetree composite samples and NIR spectra collected at much high resolution (10-mm increments from radial strips sampled at multiple heights) but basal area weighted to provide a single spectrum per tree.…”

“…Most of these studies of within-tree variation involved using a "spatial-specific" model [56] as wood property data and the corresponding spectral data are measured from pith-to-bark and at multiple heights at the same spatial resolution (e.g., 10-mm radial increments). An alternative approach is to use a "spatial-interpolated" model [56] which involves "measuring a property at 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." Recently, Schimleck et al [56] used both approaches to examine within-tree variation of density and coarseness and found that patterns of variation for maps obtained using the two approaches were comparable.…”

“…An alternative approach is to use a "spatial-interpolated" model [56] which involves "measuring a property at 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." Recently, Schimleck et al [56] used both approaches to examine within-tree variation of density and coarseness and found that patterns of variation for maps obtained using the two approaches were comparable. Density was measured using SilviScan (= spatial-specific model data) and on chips produced from bolts (= spatial-interpolated model data).…”