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首页> 外文期刊>Holzforschung >Comparison of whole-tree wood property maps based on near-infrared spectroscopic calibrations utilizing data at different spatial resolutions
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Comparison of whole-tree wood property maps based on near-infrared spectroscopic calibrations utilizing data at different spatial resolutions

机译：基于近红外光谱校准的全树木材特性图的比较，利用不同空间分辨率的数据

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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.
机译：从放射状条带或木质圆盘获得的近红外（NIR）光谱或NIR高光谱图像提供了一种经济高效的方法，可用于检查树木内木材的特性变化。用于木材性能预测的校准至关重要，可以使用两种根本不同的方法来获得。一种涉及使用空间特定模型，其中以相同的分辨率测量木材特性数据和相应的光谱数据，以进行校准和预测，例如径向增量为10毫米。另一个提供空间插值模型，并涉及在较大范围内（例如，整棵树，针对代表等效比例的NIR光谱校准此数据，然后使用校准来更高分辨率地预测属性。为了了解这些方法对树内变异的后续模式的影响，基于使用两种不同方法获得的数据，比较了整棵树的风干密度（ADD）和粗糙度图。 ADD和粗糙度变化的模式具有可比性，表明这两种方法均可用于检查树内变化。空间插值模型具有明显的优势。由于基于整棵树（或盘状）样本，它们极大地降低了木材特性分析的成本，并允许开发成本昂贵且难以衡量的特性图（例如纸浆产量）。

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