• Key message A novel non-destructive method has been developed to predict modulus of elasticity (MOE) of logs using measurements taken from cores extracted from discs. The trees were felled and cut into logs to allow validation of our method; however, similar results would be obtained if the cores were extracted from standing trees. The method shows that a single core from breast height is sufficient to predict MOE of logs, allowing early grading and sorting of logs for optimal use and processing. • Context Early estimation of log MOE allows efficient sorting and grading of logs which can improve the financial return and reduce wastage of wood. • Aims This work aims to predict the MOE of logs accurately from measurements taken on cores obtained from trees. • Methods The MOE of the logs was predicted using ultrasound measurements conducted on small segments obtained from cores using two different approaches: segment average and integral average. Sixty-eight trees from locally developed F 1 and F 2 hybrid pines (slash pine × Caribbean pine hybrids, Pinus elliottii var. elliottii × P. caribaea var. hondurensis (PEE × PCH cross)) were felled and cut into logs to validate the results. The Beam Identification by Non-destructive Grading (BING) method was used to measure a reference dynamic MOE (BING-MOE) for each log, and this was compared with the estimated log MOE. • Results Strong correlations $$(r=0.79 \mathrm\ {to}\ 0.91)$$ ( r = 0.79 to 0.91 ) between measured log MOE and estimated log MOE were obtained. This study revealed that a single core from the breast height (1.3 m) of a tree allows a good prediction of the log MOE. Tree height, spacing, and diameter had no significant effect on the log MOE prediction. The segment average MOE under predicts the BING-MOE, whereas the integral average method provides very little bias in the prediction. Furthermore, the prediction errors from the regression analysis for all logs were greater in the segment average method compared with the integral average method. • Conclusion This paper presented a novel non-destructive evaluation method capable of predicting the MOE of the whole log by combining data available from a single breast-height core extracted from standing trees with our integral average MOE approach. The integral average method predicted the BING-MOE more accurately with lower bias compared with other existing tools without any complex equipment, analysis, and statistical calibration for segregating out individual trees or stands. The method can potentially be used to predict the log MOE of other tree species and extended to predict MOE of individual boards that can be sawn from a log.

中文翻译：

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通过无损立树测量准确估计 log MOE

• 关键信息 已开发出一种新的非破坏性方法，可使用从圆盘中提取的岩心进行的测量来预测测井的弹性模量 (MOE)。树木被砍伐并切成原木以验证我们的方法；然而，如果从立树中提取核心，将获得类似的结果。该方法表明，来自胸高的单个核心足以预测原木的 MOE，允许对原木进行早期分级和分类，以实现最佳使用和加工。• 背景 对原木 MOE 的早期估计允许对原木进行有效的分类和分级，从而提高财务回报并减少木材浪费。• 目的 这项工作的目的是根据对从树木获得的核心进行的测量准确预测原木的 MOE。• 方法 使用两种不同的方法：分段平均和积分平均，对取自岩心的小分段进行超声测量，预测测井的 MOE。砍伐当地开发的 F 1 和 F 2 杂交松树（西洋松×加勒比松杂交种，Pinus elliottii var. elliottii × P. caribaea var. hondurensis（PEE × PCH 杂交））的 68 棵树并切成原木以验证结果。无损分级光束识别 (BING) 方法用于测量每个日志的参考动态 MOE (BING-MOE)，并将其与估计的日志 MOE 进行比较。• 结果 获得了测量 log MOE 和估计 log MOE 之间的强相关性 $$(r=0.79 \mathrm\ {to}\ 0.91)$$（r = 0.79 到 0.91）。这项研究表明，乳房高度的单个核心 (1. 3 m) 的树可以很好地预测 log MOE。树高、间距和直径对 log MOE 预测没有显着影响。段平均 MOE 低于预测 BING-MOE，而积分平均方法在预测中提供的偏差很小。此外，与积分平均法相比，分段平均法对所有测井的回归分析的预测误差更大。• 结论 本文提出了一种新的非破坏性评估方法，该方法能够通过将从立树提取的单个胸高核心的可用数据与我们的积分平均 MOE 方法相结合来预测整个原木的 MOE。与其他现有工具相比，无需任何复杂设备、分析、和统计校准，用于隔离个别树木或林分。该方法可以潜在地用于预测其他树种的对数 MOE，并扩展到预测可以从原木上锯开的单个木板的 MOE。