Comparison of Non-Destructive Tools for Measuring MOE of Southern Pine Trees

Various non-destructive technologies have been employed to assess log quality, facilitating informed decision regarding sorting, segregation and processing decisions. However, there is a lack of comprehensive comparative evaluations of non-destructive tools for MOE assessment in standing trees, specifically regarding their accuracy, efficiency, practical advantages, and their correlation with the quality of sawn boards. Therefore, this study first investigated the correlations between log MOE, log density, and average MOE and MOR of boards obtained from each log. Then, the study evaluated the predictive performance of estimating measured log MOE (Measured_MOE) and average board MOE using a destructive (HM200_MOE), a lab-based non-destructive (USMOE), and two field-deployable non-destructive (ST300_MOE and Resi_MOE) log MOE measurement tools. Finally, a comparison between the destructive and non-destructive tools for log stiffness measurement, focusing on deployability, efficiency, and prediction power was presented. The results showed that as tools become less destructive, their predictive power in estimating log MOE decreases. The destructive method, HM200_MOE, was the most precise (with an 0.78), in estimating log MOE due to its direct measurement on felled logs, followed by the non-destructive m\(\:{R}^{2}=\:\)ethods: USMOE (\(\:{R}^{2}=0.67\)), ST300_MOE (\(\:{R}^{2}=0.61\)), and Resi_MOE (\(\:{R}^{2}=0.57\)). USMOE explained the highest variability in the average board MOE (\(\:{R}^{2}=0.71\)), followed by HM200_MOE (\(\:{R}^{2}=0.69\)), ST300_MOE (\(\:{R}^{2}=0.64\)), and Resi_MOE (\(\:{R}^{2}=0.43\)). Green log density showed very weak correlation with log MOE and average board MOE. In contrast, resin extracted log density had moderate to strong correlations with log MOE (\(\:{R}^{2}\) from 0.39 to 0.63) and a high positive correlation with average board MOE (\(\:{R}^{2}=\:0.84\)).Destructive methods like HM200 are very precise but costly and unsuitable for field applications. Lab-based non-destructive tools such as USMOE can achieve a balance between precision and accuracy and potential field applicability but are slower, more expensive, and not field-deployable like Resi_MOE and ST300_MOE. Field-deployable tools, such as ST300 and Resi, offer practical solutions for operational forestry due to their efficiency and portability. However, their reduced predictive performance underscores the need for improved precision and accuracy through improved modelling or calibration. These findings highlight the trade-offs between predictive performance and operational efficiency, with the choice of tools depending on the required predictive performance, field deployability, sampling needs, and the cost of equipment and measurements.