Journal article
Accurate estimation of log MOE from non-destructive standing tree measurements
Annals of Forest Science, Vol.78(1), pp.1-15
2021
Abstract
• 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 F1 and F2 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 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.
Details
- Title
- Accurate estimation of log MOE from non-destructive standing tree measurements
- Authors
- Chandan Kumar (Corresponding Author) - Queensland Department of Agriculture and FisheriesSteven Psaltis (Author) - Queensland University of TechnologyHenri Bailleres (Author) - Queensland Department of Agriculture and FisheriesIan Turner (Author) - Queensland University of TechnologyLoic Brancheriau (Author) - University of MontpellierGary Hopewell (Author) - Queensland Department of Agriculture and FisheriesElliot J Carr (Author) - Queensland University of TechnologyTroy Farrell (Author) - Queensland University of TechnologyDavid J Lee (Author) - University of the Sunshine Coast, Queensland, Forest Industries Research Centre
- Publication details
- Annals of Forest Science, Vol.78(1), pp.1-15
- Publisher
- Springer-Verlag France
- DOI
- 10.1007/s13595-021-01041-8
- ISSN
- 1297-966X
- Organisation Unit
- Forest Research Institute; University of the Sunshine Coast, Queensland; Forest Industries Research Centre; Tropical Forests & People Research Centre
- Language
- English
- Record Identifier
- 99518306002621
- Output Type
- Journal article
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