Canadian Forest Service Publications

Near infrared detection of decay in post-mountain pine beetle lumber. 2009. Stirling, R. Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, BC. Mountain Pine Beetle Working Paper 2009-08. 28 p.

Year: 2009

Available from: Pacific Forestry Centre

Catalog ID: 30807

Language: English

Series: Mountain Pine Beetle Working Paper (PFC - Victoria)

CFS Availability: Order paper copy (free), PDF (download)

Abstract

As mountain pine beetle-affected trees age in the forest, the incidence of decay increases and the type of decay is likely to change from limited amounts of heart-rot to increasing amounts of sap-rot. Given the scale of the current beetle epidemic in British Columbia, sawmills can expect to process increasing amounts of sap-rot in post-mountain pine beetle wood. In the normal process of grading lumber, a grader visually inspects the lumber, and places the small amounts of visibly decayed lumber into lower grades. However, increasing amounts of decay, the speed at which lumber is produced from a modern planer mill, and the presence of blue stain will make it more difficult for graders to visibly detect decay, other than advanced pocket rot or similar defects. Automated grading systems that use colour cameras are able to do a reasonable job of detecting decay. This project investigated whether the use of visible/near infrared spectroscopy to identify decay in beetle-affected lumber could improve decay detection. Visible/near infrared spectroscopy was able to differentiate sound and decayed wood using a small beam centred on well-defined sound and decayed areas. However, when a larger beam was used, spectra often contained information from adjacent sound and decayed regions. This resulted in poorer classification. In all models the visible region had a greater influence than the near infrared region. In fact, models developed on colourimetric data alone (Lab*) performed similarly to those developed over the entire visible/near infrared range. None of the developed models were able to detect decay more accurately than existing automated grading systems.

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