There are a number of wood properties which affect the quality of forest products such as lumber and pulp. Of these, wood density is considered by some to be the single most important physical characteristic because it is an excellent predictor of strength, stiffness, hardness, and paper-making capacities. Accurately assessing density in real-time can be a challenge for log supply managers wanting to segregate logs into different product classes based on density.
Mechanized harvesting machines are frequently fitted with computer technology and rudimentary sensor systems for measuring external stem dimensions. Research into technologies for measuring stem quality attributes is progressing on a number of fronts with varying levels of success. Some of these scanning technologies could be integrated into the design of mechanized harvesting systems.
In this dissertation:
• It is shown how Douglas-fir wood density can be predicted from near infrared (NIR) spectroscopy measurements of chain saw chips, ejected as a stem is cut into logs by a mechanized harvester,
• it is provided an analysis of the potential use of NIR technology for log segregation based on wood density,
• it is presented a general methodology to estimate log prices of Douglas-fir based on the net return obtained when logs of different wood density classes are processed and converted into end products (lumber and pulp),
• it is demonstrated how wood density could be included in optimal bucking procedures, and
• it is analyzed the effect of market requirements for density on log yields, total volume and revenue from a representative sample of Douglas-fir stems.
New sensor technologies are likely to lead to measurement, segregation and supply of a wider range of wood properties for forest product markets.