Acoustic-Based Prediction of End-Product-Based Fibre Determinates within Standing Jack Pine Trees

Abstract

The objective of this study was to specify, parameterize, and evaluate an acoustic-based inferential framework for estimating commercially-relevant wood attributes within standing jack pine (Pinus banksiana Lamb) trees. The analytical framework consisted of a suite of models for predicting the dynamic modulus of elasticity (m e ), microfibril angle (m a ), oven-dried wood density (w d ), tracheid wall thickness (w t ), radial and tangential tracheid diameters (d r and d t , respectively), fibre coarseness (c o ), and specific surface area (s a ), from dilatational stress wave velocity (v d ). Data acquisition consisted of (1) in-forest collection of acoustic velocity measurements on 61 sample trees situated within 10 variable-sized plots that were established in four mature jack pine stands situated in boreal Canada followed by the removal of breast-height cross-sectional disk samples, and (2) given (1), in-laboratory extraction of radial-based transverse xylem samples from the 61 disks and subsequent attribute determination via Silviscan-3. Statistically, attribute-specific acoustic prediction models were specified, parameterized, and, subsequently, evaluated on their goodness-of-fit, lack-of-fit, and predictive ability. The results indicated that significant (p ≤ 0.05) and unbiased relationships could be established for all attributes but d t . The models explained 71%, 66%, 61%, 42%, 30%, 19%, and 13% of the variation in m e , w t , s a , c o , w d , m a , and d r , respectively. Simulated model performance when deploying an acoustic-based wood density estimate indicated that the expected magnitude of the error arising from predicting d t , c o , s a , w t , m e, and m a prediction would be in the order of ±8%, ±12%, ±12%, ±13%, ±20%, and ±39% of their true values, respectively. Assessment of the utility of predicting the prerequisite w d estimate using micro-drill resistance measures revealed that the amplitude-based w d estimate was inconsequentially more precise than that obtained from v d (≈ <2%). A discourse regarding the potential utility and limitations of the acoustic-based computational suite for forecasting jack pine end-product potential was also articulated. Keywords: wood quality; dilatational stress wave velocity; absolute and relative error intervals; ResistographForests 2019, 10, 605 2 of 29 however, a priori knowledge of the internal fibre attributes, which are among the principal determinates underlying end-product quality (sensu [4]), are not readily observable or measurable within standing trees prior to harvest nor within the derived logs following harvest. Furthermore, forecasting end-product potential based on correlative relationships between external morphological tree characteristics and internal fibre attributes have yielded mixed results of generally limited utility [5]. Thus, apart from the implementation of a logistically-challenging destructive-based field sampling initiative combined with the subsequent expensive and time-consuming laboratory processing of extracted wood samples...

Development of Spatiotemporal Whole-Stem Models for Estimating End-Product-Based Fibre Attribute Determinates for Jack Pine and Red Pine

“…For jack pine, as part of a dual objective investigation examining the potential of nondestructive attribute estimation methods (e.g., acoustic velocity; [15,16]) and development of fibre attribute models (e.g., [9] and this study), two geographically separated jack pine thinning experiments established in northeastern (Sewell) and northcentral (Tyrol) Ontario were selected for sampling. At the Sewell site, 31 jack pine sample trees were selected within six variable-size plots that were established in two even-aged natural-origin semi-mature (≈60 year) monospecific stands.…”

“…Furthermore, the distributions of each of the absolute variates for both species were assumed to be approximately normal at each stem height position as inferred from the approximate equivalence of the mean and median values. Additional details regarding the sites in terms of their silvicultural histories, site productivity and mensurational characteristics specific to the sampled stands and destructively sampled trees can be found in [15,16] for jack pine and in [17,18] for red pine. The cross-sectional fibre attribute determination was completed at the Silviscan-3 processing laboratory located at the FPInnovations headquarters situated at the University of British Columbia in Vancouver, British Columbia, Canada.…”