Predicting knottiness of <i>Pinus sylvestris</i> for use in tree bucking procedures

Uusitalo, Jori; Isotalo, Jarkko

doi:10.1080/02827580500407109

Cited by 14 publications

(19 citation statements)

References 14 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, bucking simulations conducted by commercial bucking simulators are typically based only on stem dimensions giving most often large overestimates of valuable timber assortments due to the lack of external quality indicators. It is well known that external quality indicators affecting tree and stand value include tree defects such deviations from straightness or branchiness (Uusitalo and Isotalo 2005;Price et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Validating the predicted saw log and pulpwood proportions and gross value of Scots pine and Norway spruce harvest at stand level by Most Similar Neighbour analyses and a stem quality database

Malinen¹,

Kilpeläinen²,

Verkasalo³

2018

Silva Fenn.

View full text Add to dashboard Cite

Detailed pre-harvest information about the volumes and properties of growing stocks is needed for increased precision in wood procurement planning for just-in-time wood deliveries by cut-to-length (CTL) harvesters. In the study, the non-parametric Most Similar Neighbour (MSN) methodology was evaluated for predicting external quality of Scots pine and Norway spruce, expressed as stem sections fulfilling the saw log dimension and quality requirements of Finnish forest industry, as they affect the recovery of timber assortments and the value of a pre-harvest stand. Effects of external tree quality were evaluated using saw log recovery and saw log reduction caused by stem defects, as well as total timber value (â¬) and average unit value (â¬ m) in a stand. Root mean square error (RMSE) of saw log recovery and reduction were 9.12 percentile points (pp) for Scots pine and 6.38 pp for Norway spruce stands. In the unit value considerations, the predictions compared with measurements resulted in the RMSE of 3.50 â¬ m and the bias of 0.58 â¬ m in Scots pine stands and 2.60 â¬ m, and 0.35 â¬ m in Norway spruce stands, respectively. The presented MSN based approach together with the utilization of the external stem quality database included in the ARVO software could provide dimension and external quality predictions usable for pre-harvest assessment of timber stock at a stand level. This prediction methodology is usable especially in analyses where timber assortment recoveries, values and unit prices are compared when different bucking objectives are used.â3â3â3â3â3

show abstract

Section: Discussionmentioning

confidence: 99%

Validating the predicted saw log and pulpwood proportions and gross value of Scots pine and Norway spruce harvest at stand level by Most Similar Neighbour analyses and a stem quality database

Malinen¹,

Kilpeläinen²,

Verkasalo³

2018

Silva Fenn.

View full text Add to dashboard Cite

show abstract

“…The widths of the individual annual rings were manually measured from the pith to the bark by using image analysis software (ImageJ, Schneider et al 2012). The mean width of rings 11−20 from the pith was used in the modelling for describing the growth rate at the pole stage, which is critical for quantifying the branchiness of the lower stem (Uusitalo and Isotalo 2005).…”

Section: Stands and Field Measurementsmentioning

confidence: 99%

Predicting knottiness of Scots pine stems for quality bucking

et al. 2019

Self Cite

View full text Add to dashboard Cite

Stem shapes and wood properties are typically unknown at the time of harvesting. To date, approaches that integrate information about past tree growth into the harvesting and bucking process are rarely used. New models were developed and their potential demonstrated for stem bucking procedures for cut-to-length harvesters that integrate information about external and internal stem characteristics detected during harvesting. In total 221 stems were sampled from nine Scots pine (Pinus sylvestris L.) stands in Finland. The widths of rings 11−20 from the pith were measured using images taken from the end face of each butt log. The total volume of knots in each whorl was measured by using a 4D X-ray log scanner. In addition, 13 stems were test sawn, and the diameters of individual knots were measured from the sawn boards. A model system was developed for predicting the horizontal diameter of the thickest knot for each whorl along a stem. The first submodel predicts the knot volume profile from the stem base upwards, and the second submodel converts the predicted knot volume to maximum knot diameter. The results showed that the knottiness of stems of a given size may vary greatly depending on their early growth rate. The developed system will be used to guide logging operations to achieve more profitable bucking procedures.

show abstract

“…This includes the prediction of internal stem characteristics and lumber quality from external stem or log properties. Most evaluation methods currently in use are based on the prediction of log or lumber grade from external indicators, such as diameter at breast height (1.3 m, DBH), or height of the lowest dead branch (Uusitalo and Kivinen, 1998;Uusitalo and Isotalo, 2005). Modern industrial wood conversion simulators, however, could make use of much more detailed information about stem structure, both in sawing and pulping simulation (Lundgren, 2000;Lundqvist, 2002;Poukka et al, 2003;Pinto, 2004).…”

Section: Introductionmentioning

confidence: 97%