Modelling of knots in logs

Samson, M. V.

doi:10.1007/bf00193865

Cited by 25 publications

(20 citation statements)

References 3 publications

(2 reference statements)

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“…Recent Swedish studies have focused on finding appropriate stem characteristics 522 J. Uusitalo & J. Isotalo combined with some stand characteristics in predicting knottiness at a general level (Bjö rklund & Petersson, 1999;Moberg, 1999). There have also been attempts to construct models to predict the shape and characteristics of the dead/sound knot border (Kärkkäinen, 1986;Moberg, 2000) and to link tree growth models to simulated sawing (Samson, 1993;Mäkinen & Song, 2002;Ikonen et al, 2003;Mäkelä & Mäkinen, 2003).…”

Section: Introductionmentioning

confidence: 97%

Predicting knottiness of Pinus sylvestris for use in tree bucking procedures

Uusitalo

Isotalo

2005

Scandinavian Journal of Forest Research

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The quality and accompanying value of Scots pine (Pinus sylvestris L.) lumber varies markedly in the Nordic countries. Consequently, tree bucking has a great impact on the economic result. The objectives of this study were (1) to analyse which knot characteristics usually reduce pine lumber sawn from the butt-logs to the next quality grade, (2) to determine the most appropriate characteristics that could be used in predicting lumber quality, and (3) to develop models that could be used in practice for predicting the probability of certain quality grades. The study was based on field experiments and test sawing data on 100 Scots pine stems from south-western Finland. Since the results showed that the maximum dead knot is the most crucial knot characteristic when the first cross-cutting point is determined, models were created that predict the height of the first dead knot that lowers grade A to B (B-grade dead knot). Both early growth rate and dead branch height should be measured to predict pine butt-log quality. Early growth rate seems to be appropriate in predicting between-stand variation, while dead branch height is appropriate for predicting within-stand variation.

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Section: Introductionmentioning

confidence: 97%

Predicting knottiness of Pinus sylvestris for use in tree bucking procedures

Uusitalo

Isotalo

2005

Scandinavian Journal of Forest Research

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“…the knot structure, has to be extracted. An effective way to do this is to use knot models (Samson 1993, Grundberg 1994). Grundberg has developed image analysis algorithms which use knot models to extract the knot structure of CTscanned Scots pine (Pinus sylvestris L.) saw logs automatically.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of knot parameters measured automatically in CT-images of Norway spruce ( Picea abies (L.) Karst.)

Oja

2000

Holz als Roh- und Werkstoff

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Knot parameters measured automatically in CT-images of Norway spruce have been evaluated on logs from four plots in the Norway spruce stem bank. The evaluation was made through comparisons between measurements on real centre boards and measurements on reconstructed boards. The reconstructions were based on the automatically measured parameter descriptions of knots and log shape and on simulated sawing. The diameter of knots was measured with a mean error of )2 mm and a standard deviation of 3 mm. It was shown that the reconstructed centre boards were well in accordance with the real centre boards in terms of position, number, size and type of knots. The exception is knot type for small knots in large butt logs. Bestimmen der Astdurchmesser von Fichtenholz auf Grund automatischer CT-BilderAstdurchmesser wurden aus automatisch aufgenommenen CT-Bildern von nordischem Fichtenholz gescha Ètzt. Das Rundholz wurde aus 4 Auszu Ègen der norwegischen Datenbank fu Èr Fichten-Rundholz entnommen. Verglichen wurden Messungen an realen Mittelbrettern und an Rekonstruktionen aus den CT-Bildern. Die Rekonstruktionen gru Èndeten sich auf die automatisch gemessenen Parameter fu Èr die A È ste und die Rundholzform, sowie auf eine Simulation des Sa Ègevorgangs. Die Messung der Astdurchmesser erfolgte mit einem mittleren Fehler von 2 mm und einer Standardabweichung von 3 mm. Die Rekonstruktion der Mittelbretter war in guter U È bereinstimmung mit den tatsa Èchlichen Brettern in Bezug auf Position, Anzahl, Gro Èûe und Asttyp. Ausnahmen gab es bei kleinen Asttypen in groûen Erdstammabschnitten.

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“…This is important because dead knots can have a detrimental effect on end-use properties and therefore are an important wood quality parameter (Vestol and Hoibo 2000). To account for the presence of dead knots, previous models have considered the live portion of a knot as being conical in shape and the dead portion as being cylindrical (Samson 1993;Trincado and Burkhart 2008). This implied that once the maximum diameter was reached, the branch died and the diameter remained constant until self-pruning occurred.…”

Section: Discussionmentioning

confidence: 98%

“…Previous studies by Samson (1993) and Lemieux et al (1997a) reported some ovality in knot cross sections in black spruce, with the vertical diameter approximately 10% to 15% greater than the horizontal diameter. However, because external branch diameter was only recorded in the vertical direction, we chose to model the knot profile along the same axis.…”

Section: Knot Profilesmentioning

confidence: 95%

Modelling knot morphology as a function of external tree and branch attributes

Duchâteau

Longuetaud²,

Mothe³

et al. 2013

Can. J. For. Res.

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Existing models for describing knot morphology are typically based on polynomial functions with parameters that are often not biologically interpretable. Hence, they are difficult to integrate into tree growth simulators due to the limited possibilities for linking knot shape to external branch and tree characteristics. X-ray computed tomography (CT) images taken along the stems of 16 jack pine (Pinus banksiana Lamb.) trees and 32 black spruce (Picea mariana (Mill.) B.S.P.) trees were used to extract the three-dimensional shape of 3450 and 11 276 knots from each species, respectively. Using a nonlinear approach, we firstly fitted a model of knot geometry adapted from a Weibull function. Separate equations were used to describe both the curvature and the diameter of the knot along its pith. Combining these two equations gave an accurate representation of knot shape using only five parameters. Secondly, to facilitate the integration of the resulting model into a tree growth simulator, we extracted the parameters obtained for each knot and modelled them as functions of external branch and tree characteristics (e.g., branch diameter, insertion angle, position in the stem, tree height, and stem diameter). When fitted to a separate data set, the model residuals of the black spruce knot curvature equation were less than 2.9 mm in any part of the knot profile for 75% of the observations. The corresponding value from the diameter equation was 2.8 mm. In jack pine, these statistics increased to 5.4 mm and 3.2 mm, respectively. Overall, the ability to predict knot attributes from external tree-and branch-level variables has the potential to improve the simulation of internal stem properties.Résumé : Les modèles utilisés actuellement pour décrire la morphologie des noeuds sont généralement basés sur des fonctions polynomiales dont les paramètres sont difficilement interprétables. Ainsi, ils sont difficiles à intégrer dans les simulateurs de croissance en raison de la difficulté à relier la forme des noeuds et les caractéristiques externes des branches et des arbres. Des images de tomographie à rayons-X le long des tiges de 16 pins gris (Pinus banksiana Lamb.) et de 32 épinettes noires (Picea mariana (Mill.) BSP) ont été utilisées pour extraire, respectivement pour chaque espèce, la forme tridimensionnelle de 3450 et 11 276 noeuds. Dans un premier temps, nous avons utilisé une approche de modélisation non linéaire basée sur une fonction de Weibull afin de décrire la géométrie de chaque noeud. Des équations distinctes ont été utilisées afin de décrire à la fois l'évolution de la courbure et du diamètre du noeud de la moelle à l'écorce. Cette combinaison de deux équations a permis une représentation exacte de la forme noeud en utilisant seulement cinq paramètres. Dans un second temps, les paramètres obtenus pour chacun des noeuds ont été extraits et modélisés en fonction des caractéristiques externes de la branche et de l'arbre (p. ex. le diamètre de la branche, l'angle d'inclinaison, la position dans la tige, la hauteur et le ...

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Modelling of knots in logs

Cited by 25 publications

References 3 publications

Predicting knottiness of Pinus sylvestris for use in tree bucking procedures

Predicting knottiness of Pinus sylvestris for use in tree bucking procedures

Evaluation of knot parameters measured automatically in CT-images of Norway spruce ( Picea abies (L.) Karst.)

Modelling knot morphology as a function of external tree and branch attributes

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