A multidimensional statistical model for wood data analysis, with density estimated from CT scanning data as an example

Canadian Journal of Forest Research

Volumn 42, Issue 6, 2012, Pages 1038-1049

  Manceur, Ameur M ^a   Beaulieu, Jean ^b   Han, Liwen ^a   Dutilleul, Pierre ^a  

^a MCGILL UNIVERSITY   (Canada)

^b LAURENTIAN FORESTRY CENTRE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ANNUAL GROWTH; COMPUTED TOMOGRAPHY; CT SCANNING; DATA COLLECTION; ESTIMATED VARIANCE; GROWTH RINGS; MULTIPLICATIVE CONSTANTS; PICEA GLAUCA; PROPERTY MEASUREMENT; SCANNING DATA; STATISTICAL MODELS; VARIANCE-COVARIANCE; WHITE SPRUCE; WOOD DENSITY;

COMPUTERIZED TOMOGRAPHY; HARDWOODS; NORMAL DISTRIBUTION; SAMPLING;

FORESTRY;

COVARIANCE ANALYSIS; DENSITY; HEIGHT; SAMPLING; SPATIOTEMPORAL ANALYSIS; TOMOGRAPHY; TREE; VARIANCE ANALYSIS; WOOD;

DISTRIBUTION; FORESTRY; HARDWOODS; SAMPLING;

PICEA GLAUCA;

EID: 84862115703     PISSN: 00455067     EISSN: 12086037     Source Type: Journal    
DOI: 10.1139/X2012-053     Document Type: Article

References (43)

1. Antony, F., Schimleck, L., Daniels, R., Clark, A., III, and Hall, D. 2010. Modeling the longitudinal variation in wood specific gravity of planted loblolly pine (Pinus taeda) in the United States. Can. J. For. Res. 40(12): 2439-2451. doi:10.1139/X10-187.
2. Apiolaza, L., and Garrick, D. 2001. Analysis of longitudinal data from progeny test: some multivariate approaches. For. Sci. 47: 129-140.
3. Apiolaza, L., Gilmour, A., and Garrick, D. 2000. Variance modeling of longitudinal height data from a Pinus radiata progeny test. Can. J. For. Res. 30(4): 645-654. doi:10.1139/x99-246.
4. Boik, R.J. 1991. Scheffé's mixed model for multivariate repeated measures: a relative efficiency evaluation. Commun. Stat. (Theory Methods), 20(4): 1233-1255. doi:10.1080/03610929108830562.
5. Box, G.E.P. 1954a. Some theorems on quadratic forms applied in the study of analysis of variance problems, I. Effect of inequality of variance in the one-way classification. Ann. Math. Stat. 25(2): 290-302. doi:10.1214/aoms/1177728786.
6. Box, G.E.P. 1954b. Some theorems on quadratic forms applied in the study of analysis of variance problems, II. Effects of inequality of variance and of correlation between errors in the two-way classification. Ann. Math. Stat. 25(3): 484-498. doi:10.1214/aoms/1177728717.
7. Corriveau, A., Beaulieu, J., Mothe, F., Poliquin, J., and Doucet, J. 1990. Densité et largeur des cernes des populations d'Épinettes blanches de la région forestière des Grands Lacs et du Saint Laurent. Can. J. For. Res. 20(2): 121-129. doi:10.1139/x90-018.
8. Crowder, M.J., and Hand, D.J. 1990. Analysis of repeated measures. Chapman Hall, London, U.K.
9. Defo, M., Goodison, A., and Uy, N. 2009. A method to map withintree distribution of fibre properties using SilviScan-3 data. For. Chron. 85: 409-414.
10. Dutilleul, P. 1998. Tests of classification with simply, doubly and triply repeated measures. Invited talk at the Statistical Society of Canada Meeting, Sherbrooke, Quebec, 31 May - 3 June.
11. Dutilleul, P. 1999. The MLE algorithm for the matrix normal distribution. J. Statist. Comput. Simulation, 64(2): 105-123. doi:10.1080/00949659908811970.
12. Dutilleul, P. 2011. Spatio-temporal heterogeneity: concepts and analyses. Cambridge University Press, Cambridge, U.K.
13. Dutilleul, P., and Pinel-Alloul, B. 1996. A doubly multivariate model for statistical analysis of spatio-temporal environmental data. Environmetrics, 7(6): 551-565. doi:10.1002/(SICI)1099-095X(199611)7:6<551::AID-ENV233>3.0.CO;2-9.
14. Dutilleul, P., Herman, M., and Avella-Shaw, T. 1998. Growth rate effects on correlations among ring width, wood density and mean tracheid length in Norway spruce (Picea abies). Can. J. For. Res. 28(1): 56-68. doi:10.1139/x97-189.
15. Evans, R., Gartside, G., and Downes, G. 1995. Present and prospective use of Silviscan for wood microstructure analysis. Appita J. 95: 91-96.
16. Freyburger, C., Longuetaud, F., Mothe, F., Constant, T., and Leban, J.-M. 2009. Measuring wood density by means of X-ray computer tomography. Ann. For. Sci. 66(8): 804-813. doi:10.1051/forest/2009071.
17. Fritts, H.C. 1976. Tree rings and climate. Academic Press, London, U.K.
18. Gartner, B.L., Aloni, R., Funada, R., Lichtfuss-Gautier, A.N., and Roig, F.A. 2002. Clues for dendrochronology from studies of wood structure and function. Dendrochronologia, 20(1/2): 53-61. doi:10.1078/1125-7865-00007.
19. Gjerdum, P., and Bernabei, M. 2009. Three-dimensional spiral grain pattern in five large Norway spruce stems. Silva Fenn. 43: 457-464.
20. Han, L., Dutilleul, P., Prasher, S., Beaulieu, C., and Smith, D.L. 2008. Assessment of common scab-inducing pathogen effects on potato underground organs via computed tomography scanning. Phytopathology, 98(10): 1118-1125. doi:10.1094/PHYTO-98-10-1118. PMID:18943458.
21. Henze, N., and Zirkler, B. 1990. A class of invariant consistent tests for multivariate normality. Commun. Stat. (Theory Methods), 19 (10): 3595-3617. doi:10.1080/03610929008830400.
22. Herman, M., Dutilleul, P., and Avella-Shaw, T. 1999. Growth rate effects on intra-ring and inter-ring trajectories of microfibril angle in Norway spruce (Picea abies). IAWA J. 20: 3-21.
23. Jordan, L., Daniels, R., Clark, A., III, and He, R. 2005. Multilevel nonlinear mixed-effects models for the modeling of early wood and latewood microfibril angle. For. Sci. 51: 357-371.
24. Jungnikl, K., Goebbels, J., Burgert, I., and Fratzl, P. 2009. The role of material properties for the mechanical adaptation at branch junctions. Trees (Berl.), 23(3): 605-610. doi:10.1007/s00468-008-0305-9.
25. Kalender, W.A. 2000. Computed tomography. Publicis MCD Verlag, Munich, Germany.
26. Keunecke, D., Evans, R., and Niemz, P. 2009. Microstructural properties of common yew and Norway spruce determined with Silviscan. IAWA J. 30: 165-178.
27. Lindgren, L.O. 1991. Medical CAT-scanning: X-ray absorption coefficients, CT-numbers and their relation to wood density. Wood Sci. Technol. 25(5): 341-349. doi:10.1007/BF00226173.
28. Manceur, A.M. 2012. Separable variance-covariance structure: estimation, testing, and environmental application. Ph.D. thesis, Department of Plant Science, McGill University, Montreal, Que.
29. Mardia, K.V. 1974. Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhya B, 36: 115-128.
30. Mardia, K.V., and Goodall, C. 1993. Spatial-temporal analysis of multivariate environmental monitoring data. In Multivariate environmental statistics. Vol. 6. Edited by G.P. Patil and C.R. Rao. North-Holland, New York. pp. 347-385.
31. Molteberg, D., and Høibø, O. 2007. Modelling of wood density and fibre dimensions in mature Norway spruce. Can. J. For. Res. 37(8): 1373-1389. doi:10.1139/X06-296.
32. Muirhead, R.J. 1982. Aspects of multivariate statistical theory. Wiley, New York.
33. Njue, C. 2001. On the efficiency of testing procedures in the linear model for multivariate longitudinal data. Ph.D. thesis, Department of Statistics, University of Manitoba, Winnipeg, Man.
34. Pont, D., Brownlie, R., and Grave, J. 2007. Disc image-processing software for three-dimensional mapping of stem ring width and compression wood. N.Z. J. For. Sci. 37: 168-185.
35. Potvin, C., Lechowicz, M.J., and Tardif, S. 1990. The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71(4): 1389-1400. doi:10.2307/1938276.
36. Roy, A., and Khattree, R. 2005. Testing the hypothesis of a Kroneckar Product Covariance Matrix in multivariate repeated measures data. Paper 199-30, Statistics and Data Analysis, SUGI 30 Proceedings, Philadelphia, Pa.
37. Schott, J.R. 1997. Matrix analysis for statistics. Wiley, New York.
38. Tian, X., Cown, D., and McConchie, D. 1995. Modelling of Pinus radiata wood properties. Part II: Basic density. N.Z. J. Sci. 25: 214-230.
39. van Loan, C.F. 2000. The ubiquitous Kronecker product. J. Comput. Appl. Math. 123(1/2): 85-100. doi:10.1016/S0377-0427(00)00393-9.
40. Via, B., So, C., Groom, L., Shupe, T.F., Stine, M., and Wikaira, J. 2007. Within tree variation of lignin, extractives and microfibril angle coupled with the theoretical and near infrared modeling of microfibril angle. IAWA J. 28: 189-209.
41. Wang, E.I.C., and Micko, M.M. 1984. Wood quality of white spruce from north central Alberta. Can. J. For. Res. 14(2): 181-185. doi:10.1139/x84-035.
42. Wilhelmsson, L., Arlinger, J., Spångberg, K., Lundqvist, S.-O., Grahn, T., Hedenberg, Ö., and Olsson, L. 2002. Models for predicting wood properties in stems of Picea abies and Pinus sylvestris in Sweden. Scand. J. For. Res. 17(4): 330-350. doi:10.1080/02827580260138080.
43. Wolfinger, R.D. 1996. Heterogeneous variance: covariance structures for repeated measures. J. Agric. Biol. Environ. Stat. 1(2): 205-230. doi:10.2307/1400366.