Localization of growth estimates using non-parametric imputation methods

Forest Ecology and Management

Volumn 256, Issue 4, 2008, Pages 674-684

  Sironen, S ^a   Kangas, A ^b   Maltamo, M ^a   Kalliovirta, J ^c  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

^b UNIVERSITY OF HELSINKI   (Finland)

^c Simosol Oy   (Finland)

Author keywords

Imputation; Individual tree growth; k Nearest neighbour; Localization; Non parametric; Norway spruce; Scots pine

Indexed keywords

DATABASE SYSTEMS; ECOLOGY; EIGENVALUES AND EIGENFUNCTIONS; FOOD PROCESSING; FORESTRY; LINEAR REGRESSION; REGRESSION ANALYSIS; TREES (MATHEMATICS);

BREAST HEIGHT; CORRELATION STRUCTURES; DIAMETER INCREMENT; ELSEVIER (CO); FINLAND; GROWTH MODELING; IMPUTATION METHODS; INDEPENDENT VARIABLES; K NEAREST NEIGHBOUR (K NN); LINEAR REGRESSION ANALYSIS; MAHALANOBIS DISTANCE (MD); NON PARAMETRIC ESTIMATES; NON-PARAMETRIC; NORWAY SPRUCE (PICEA ABIES [L.] KARST.); PARAMETRIC MODELING; PICEA ABIES; PINUS SYLVESTRIS L.; REGRESSION (R2); SCOTS PINES; SPATIAL COORDINATES; SPATIAL MEASURES;

ESTIMATION;

COMPARATIVE STUDY; CONIFEROUS TREE; ESTIMATION METHOD; GROWTH DETERMINATION; GROWTH MODELING;

DATA BASES; ECOLOGY; ESTIMATION; FORESTRY; GROWTH; PICEA ABIES; PINUS SYLVESTRIS; REGRESSION ANALYSIS;

EURASIA; EUROPE; FINLAND; NORTHERN EUROPE; SCANDINAVIA;

PICEA ABIES; PINUS SYLVESTRIS;

EID: 47049095198     PISSN: 03781127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foreco.2008.05.013     Document Type: Article

References (36)

1. Altman N.S. An introduction to kernel and nearest neighbor nonparametric regression. Am. Stat. 46 (1992) 175-184
2. Atkeson C.G., Moore A.W., and Schaal S. Locally weighted learning. Artif. Intell. Rev. 11 (1997) 11-73
3. Calama R., and Montero G. Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea): a calibrating approach. Silva Fenn. 39 1 (2005) 37-54
4. Cleveland W.S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Ass. 74 (1979) 829-836
5. Cleveland, W.S., Loader, C., 1994. Smoothing by local regression: principles and methods. Technical report 95.3. AT&T Bell Laboratories, Statistics Department, Murray Hill, NJ, 38 pp. Available at: http://cm.bell-labs.com/cm/ms/departments/sia/doc/smoothing.springer.pdf .
6. Eerikäinen K. Random parameter model for the relationship between stand age and tree height in Zambia. In: Pukkala T., and Eerikäinen K. (Eds). Growth and Yield Modelling of Tree Plantations in South and East Africa (1999), University of Joensuu, Faculty of Forestry, Research Notes 97 153-165
7. Gregoire T.G. Generalized error structure for forestry yield models. For. Sci. 33 2 (1987) 423-444
8. Gustavsen, H.G., Roiko-Jokela, P., Varmola, M., 1988. Kivennäismaiden talousmetsien pysyvät (INKA ja TINKA) kokeet. Suunnitelmat, mittausmenetelmät ja aineistojen rakenteet. The Finnish Forest Research Institute. Research Papers 292, 212 pp. (in Finnish).
9. Gustavsen, H.G., 1998. Volymtillväxten och övre höjdens utveckling i tall-dominerade bestånd i Finland-en utvärdering av några modellers validitet i nuvarande skogar. The Finnish Forest Research Institute. Research Papers 707, 190 pp. (in Swedish).
10. Holmström H., Nilsson M., and Ståhl G. Simultaneous estimations of forest parameters using aerial photograph-interpreted data and the k nearest neighbor method. Scand. J. For. Res. 16 (2001) 67-78
11. Hynynen, J., Ojansuu, R., Hökkä, H., Siipilehto, J., Salminen, H., Haapala, P., 2002. Models for Predicting Stand Development in MELA System. The Finnish Forest Research Institute. Research Papers 835, 116 pp.
12. Härdle W. Applied Nonparametric Regression (1989), Cambridge University Press, UK 323 pp.
13. Kalliola R. Suomen kasvimaantiede. (1973), WSOY 308 pp. (in Finnish)
14. Kangas A., and Korhonen K.T. Generalizing sample tree information with semiparametric and parametric models. Silva Fenn. 29 (1995) 151-158
15. Korhonen K.T., and Kangas A. Application of nearest-neighbour regression for generalizing sample tree information. Scan. J. For. Res. 12 (1997) 97-101
16. Laasasenaho J. Taper curve and volume functions for pine, spruce and birch. Commun. Inst. For. Fenn. 108 (1982) 72
17. Lappi J. Mixed linear models for analysing and predicting stem form variation of Scots pine. Commun. Inst. For. Fenn. 134 (1986) 69
18. Lappi J. Calibration of height and volume equations with random parameters. For. Sci. 37 (1991) 781-801
19. Lappi J. Metsäbiometrian menetelmiä. University of Joensuu. Silva Carelica 24 (1993) 182 (in Finnish)
20. LeMay V., Maedel J., and Coops N.C. Estimating stand structural details using variable-space nearest neighbour analyses to link ground data, forest cover maps, and landsat imagery. Rem. Sens. Environ. 118 (2008) 2578-2591
21. Mahalanobis P.C. On the generalized distance in statistics. Proc. Natl. Inst. Sci. India 12 (1936) 49-55
22. Maltamo M., and Kangas A. Methods based on k nearest neighbor regression in the prediction of basal area diameter distribution. Can. J. For. Res. 28 (1998) 1107-1115
23. Maltamo M., Malinen J., Kangas A., Härkönen S., and Pasanen A.-M. Most Similar Neighbour-based stand variable estimation for use in inventory by compartments in Finland. Forestry 76 4 (2003) 449-464
24. McRoberts R.E., Nelson M.D., and Wendt D.G. Stratified estimation of forest area using satellite imagery, inventory data, and the k-nearest neighbors technique. Rem. Sens. Environ. 82 (2002) 457-468
25. Moeur M., and Stage A.R. Most Similar Neighbor. An improved sampling inference procedure for natural resource planning. For. Sci. 41 (1995) 337-359
26. Moeur M., and Hershey R.R.. Preserving spatial and attribute correlation in the interpolation of forest inventory data. In: Lowell K., and Jaton A. (Eds). Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources. Papers Presented at the Third International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences in Quebec City, Canada. May 20-22, 1998, Ann Arbor Press, Chelsea, Michigan (1999) 419-430
27. Packalén P., and Maltamo M. The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs. Rem. Sens. Environ. 109 (2007) 328-341
28. Rasinmäki, J., Kalliovirta, J., Mäkinen, A., 2007. An adaptable simulation framework for multiscale forest resource data, submitted.
29. SAS, 1992. Technical Report P-229. SAS/STAT Software: Changes and Enhancements, Release 6.07. SAS Institute Inc., Cary, NC, 620 pp.
30. Searle S.R. Linear Models for Unbalanced Data (1987), Wiley, New York 536 pp.
31. Searle S.R., Case A.G., and McCulloch C.E. Variance Components (1992), Wiley, New York 501 pp.
32. Sironen S., Kangas A., Maltamo M., and Kangas J. Estimating individual tree growth with the k-nearest neighbour and k-Most Similar Neighbour methods. Silva Fenn. 35 4 (2001) 453-467
33. Sironen S., Kangas A., Maltamo M., and Kangas J. Estimating individual tree growth with nonparametric methods. Can. J. For. Res. 33 (2003) 444-449
34. Theodoridis S., and Koutroumbas K. Pattern Recognition. third edition (2006), Academic Press, USA 837 pp.
35. Tokola, T., Kangas, A., Kalliovirta, J., Mäkinen, A., Rasinmäki, J., 2006. SIMO-SIMulointi ja Optimointi uuteen metsäsuunnitteluun. Metsätieteen aikakauskirja 1/2006, 60-65 (in Finnish).
36. West P.W., Ratkowsky D.A., and Davis A.W. Problems of hypothesis testing of regression with multiple measurements from individual sampling units. For. Ecol. Manage. 7 (1984) 207-224