Email this article GENERALIZED HEIGHT-DIAMETER MODELS FOR Acacia mangium Willd. PLANTATIONS IN SOUTH SUMATRA
DOI: 10.20886/ijfr.2010.7.1.1-19Abstract
The aim of this study was to develop a generalized height-diameter model for predicting tree height of Acacia mangium plantations in South Sumatra that could account for the variability of site and stand conditions. Six commonly used non-linear growth functions (i.e. Gompertz, Chapman-Richards, Lundqvist-Korf, Weibull, modified logistic, and exponential) were selected as candidate base models and were fitted to individual tree’s height-diameter data of A.mangium plantations. A total of 13,302 trees collected from permanent sample plots with various spacing , stand age, and site quality were available for this study. The data were split into two sets: one set being the majority (75%) was used to estimate model parameters and the remaining data set (25%) was used to validate the models. The results showed that the six base models produced almost identical fits with a relatively high root mean squared error (± 3.4 m) and a relatively low proportion of the total variation in obser ved tree height (52.5 - 53.4%). The Lundqvist-Korf (LK) model performed slightly better than the other models based on the goodness of fit as well as bias and standard errors of the predictions. This LK model can be fitted easily and provided more satisfactory fit when additional variables were included into the model, hence was selected as the base model. Introducing stand variables into the selected base model resulted in a significant improvement of the accuracy for predicting heights. The root mean squared error decreased by the value between 0.5564 and 1.4252 m and the proportion of variation explained by the model increased by the value between 13.88 and 33.21%. The best improvement based on fit and model validation was achieved by the generalized height-diameter model with inclusion of stand age and site index.
Keywords
Full Text:
PDFReferences
Arabatzis, A.A. and H.E. Burkhart. 1992. An evaluation of sampling methods and model forms for estimating height-diameter relationships in loblolly pine plantations. Forest Science 38: 192-198.
Bi, H., V. Jurskis, and J. O’Gara. 2000. Improving height prediction of regrowth eucalypts by incorporating the mean size of site trees in a modified Chapman-Richards equation. Australian Forestry 63: 255-264.
Borders, B.E. and W.D. Patterson. 1990. Projecting stand tables: a comparison of the Weibull diameter distribution method, a percentile-based projection method, and a basal area growth projection method. Forest Science 36: 413-424.
Chapman, D.G. 1961. Statistical problems in dynamics of exploited fisheries populations. In: Newman, J. (Ed ). Mathematical Statistics and Probability. University of California Press, Berkeley, pp. 153-168.
Clutter, J.L., J.C.Fortson, L.V. Pienaar, G.H. Brister, and R .L. Bailey. 1983. Timber Management: A Quantitative Approach. Wiley, New York. 333p.
Colbert, K.C., D.R . Larsen, and J.R . Lootens. 2002. Height-diameter equations for thirteen Midwestern Bottomland hardwood species. Northern Journal of Applied Forestry 19: 171-176.
Curtis, R .O. 1967. Height-diameter and height-diameter-age equations for second- growth douglas-fir. Forest Science 13: 365-375.
Dorado, F.C., U. Diéguez-Aranda, M.B. Anta, M.S. Rodríguez, and K. Gadow. 2006. A generalised height-diameter model including random components for radiata pine plantations in northwestern Spain. Forest Ecolog y and Management 229:202-213.
Draper, N.R . and H. Smith. 1998. Applied Regression Analysis (3rd Edition). John Wiley and Sons, New York. 736p.
Eerikäinen, K. 2003. Predicting the height-diameter pattern of planted Pinus kesiya stands in Zambia and Zimbabwe. Forest Ecolog y and Management 175: 355-366.
Fang , Z. and R .D. Bailey. 1998. Height-diameter models for tropical forests on Hainan Island in southern China. Forest Ecolog y and Management 110: 315-327.
Flawelling , J. and R . de Jong. 1994. Considerations in simultaneous cur ve fitting for repeated height-diameter measurements. Canadian Journal of Forest Research 24: 1408-1414.
Hilt, D.E. and M.E. Dale. 1982. Height prediction equations for even-aged upland oak stands. USDA Forest Ser vice, RP-NE-493. 9p.
Hökkä, H. 1997. Height-diameter cur ves with random intercepts and slopes for trees growing on drained peatlands. Forest Ecolog y and Management 97: 63-72.
Huang , S. 1999. Ecoregion-based individual tree height-diameter models for lodgepole pine in Alberta. Western Journal of Applied Forestry 14: 186-193.
Huang , S., S.J. Titus and D.P. Wiens. 1992. Comparison of nonlinear height-diameter functions for major Alberta tree species. Canadian Journal of Forest Research 22:1297-1304.
Lanner, R .M. 1985. On the insentivity of height growth to spacing. Forest Ecology and Management 13: 143-148.
Lappi, J. 1997. A longitudinal analysis of height/diameter cur ves. Forest Science 43: 555-570.
Larsen, D.R . and D.W. Hann. 1987. Height-diameter equations for seventeen tree species in southwest Oregon. Oregon State University Forest Research Laboratory, Cor vallis. Research Paper 49. 16p.
Lei, Y. and B.R . Parresol. 2001. Remarks on height-diameter modeling. USDA Forest Ser vice, RN-SRS-10. 6p.
Mehtätalo, L. 2004. A longitudinal height-diameter model for Nor way spruce in Finland. Canadian Journal of Forest Research 34: 131-140.
Mehtätalo, L. 2005. Height-diameter models for Scots pine and birch in Finland. Silva Fennica 39: 55-66.
Moore, J.A., L. Zhang , and D. Stuck. 1996. Height-diameter equations for ten tree species in the Inland Northwest. Western Journal of Applied Forestry 11: 132-137.
Parresol, B.R . 1992. Baldc ypress heig ht-diameter equations and their prediction confidence inter vals. Canadian Journal of Forest Research 22: 1429-1434.
Peng , C., L. Zhang , and J. Liu. 2001. Developing and validating nonlinear height- diameter models for major tree species of Ontario’s boreal forests. Northern Journal of Applied Forestry 18: 87-94.
Pienaar, L.V., H.H. Page, and J.W. Rheney. 1990. Yield prediction for mechanically site- prepared slash pine plantations. Southern Journal of Applied Forestry 14: 104-109.
Ratkowsky, D.A. 1990. Handbook of Nonlinear Regression Models. Marcel Dekker, New York. 241p.
Ratkowsky, D.A. and T.J. Reedy. 1986. Choosing the near-linear parameters in the four-parameter logistic model for radioligand and related assays. Biometrics 42:575-582.
Richards, F.J. 1959. A flexible growth function for empirical use. Journal of Experimental Botany 10: 290-300.
Sánchez, C.A .L., J.G. Varela, F.C. Dorado, A .R . Alboreca , R .R . Soalleiro, J.G.A. González, and F.S. Rodríguez. 2003. A height-diameter model for Pinus radiata
D. Don in Galicia (Northwest Spain). Annals of Forest Science 60: 237-245.
SAS Institute Inc. 2005. SAS/STAT User’s Guide, Version 9.1. SAS Institute Inc, Cary, NC. 5121p.
Sharma, M. and S.Y. Zhang. 2004. Height-diameter models using stand characteristics for Pinus banksiana and Picea mariana. Scandinavian Journal of Forest Research 19: 442-451.
Smith, D.M., B.C. Larson, and M.J. Kelty. 1997. The Practice of Silviculture: Applied Forest Ecolog y (9th Edition). John Wiley, New York. 537p.
Smith, W.D. and M.R . Strub. 1991. Initial Spacing : How many trees to plant. In: Duryea, M.L. and P.M. Dougherty (Eds). Forest Regeneration Manual. Kluwer Academic Publishers, Netherlands, pp. 281-289.
Stage, A.R . 1963. A mathematical approach to polimorphic site index cur ves for grand fir. Forest Science 9: 167-180.
Staudhammer, C. and V. Lemay. 2000. Height prediction equations using diameter and stand density measures. The Forestry Chronicle 76: 303-309.
Temesgen, H. and K. Gadow. 2004. Generalised height-diameter models - an application for major tree species in complex stands of interior British Columbia. European Journal of Forest Research 123: 45-51.
Wagner, R .G. and S.R . Radosevich. 1991. Neighborhood predictors of interspecific competition in yound Doug las-fir plantations. Canadian Journal of Forest Research 21: 821-828.
Wang , C. and D.W. Hann. 1988. Height-diameter equations for sixteen tree species in the central western Willamette Valley of Oregon. Oregon State University Forest Research Laboratory, Research Paper 51. 12p.
Refbacks
- There are currently no refbacks.
