Research Institute of Forests and Rangelands. P. O. Box: 13185-116. Tehran, Iran.
Islamic Azad University, Science and Research branch, Tehran, Iran. *Corresponding Author's E-mail: email@example.com
This research was conducted to investigate spatial variability and estimate tree attributes in a plantation forest in the Caspian region of Iran using geostatistical analysis. Sampling was performed based on a 50m?125m systematic grid in a maple stand (Acer velutinum Boiss) 18 years of age using circular samples of 200m2 area. Totally, 96 sample plots were measured in 63 hectares and 14.25 hectare was inventoried as full census area. Experimental variograms for forest stem basal area, stem density and tree height attributes were calculated and plotted using the geo-referenced inventory plots. The calculated variograms of basal area and height showed a high spatial auto-correlation, which is fitted by spherical model. However, stem density showed a large nugget effect. Estimations for basal area and height interpolated by ordinary block kriging and cross validation results showed that all the estimations were accurate. Furthermore, the estimated kriged mean of basal area showed no significant difference to the real mean in the full census area. Therefore, geostatistical analysis is able to capture and explain the spatial variability as well as estimate tree attributes (not stem density) in this kind of plantation forest, accurately.
Akhavan, R., Zahedi Amiri, Gh. and Zobeiri, M. (2010) Spatial variability of forest growing stock using geostatistics in the Caspian region of Iran. Caspian J. Env. Sci, 8 (1), In Press.
Bellehumeur, C. and Legendre, P. (1998) Multiscale sources of variation in ecological variables: modeling spatial dispersion, elaborating sampling designs. Landsc. Ecol., 13, 15-25.
Biondi, F., Myers D.E. and Avery, C.C. (1994) Geostatistically modeling stem size and increment in an old-growth forest. Can. J. For. Res., 24, 1354-1368.
Cressie, N.A.C. (1993) Statistics for spatial data. John Willy and Sons Inc., New York. pp. 900. Dale, M.R.T. (2000) Spatial pattern analysis in plant ecology. Cambridge University press, Cambridge, United Kingdom, 326 p.
Freeman, E.A. and Moisen, G.G. (2007) Evaluating kriging as a tool to improve moderate resolution maps of forest biomass. Environ. Monit. Assess. 128, 395- 410.
Goovaerts, P. (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York, 483 p.
Gunnarsson, F., Holm, S. Holmgren, P. and Thuresson, T. (1998) On the potential of kriging for forest management planning. Scan. J. For. Res., 13, 237- 245.
Hernández, J. and Emery, X. (2009) A geostatistical approach to optimize sampling designs for local forest inventories. Can. J. For. Res., 39, 1465-1474.
Husch, B., Miller C.I. and Beers, T.W. (1982) Forest mensuration. 3rd ed. John Wiley and Sons, inc., New York. pp. 443. Isaaks, E.H. and Srivastava, R.M. (1989) An introduction to applied geostatistics. Oxford University Press, New York, 561 p. Jeffers,
J.N.R. (1982) Modeling. Chapman and Hall, London, 80 p.
Kint, V., Meirvenne, M.V. Nachtergale, L. Geudens G. and Lust, N. (2003) Spatial methods for quantifying forest stand structure development: a comparison between nearest neighbor indices and variogram analysis. Forest science, 49, 36-49.
Mandallaz, D. (1991) A unified approach to sampling theory for forest inventory based on infinite population model. PhD. Dissertation, Academic Press, ETH Zürich, Switzerland, Chair of Forest Inventory and Planning. http://www. e-collection.ethb.ethz.ch/.
Mandallaz, D. (1993) Geostatistical methods for double sampling schemes: application to combined forest inventory. Technical report, ETH Zürich, Chair of Forest Inventory and Planning, 133 p.
Montes, F., Hernandez M.J. and Canellas, I. (2005) A geostatistical approach to cork production sampling in Quercus suber forests. Can. J. For. Res., 35, 2787-2796.
Pierce Jr., K.B., J.L. Ohmann, M.C. Wimberly, M.J. Gregory and J.S. Fried, (2009) Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods. Can. J. For. Res., 39, 1901-1916.
Samra, J.S., Gill H.S. and Bhatia, V.K. (1989) Spatial stochastic modeling of growth and forest resource evaluation. Forest Science, 35, 663-676.
Tuominen, S. Fish S. and Poso, S. (2003) Combining remote sensing, data from earlier inventories, and geostatistical interpolation in multi-source forest inventory. Can. J. For. Res., 33, 624- 634.
Webster, R. and Oliver, M.A. (2000) Geostatistics for environmental scientists, Wiley Press, 271 p.