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Spatial variability of forest growing stock using geostatistics in the Caspian region of Iran | ||
| Caspian Journal of Environmental Sciences | ||
| مقاله 5، دوره 8، شماره 1، فروردین 2010، صفحه 43-53 اصل مقاله (329.3 K) | ||
| نویسندگان | ||
| R. Akhavan* 1؛ Gh Zahedi Amiri2؛ M. Zobeiri3 | ||
| 1Research Institute of Forests and Rangelands, P.O. Box, 13185-116, Tehran, Iran. | ||
| 2Department of Forestry, Faculty of Natural Resources, University of Tehran, P.O. Box, 31585-4314, Karaj, Iran. | ||
| 3Department of Forestry, Faculty of Natural Resources, University of Tehran, P.O. Box, 31585-4314, Karaj, Iran. *Corresponding author's E-mail: akhavan@rifr.ac.ir | ||
| چکیده | ||
| Estimating the amount of variation due to spatial dependence at different scales provides a basis for designing effective experiments. Accurate knowledge of spatial structures is needed to inform silvicultural guidelines and management decisions for long term sustainability of forests. Furthermore, geostatistics is a useful tool to describe and draw map the spatial variability and estimation of forest variables. Therefore, this research was conducted to investigate on spatial variability and to estimate forest stock variables using geostatistical approach in a mixed hardwood forest, located in the Caspian region of Iran. Field sampling was performed based on a 150m by 200m systematic rectangular grid of 3 clustered plots (50m away). Each sample plot consisted of two concentric circles. Overall, 434 sample plots were measured in 502 hectares. Experimental variograms for forest basal area, volume and tree density were calculated and plotted using the geo- referenced inventory plots. All the variograms showed weak spatial auto- correlations between samples, even in short distances. Estimations were made using fitted variogram models and ordinary block kriging. Cross- validation results showed that all the estimations are biased, because of the large variability and weak spatial structure in the forest stock variables. Therefore, kriging could not make accurate estimations because of high spatial variability of forest growing stock related variables in this heterogeneous and uneven-aged forest. REFERENCES 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. Chiles, J.P. and Delfiner, P., 1999. Geostatistics: modeling spatial uncertainty. Wiley, New York. 695 p. Clark, I., 1979. Practical geostatistics. Applied Science Publishers, London. Cressie, N.A.C., 1993. Statistics for spatial data. John Willy and Sons, Inc., New York. 900 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. Guibal, D., 1973. L´ estimation des oukoumés du Gabon, note interne 333, centre de Morphologie Mathématique, Fontainebleau, France, (In French). 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. Husch, B., Miller, C. I., and Beers, T. W., 1982. Forest mensuration. 3rd Edition. John Wiley and Sons, inc., New York. 443 p. Isaaks, E.H. and Srivastava, R.M., 1989. An introduction to applied geostatistics. Oxford University Press, New York. 561 p. Jost, A., 1993. Geostatistische analyse des Stichprobenfehlers systematischer Stichproben, PhD Dissertation, University of Freiburg, Breisgau, Germany, 113 p. (In German). 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 neighbour indices and variogram analysis. For. Sci., 49: 36-49. Krige, D. G., 1951. A statistical approach to some mine valuation and allied problems on the Witwatersrand. Master’s thesis, University of Witwatersrand, 190 p. 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. 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. Mandallaz, D., 2000. Estimation of the spatial covariance in universal kriging: Application to forest inventory. Environ. Ecol. Stat., 7: 263-284. 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. 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. Zahedi Amiri, Gh., 1991. Determination of tree species increment in Kheiroudkenar forest. MSc. Dissertation, University of Tehran, Iran, 120 p. Zahedi Amiri, Gh., 1998. Relation between ground vegetation and soil characteristic in a mixed hardwood stand, Ph.D. thesis, academic press, University of Gent, Belgium, 319 p. | ||
| کلیدواژهها | ||
| Geostatistics؛ Growing stock؛ Kriging؛ Spatial variability؛ Variogram | ||
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