Investigation on application of k-nn (k- nearest neighbor) sampling method in Zagros forests (Case study: Karzan forest, Ilam)

Karamshahi, Abdolali; Zobeiri, Mahmoud; Namiranian, Manouchehr; Feghhi, Jahangir

doi:10.22092/ijfpr.2011.107503

	Investigation on application of k-nn (k- nearest neighbor) sampling method in Zagros forests (Case study: Karzan forest, Ilam)
تحقیقات جنگل و صنوبر ایران
Article 2, Volume 19, Issue 4 - Serial Number 46, December 2012, Pages 465-453 PDF (392.06 K)
Document Type: Research article
DOI: 10.22092/ijfpr.2011.107503
Authors
Abdolali Karamshahi^* ¹; Mahmoud Zobeiri²; Manouchehr Namiranian²; Jahangir Feghhi³
¹Ph.D. student of forestry, Faculty of Natural Resources, University of Tehran
²Prof., Faculty of Natural Resources, University of Tehran
³Associate Prof., Faculty of Natural Resources, University of Tehran
Abstract
For maintaining of Zagros forests role in wild life, water and soil conservation, the suitable solutions and methods for assessing the existing conditions and planning for management of this forests should be given. This study was carried out in forest regions around Ilam city (Karzan region) in a forest area of 86 ha with coppice and seed origin. At first, a 100 percent forest inventory was implemented. The coordinates of each tree on the ground was recorded and their quantitative attributes (crown diameter, DBH of seed origin trees) were measured. Using Arcview software in GIS environment the eight cells sample plots for different inventory networks namely 100 m×100m, 100 m×200m and 200 m×200 m were simulated and statistical analysis were done. The mean value of trees density per hectare for all three networks were calculated and compared with their real data (100 percent inventory) by t-test (α=0.05). Results showed that there is no significant difference between mean values in three inventory networks and the real mean value.
Keywords
Karazan forests; inventory; eight cells sampling plots; Zagros forests

References
- اسحاق نیموری، ج.، 1379. مقایسه روش آماربرداری سیستماتیک تصادفی با قطعات نمونه دایره‌ای و روش ترانسکت از نظر دقت و هزینه در جنگلهای بلوط غرب، منطقه سرخه‌دیزه کرمانشاه. پایان‌نامه کارشناسی ‌ارشد، دانشکده منابع طبیعی، دانشگاه تهران، 127 صفحه. - حیدری، ر.ح.، 1385. بررسی روشهای مختلف آماربرداری فاصله‌ای در جنگلهای زاگرس (مطالعه موردی: منطقه سرخه‌دیزه کرمانشاه). رساله دکتری، دانشکده منابع طبیعی، دانشگاه تهران، 132 صفحه. - زبیری، م.، 1381. زیست‌سنجی (بیومتری) جنگل. انتشارات دانشگاه تهران، 366 صفحه. - طهماسبی، م.، 1374. بررسی ابعاد شبکه آماربرداری و سطح قطعه نمونه برای دقت معین در جنگلهای بلوط غرب. پایان‌نامه کارشناسی ‌ارشد، دانشگاه تهران، 108 صفحه. - کرمشاهی، ع. و نجفی، ع.، 1385. برآورد تاج‌پوشش و تعداد در هکتار درختان با استفاده از روش ترانسکت با تعداد درخت ثابت و مقایسه آن با روش تصادفی سیستماتیک و استراتیفیکاسیون در جنگلهای استان ایلام. دانشگاه ایلام، طرح پژوهشی خاتمه یافته، 59 صفحه. - محمدپور، م.، 1378. بررسی مقدماتی فیتواکولوژی در استان ایلام. پایان‌نامه کارشناسی‌ارشد، دانشگاه مازندران، 123صفحه. -Berterretche, M., Hudak, A., Tcohen, W.B., Maiersperger, T., Kgower, S.T. and Dungan, J., 2005. Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM⁺ data over a boreal forest. Remote Sensing of Environment, 96: 49-61. - Cressie, N.A.C., 1993. Statistics for spatial data. Revised edition. John Wiley & Sons, New York. 887 p. - Franco-Lopez , H., Ek, A.R. and Baura, M.F., 2001. Estimation and mapping of forest stand density, volume and cover type using the k-nn method. Remote sensing of Environmental, 77 (3): 251-274. - Goovaerts, P., 1997. Geostatistics for natural resources evaluation. Oxford University Press, New York. 323 p. - Haapanen, R., Marvin, E. and Alean, E., 2004. Delineation of forest/nonforest land use k-nn methods. Remote sensing of Environmental, 89: 265-271. - Huiyan, Gu., Dailimin, W., Gany, X. and Wang, H., 2006. Estimation of forest volume by integrating landsat ETM imagery and forest inventory data. Science China, 49: 32- 43. - Lemay, V. and Haillemariam, T., 2005. Comparison of k-nn methods for estimating basal area and stems per hectare using aerial auxiliary variables. For. Sci., 51 (2): 109-119. - Nilsson, M., 2002. Deriving nationwide estimates of forest variables for Sweden using Land sat ETM+ and field data. University of agricultural science, Umea, Sweden, 150 p. - Odeh, I.O.A., McBratney, A.B. and Chittleborough, D.J., 1995. Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kring. Geoderma, 67: 215-226. - Odeh, I.O.A. and McBratney, A.B., 2000. Using AVHRR images for spatial prediction of clay content in the lower Namoi Valley of eastern Australia. Geoderma, 97: 237-245. - Sheil, D. 2004. Biological diversity and local people's perspective (methods for a multidisciplinary landscape assessment), 283 p. - Sheil, D., Ducey, M.J., Sidiyasa, J., and Samsoedin, I., 2003. A new type of sample unit for the efficient assessment of diverse tree communities in complex forest landscapes. Journal of Tropical Forest Science, 15 (1): 1-24. - Tuominen, S., Fish, S. and Poso, S., 2003. Combining remote sensing, data from earlier inventories and geostatistical interpolation in multisource forest inventories. Canadian Journal of Forest Research, 33: 623-634. - Wackernagel, H., 1994. Multivariate spatial statistics. Geoderma, 62: 83-92.
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