مقایسه روش‌های نمونه‌برداری k-NN در برآورد تراکم درختان بنه (Pistacia atlantica Desf.) با الگوی مکانی کپه ای در یک توده تنک زاگرس(Pistacia atlantica Desf.)

نوع مقاله : علمی- پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشکده کشاورزی، دانشگاه شیراز

2 استادیار، دانشکده کشاورزی، دانشگاه شیراز

چکیده

تراکم (تعداد درختان در واحد سطح) یکی از مشخصه­‌های ساختاری مهم در توده­‌های جنگلی است که در درک پویایی جنگل مناسب است. روش kامین نزدیکترین همسایه (k-NN) یک روش فاصله‌­ای است که به‌طور متداول در آماربرداری جنگل برای برآورد مشخصه­‌های کمی به‌کار می­رود. در این مطالعه روش k-NN با پنج راهکار نزدیکترین فرد (NI)، نزدیکترین همسایه (NN)، جفت‌های تصادفی (RP)، چارک نقطه مرکز (PCQ) و همسایه چارکی (QN) برای برآورد تراکم درختان بنه (Pistacia atlantica Desf.) در جنگل‌های زاگرس استفاده شد. یک توده با مساحت 45 هکتار در رویشگاه تحقیقاتی بنه انتخاب شد و به‌منظور تعیین تراکم واقعی، آماربرداری صددرصد شد. توزیع مکانی درختان بنه، کپه­‌ای (0/79=R و 12/38-=z) و تراکم آنها 19/44 درخت در هکتار بود. سپس روش k-NN با راهکارهای مختلف و k برابر دو تا 10 در 46 نقطه نمونه­‌برداری در یک شبکه 100 × 100 متری مورد استفاده قرار گرفت. نتایج نشان داد که به‌جز راهکار PCQ، روش k-NN با سایر راهکارها مقدار تراکم را به‌طور معنی­‌داری (در سطح اطمینان 95 درصد) درست برآورد کرد. تعداد k و راهکار انتخاب آن بر صحت و دقت نتایج روشk-NN تأثیر گذاشت، به‌نحوی‌که 4=k در NI، 7=k در NN و 2=k در RP و QN تراکم درختان بنه را با کمترین خطا (RMSE و اریبی) برآورد کردند. به‌طور کلی نتایج نشان داد که روش k-NN بهینه با k و راهکار مناسب توانسته تراکم درختان بنه با توزیع مکانی کپه­‌ای را در یک توده تنک در جنگل‌های زاگرس برآورد کند.

کلیدواژه‌ها

عنوان مقاله [English]

Comparing different k-NN sampling methods for density estimation of wild pistachio (Pistaciaatlantica Desf.) with clustered spatial pattern in a Zagros open stand

نویسندگان [English]

  • Masoume Moselou 1
  • Seyyed Yousef Erfanifard 2
1 M.Sc. Student, College of Agriculture, Shiraz University
2 Assistant Prof., College of Agriculture, Shiraz University
چکیده [English]

Density (i.e. number of trees per unit area) is one of the important structural attributes of forest stands which is appropriate to understand forest dynamics. The k-Nearest Neighbour (k-NN) method is a distance sampling method which is commonly used to estimate quantitative attributes in forest inventories. In this study, the k-NN method with five strategies of Nearest Individual (NI), Nearest Neighbour (NN), Random Pairs (RP), Point-Centered Quarter (PCQ), and Quartered Neighbour (QN) was used to estimate the density of wild pistachio (Pistacia atlantica Desf.) in Zagros woodlands. A natural stand of 45 ha was selected in Bane Research Site, and was fully callipered to derive the true density. The spatial distribution of trees was clustered (R=0.79 and z = -12.38) with a true density of 19.44 trees per ha. While applying the k-NN method, different strategies as well as k ranging between 2 and 10 were tested across 46 sample points in a 100 × 100 m2 sampling grid. The results showed that all strategies except PCQ significantly estimated the density at α=0.05. Furthermore, the number of k and the strategy of k-tree selection affected the accuracy and precision of k-NN results, since NI in k=4, NN in k=7, RP and QN in k=2 estimated the density with the least error (RMSE and Bias). In conclusion, the optimum k-NN method with appropriate k and strategy could estimate the density of wild pistachio trees with clustered spatial distribution in an open stand in Zagros woodlands.

کلیدواژه‌ها [English]

  • wild pistachio
  • Density
  • Zagros
  • error
  • k-NN

مراجع

- Askari, Y., Zobeiri, M. and Sohrabi, H., 2013. Comparison of five distance sampling methods for estimating quantitative characteristics of Zagros forests. Iranian Journal of Forest and Poplar Research, 21(2): 316-328 (In Persian).
- Baffetta, F., Corona, P. and Fattorini, L., 2011. Design-based diagnostics for k-NN estimators of forest resources. Canadian Journal of Forest Resources, 41: 59-72.
- Bonham, C.D., 2013. Measurements for terrestrial vegetation. Wiley-Blackwell Publishers, West Sussex, 246p.
- Bryant, D.M., Ducey, M.J., Innes, J.C., Lee, T.D., Eckert, R.T. and Zarin, D.J., 2004. Forest community analysis and the point-centered quarter method. Plant Ecology, 175: 193-203.
- Chirici, G., Winter, S. and McRoberts, R.E., 2011. National Forest Inventories: Contributions to Forest Biodiversity Assessments. Springer, New York, 226p.
- Diggle, P.J., 2003. Statistical analysis of spatial point patterns. Arnold Publishers, New York, 159p.
- Engeman, R.M., Sugihara, R.T., Pank, L.F. and Dusenberry, W.E., 1994. A comparison of plotless density estimators using Monte Carlo simulation. Ecology, 75(6): 1769-1779.
- Erfanifard, Y. and Mahdian, F., 2012. Comparative investigation on the methods of true spatial pattern analysis of trees in forests, Case study: Wild pistachio research forests, Fars province, Iran. Iranian Journal of Forest and Poplar Research, 20(1): 62-73 (In Persian).
- Fehrmann, L. and Kleinn, C., 2005. A k-Nearest Neighbour approach for estimating of single-tree biomass. Proceedings of the 7th FIA Symposium, USA, 3-6 Oct. 2005: 251-259.
- Guebas, F.D. and Koedam, N., 2006. Empirical estimate of the reliability of the use of the Point-Centered Quarter Method (PCQM): Solutions to ambiguous field situations and description of the PCQM+ protocol. Forest Ecology and Management, 228: 1-18.
- Hanberry, B.B., Yang, J., Kabrick, J.M. and He, H.S., 2012. Adjusting forest density estimates for surveyor bias in historical tree surveys. American Midland Naturalist, 167: 285-306.
- Hijbeek, R., Koedam, N., Khan, M.N.I., Kairo, J.G., Schoukens, J. and Guebas, F.D., 2013. An evaluation of plotless sampling using vegetation simulations and field data from mangrove forest. PLoS ONE, 8(6): 1-10.
- Hill, D., Fasham, M., Tucker, G., Shewry, M. and Shaw, P., 2005. Handbook of Biodiversity Methods. Cambridge University Press, Cambridge, 573p.
- Jahan, A., Akhavan, R. and Erfanifard, Y., 2010. Application of k-NN (9-tree) sampling method in central Zagros forests (Case study: Servak forest, Yasouj). The 1st Symposium on Threats and Destroying Factors of Biodiversity in Central Zagros. Iran, 16-17 Feb. 2010: 231-233 (In Persian).
- Jazirehi, M.H. and Ebrahimi Rostaghi, M., 2003. Silviculture in Zagros. University of Tehran Press, Tehran, 560p (In Persian).
- Kangas, A. and Maltamo, M., 2006. Forest Inventory: Methodology and Applications. Springer, 365p.
- Karamshahi, A., Zobeiri, M., Namiranian, M. and Feghhi, J., 2012. Investigation on application of k-NN (k-Nearest Neighbor) sampling method in Zagros forests (Case study: Karzan forest, Ilam). Iranian Journal of Forest and Poplar Research, 19(4): 453-46 (In Persian).
- Kleinn, C. and Vilcko, F. 2005. On estimation in k-tree sampling. Proceedings of the 7th FIA Symposium. USA, 3-6 Oct. 2005: 203-208.
- Kleinn, C. and Vilcko, F., 2006. A new empirical approach for estimation in k-tree sampling. Forest Ecology and Management, 237: 522-533.
- Kung, Y.H., Lin, P.S. and Kao, C.H. 2012. An optimal k-nearest neighbor for density estimation. Statistics and Probability Letters, 82: 1786-1791.
- Leavitt, S.D. and Clair, L.L., 2011. Estimating Xanthoparmelia (Parmeliaceae) population density in subalpine communities in southern Utah, U.S.A. using two distance methods, with implications for assessing community composition. Bryologist, 114(3): 625-636.
- Magnussen, S., 2012. A new composite k-tree estimator of stem density. European Journal of Forest Research, 131: 1513-1527.
- Magnussen, S., Fehrman, L. and Platt, W.J., 2012a. An adaptive composite density estimator for k-tree sampling. European Journal of Forest Research, 131: 307-320.
- Magnussen, S., McRoberts, R.E. and Tomppo, E.O., 2012b. A resampling variance estimator for the k nearest neighbours technique. Canadian Journal of Forest Research, 40: 648-658.
- McRoberts, R.E., 2012. Estimating forest attribute parameters for small areas using nearest neighbors techniques. Forest Ecology and Management, 272: 3-12.
- Owji, M.G. and Hamzepour, M., 2012. Vegetation profile of wild pistachio experimental forest. Published by Research Institute of Forests and Rangelands, Tehran, 240p (In Persian).
- Temesgen, H., Barrett, T.M. and Latta, G., 2008. Estimating cavity tree abundance using Nearest Neighbor imputation methods for western Oregon and Washington forests. Silva Fennica, 42(3): 337-354.
- Pourreza, M., Shaw, J.D. and Zangeneh, H., 2008. Sustainability of wild pistachio (Pistacia atlantica Desf.) in Zagros forests, Iran. Forest Ecology and Management, 255: 3667-3671.
- Seo, H.S., Phua, M.H., Ong, R., Choi, B. and Lee, J.S., 2014. Determining aboveground biomass of a forest reserve in Malaysian Borneo using k-Nearest Neighbour method. Journal of Tropical Forest Science, 26(1): 58-68.
- Zhu, X. and Zhang, J., 2009. Quartered neighbor method: a new distance method for density estimation. Frontiers of Biology in China, 4(4): 574-578.
تحقیقات جنگل و صنوبر ایران
دوره 23، شماره 4
دی 1394
صفحه 626-636

فایل ها

هم رسانی

ارجاع به این مقاله

آمار