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
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