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برآورد ارتفاع درختان نمدار (.Tilia begonifolia Stev) با استفاده از مدلهای غیرخطی | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 7، دوره 27، شماره 4 - شماره پیاپی 78، دی 1398، صفحه 436-450 اصل مقاله (1.17 M) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2020.128070.1869 | ||
| نویسندگان | ||
| محمدرسول نظری سندی1؛ ایرج حسن زاد ناورودی* 2؛ امان محمد کلته3؛ حسن پور بابایی4 | ||
| 1دانشجوی دکتری مدیریت جنگل، گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه گیلان، صومعهسرا، ایران | ||
| 2دانشیار، گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه گیلان، صومعهسرا، ایران | ||
| 3استادیار، گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه گیلان، صومعهسرا، ایران | ||
| 4استاد، گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه گیلان، صومعهسرا، ایران | ||
| چکیده | ||
| قطر و ارتفاع درختان، متغیرهایی اساسی برای بررسی زیتوده، ذخیره کربن و تکامل تودههای جنگلی هستند. با توجه به کمهزینه بودن و سهولت اندازهگیری قطر و زیاد بودن خطای اندازهگیری ارتفاع درخت، در این پژوهش برای برآورد ارتفاع درختان نمدار (Tilia begonifolia Stev) در جنگلهای شفارود گیلان از مدلهای غیرخطی استفاده شد. نمدار در شفارود از ارتفاعات پایین تا 1800 متر از سطح دریا پراکنش دارد. این گونه، نقش مهمی در حفظ ترکیب و ساختار طبیعی جنگل ایفا میکند. دادههای مورد بررسی با نمونهبرداری تصادفی- منظم با ابعاد شبکه 200×200 متر مربع از 48 قطعهنمونه دایرهای 10 آری در دامنههای ارتفاعی 500 تا 950 متر از سطح دریا (پارسلهای 29 و 30) در سری 16 و 50 تا 500 متر از سطح دریا (پارسلهای 14 و 18) در سری 17 جمعآوری شدند. مدلسازی با 12 مدل غیرخطی پرکاربرد و شبکه عصبی مصنوعی پرسپترون چندلایه با الگوریتم لونبرگ- مارکوارت انجام شد. شبکه عصبی مصنوعی از مزیت تشخیص روابط پیچیده غیرخطی بین دادههای ورودی و خروجی برخوردار است. مقایسه نتایج با معیارهای کاراییسنجی RMSE، R2adj، AIC و MAD انجام شد. براساس این معیارها در بین 12 مدل مورد نظر، مدل Stoffels-Van Soeset (1953) در پایینبند و مدل Burkhart-Strub (1974) برای میانبند بیشترین کارایی را داشتند، در حالیکه شبکه عصبی مصنوعی در هر دو رویشگاه از بیشترین کارایی برخوردار بود. شبکه عصبی مصنوعی، مقدار خطا را برای مدلهای پیشنهادی در مناطق پایینبند و میانبند بهترتیب 5/54 و 7/35 درصد کاهش داد. اگرچه دقت مدلهای غیرخطی پیشنهادی برای منطقه مورد بررسی مناسب بود، اما شبکه عصبی مصنوعی بهدلیل دقت بیشتر نسبت به این مدلها برتری داشت. | ||
| کلیدواژهها | ||
| شبکه عصبی مصنوعی؛ قطر- ارتفاع؛ شفارود؛ گیلان؛ مدلسازی | ||
| عنوان مقاله [English] | ||
| Estimation of lime (Tilia begonifolia Stev.) trees height using nonlinear models | ||
| نویسندگان [English] | ||
| M.R> Nazari Sendi1؛ I. Hassanzad Navroodi2؛ A.M Kalteh3؛ H. Poorbabaei4 | ||
| 1Ph.D. Student of Forest Management, Department of Forestry, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Iran | ||
| 2Associate Prof., Department of Forestry, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Iran | ||
| 3Assistant Prof., Department of Water Resources Engineering, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Iran | ||
| 4Prof., Department of Forestry, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Iran | ||
| چکیده [English] | ||
| The diameter and height of trees are essential variables for biomass prediction, carbon storage, and forest stand development. Compared with height, measuring the diameter of trees more convenient and is associated with lower cost and error. In this study, nonlinear models were used to estimate height of lime trees in the Shafaroud forests of Guilan. Lime (Tilia begonifolia Stev.) trees are distributed from low to high altitude of 1800 m in Shafaroud forests and have an important role in preserving its natural composition and stand structure. A systematic random sampling method within a 200 × 200-meter network was applied for data collection. Data were collected from 48 circular sample plots with 1000 m2 at altitudes from 500 to 950 m (parcels no. 29 and 30 in 16th compartment) as well as from 50-500 m (parcels no. 14 and 18 in 17th compartment). Modeling was performed with 12 commonly used nonlinear models and multilayer perceptron neural networks with the Levenberg-Marquardt algorithm, which has the advantage to accommodate the complex nonlinear relationships between input and output data. Performance criteria including root mean square error (RMSE), adjusted R2, AIC, and MAD were used to compare the results. Results showed the highest performances of Burkhart-Strub (1974) in mid-altitude and Stoffels-Van Soeset (1953) models in low-altitude forests, while artificial neural network (ANN) returned the highest accuracy and performance in both sites. It decreased the RMSE by 5.54% in sub-mountain and 7.35% in low-land forests compared to the best applied nonlinear models. Although the suggested nonlinear models were accurate enough for the study site, the ANN method is preferred for its higher accuracy. | ||
| کلیدواژهها [English] | ||
| Artificial neural network, Guilan, height-diameter, modeling, Shafaroud | ||
| مراجع | ||
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