| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 3,005 |
| تعداد مقالات | 33,631 |
| تعداد مشاهده مقاله | 44,479,927 |
| تعداد دریافت فایل اصل مقاله | 30,144,398 |
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 | ||
|
- Ahmadi, F., Alavi, S.J., Tabari Kouchaksaraei, M. and Aertsen, W., 2014. Comparison of non-linear height and diameter functions for oriental beech (Fagus orientalis Lipsky) in a mixed and uneven-aged Caspian forest (Case study: Tarbiat Modares University forest research station). Iranian Journal of Forest, 6(1): 11-22 (In Persian). - Aishan, T., Halik, Ü., Betz, F., Gärtner, P. and Cyffka, B., 2016. Modeling height–diameter relationship for Populus euphratica in the Tarim riparian forest ecosystem, Northwest China. Journal of Forestry Research, 27(4): 889-900. - Alemi, A., Oladi, J., Fallah, A. and Maghsoodi, Y., 2018. Evaluation of height-diameter nonlinear models for Alnus species in Hyrcanian forests (Case study: Golestan Rezaeian forest). Journal of Natural Ecosystems of Iran, 9(2): 1-12 (In Persian). - Batista, J.L., Couto, H.T.Z. and Marquesini, M., 2001. Performance of height-diameter relationship models: analysis in three forest types. Scientia Forestalis, 60: 149-163. - Burnham, K.P. and Anderson, D.R., 2004. Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2): 261-304. - Castaño-Santamaría, J., Crecente-Campo, F., Fernández-Martínez, J.L., Barrio-Anta, M. and Obeso, J.R., 2013. Tree height prediction approaches for uneven-aged beech forests in northwestern Spain. Forest Ecology and Management, 307: 63-73. - Delfan Abazari, B., Sagheb-Talebi, Kh. and Namiranian, M., 2004. Regeneration gaps and quantitative characteristics of seedlings in different development stages of undisturbed beech stands (Kelardasht, Northern Iran). Iranian Journal of Forest and Poplar Research, 12(2): 251-266 (In Persian). - Diamantopoulou, M.J. and Özçelik, R., 2012. Evaluation of different modeling approaches for total tree-height estimation in Mediterranean Region of Turkey. Forest Systems, 21(3): 383-397. - Elamir, E.A.H., 2012. Mean absolute deviation about median as a tool of explanatory data analysis. Proceedings of the World Congress on Engineering. London, 4-6 July 2012: 6p. - Ghanbari, S. and Sheidai Karkaj, E., 2018. Diversity of tree and shrub species in woodlands of Guijeh-bel region of Ahar. Iranian Journal of Forest and Poplar Research, 26(1): 118-128 (In Persian). - Hagan, M.T. and Menhaj, M.B., 1994. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6): 989-993. - Hassanzad Navroodi, I., Alavi, S.J., Ahmadi, M.K. and Radkarimi, M., 2016. Comparison of different non-linear models for prediction of the relationship between diameter and height of velvet maple trees in natural forests (Case study: Asalem Forests, Iran). Journal of Forest Science, 62(2): 65-71. - Kalteh, A.M., 2017. Enhanced monthly precipitation forecasting using artificial neural network and singular spectrum analysis conjunction models. INAE Letters, 2(3): 73-81. - Lei, X., Peng, C., Wang, H. and Zhou, X., 2009. Individual height-diameter models for young black spruce (Picea mariana) and Jack pine (Pinus banksiana) plantations in New Bruswick, Canada. The Forestry Chronicle, 85(1): 43-56. - Maier, H.R. and Dandy, G.C., 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software, 15(1): 101-124. - Mohammadi, J. and Shataee, Sh., 2017. Study of different height-diameter models for hornbeam (Carpinus betulus L.) in uneven-aged stands of Shastkalateh forest of Gorgan. Iranian Journal of Forest and Poplar Research, 24(4): 700-712 (In Persian). - Nazari Sendi, M.R., Hassanzad Navroodi, I., Poorbabaei, H., Sheikhkanlu Milan, M. and Bakhshandeh, B., 2014. Determination of lime tree (Tilia begonifolia Stev.) stems form based on quantitative parameters (Study area: Shafaroud forests of Guilan province, Iran). Folia Forestalia Polonica, 56(4): 165-170. - Ng'andwe, P., Chungu, D., Yambayamba, A.M. and Chilambwe, A., 2019. Modeling the height-diameter relationship of planted Pinus kesiya in Zambia. Forest Ecology and Management, 447: 1-11. - Özçelik, R., Yavuz, H., Karatepe, Y., Gürlevik, N. and Kiriş, R., 2014. Development of ecoregion-based height-diameter models for 3 economically important trees species of southern Turkey. Turkish Journal of Agriculture and Forestry, 38: 399-412. - Peng, C., Zhang, L., Huang, S., Zhou, X., Parton, J. and Woods, M., 2001. Developing ecoregion-based height-diameter models for jack pine and black spruce in Ontario. Forest Research Report, No. 159, Ontario Ministry of Natural Resources, Ontario, 18p. - Plotnik, A., 2000. The Urban Tree Book: An Uncommon Field Guide for City and Town. Three Rivers Press, New York, 433p. - Rezaei, S.A., 2012. The Feature of Natural Resources and Watersheds of Iran. Pouneh Publication, Tehran, 304p (In Persian). - Sadeghi Kaji, H., Jafari, A. and Yarali, N., 2015. An assessment of forest management sustainability in Do-Polan district, Chaharmahal and Bakhtiari Province, Iran. Iranian Journal of Forest and Poplar Research, 23(3): 490-501 (In Persian). - Scaranello, M.A.S.,Alves, L.F., Vieira, S.A.,Camargo, P.B., Joly, C.A. and Martinelli, L.A., 2012. Height-diameter relationships of tropical Atlantic moist forest trees in southeastern Brazil. Scientia Agricola, 69(1): 26-37. - Sheikhcanlou Milan, M., Hassan Zad Navroodi, I., Nazari Sendi, M.R. and Bakhshandeh Navroud, B., 2014. Effect of elevation on quantitative and qualitative characteristics of lime trees in Shafaroud basin of Guilan province. Journal of Forest and Wood Product, 67(2): 245-253 (In Persian). - Sheikholeslami, A., Namiranian, M. and Sagheb Talebi, Kh., 2005. A study of the effects of some environmental variables on large-leaved lime (Tilia platyphyllos Scop) in forests of Western Mazandaran. Iranian Journal of Natural Resources, 58(3): 553-566 (In Persian). - Tsega, M., Guadie, A., Teffera, Z.L., Belayneh, Y. and Niu D., 2018. Development and validation of height-diameter models for Cupressus lusitanica in Gergeda forest, Ethiopia. Forest Science and Technology, 14(3): 138-144. - Wilamowski, B.M., Chen, Y. and Malinowski, A., 1999. Efficient algorithm for training neural networks with one hidden layer. Proceedings of International Joint Conference on Neural Networks (IJCNN'99), Cat. No.99CH36339. Washington, D.C., 10-16 Jul. 1999: 1725-1728. - Yosefzadeh, H., Tabari, M., Hosseinzadeh Colagar, A., Assadi, M., Sattarian, A. and Zare, H., 2010. Variation in leaf morphology of Tilia spp. of in Hyrcanian forests. Journal of Taxonomy and Biosystematics, 2(3): 11-24 (In Persian). - Zobeiry, M., 2005. Forest Inventory (Measurement of Tree and Stand). University of Tehran Press, Tehran, 401p (In Persian). | ||
|
آمار تعداد مشاهده مقاله: 1,143 تعداد دریافت فایل اصل مقاله: 677 |
||