قابلیت داده‌‌های سنجنده OLI ماهواره لندست 8 در برآورد زی‌‌توده روی زمینی توده‌‌های ممرز (Carpinus betulus) در جنگل خیرود

نوع مقاله : علمی- پژوهشی

نویسندگان

1 کارشناس ارشد جنگل‌‌داری، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

2 استاد، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

3 دانشجوی دکتری جنگل‌‌داری، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

DOR: 98.1000/1735-0883.1397.26.406.73.3.1602.1583
 
در پژوهش پیش‌رو به‌‌بررسی قابلیت داده‌‌های سنجنده OLI ماهواره لندست 8 به‌‌منظور برآورد زی‌‌توده روی زمینی توده‌‌های به‌نسبت خالص ممرز در بخشی از جنگل‌‌های هیرکانی پرداخته شد. برای مدل‌‌سازی و اعتبارسنجی نتایج، 55 قطعه‌‌نمونه به‌‌طور انتخابی تعیین شد و مقدار واقعی زی‌‌توده روی زمینی در آن­‌ها اندازه‌‌گیری شد. در هر قطعه‌نمونه، قطر برابر سینه تمام درختان قطورتر از 7/5 سانتی‌‌متر اندازه‌‌گیری شد. سپس با استفاده از جدول حجم محلی و چگالی بحرانی، زی‌‌توده روی زمینی این درختان محاسبه شد. پیش‌‌پردازش‌‌ها و پردازش‌‌های لازم بر روی داده‌‌های ماهواره‌‌ای انجام شد. مدل‌‌سازی ‌‌به‌روش‌های پارامتریک رگرسیون گام‌‌به‌‌گام، رگرسیون پس‌رو و روش‌‌های ناپارامتریک شبکه عصبی مصنوعی، k نزدیک‌‌ترین همسایه و جنگل‌‌تصادفی انجام شد. بررسی ضریب همبستگی پیرسون بین زی‌‌توده روی زمینی در قطعه‌نمونه­‌ها و ارزش‌‌های طیفی متناظر آن­‌ها در باندهای اصلی و محاسباتی نشان داد که باند مادون قرمز نزدیک، بیشترین مقدار همبستگی را با زی‌‌توده روی زمینی داشت (0/52-). از میان روش‌‌های پارامتریک، رگرسیون پس‌‌رو با ضریب تعیین تعدیل‌شده 0/295 و درصد مجذور میانگین مربع خطا 28/63 درصد و از میان روش‌‌های ناپارامتریک، شبکه عصبی مصنوعی با کمترین درصد مجذور میانگین مربع خطا (23/45 درصد) مناسب‌‌ترین عملکرد را برای برآورد زی‌‌توده روی ‌‌زمینی داشتند. نتایج این پژوهش را می‌‌توان ضعیف دانست. از این­‌رو، داده‌‌های سنجنده OLI ماهواره لندست 8 را فقط می‌‌توان در سطوح وسیع و به‌‌صورت کوچک‌مقیاس برای برآورد زی‌‌توده روی زمینی توده‌‌های ممرز به‌کار برد. البته برای کسب اطمینان از این نتیجه‌‌گیری کلی و تعمیم آن به جنگل‌‌های هیرکانی لازم است که مطالعات تکمیلی انجام شود.

کلیدواژه‌ها


عنوان مقاله [English]

Investigating the capability of Landsat 8 OLI data for estimation of aboveground woody biomass of common hornbeam (Carpinus betulus L.) stands in Khyroud Forest

نویسندگان [English]

  • Fardin Moradi 1
  • Ali Asghar Darvishsefat 2
  • Manouchehr Namiranian 2
  • Ghasem Ronoud 3
1 M.Sc. of Forestry, Faculty of Natural Resources, University of Tehran, Karaj, Iran
2 Prof., Faculty of Natural Resources, University of Tehran, Karaj, Iran
3 Ph.D. Student, Faculty of Natural Resources, University of Tehran, Karaj, Iran
چکیده [English]

In this research, capability of Landsat 8 OLI was studied for estimation of aboveground biomass in pure stands of the common hornbeam (Carpinus betulus L.) in Hyrcanian forests of Iran. In order to obtain in situ aboveground biomass, diameters at breast height (DBH) of all trees greater than 7.5 cm were measured in 55 sample plots. Then, in situ aboveground biomass was calculated using local volume table and specific gravity in each plot. About 70 percentages of in situ measurements (40 sample plots) were used for modeling aboveground biomass based on Landsat 8 OLI data using different methods of stepwise regression, backward regression, artificial neural network, k-nearest neighbor and random forest. Validation of the models was done using 30 percentages of in situ measurements (15 sample plots). Based on the Pearson correlation coefficient, near-infrared band showed the highest correlation with aboveground biomass (0.52). Backward regression with adjusted R2 of 0.295 and RMSE% of 28.63%, and artificial neural network with RMSE% of 23.45% showed the best performance among parametric and non-parametric methods, respectively. Based on the results, Landsat 8 OLI data seems suitable for aboveground biomass estimation in pure stands of the common hornbeam only over large areas and small scale. Although more investigations are required to verify and generalize the results to the entire Hyrcanian forests of Iran.

کلیدواژه‌ها [English]

  • Artificial Neural Network
  • biomass
  • Hyrcanian forests
  • k-nearest neighbor
  • modeling
  • random forest
  • satellite image
- Anonymous, 1995. The First Revision of Forest Management Plan of Patom District, Educational and Research Forest of the University of Tehran (Kheyroud Forest). Faculty of Natural Resources, University of Tehran, Karaj, 500p (In Persian).
- Anonymous, 2013. The Second Revision of Forest Management Plan of the Namekhaneh District, Educational and Research Forest of the University of Tehran (Kheyroud Forest). Faculty of Natural Resources, University of Tehran, Karaj, 800p (In Persian).
- Azizi, Z., Najafi, A., Fatehi, P. and Pirbavaghar, M., 2010. Forest stand volume estimation using satellite IRS_P6 (LISS-IV) data (Case study: Lirehsar, Tonekabon). Iranian Journal of Forest and Poplar Research, 18(1): 143-151 (In Persian).
- Bayati, H. and Najafi, A., 2013. Performance comparison artificial neural networks with regression analysis in trees trunk volume estimation. Journal of Forest and Wood Products (Iranian Journal of Natural Resources), 66(2): 177-191 (In Persian).
- Brown, S., 1997. Estimating Biomass and Biomass Change of Tropical Forests: A Primer. FAO Forestry Paper, vol. 134, Rome, 55p.
- Cole, T.G. and Ewel, J.J., 2006. Allometric equations for four valuable tropical tree species. Forest Ecology and Management, 229(1-3): 351-360.
- Enayati, A.A., 2011. Wood Physics. University of Tehran Press, Tehran, 265p (In Persian).
- Fatehi, P., Damm, A., Schweiger, A.K., Schaepman, M.E. and Kneubühler, M., 2015. Mapping alpine aboveground biomass from imaging spectrometer data: A comparison of two approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 3123-3139.
- Fatholahi, M., 2013.  Investigation of aboveground carbon stock estimation possibility using SPOT-HRG data (Case study: Forest of Darabkola). M.Sc. thesis, Sari Agricultural Sciences and Natural Resources University, Sari, 70p (In Persian).
- Holmgren, J., Joyce, S. and Nilsson, M. and Olsson, H., 2000. Estimating stem volume and basal area in forest compartments by combining satellite image data with field data. Scandinavian Journal of Forest Research, 15(1): 103-111.
- Kalbi, S., Fallah, A. and Shataei Joybari, Sh., 2014. Estimation of forest biophysical properties using SPOT HRG data (Case Study: Darabkola Experimental Forest). Journal of Wood & Forest Science and Technology, 20(4): 117-133 (In Persian).
- Khorrami, R., Darvishsefat, A.A. and Namiranian, M., 2008. Investigation on the capability of landsat7 ETM+ data for standing volume estimation of beech stands (Case study: Sangdeh Forests). Iranian Journal of Natural Resources, 60(4): 1281-1289 (In Persian).
- Kim Phat, N., Knorr, W. and Kim, S., 2004. Appropriate measures for conservation of terrestrial carbon stocks—analysis of trends of forest management in Southeast Asia. Forest Ecology and Management, 191(1-3): 283-299.
- Lu, D., 2005. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. International Journal of Remote Sensing, 26(12): 2509-2525.
- McRoberts, R.E., 2008. Using satellite imagery and the k-nearest neighbors technique as a bridge between strategic and management forest inventories. Remote Sensing of Environment, 112(5): 2212-2221.
- Mohammadi, J., 2008. Investigating estimation some quantitative characteristics for presentation location models using Landsat ETM+ satellite data. M.Sc. thesis, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, 78p (In Persian).
- Mohammadi, J. and Shataee, Sh., 2009. Sensitivity evaluation of spectral vegetation indices using sensitivity functions for stand volume estimation. Journal of Wood & Forest Science and Technology, 16(2): 101-120 (In Persian).
- Mohammadi, J., Shataee, Sh. and Babanezhad, M., 2011. Estimation of forest stand volume, tree density and biodiversity using Landsat ETM+ Data, comparison of linear and regression tree analyses. Procedia Environmental Sciences, 7: 299-304.
- Nalaka, G.D.A., Sivananthawerl, T. and Iqbal, M.C.M., 2013. Scaling aboveground biomass from small diameter trees. Tropical Agricultural Research, 24(2): 150-162.
- Noorian, N., Shataee, Sh., Mohammadi, J. and Yazdani, S., 2014. Estimating forest structural attributes by means of ASTER imagery and CART algorithm (Case study: Shastkolateh forest, Gorgan). Iranian Journal of Forest and Poplar Research, 22(3): 434-446.
- Poorazimy, M., Shataee, Sh., Attarchi, S. and Mohammadi, J., 2017. Estimation of aboveground biomass using Alos-Palsar data in Hyrcanian forests (Case study: ShastKalateh, Gorgan). Journal of Forest and Wood Products (Iranian Journal of Natural Resources), 70(3): 479-488.
- Rajab Pourrahmati, M., Darvishsefat, A.A., Baghdadi, N., Namiranian, M. and Soofi Mariv, H., 2015. Estimation of forest canopy height in mountainous areas using ICESat-GLAS data. Iranian Journal of Forest and Poplar Research, 23(1): 90-103.
- Ronoud, Gh. and Darvishsefat, A.A., 2018. Estimating aboveground woody biomass of Fagus orientalis stands in Hyrcanian forest of Iran using Landsat 5 satellite data (Case study: Khyroud Forest). Geographic Space, 17(60): 117-129.
- Ronoud, Gh., Darvishsefat, A.A. and Namiranian, M., 2018. Estimation of aboveground woody biomass of Fagus orientalis stands in Hyrcanian forest of Iran using OLI data (Case study: Gorazbon and Namkhaneh Districts, Kheyrud Forest). Journal of Forest and Wood Products (Iranian Journal of Natural Resources), 70(4): 559-568.
- Rostami Andargoli, M., 2008. Forest woody biomass estimation using SPOT 5 satellite data (Case study in seri 4 forest at Astara district). M.Sc. thesis, University of Guilan, Guilan, 94p (In Persian).
- Roy, P.S. and Ravan, S.A., 1996. Biomass estimation using satellite remote sensing data-An investigation on possible approaches for natural forest. Journal of Biosciences, 21(4): 535-561.
- Shataee, Sh., Kalbi, S., Fallah, A. and Pelz, D., 2012. Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms. International Journal of Remote Sensing, 33(19): 6254-6280.
- Tóth, T., Schaap, M.G. and Molnár, Z., 2008. Utilization of soil-plant interrelations through the use of multiple regression and artificial neural network in order to predict soil properties in Hungrian Solonetzic grasslands. Cereal Research Communications, 36(5): 1447-1450.
- Vafaei, S., Soosani, J., Adeli, K., Fadaei, H. and Naghavi, H., 2017. Estimation of aboveground biomass using optical and radar images (Case study: Nav-e Asalem forests, Gilan). Iranian Journal of Forest and Poplar Research, 25(2): 320-331.
- Yadav, B.K.V. and Nandy, S., 2015. Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques. Environmental Monitoring and Assessment, 187: 308.