Document Type : علمی - پژوهشی


University of Tehran


Estimating the biomass values in forests stands through remote sensing is important. It has been reported that the major reasons of uncertainty are the lack of concurrency in satellite data and field information as well as the use of global allometric equations for estimating the weight of biomass of forest trees inside the country. Minimizing the above problems and the investigation of data performance in developing appropriate model for estimating the forest biomass in the Bankoll region of Karazan District of Sirvan County in Ilam province using Sentinel-1 satellite data in 27th of June, 2017 was the main goal of this study. Average size of the trees crown in 53 rectangular plots related to the coppice growth form with dimensions of 30×30 mwhich during 23 may 2017 to 10 June 2017 through applying DGPS by RTK method have been implemented on the ground were entered in the process of estimation the value of biomass. The average harvested field biomass was 10.63 Mg ha-1. After extraction of radar features, those features which had the greatest correlation with the values of biomass were selected using genetic algorithm by two models including K-Nearest Neighbor (K-NN) regression and Support-Vector Regression (SVR), then the most appropriate combination was identified and the biomass values were modelled. Models were validated using 26 test plots. Correlation of features obtained from radar data and the value of biomass indicated that features of VH، Mean VV، Mean VV GLCM (Correlation) and Mean VH GLCM (Dissimilarity) had the greatest sensitivity towards the value of biomass. Using regression models indicated that SVR model (Relative RMSE of 0.08) was more precise compared with K-NN regression (relative RMSE of 0.10). The best combination in the use of K-NN regression model with a relative RMSE of almost 0.99 Mg ha-1 (equal to 10%) and the coefficient of determination (R2) of 0.22 and the best combination when using SVR model was a relative RMSE of 0.87 Mg ha-1 (equal to 8%) and the R2 of 0.14. The results indicated that the final models, obtained from the optimal features extracted from radar data in the wavelength of C band and used parametric and non-parametric regressional methods in this research, were not abled to improve the saturated effect in data for estimation of biomass in the studied forests and it was not resulted in presenting an estimating model with an acceptable accuracy.


Amini, J. & Sadeghi, Y., 2013, Optical and Radar Images in Modeling the Forests Biomass in North of Iran.
Amini, J. & Sumantyo, J.T.S., 2009, Employing a Method on SAR and Optical Images for Forest Biomass Estimation, IEEE Transactions on Geoscience and Remote Sensing, 47(12), PP. 4020-4026.
Attarchi, S. & Gloaguen, R., 2014, Improving the Estimation of Above-Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran), Remote Sensing, 6(5), PP. 3693-3715.
Attarod, P., Sadeghi, S.M.M., Sarteshnizi, F.T., Saroei, S., Abbasian, P., Masihpoor, M., Kordrostami, F. & Dirikvandi, A., 2016, Meteorological Parameters and Evapotranspiration Affecting the Zagros Forests Decline in Lorestan Province, Iranian Journal of Forest and Range Protection Research, 13(2).
Baghdadi, N., Le Maire, G., Bailly, J.S., Osé, K., Nouvellon, Y., Zribi, M., Lemos, C. & Hakamada, R., 2014, Evaluation of ALOS/PALSAR L-Band Data for the Estimation of EucalyptusPlantations Above-Ground Biomass in Brazil, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8), PP. 3802-3811.
Behera, M.D., Tripathi, P., Mishra, B., Kumar, S., Chitale, V.S. & Behera, S.K., 2016, Above-Ground Biomass and Carbon Estimates of Shorea Robusta and Tectona Grandis Forests Using QuadPOL ALOS PALSAR Data, Advances in Space Research, 57(2), PP. 552-561.
Burrows, W.H., Hoffmann, M.B., Compton, J.F., Back, P.V. & Tait, L.J., 2000, Allometric Relationships and Community Biomass Estimates for Some Dominant Eucalypts in Central Queensland Woodlands, Australian Journal of Botany, 48(6), PP. 707-714.
Chang, J. & Shoshany, M., 2016, Mediterra-nean Shrublands Biomass Estimation Using Sentinel-1 and Sentinel-2, In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (PP. 5300-5303), IEEE.
Charlton, M.H., Docherty, R. & Hutchings, M.G., 1995, Quantitative Structure–Sublimation Enthalpy Relationship Studied by Neural Networks, Theoretical Crystal Packing Calculations and Multilinear Regression Analysis, Journal of the Chemical Society, Perkin Transactions 2, (11), PP. 2023-2030.
Chen, L., Ren, C., Zhang, B., Wang, Z. & Xi, Y., 2018, Estimation of Forest Above-Ground Biomass by Geographi-cally Weighted Regression and Machine Learning with Sentinel imagery, Forests, 9(10), P. 582.
Deng, S., Katoh, M., Guan, Q., Yin, N. & Li, M., 2014, Estimating Forest Above-Ground Biomass by Combining ALOS PALSAR and WorldView-2 Data: A Case Study at Purple Mountain National Park, Nanjing, China, Remote Sensing, 6(9), PP. 7878-7910.
Eini-Zinab, S., Maghsoudi, Y. & Sayedain, S.A., 2019, Assessing the Performance of Indicators Resulting from Three-Component Freeman–Durden Polarimetric SAR Interferometry Decomposition at P-and L-band in Estimating Tropical Forest Above-Ground Biomass, International Journal of Remote Sensing, 41(2), PP. 433-454.
Galidaki, G., Zianis, D., Gitas, I., Radoglou, K., Karathanassi, V., Tsakiri–Strati, M., Woodhouse, I. & Mallinis, G., 2017, Vegetation Biomass Estimation with Remote Sensing: Focus on Forest and Other Wooded Land over the Mediterranean Ecosystem, International Journal of Remote Sensing, 38(7), PP. 1940-1966.
Ghasemi, N., Sahebi, M.R. & Mohammadzadeh, A., 2012, Biomass Estimation of a Temperate Deciduous Forest Using Wavelet Analysis, IEEE Transactions on Geoscience and Remote Sensing, 51(2), PP. 765-776.
Haddadi G., A., Sahebi, M.R. & Mansourian, A., 2011, Polarimetric SAR Feature Selection Using a Genetic Algorithm, Canadian Journal of Remote Sensing, 37(1), PP. 27-36.
Heydari, M., Pourbabaei, H. & Esmailzadeh, O., 2015, The Effects of Habitat Characteristics and Human Destruc-tions on Understory Plant Species Biodiversity and Soil in Zagros Forest Ecosystem, Iranian Journal of Biology, 28(3), PP. 535-548.
Iranmanesh, Y., Talebi, K.S., Sohrabi, H., Jalali, S.G. & Hosseini, S.M., 2014, Biomass and Carbon Stocks of Brant's Oak (Quercus Brantii Lindl.) in Two Vegetation Forms in Lordegan, Chaharmahal & Bakhtiari Forests, Iranian Journal of Forest and Poplar Research, 22(4).
Laurin, G.V., Balling, J., Corona, P., Mattioli, W., Papale, D., Puletti, N., Rizzo, M., Truckenbrodt, J. & Urban, M., 2018, Above-Ground Biomass Prediction by Sentinel-1 Multi-temporal Data in Central Italy with Integration of ALOS2 and Sentinel-2 Data, Journal of Applied Remote Sensing, 12(1), P. 016008.
Marvie Mohajer M.R., 2006, Silviculture, Tehran, University of Tehran: 404.
Namiranian, M., 2010, Measerment of the Tree and Forest Biometry, University of Tehran press.
Nichol, J.E. & Sarker, M.L.R., 2010, Improved Biomass Estimation Using the Texture Parameters of Two High-Resolution Optical Sensors, IEEE Transactions on Geoscience and Remote Sensing, 49(3), PP. 930-948.
Ramezani, M.R. & Sahebi, M.R., 2015, Forest Biomass Estimation Using SAR and Optical Images, Journal of Geospatial Information Technology, 3(1), PP. 15-26.
Ronoud, G. & Darvishsefat, A.A., 2016, Estimating Above-Ground Woody Biomass of Fagus Orientalis Stands in Hyrcanian Forest of Iran Using Landsat 8 Satellite Data (Case Study: Khyroud Forest), A thesis of Master Student in Forest Science, University of Tehran.
Santi, E., Paloscia, S., Pettinato, S., Fontanelli, G., Mura, M., Zolli, C., Maselli, F., Chiesi, M., Bottai, L. & Chirici, G., 2017, The Potential of Multifrequency SAR Images for Estimating Forest Biomass in Mediterranean Areas, Remote Sensing of Environment, 200, PP. 63-73.
Shataee, S., Kalbi, S., Fallah, A. & 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), PP. 6254-6280.
Smola, A.J. & Schölkopf, B., 2004, A Tutorial on Support Vector Regression, Statistics and Computing, 14(3), PP. 199-222.
Sohrabi, H. & Shirvani, A., 2012, Allometric Equations for Estimating Standing Biomass of Atlantic Pistache (Pistacia Atlantica Var. Mutica) in Khojir National Park, Iranian Journal of Forest, 4(1), PP. 55-64.
Souza, D.V., Nievola, J.C., Santos, J.X., Wojciechowski, J., Gonçalves, A.L., Corte, A.P.D. & Sanquetta, C.R., 2019, k-Nearest Neighbor Regression in the Estimation of Tectona G Randis Trunk Volume in the State of Pará, Brazil, Journal of Sustainable Forestry, 38(8), PP. 755-768.
Sukawattanavijit, C., Chen, J. & Zhang, H., 2017, GA-SVM Algorithm for Improving Land-Cover Classification Using SAR and Optical Remote Sensing Data, IEEE Geoscience and Remote Sensing Letters, 14(3), PP. 284-288.
Vafaei, S., Soosani, J., Adeli, K., Fadaei, H. & Naghavi, H., 2017, Estimation of Above-Ground Biomass Using Optical and Radar Images (Case Study: Nav-e Asalem Forests, Gilan), Iranian Journal of Forest and Poplar Research, 25(2).
Vose, M.D., 1999. The Simple Genetic Algorithm: Foundations and Theory, Vol. 12, MIT press.
Wang, X. & Ge, L., 2012, Evaluation of Filters for ENVISAT ASAR Speckle Suppression in Pasture Area.
West, P.W. & West, P.W., 2009, Tree and Forest Measurement, Heidelberg: Springer.
Wu, X. & Kumar, V. (eds.), 2009, The Top Ten Algorithms in Data Mining, CRC press.
Yadav, B.K. and Nandy, S., 2015. Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques. Environmental monitoring and assessment, 187(5), p.308.