- Mohebbi Gargari, R., Bayat Kashkoli, A., & Moazami, V. (2018). Survey of effective criteria for sustainable development of poplar wood farming in Iran by pair comparisons method. Iranian Journal of Wood and Paper Industries, 9(2), 49-235.
- Lashkarbolouki, E, Pourtahmasi, K., Oladi R., Kalagari, R, Alizadeh, H.) 2016.( Recognition and ratting off effecting indexes on the consumption of pulp and paper industry production from different poplar plantation sites in Iran. Iranian Journal of Wood and Paper Industries, 7(3), 36-425.
- Bombrun, M., Dash, J.P., Pont, D., Watt, M.S., Pearse, G.D., Dungey, H.S. )2020.( Forest-scale phenotyping: Productivity characterisation through machine learning. Frontiers in Plant Science, 11:99.
- Lhotka, J.M., & Loewenstein, E.F. )2011). An individual-tree diameter growth model for managed uneven-aged oak-shortleaf pine stands in the Ozark highlands of Missouri, USA. Forest Ecology and Management, 261(3):8-770.
- Sharma, R., Vacek, Z., & Vacek, S. ) 2016.( Nonlinear mixed effect height-diameter model for mixed species forests in the central part of the Czech Republic. Journal of Forest Science, 62(10): 470-484.
- Rex, F.E., Silva, C.A., Dalla Corte, A.P., Klauberg, C., Mohan, M., Cardil, A., da Silva, V.S., de Ameida, D.R.A., Garcia, M., Braodbent, E.N., Valbuena R., Stoddart, J., Merrick, T., & Hudak, A.T. (2020). Comparison of statistical modelling approaches for estimating tropical forest aboveground biomass stock and reporting their changes in low-intensity logging areas using multi-temporal LiDAR data. Remote Sensing, 12(9): 1498.
- Bayat, M., Pukkala, T., Namiranian, M., & Zobeiri, M. (2013). Productivity and optimal management of the uneven-aged hardwood forests of Hyrcania. European Journal of Forest Research, 132(5), 64-851.
- Ou, Q., Lei, X., & Shen, C. (2019). Individual tree diameter growth models of larch–spruce–fir mixed forests based on machine learning algorithms. Forests, 10(2), 187.
- Diamantopoulou, M.J. (2005). Predicting fir trees stem diameters using artificial neural network models. Southern African Forestry Journal, 205(1), 44-39.
- Özçelik, R., Cao, Q.V., Trincado, G., & Göçer, N. (2018). Predicting tree height from tree diameter and dominant height using mixed-effects and quantile regression models for two species in Turkey. Forest Ecology and Management, 419, 8-240.
- Júnior, I.S.T., de Souza, J.R.M., de Sousa Lopes, L.S., Fardin, L.P., Casas, G.G., de Oliveira Neto, R.R., Leite R.V., Leite H.G. (2021). Machine learning and regression models to predict multiple tree stem volumes for teak. Southern Forests, 83(4), 294-302.
- Lacerda, T.H.S., Cabacinha, C.D., Araújo, C.A., Maia, R.D., & Lacerda, K.W.S. (2017). Artificial neural networks for estimating tree volume in the Brazilian savanna. Cerne, 23: 483-491.
- Özçelik, R., Diamantopoulou M.J., Brooks J.R., Wiant Jr H.V. (2010). Estimating tree bole volume using artificial neural network models for four species in Turkey. Journal of Environmental Management, 91(3), 53-742.
- Karatepe, Y., Diamantopoulou, M.J., Özçelik, R., & Sürücü, Z. (2022). Total tree height predictions via parametric and artificial neural network modeling approaches. iForest-Biogeosciences and Forestry, 15(2):95.
- Lima, R.B.D., Ferreira, R.L.C., da Silva, J.A.A., Alves Junior, F.T., & de Oliveira, C.P. (2021). Estimating tree volume of dry tropical forest in the Brazilian semi-Arid region: a comparison between regression and artificial neural networks. Journal of Sustainable Forestry, 40(3): 281-299.
- Bayati, H., & Najafi, A. (2013). Performance comparison artificial neural networks with regression analysis in trees trunk volume estimation. Forest and Wood Products, 66(2): 177-191.
- Leite, R.V., do Amaral, C.H., Pires, R.P., Silva, C.A., Soares, C.P.B., Macedo, R.P., da Silva, A.A.L., Broadbent, E.N., Mohan, M., & Leite, H.G. (2020). Estimating stem volume in eucalyptus plantations using airborne LiDAR: A comparison of area-and individual tree-based approaches. Remote Sensing, 12(9): 1513.
- Zobeiri M. (2005). Forest Inventory (Measurement of Tree and Forest).University of Tehran Press, Tehran (In Persian)
- Jahani, A., Kalagari, M., Modirrahmati, A., & Ghasemi, R. (2014). Determining the best stem form factor equation for populous deltoides in poplar plantations of Alborz Research Station, Karaj. Iranian Journal of Forest and Poplar Research, 22(2), 216-224.
- Gorzin, F., Namiranian, M., Omid, M., & Bayat, M. (2018). Comparison between artificial neural network and regression analysis methods to predict and estimate the volume of logging trees in the kheyroud forest of Noshahr. Forest and Wood Products, 71(2): 117-126.
- Bhering, L.L., Cruz, C.D., Peixoto, L.D, Rosado, A.M., Laviola, B.G., & Nascimento, M. (2015). Application of neural networks to predict volume in eucalyptus. Crop Breeding and Applied Biotechnology, 15, 31-125.
- Bayat, M., Namiranian, M., Omid, M., Rashidi, A., & Babaei, S. (2016). Applicability of artificial neural network for estimating the forest growing stock. Iranian Journal of Forest and Poplar Research, 24(2),14-226.
- Lewis, C. (1982). International and Business Forecasting Methods, London, Boston, Butterworths Sceintific Publishing.
|
