Modeling the commercial volume of pure and mixed stands of beech trees using non-parametric algorithms in the educational-research Forest of Darabkola, Sari, Iran

Document Type : Research article

Authors

1 Prof., Department of Forestry, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources,, Sari, Iran

2 Postdoctoral Researcher in Forestry, Department of Forestry, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

3 Ph.D. of Forestry, Department of Forestry, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

4 M.Sc. student of Forestry, Department of Forestry, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

Abstract

   Commercial volume trees are an important variable that contributes to economic decision-making and analysis in forest management. In this regard, Commercial Volume modeling in Hyrcanian forests is the key to implementing sustainable forest management plans. Due to time and cost constraints and the lack of local or public volumetric measurements in the equations, most forest managers still use traditional expansion factors to estimate volume. Therefore, using independent variables in volumetric modeling is an essential step in fitting models to representation. Therefore, the present study aims to model and predict business volume with minimal error using two ANN and CART algorithms. The study area was parcels 14, 16 and 24 of the educational-research forest of Sari Faculty of Natural Resources located in Darabkola, one of the functions of Sari city. After rotating the forest, masses with different forest types were studied according to the purpose of the study: Trees of pure beech, pure hornbeam and beech-hornbeam. For this purpose, at least 20 trees fell in each type and all standing trees in each sample parcel were measured. In each plot, quantitative characteristics of all trees including total tree height, trunk height (length), diameter per chest and qualitative characteristics including the degree of tree rot were measured. After measuring all the required characteristics of fallen trees at the level of Study forest types by species, to accurately estimate the volume was measured. Then the actual volume of the tree was calculated in the form of different trunk parts based on the ESmalian relationship. Finally, ANN and CART algorithms were used for modeling in STATISTICA12.0 software environment. The results of modeling the commercial volume of three masses of pure beech, pure hornbeam and beech-hornbeam with two ANN and CART algorithms showed the values of R2 explanation coefficient (0.82; 0.77), (0.44; 0.72) respectively and (0.91; 0.84). The results of modeling the commercial volume of three masses of pure beech, pure hornbeam and beech-hornbeam with two algorithms ANN and CART showed that the ANN algorithm with a R2=0.91and the percentage of RMSE%= 10.51 is more precision. Finally, the findings showed that the ANN algorithm leads to better prediction than the CART algorithm. Also, the performance of this algorithm for beech-border mixed mass is higher than any of the pure beech and hornbeam masses.

Keywords


- Alijani, V., Sagheb Talebi, Kh. and Akhavan, R., 2014. Quantifying structure of intact beech (Fagus orientalis Lipsky) stands at different development stages (Case study: Kelardasht area, Mazandaran). Iranian Journal of Forest and Poplar Research, 21 (3): 396-410 (In Persian with English Summary).
- Bayat, M., Pukkala, T., Namiranian, M. and Zobeiri, M., 2013. Productivity and optimal management of the uneven-aged hardwood forests of Hyrcania. European Journal of Forest Research, 132(5–6): 851–864.
- Bayati, H. and Najafi A., 2013. Performance Comparison Artificial Neural Networks with Regression Analysis in Trees Trunk Volume Estimation. Forest and Wood Products, 2(2): 177-191 (In Persian with English Summary).
- Breiman, L., Friedman, J., Stone, C. J. and Olshen, R.  A., 1984. Classification and regression trees, CRC press.
- Che, S., Tan, X., Xiang, C., Sun, J., Hu, X., Zhang, X., Duan, A. and Zhang, J., 2019. Stand basal area modelling for Chinese fir plantations using an artificial neural network model. Journal of Forestry Research, 30(5):1641-1649.
- De Oliveira, D.V., Rode, R., de Oliveira Neto, R.R., J.R.V. Gama and Leite, H.G., 2021. Use of artificial neural networks for predicting volume of forest species in the Amazon Forest. Scientia Forestalis, 49(131).
- Diamantopoulou, M. J. and Milios, E., 2010. Modelling total volume of dominant pine trees in reforestations via multivariate analysis and artificial neural network models. Biosystems Engineering, 105(3): 306-315.
- Lawrence, R. L. and Wright, A., 2001. Rule-based classification systems using classification and regression tree (CART) analysis, Photogrammetric Engineering and Remote Sensing, 67(10): 1137-1142.
- Lhotka, J.M. and 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): 770–778.
- Mehtatalo, L. 2020. Biometry for Forestry and Environmental Data: With Examples in R. CRC Press, Boca Raton, FL.
- Moridi, M., Fallah, A., Pourmajidian, M.R., Sefidi, K., 2021. Quantitative Analysis of Forest Structure at Growing Up Volume Stage in the Evaluation of Natural Beech Stands (Case Study: Kheyroud Forest). Iranian Journal of Forest, 13(2): 115-128 (In Persian with English Summary).
- 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 (In Persian with English Summary).
- Ozcelik, R., Diamantopoulou, M.J., Brooks, J.R. and Wiant, H.V., 2010. Estimating tree bole volume using artificial neural network models for four species in Turkey. Journal of Environmental Management, 91(3): 742-753.
- Pourhashemi, M., Eskandari, S., Dehghani, M., Najafi, T., Asadi, A. and Panahi, P., 2012. Biomass and leaf area index of Caucasian Hackberry (Celtis caucasica Willd.) in Taileh urban forest, Sanandaj, Iran. Iranian Journal of Forest and Poplar Research, 19(4): 609-620 (In Persian with English Summary).
- Reis, L.P., Souza, A.L., Reis, P.C.M., Mazzei, L., Soares, C.P.B., Torres, C.M.M.E., Silva, L.F., Ruschel, A.R. and Rêgo, L.J.S., 2018. Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest. Ecological Engineering, 112: 140–147.
- Sadeghi Kaji, H. and Soltani, A., .2017. Single tree volume modeling for even age Persian oak (Quercus brantii) coppice in Central Zagros (Case study: Chaharmahal VA Bakhtiari province, Ardal and Kiar district). Iranian Journal of Forest, 9(3): 361-372 (In Persian with English Summary).
- Tavares Júnior, I.D.S., de Souza, J.R.M., Lopes, L.S.D.S., Fardin, L.P., Casas, G.G., Oliveira Neto, R.R.D., Leite, R.V. and Leite, H.G., 2022. Machine learning and regression models to predict multiple trees stem volumes for teak. Southern Forests: Journal of Forest Science, 1-9.
- Tiryaki, S. and Aydin, A., 2014. An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Construction and Building Materials, 62: 102-108.
- Tooke, T. R., Coops, N. C., Goodwin, N. R. and Voogt, J. A., 2009. Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications. Remote Sensing of Environment. 113: 398–407.
- Vahedi, A. A., 2016. Simulating commercial biomass in the Hyrcanian mixed-beech stands. Iranian Journal of Forest and Poplar Research, 24(3): 451-462 (In Persian with English Summary).
- Vahedi, A., Mataji, A. and Akhavan, R., 2017. Modeling the commercial volume of trees in mixed beech stands of Hyrcanian forests through artificial neural network. Forest and Wood Products, 70(1):49-60 (In Persian with English Summary).
- Zobeiri, M., 2005. Forest inventory measurement of tree and forest. Second edition, University of Tehran Publication and Printing Institute, Tehran, Iran, 401p (In Persian with English Summary).