Iranian Journal of Forest

Iranian Journal of Forest

Evaluation of tree basal area increment models using machine learning algorithms

Document Type : Research Paper

Authors
1 Assistant Prof., Dept. of Forestry, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resource University, Sari, I. R. Iran
2 Prof., Dept. of Forestry, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resource University, Sari, I. R. Iran
Abstract
Forest management planning is a critical decision-making tool in forestry, resulting in a plan that outlines anticipated activities, their timing, and control measures to achieve forest management goals. Investigating growth and product models is one of the most important methods for obtaining information about the future state of a forest. In other words, assessing stand growth and yield is a basic prerequisite for forest management planning. Therefore, determining and estimating the basal area increment of trees is crucial for understanding forest dynamics and informing planning and management efforts. Since  Hyrcanian forest species are considered among the most valuable, this study aims to investigate basal area increment using machine learning algorithms and model itin the uneven-aged forest of Farim in Mazandaran province. In this study, the basal area increment (BAI) of trees was modeled using Machine Learning (ML) algorithms (Artificial Neural Networks, Support Vector Machine, Random Forest, and Generalized Additive Model) over 10 years. Biometric indices (e.g., diameter, height, basal area, basal area of the largest trees), physiographic factors (aspect, slope, altitude), and climatic variables (temperature, precipitation, evaporation and transpiration) were used as input for model development. The performance  of the machine learning algorithms were compared using bias, RMSE, and R2. The ANN model, specifically an MLP network with seven hidden layer neurons, achieved the highest accuracy (88%) in predicting basal area increment compared to other models. These results demonstrate the effectiveness of ANN models for accurately modeling basal area increment, making them valuable tools in forest management. The strong performance of the generated models, attributed to their optimal structure (e.g., number of neurons, activation function, and input variables), highlights their stability and generalization capacity  across diverse datasets. The potential to improve forest parameter modeling using machine learning techniques, specifically ANN, is crucial for sustainable forest management. Such improvements can enhance the conservation of species composition and the structural characteristics of the forest.
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Adjuik, T.A., & Davis, S.C., (2022). Machine Learning Approach to Simulate Soil CO2 Fluxes under Cropping Systems. Agronomy, 12(1), 197.
Baskent, E.Z., & Keles, S. (2005). Spatial forest planning: Areview. Ecological modeling, 188,145-173.
Barbosa, L.O., Costa, E.A., Schons, C.T., Finger, C.A.G., Liesenberg, V., & Bispo, P.d.C. (2022). Individual Tree Basal Area Increment Models for Brazilian Pine (Araucaria angustifolia) Using Artificial Neural Networks. Forests13, 1108. https://doi.org/10.3390/f13071108.
Bayat, M., & Hamidi, S. K. (2019). Investigation some of the biotic and abiotic variables effective on the diameter increment of the beech trees at fixed sample plots level by growth models. Ecology of Iranian Forest7(13), 91-99.‏
Bayat, M., Bettinger, P., Heidari, S., Henareh Khalyani, A., Jourgholami, M., & Hamidi, S. K. (2020). Estimation of tree heights in an uneven-aged, mixed forest in northern Iran using artificial intelligence and empirical models. Forests11(3), 324.‏
Bayat, M., Hamidi, S.K., & Sadeghzadeh, M.H. (2019). Investigation some of the Biotic and Abiotic Variables Effective on the Diameter Increment of the Beech Trees at Fixed Sample Plots level by Growth Models. Ecology of Iranian Forests7(13), 91-99. doi:10.29252/ifej.7.13.91. (In Persian)
Bayat, M., Knoke, T., Heidari, S., Hamidi, S. K., Burkhart, H., & Jaafari, A. (2022). Modeling tree growth responses to climate change: a case study in natural deciduous mountain forests. Forests13(11), 1816.‏
Bayat, M., Burkhart, H., Namiranian, M., Hamidi, S. K., Heidari, S., & Hassani, M. (2021). Assessing biotic and abiotic effects on biodiversity index using machine learning. Forests12(4), 461.‏ https://doi.org/10.3390/f12040461
Bayat, M., Bettinger, P., Masteali, S. H., Hamidi, S. K., Masood Awan, H. U., & Abolhasani, A. (2023). Recreation potential assessment at Tamarix forest reserves: A method based on multicriteria evaluation approach and landscape metrics. Forests14(4), 705.‏
Bettinger, P., Gratez, D., & Sessions, J. (2005). A Density- dependent stand-level optimization approach for deriving management prescriptions for interior northwest (USA) landscapes. Forest Ecology and Management, 217(2-3), 171-186.
Breiman, L. (2001). Random forests. Machine learning45, 5-32.‏
Burkhart, H.E. (1990). Status and future of growth and yield models. In: proc. A symp. On state-of the methodology of forest inventory. USDA Forest Service, 283, 409-414.
Crecente-Campo, F., Tomé, M., Soares, P., & Diéguez-Aranda, U. (2010). A generalized nonlinear mixed-effects height-diameter model for Eucalyptus gobulus L. in northwestern Spain. Forest Ecology Management, 259(5), 943–952. https://doi.org/10.1016/j.foreco.2009.11.036.
da Rocha, S. J. S. S., Torres, C. M. M. E., Jacovine, L. A. G., Leite, H. G., Gelcer, E. M., Neves, K. M., ... & Zanuncio, J. C. (2018). Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil. Science of the total environment645, 655-661.‏
Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine learning40, 139-157.‏
Eisfeld, R.D.L., Arce, J.E., Sanquetta, C.R., & Braz, E.M. (2019). Is it forbidden the wood use of Araucaria angustifolia? An analysis on the current legal budget. Floresta, 50, 971–982.
Ghaderi, I., Hassanzad Navroodi, I., & Torkaman, J. (2013). Effect of altitude on annual diameter growth of Quercus libani Oliv in Kurdistan province. Iranian Journal of plant research, 26(4), 434-443. (In Persian)
Görgens, E. B., Montaghi, A., & Rodriguez, L. C. E. (2015). A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics. Computers and Electronics in Agriculture116, 221-227.‏
Hai, F.Z., Xue, M.S., Zhi, Y.Y., Peng, X., Yan, X., & Hua, T. (2011). August temperature variability in the southeastern Tibetan Plateau since A.D.1385 inferred from tree rings. PALAEO, 5, 703.
Hamidi, S.K., Fallah, A., Bayat, M., & Hosseini Yekani, S.A. (2017). Determining the Forest Volume Growth using Permanent Sample Plots (Case Study: Farim Forest, Jojadeh District). Ecology of Iranian Forests 4(8), 1–8. (In Persian)
Hamidi, S.K., Fallah, A., Bayat, M., & Hosseini Yekani, S.A. (2019). Individual Tree Growth Models for Management of Uneven aged and Mixed Hyrcanian Forests (Case Study: Farim Forest). Iranian Journal of Forest, 3(11), 373–386. (In Persian)
Hamidi, S. K., Zenner, E. K., Bayat, M., & Fallah, A. (2021). Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest. Annals of Forest Science78, 1-16. https://doi.org/10.1007/s13595-020-01011-6
Hamidi, S. K., Weiskittel, A., Bayat, M., & Fallah, A. (2021). Development of individual tree growth and yield model across multiple contrasting species using nonparametric and parametric methods in the Hyrcanian forests of northern Iran. European Journal of Forest Research140(2), 421-434.‏https://doi.org/10.1007/s10342-020-01340-1
Hamidi, S.K., Fallah, A., Bayat, M., & Hosseini, Y.S.A. (2021). Investigating the diameter and height models of beech trees in uneven age forest of Northern Iran (Case study: Forest Farim). Ecology of Iranian Forests9(17), 30-40. doi:10.52547/ifej.9.17.30 (In Persian)
Hamidi, S. K., de Luis, M., Bourque, C. P. A., Bayat, M., & Serrano-Notivoli, R. (2023). Projected biodiversity in the Hyrcanian Mountain Forest of Iran: An investigation based on two climate scenarios. Biodiversity and Conservation32(12), 3791-3808.‏ https://doi.org/10.1007/s10531-022-02470-1
Hamidi, S.K., Fallah, A., Nazaryani, N. (2023). Modelling the most appropriate vegetation indices under the influence of climatic factors using sentinel 2 images - Case study: Farim Forest. Scientific- Research Quarterly of Geographical Data (SEPEHR), 32(127), 55-76. doi: 10.22131/sepehr, (2023).1983358.2935. (In Persian)
Hamidi, S.K., & Fallah, A. (2024). Evaluation of the Performance of Climatic Scenarios in the Basal Area Model of Trees (Case Study: the Farim Forest). Ecology Iranian Forest, 12(2), 1-14. doi:10.61186/ifej.12.2. (In Persian)
Huang, H. X., Li, J. C., & Xiao, C. L. (2015). A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm. Expert Systems with Applications42(1), 146-155.‏
Huy, B., Poudel, K.P., & Temesgen, H. (2021). Individual tree diameter growth modeling system for Dalat pine (Pinus dalatensis Ferré) of the upland mixed tropical forests. Forest Ecology and Management, 480, 118612.
IPCC. (2019). Summary for policymakers. In: Abe-Ouchi, A., Gupta, K., Pereira, J. (Eds.), IPCC Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities.
Jafarian, Z., & Karger, M. (2016). Modeling the distribution of protective and valuable plant species in the Pleur tourist area using the generalized linear model (GLM) and the generalized aggregate model (GAM). Geography and Development Quarterly, 15(4), 117-132.
Kisi, O., & Kilic, Y. (2016). An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration. Theoretical and applied climatology126, 413-425.‏
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors18(8), 2674.‏
Lessard, V.C., McRoberts, R.E., & Holdaway, M.R. )2001(. Diameter growth models using Minnesota forest inventory and analysis data. Forest Science, 47(3), 301-310.
Lohmander, P. (2017) A general dynamic function for the basal area of individual trees derived from a production theoretically motivated autonomous differential equation. Interdisciplinary Journal of Management Studies, 10, 917-928.
Mauya, E. W., Hansen, E. H., Gobakken, T., Bollandsås, O. M., Malimbwi, R. E., & Næsset, E. (2015). Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania. Carbon balance and management10, 1-14. doi: 10.1186/s13021-015-0021-x.
Moreno, P.C., Palmas, S., Escobedo, F.J., Cropper, W.P., & Gezan, S.A. (2017). Individual-tree diameter growth models for mixed Nothofagus second growth forests in southern Chile. Forests, 8, 12. 506.
Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives31(2), 87-106.‏
Nazariani, N., Fallah, A., Hamidi, S. K., & Varamesh, S. (2022). Estimation of quantitative characteristics of Zagros forests using data mining‎ nonparametric algorithms (case study: Olad Ghobad Watershed, Koohdasht, Lorestan)‎. Forest Research and Development8(3), 249-263.‏ (In Persian)
Nguyen, T. T., Huang, J. Z., & Nguyen, T. T. (2015). Unbiased Feature Selection in Learning Random Forests for High‐Dimensional Data. The Scientific World Journal2015(1), 471371.‏ DOI: 10.1155/2015/471371
Pokharel, B., & Froese, R.E. (2009). Representing site productivity in the basal area increment model for FVS-Ontario. Forest Ecology and Management, 258, 666-675.
Rees, F., Doherty, M., Grainge, M. J., Lanyon, P., & Zhang, W. (2017). The worldwide incidence and prevalence of systemic lupus erythematosus: a systematic review of epidemiological studies. Rheumatology56(11), 1945-1961.‏
Silva, J.P.M., da Silva, M.L.M., de Mendonça, A.R., da Silva, G.F., de Barros Junior, A.A., da Silva, E.F., Aguiar, M.O., Santos, J.S., & Rodrigues, N.M.M. (2023). Prognosis of forest production using machine learning techniques. Information Processing in Agriculture, 10(1), 71-84.
Sharma, M., & Parton, J. (2007). Height-diameter Equations for boreal tree species in Ontario using a mixed-effects modeling approach.  Forest Ecology Management, 249, 187-198.
Sharma, R.P., Stefancik, I., Vacek, Z., & Vacek, S. (2019). Generalized nonlinear mixed-effects individual tree diameter increment models for beech forests in Slovakia. Forests, 10(5), 451.
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 sensing33(19), 6254-6280.‏
Stavins, Robert N., and Kenneth R. Richards. 2005. “The Cost of U.S. Forest-Based Carbon Sequestration.” Arlington, VA: Pew Center on Global Climate Change.
Stepka, T.F., de Mattos, P.P., Figueiredo Filho, A., Braz, E.M., do Amaral Machado, S. (2021). Growth of Araucaria angustifoliaCedrela fissilis and Ocotea porosa in different locations within the Araucaria Forest in the Southern Brazil. Forest Ecology Management486, 118941.
Strobl, R. O., & Forte, F. (2007). Artificial neural network exploration of the influential factors in drainage network derivation. Hydrological Processes: An International Journal21(22), 2965-2978.‏
Townsend, P. A. (2002). Estimating forest structure in wetlands using multitemporal SAR. Remote sensing of environment79(2-3), 288-304.‏
Vahedi, A.A., Fallah, A., Akhavan, R., Nazariani, N., Khatibnia, E., & Hamidi, S.K. (2024). Spatial Analyses for Fine Woody Debris Volume Stock in the Hyrcanian Research Forest of Kheyrood-Kenar. Ecology Iranian Forest12(1), 73-87. doi:10.61186/ifej.12.1.73. (In Persian)
Vanclay, J. K. (1994). Sustainable timber harvesting: Simulation studies in the tropical rainforests of north Queensland. Forest ecology and management69(1-3), 299-320.‏
Vargas-Larreta, B., Castedo-Dorado, F., Alvarez-Gonzalez, J.G., Barrio-Anta, M., & Cruz-Cobos, F.  (2009).  A generalized height-diameter model with random coefficients for uneven-aged stands in El Salto, Durango (Mexico).  Forestry, 82, 445-462.
Vrushali, Y., Kulkarni, P., Pradeep, K. (2014). Effective learning and classification using Random Forest algorithm, International Journal of Engineering and Innovative Technology (IJEIT), 3(11), May (2014).
West, P.W. (2021). Modelling maximum stem basal area growth rates of individual trees of Eucalyptus pilularis Smith. Forest Science, 67(6), 633– 636. https://doi.org/10.1093/forsci/fxab047
Wykoff, W.R. (1986). Supplement to the user’s guide for the stand Prognosis model:  Version 5.0.  General Technical Report INT-208, USDA Forest Service, Intermountain Research Station, Ogden, UT pp: 36.
Wykoff, W.R. (1990). A basal area increment model for individual conifers in the northern Rocky Mountains.  Forest Science, 36, 1077-1104.
Yang, Y., Monserud, R. A., & Huang, S. (2004). An evaluation of diagnostic tests and their roles in validating forest biometric models. Canadian Journal of Forest Research34(3), 619-629.‏
Zhao, D., Borders, B., & Wilson, M. (2004). Individual-tree diameter growth and mortality models for bottomland mixed-species hardwood stands in the lower Mississippi alluvial valley. Forest Ecology and Management, 199(2-3), 307-322.
Zhao, K., Popescu, S., Meng, X., Pang, Y., & Agca, M. (2011). Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sensing of Environment115(8), 1978-1996.‏
Zhang, X., Duan, A., Zhang, J., & Xiang, C. (2014). Estimating Tree Height‐Diameter Models with the Bayesian Method. The Scientific World Journal2014(1), 683691.‏
Volume 16, Issue 5
Winter 2025
Pages 71-86

  • Receive Date 04 November 2024
  • Revise Date 16 December 2024
  • Accept Date 04 January 2025