Estimation of Individual Tree Height in Eucalyptus spp. Plantations Using Regression Methods and Machine Learning Algorithms in Rio Grande do Sul
Abstract
The estimation of total tree height is a fundamental step in forest inventories, being essential for quantifying timber stock and supporting management planning in planted forests. However, direct measurement of this variable requires considerable operational effort and is usually performed only on subsamples, making it necessary to use hypsometric models to estimate the height of remaining trees. In this context, the present study aimed to configure, train, and validate regression models and machine learning algorithms to estimate the total height of individual trees in Eucalyptus spp. plantations located in Lavras do Sul, Rio Grande do Sul, Brazil. Two areas with different ages (12 and 15 years) were analyzed, totaling 395 trees measured for diameter at breast height (DBH) and total height. Logarithmic and N¨aslund models were fitted, along with Random Forest and Artificial Neural Network algorithms, using data partitioning into training (80%) and validation (20%) datasets. Model performance was evaluated using root mean square error (RMSE), percentage bias (BIAS), and coefficient of determination (R²). For the overall dataset, the logarithmic model showed the best predictive performance. In the 12-year-old stand, the N¨aslund model presented better results, whereas in the 15-year-old stand, the Artificial Neural Network achieved superior performance. Overall, all tested techniques demonstrated satisfactory performance for total height estimation, indicating that regression methods remain efficient and operationally simpler, while machine learning algorithms tend to provide advantages for larger and more complex datasets. The integration of traditional statistical approaches and artificial intelligence represents a promising alternative for advancing forest modeling.
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Marangon, G. P.; Duran, P. P. M.; Schunemann, A. L.; Silveira, B. D. D.; Bueno, G. D.; Lisboa, G. D. S. (2026). Estimation of Individual Tree Height in Eucalyptus spp. Plantations Using Regression Methods and Machine Learning Algorithms in Rio Grande do Sul. TreeDimensional Journal, 16(e026288), 1-10. https://doi.org/10.55746/treed.2026.04.288.
@article{marangon2026,
title={Estimation of Individual Tree Height in Eucalyptus spp. Plantations Using Regression Methods and Machine Learning Algorithms in Rio Grande do Sul},
author={Marangon, Gabriel Paes and Duran, Pietro Pimentel Morales and Schunemann, Adriano Luis and Silveira, Bruna Denardin da and Bueno, Giuliano Dalenogare and Lisboa, Gerson dos Santos},
journal={TreeDimensional Journal},
year={2026},
volume={16},
number={e026288},
pages={1-10},
doi={10.55746/treed.2026.04.288}
}TY - JOUR AU - Marangon, Gabriel Paes AU - Duran, Pietro Pimentel Morales AU - Schunemann, Adriano Luis AU - Silveira, Bruna Denardin da AU - Bueno, Giuliano Dalenogare AU - Lisboa, Gerson dos Santos TI - Estimation of Individual Tree Height in Eucalyptus spp. Plantations Using Regression Methods and Machine Learning Algorithms in Rio Grande do Sul JO - TreeDimensional Journal PY - 2026 VL - 16 IS - e026288 SP - 1 EP - 10 DO - 10.55746/treed.2026.04.288 AB - The estimation of total tree height is a fundamental step in forest inventories, being essential for quantifying timber stock and supporting management planning in planted forests. However, direct measurement of this variable requires considerable operational effort and is usually performed only on subsamples, making it necessary to use hypsometric models to estimate the height of remaining trees. In this context, the present study aimed to configure, train, and validate regression models and machine learning algorithms to estimate the total height of individual trees in Eucalyptus spp. plantations located in Lavras do Sul, Rio Grande do Sul, Brazil. Two areas with different ages (12 and 15 years) were analyzed, totaling 395 trees measured for diameter at breast height (DBH) and total height. Logarithmic and N¨aslund models were fitted, along with Random Forest and Artificial Neural Network algorithms, using data partitioning into training (80%) and validation (20%) datasets. Model performance was evaluated using root mean square error (RMSE), percentage bias (BIAS), and coefficient of determination (R²). For the overall dataset, the logarithmic model showed the best predictive performance. In the 12-year-old stand, the N¨aslund model presented better results, whereas in the 15-year-old stand, the Artificial Neural Network achieved superior performance. Overall, all tested techniques demonstrated satisfactory performance for total height estimation, indicating that regression methods remain efficient and operationally simpler, while machine learning algorithms tend to provide advantages for larger and more complex datasets. The integration of traditional statistical approaches and artificial intelligence represents a promising alternative for advancing forest modeling. KW - Forest management KW - Forest Plantations KW - Forest Inventory KW - Hypsometric Relationship KW - Forest Modeling and Simulation. ER -
Marangon, G. P.; Duran, P. P. M.; Schunemann, A. L.; Silveira, B. D. D.; Bueno, G. D.; Lisboa, G. D. S. (2026). Estimation of Individual Tree Height in Eucalyptus spp. Plantations Using Regression Methods and Machine Learning Algorithms in Rio Grande do Sul. TreeDimensional Journal, 16(e026288), 1-10. https://doi.org/10.55746/treed.2026.04.288. Import via Mendeley Web Importer using DOI 10.55746/treed.2026.04.288
Add to Zotero using DOI 10.55746/treed.2026.04.288 Marangon, G. P.; Duran, P. P. M.; Schunemann, A. L.; Silveira, B. D. D.; Bueno, G. D.; Lisboa, G. D. S. (2026). Estimation of Individual Tree Height in Eucalyptus spp. Plantations Using Regression Methods and Machine Learning Algorithms in Rio Grande do Sul. TreeDimensional Journal, 16(e026288), 1-10. https://doi.org/10.55746/treed.2026.04.288.
Marangon, G. P.; Duran, P. P. M.; Schunemann, A. L.; Silveira, B. D. D.; Bueno, G. D.; Lisboa, G. D. S. (2026). Estimation of Individual Tree Height in Eucalyptus spp. Plantations Using Regression Methods and Machine Learning Algorithms in Rio Grande do Sul. TreeDimensional Journal, 16(e026288), 1-10. https://doi.org/10.55746/treed.2026.04.288.