Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees

doi:10.3832/ifor4328-016

Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees

Maria J Diamantopoulou⁽¹⁾ , Aydin Çömez⁽²⁾, Ramazan Özçelik⁽³⁾, Sükrü Teoman Güner⁽⁴⁾

iForest - Biogeosciences and Forestry, Volume 17, Issue 1, Pages 19-28 (2024)
doi: https://doi.org/10.3832/ifor4328-016
Published: Feb 12, 2024 - Copyright © 2024 SISEF

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Accurate estimates of total tree biomass are of critical importance to obtain reliable estimation of the carbon dioxide weight sequestered from the atmosphere by trees and forest stands. This information has the potential to guide appropriate forest management decisions which allow for both the improvement of forest sustainability and the implementation of multi-task reforestation designs aimed to mitigate the detrimental effects of climate change. The current laborious and tree-destructive procedures needed to attain such information has led to the development of machine learning (ML) models aimed at providing accurate estimations of the tree biomass sequestering the atmospheric carbon dioxide. We tested the Levenberg-Marquardt artificial neural network and the support vector machine for regression techniques as an alternative to non-linear allometric regression (NLR) modelling approaches commonly used for tree biomass estimation. We tested the developed ML models using primary ground-truth data from the Lebanon cedar forests in the Western Inner Anatolian regions of Turkey, and their predictions were compared to those of NLR models developed using the same dataset. The results showed that the ML approaches outperformed the NLR models in accurately estimating tree biomass and its components (above- and belowground dry biomass, dry branches biomass, etc.), and the support vector regression (SVR) models gave the highest accuracy of estimates. Therefore, the carbon dioxide weight sequestered in Lebanon cedar trees were reliably estimated, with the aim of supporting the best forest management practices to be applied in Lebanon cedar tree stands in Turkey.

Keywords

Tree Biomass, Carbon Dioxide Weight, Levenberg-Marquardt Artificial Neural Network, Support Vector Machine For Regression, Lebanon Cedar Trees

Authors’ address

(1)

0000-0002-6003-1285
Faculty of Agriculture, Forestry and Natural Environment, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, GR-54124 Thessaloniki (Greece)

(2)

Aegean Forestry Research Institute, 35515 Urla, Izmir (Turkey)

(3)

0000-0003-2132-2589
Faculty of Forestry, Isparta University of Applied Sciences, 32260 Isparta (Turkey)

(4)

0000-0002-3058-7899
Department of Forestry, Ulus Vocational School, Bartin University, 74600 Ulus, Bartin (Turkey)

Corresponding author

Maria J Diamantopoulou
mdiamant@for.auth.gr

Citation

Diamantopoulou MJ, Çömez A, Özçelik R, Güner ST (2024). Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees. iForest 17: 19-28. - doi: 10.3832/ifor4328-016

Academic Editor

Giorgio Alberti

Paper history

Received: Feb 15, 2023
Accepted: Nov 15, 2023

First online: Feb 12, 2024
Publication Date: Feb 29, 2024
Publication Time: 2.97 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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