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

Abstract

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.