Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches

This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics-stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)-for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees....

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Veröffentlicht in:Forest science and technology Jg. 21; H. 1; S. 110 - 122
Hauptverfasser: Antúnez, Pablo, Wehenkel, Christian, Basave-Villalobos, Erickson, Calixto-Valencia, Celi Gloria, Valenzuela-Encinas, César, Ruiz-Aquino, Faustino, Sarmiento-Bustos, David
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Seoul Taylor & Francis 02.01.2025
Taylor & Francis Ltd
Taylor & Francis Group
한국산림과학회
Schlagworte:
Algorithms
allometric modeling
allometry
Biomass
Branches
Effectiveness
Forest management
forests
heteroskedasticity
Learning algorithms
Least squares
Machine learning
machine learning algorithms in forestry
Neural networks
Pinus pseudostrobus
predicting forest biomass
Prediction models
quantile regression in forest management
Quantiles
Random Forest algorithm
regression analysis
root systems
Stems
Support vector machines
technology
Trees
임학
ISSN:2158-0103, 2158-0715, 2158-0715
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Zusammenfassung:This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics-stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)-for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees. The novelty of this study lies in applying five machine learning algorithms-Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)-to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. For comparison, we also applied univariate allometric models, adjusted with nonlinear least squares and quantile regression. The results indicate that Random Forest, k-NN, and SVM outperformed the other algorithms, demonstrating superior predictive accuracy for both biomass and volume. A key innovation of this study is its demonstration of how machine learning, with its ability to model complex, nonlinear relationships, can serve as a powerful tool for forest management. Quantile regression, combined with nonlinear least squares, proves most effective when the relationships are well-defined, allowing for tailored parameter adjustments that enhance predictions, particularly in the presence of heteroscedasticity. Innovative use of machine learning in forest management practices Enhanced predictive accuracy for plant biomass and volume metrics Assessment of total biomass and its components (leaves, branches, stems, and roots) Leveraging machine learning to predict essential forest metrics
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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https://www.tandfonline.com/doi/full/10.1080/21580103.2025.2456295
ISSN:2158-0103
2158-0715
2158-0715
DOI:10.1080/21580103.2025.2456295