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
Online-Zugang:	Volltext
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