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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Bibliographic Details
Main Authors:	Pablo Antúnez, Christian Wehenkel, Erickson Basave-Villalobos, Celi Gloria Calixto-Valencia, César Valenzuela-Encinas, Faustino Ruiz-Aquino, David Sarmiento-Bustos
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2025-01-01
Series:	Forest Science and Technology
Subjects:	Forest management machine learning algorithms in forestry predicting forest biomass Random Forest algorithm allometric modeling quantile regression in forest management
Online Access:	https://www.tandfonline.com/doi/10.1080/21580103.2025.2456295
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Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches

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