Evaluating organic carbon in living and dead trees using GLCM features and explainable machine learning: insights from Italian national forest

Evaluating organic carbon in living and dead trees using GLCM features and explainable machine learning: insights from Italian national forest

An accurate estimation of organic carbon (OC) in forest ecosystems is essential for understanding carbon dynamics and informing climate change mitigation strategies. This study presents a novel, explainable machine learning framework to estimate two key carbon pools: carbon sequestration in living t...

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Bibliographic Details
Main Authors: Mehdi Fasihi, Alex Falcon, Giorgio Alberti, Luca Cadez, Francesca Giannetti, Antonio Tomao, Giuseppe Serra
Format: Article
Language:English
Published: Taylor & Francis Group 2025-06-01
Series:Annals of GIS
Subjects:
Organic carbon
carbon sequestration
standing deadwood
explainable machine learning
remote sensing
Online Access:https://www.tandfonline.com/doi/10.1080/19475683.2025.2523737
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