Abstract

Advancements in remote sensing, machine learning (ML), and hierarchical modeling have revolutionized the mapping and quantification of forest carbon stocks. Accurate and scalable carbon mapping is paramount for climate policy, sustainable management, and carbon finance, given the globally significant role of forests as carbon sinks. This research paper presents a comprehensive framework integrating satellite imagery with hierarchical machine learning strategies to enhance the precision and reliability of forest carbon stock assessments. The emphasis is on methodology, results, and validation, highlighting key innovations such as the sequential modeling of forest structure parameters, robust ML algorithm comparisons, and the integration of multi-source data. These innovations collectively contribute to significant improvements in the accuracy of carbon stock assessment.
The paper features detailed charts, workflow diagrams, key formulas, and visual imagery to thoroughly communicate methods and outcomes, ensuring clarity and reproducibility. The emergence of hierarchical approaches in remote sensing, leveraging both machine learning and satellite imagery, allows for the sequential modeling of structural forest parameters. This culminates in higher-fidelity carbon stock estimates compared to direct approaches. This research synthesizes the latest methodologies and demonstrates results through comparative analysis and visualizations.