Machine learning-based geospatial assessment of forest structure characteristics and sequestration potential for informed carbon stocks inventories

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a robust ML pipeline for forest carbon estimation with uncertainty quantification, useful for remote sensing and carbon cycle science.

🏢実務担当者:Offers a practical tool for forest managers and carbon project developers to generate high-resolution carbon stock maps.

🏛政策担当者:Demonstrates potential for technology-driven improvement of national greenhouse gas inventories under the Paris Agreement.

Managed forest lands are key contributors to the carbon balance assessment needed for the greenhouse gas inventories on local, regional, national, and global levels. However, forest lands, due to size and complexity, are challenging for detailed spatially-explicit monitoring and, therefore, reliable and automatic assessment of spatial-temporal changes of carbon stocks is limited. This study presents an effective methodology for estimating key forest structure characteristics relevant to sequestration potential by combining management-level inventory data with remote sensing covariates. It primarily focuses on a machine learning (ML) pipeline that integrates an uncertainty quantification stage to support reliable decision-making and environmental analysis. We evaluated three algorithms - Random Forest (RF), Extreme Gradient Boosting (XGBoost), and TabNet-applied for multispectral satellite measurements. Predictions were made at the stand level inventory data, addressing classification tasks for dominant species and age group, and regression tasks for timber stock, stand height, and average basal area. Forest carbon stock was also derived as a target variable. XGBoost achieved the best overall performance across tasks. For regression, it reached mean absolute percentage error (MAPE) equal to 0.18 for height; 0.24 for basal area; 0.47 for timber stock and 0.37 for carbon stock. The coefficient of determination ([Formula: see text]) of 0.68 across all regression tasks. For classification, XGBoost achieved an average F1-score of 0.70 for age group prediction and 0.83 for dominant species prediction. To address the 'black-box' nature of machine learning models and enhance interpretability, we incorporated a refinement of conformal prediction to quantify predictive uncertainty at a nominal 90% coverage level. As a result, a geospatial mapping tool was developed, enabling the generation of stand-level forest attributes at 10 m spatial resolution, together with corresponding uncertainty estimates, supporting more informed forest management and carbon accounting.

• openalex https://doi.org/10.1038/s41598-026-50929-wfirst seen 2026-05-25 04:42:01 · last seen 2026-05-27 04:32:06