Quantifying Stand-level Carbon Using an Alternative Method: A Case Study of National Forests and Parks in Southern Appalachian Mountains

Abstract

As vital components of the global carbon cycle, forest ecosystems play an important role in sustainable forest management. With the new data collected and recent development of carbon equations in the region, there is a need to update the essential quantitative information about forest carbon for monitoring these vulnerable populations and making appropriate management decisions. This study aims to quantify stand-level aboveground live carbon using an alternative method among different national forests and parks in the southern Appalachian region. Hybrid models with linear mixed model and random forests were built using forest type as a random effect with common stand variables as predictors. Long-term data collected by the USDA Forest Service, Forest Inventory and Analysis (FIA) program were used in analyses. Results show that the average aboveground live carbon per hectare varies among study sites, ranging from 58.2 to 91.5 Mg C/ha. Monongahela National Forest and Great Smoky Mountains National Park have higher average carbon per hectare than other sites. Within a site, forest carbon per hectare varies among different forest type groups. The hybrid model can provide satisfactory predictions of total carbon at the stand level, even for data not used in model fitting. As parametric models remain the most popular quantitative methods in forestry practice, the proposed method retains the interpretability and portability of the parametric models, but also improves prediction accuracy by leveraging the strengths of the machine learning algorithm.

Citation

Yang, SI., Brandeis, T.J. & Skiba, T. 2025. Quantifying Stand-level Carbon Using an Alternative Method: A Case Study of National Forests and Parks in Southern Appalachian Mountains. J. For. (. 2025) https://doi.org/10.1007/s44392-025-00044-x