Accurate estimation of aboveground biomass in Moso bamboo (Phyllostachys edulis) forests under Pantana phyllostachysae Chao stress using UAV multispectral remote sensing and self‐establish allometric equations

Abstract BACKGROUND Moso bamboo ( Phyllostachys edulis ) plays a pivotal role in the global carbon cycle because of its rapid growth and significant ecological benefits. Accurate estimation of its aboveground biomass (AGB) is therefore essential for effective carbon management. However, the influence of its primary leaf‐feeding pest, Pantana phyllostachysae Chao ( P. phyllostachysae ), on AGB remains poorly understood, potentially compromising estimation accuracy. This study aims to develop allometric equations and integrate them with machine learning algorithms to accurately estimate the AGB of Moso bamboo forests under varying levels of pest stress. RESULTS Allometric equations exhibited strong estimation performance across all pest infestation levels, with R 2 values exceeding 0.93, root mean square error (RMSE) values below 0.66 kg, and mean absolute error (MAE) values under 0.51 kg. Among the machine learning approaches evaluated, the Extreme Gradient Boosting (XGBoost) algorithm demonstrated superior performance, yielding an R 2 of 0.8593, RMSE of 0.5176 kg, and MAE of 0.4313 kg. A clear negative correlation was identified between the severity of P. phyllostachysae infestation and AGB, with biomass values decreasing progressively from healthy to severely infested stands. CONCLUSION Incorporating pest factors into AGB estimation models significantly enhances model accuracy and captures the nuanced effects of pest stress on biomass accumulation. This integration improves model generalizability and ecological relevance, offering valuable insights for sustainable forest management and carbon accounting. The findings highlight the importance of explicitly considering pest dynamics in biomass modeling and carbon management strategies, laying a robust foundation for future research on pest–biomass interactions in forest ecosystems. © 2025 Society of Chemical Industry.