UAV-Based Stand Density Estimation for Aboveground Biomass Mapping in Moso Bamboo Forests

The accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is critical for assessing their carbon sequestration potential and supporting sustainable management. Satellite-based approaches are often constrained by signal saturation and mixed-pixel effects, whereas Unmanned Aerial Vehicle (UAV) imagery enables precise individual tree detection, overcoming these limitations. In this study, we propose a stand density (SD)-driven AGB estimation framework using high-resolution UAV RGB imagery. Individual bamboo positions were extracted using the Revised Local Maximum (RLM) algorithm, which achieved an optimal accuracy at a 2.5 m sampling interval (OA = 82.20%). Using 85 ground-truth plots, we developed six SD-AGB models and evaluated them via 10-fold cross-validation and independent UAV validation (10 plots). The Artificial Neural Network (ANN) model outperformed others, with strong calibration (R2 = 0.94, RMSE = 3.78 Mg/ha), robust cross-validation (R2 = 0.84 ± 0.06, RMSE = 5.24 ± 0.67 Mg/ha), and reliable independent validation (R2 = 0.87, RMSE = 4.56 Mg/ha). Spatial mapping revealed a total of 14,190 bamboo plants with an average AGB of 32.80 Mg/ha. This UAV-based SD-AGB framework provides a robust, scalable, and cost-effective tool for precise biomass estimation, supporting sustainable bamboo forest management and carbon sequestration strategies and progress towards SDG 15.