Forest aboveground biomass (AGB) is a key indicator for evaluating carbon sequestration capacity and forest productivity. Accurate regional-scale AGB estimation is crucial for advancing research on global climate change, ecosystem carbon cycles, and ecological conservation. Traditional methods, whether based on LiDAR or optical remote sensing, estimate AGB using planar density (t/ha) multiplied by pixel area, which fails to account for vertical forest structure variability. This study proposes a novel “stereoscopic (stereo) density × volume” approach, upgrading planar density to stereo density (t/ha/m) by integrating canopy height information, thereby improving estimation accuracy and exploring the feasibility of this new method. In the Daxing’anling region, plot-scale AGB estimation models were developed using stepwise linear regression (SLR) for both “planar density × area” and “stereo density × volume” methods. Results indicated that the stereo model using arithmetic mean height (H_AM) achieved comparable accuracy (R² = 0.83, RMSE = 2.77 t) with the planar model (R² = 0.83, RMSE = 2.52 t). At the regional scale, high-precision AGB estimates derived from airborne LiDAR were combined with vegetation indices from the Landsat Thematic Mapper (TM), and topographic factors from DEM to develop regional-scale AGB estimation models, using SLR and random forest (RF) algorithms. The results of 10-fold cross-validation demonstrated the superiority of the stereo method over the planar method, with RF outperforming SLR. The optimal RF-based stereo model of H_AM (R² = 0.65, rRMSE = 26.05%) significantly improved AGB estimation compared to the planar model (R² = 0.59, rRMSE = 30.41%). Independent accuracy validation using 75 field plots demonstrated that the stereo model achieved a higher validation R² of 0.45 compared to the planar model’s R² of 0.35. These findings suggest that the stereo approach mitigates the underestimation of AGB caused by forest height variability in planar methods, with no significant differences observed across forest types. In conclusion, the use of the stereo method to estimate forest AGB is superior to the planar method in optical remote sensing. This approach offers a scalable solution for forest AGB estimation and carbon stock assessment.

Accurate estimation of plantation aboveground biomass (AGB) is critical for quantifying carbon cycles and informing sustainable forest resource management, but enhancing estimation accuracy remains a key challenge. Although tree height and stand age are recognized as critical predictors for enhancing AGB models in addition to spectral vegetation indices, their individual and combined contributions in regional plantation forests remain insufficiently quantified, especially concerning the potential for leveraging the distinct characteristics of fast-growing plantations to facilitate AGB estimation. This study developed multi-source remote sensing-based Eucalyptus AGB estimation models for Nanning, Guangxi, integrating stand age and tree height to assess their impacts. Stand age was mapped from Landsat time-series imagery, and tree height was derived from UAV-LiDAR data. Plot-level reference AGB was obtained using fused UAV and terrestrial LiDAR point clouds. A random forest model, incorporating these variables with Sentinel-2 spectral information and topography, then achieved regional AGB estimation. The findings demonstrate that (1) tree height serves as the most influential predictor for AGB estimation at the regional scale, yielding a robust model performance (R² = 0.84). (2) Tree height captures the majority of the explanatory power associated with stand age. Once tree height was included as a predictor, the subsequent addition of stand age offered no significant improvement in model accuracy (R² = 0.85). (3) Given the challenges in obtaining precise tree height data and the robust correlation between stand age and tree height in fast-growing plantations, the integration of stand age substantially improved the accuracy of AGB estimations (from the spectral model of R² = 0.54 to R² = 0.74), with performance approaching that of tree height-based models (ΔR² = 0.10). Consequently, in fast-growing plantations, which are often characterized by high stand homogeneity, a hybrid model incorporating stand age can offer a reliable and cost-effective solution for AGB estimation.