Can Stereoscopic Density Replace Planar Density for Forest Aboveground Biomass Estimation? A Case Study Using Airborne LiDAR and Landsat Data in Daxing’anling, China

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 (HAM) 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 HAM (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.