| Authors |
Xuan Mu, Dan Zhao, Zhaoju Zheng, Cong Xu, Jinchen Wu, Ping Zhao, Xiaomin Li, Yong Pang, Yujin Zhao, Tianyu An, Yuan Zeng, Bingfang Wu |
| Intro |
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, highprecision
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 RFbased
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. |
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