Three-Stage Up-Scaling and Uncertainty Estimation in Forest Aboveground Biomass Based on Multi-Source Remote Sensing Data Considering Spatial Correlation

Airborne LiDAR (ALS) data have been extensively utilized for aboveground biomass (AGB) estimation; however, the high acquisition costs make it challenging to attain wall-to-wall estimation across large regions. Some studies have leveraged ALS data as intermediate variables to amplify sample sizes, thereby reducing costs and enhancing sample representativeness and model accuracy, but the cost issue remains in larger-scale estimations. Satellite LiDAR data, offering a broader dataset that can be acquired quickly with lower costs, can serve as an alternative intermediate variable for sample expansion. In this study, we employed a three-stage up-scaling approach to estimate forest AGB and introduced a method for quantifying estimation uncertainty. Based on the established three-stage general-hierarchical-model-based estimation inference (3sGHMB), an RK-3sGHMB inference method is proposed to make use of the regression-kriging (RK) method, and then it is compared with conventional model-based inference (CMB), general hierarchical model-based inference (GHMB), and improved general hierarchical model-based inference (RK-GHMB) to estimate forest AGB and uncertainty at both the pixel and forest farm levels. This study was carried out by integrating plot data, sampled ALS data, wall-to-wall Sentinel-2A data, and airborne P-SAR data. The results show that the accuracy of CMB (Radj2 = 0.37, RMSE = 33.95 t/ha, EA = 63.28%) is lower than that of GHMB (Radj2 = 0.38, RMSE = 33.72 t/ha, EA = 63.53%), while it is higher than that of 3sGHMB (Radj2 = 0.27, RMSE = 36.58 t/ha, EA = 60.43%). Notably, RK-GHMB (Radj2 = 0.60, RMSE= 27.07 t/ha, EA = 70.72%) and RK-3sGHMB (Radj2 = 0.55, RMSE = 28.55 t/ha, EA = 69.13%) demonstrate significant accuracy enhancements compared to GHMB and 3sGHMB. For population AGB estimation, the precision of the proposed RK-3sGHMB (p = 94.44%) is the highest, providing that there are sufficient sample sizes in the third stage, followed by RK-GHMB (p = 93.32%) with sufficient sample sizes in the second stage, GHMB (p = 90.88%), 3sGHMB (p = 88.91%), and CMB (p = 87.96%). Further analysis reveals that the three-stage model, considering spatial correlation at the third stage, can improve estimation accuracy, but the prerequisite is that the sample size in the third stage must be sufficient. For large-scale estimation, the RK-3sGHMB model proposed herein offers certain advantages.