A hybrid method for forest aboveground biomass estimation: fusion of individual tree- and area-based approaches over northeast China - Zhen Zhen; Xiang Li; Ye Ma; Yinghui Zhao; Xiaochun Wang (GIScience & Remote Sensing)
Currently, large-scale above-ground biomass (AGB) estimation mainly depends on the area-based approach (ABA). However, with advancements in individual tree detection technology and the availability of multi-platform remote sensing data, the individual tree-based approach (ITA) presents new opportunities for accurate, nondestructive AGB estimation. Nevertheless, research on integrating ITA with ABA for scaling AGB estimates from individual trees to the regional level remains limited. This study introduced an innovative hybrid framework incorporating ITA and ABA for regional-scale estimation of forest AGB through the combination of handheld, unmanned, and satellite LiDAR, together with Landsat 8 imagery. The results demonstrated that the segmentation of individual trees using a fusion of unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) was effective (r = 0.84, p = 0.76, F-score = 0.79) for the secondary forests in northeast China (NEC), particularly achieving high accuracy of diameter at breast height (DBH) with R2 = 0.93 and RMSE of only 1.84 cm. Both individual tree and plot-level AGB estimates achieved satisfactory accuracy, with biases of  −3.9 kg and  −16,56 Mg/ha, respectively. The error-in-variable (EIV) model was established for stand-level AGB estimation using the plot AGB estimates from ITA (R2 = 0.69; RMSE = 19.59 Mg/ha; rRMSE = 15.1%) and employed to estimate footprint-level AGB based on the canopy height data extracted from ICESat-2/ATL08, significantly expanding the limited sample size (by 150 times) for regional AGB estimation. Two periods (2019–2020, 2021–2022) of continuous AGB of NEC were mapped using the integrated method of random forests (RF) and empirical Bayesian Kriging (EBK). The accuracy of the RF-EBK model is markedly enhanced in comparison to that of the RF model for AGB estimation (R2 increased by about 37% − 49%, RMSE and rRMSE declined by 25%). This study provides technical support for upscaling AGB estimation from individual tree to extensive forest and lays a solid foundation for precise forestry, sustainable forest management, and the attainment of carbon neutrality.