A framework for upscaling aboveground biomass from an individual tree to landscape level and qualifying the multiscale spatial uncertainties for secondary forests

Abstract

Secondary forests, a typical forest type in the sub-frigid zone of Northeast China, have significant potential for carbon sequestration. Accurate estimation of the Aboveground Biomass (AGB) of secondary forests and assessment of multiscale uncertainties are crucial for promoting Reduced Emissions from Deforestation and Degradation. This study developed a novel framework to upscale the AGB estimation from the tree to the landscape level and assessed multiscale uncertainties based on multi-platform laser scanning data and Unmanned Aerial Vehicle (UAV) hyperspectral images. The framework included two stages: (1) quantifying multiple uncertainties (uncertainties of individual tree crown delineation, individual tree parameters estimation, and tree species classification) in individual tree-based AGB estimation using Monte Carlo simulations; (2) upscaling the plot to the landscape level estimated AGB using the Nonlinear Simultaneous Equation (NSE) with error-in-variables and quantifying the uncertainties of model residuals, model parameters, and model independent variables. The findings revealed a high accuracy from tree to plot AGB estimation (R2: 0.75, Root Mean Square Error (RMSE): 6.65 Mg/ha, relative RMSE (rRMSE): 5.40%), with the total and relative uncertainties of 16.85 Mg/ha and 16.29%, respectively, with the highest uncertainty (9.73 Mg/ha) observed in tree species classification. The AGB estimation using NSE achieved an R2 of 0.69, with an RMSE of 9.91 Mg/ha and an rRMSE of 10.43% from the plot to landscape level; and the uncertainties caused by model parameters, independent variables, and residuals were 5.52 Mg/ha, 14.56 Mg/ha, and 25.25 Mg/ha, respectively, accounting for 3.46%, 24.09%, and 72.45% of the total uncertainty. This study develops a framework for large-scale AGB estimation of mixed forests based on the individual tree approach and uncertainty quantification of multiscale estimates and provides a foundation for precise forestry, sustainable forest management, and carbon neutrality.