Effects of uncertainty in model predictions of individual tree volume on large area volume estimates

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Hybrid estimators for mean aboveground carbon per unit area (2016, methodology): “84 reported by McRoberts et al. (2013) for two Norwegian study areas; greater than R2⁄ = 0.74 reported by Strunk et al. (2012) for a study area in Washington, USA; and greater than 0.”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “Many models have been developed based on various combinations of the aforementioned three parameters through linear or nonlinear regression models (Saldarriaga et al. 1988; Overman et al. 1994; Parresol 1999; Segura and Kanninen 2005; Seidel et al. 2011; McRoberts and Westfall 2014).”
Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data (2015, methodology): “For both years, plot-level AGBswere then estimated as the sums of individual tree AGB predictions, scaled toMg/ha, and considered to be observations without error (McRoberts & Westfall, 2014).”
Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data (2015, background): “Plot-level AGBwas estimated as the sum of biomass estimates for individual trees on the plot, scaled to a per unit area basis, and considered to be an observation without error (McRoberts & Westfall, 2014).”