Duncanson et al. (2010) reported that space borne LiDAR data is useful for aboveground biomass (AGB) estimation over a wide range of biomass values and forest types, but the application of these data is limited, given their spatially discrete nature. The authors then used an integration of ICESat Geospatial Laser Altimeter System (GLAS) LiDAR and Landsat data to develop methods to estimate AGB in an area of south-central British Columbia, Canada. They compared estimates with a reliable AGB map of the area, derived from high-resolution airborne LiDAR data, to assess the accuracy of satellite-based AGB estimates. GLAS AGB models were shown to reliably estimate AGB (R2 = 0.77) over a range of biomass conditions. A partial least squares AGB model, using Landsat input data to estimate AGB (derived from GLAS), had an R2 of 0.60 and was found to underestimate AGB by an average of 26 Mg ha_1 per pixel when applied to areas outside of the GLAS transect. This study demonstrates that Landsat and GLAS data integration are most useful for forests with less than 120 Mg ha_1 of AGB, less than 60 years of age, and less than 60 % canopy cover. These techniques have high associated errors when applied to areas with greater than 200 Mg ha_1 of AGB. Airborne studies, however, have shown reasonable accuracies and precisions when it comes to forest biomass or volume estimation, e. g., Lefsky et al. (2002a, b), Popescu et al. (2002,2004), and van Aardt et al. (2006). In fact, van Aardt et al. (2008) have proven that wall-to-wall enumeration is possible at the taxanomic group level at high accuracies. It is likely obvious that (i) estimation quality improves as stands become more homogeneous, (ii) validation and calibration protocol for remote sensing assessments need to be put in place, and (iii) estimation outcomes are often time, species, and site dependent.