Quantifying scaling effects on satellite-derived forest area estimates for the conterminous USA

We quantified the scaling effects on forest area estimates for the conterminous USA using regression analysis and the National Land Cover Dataset 30m satellite-derived maps in 2001 and 1992. The original data were aggregated to: (1) broad cover types (forest vs. non-forest); and (2) coarser resolutions (1km and 10km). Standard errors of the model estimates were 2.3% and 4.9% at 1km and 10km resolutions, respectively. Our model improved the accuracies for 1km by 0.6% (12556km2) in 2001 and 1.9% (43198km2) in 1992, compared to the forest estimates before the adjustments. Forest area observed from Moderate Resolution Imaging Spectroradiometer (MODIS) 2001 1km land-cover map for the conterminous USA might differ by 80811km2 from what would be observed if MODIS was available at 30m. Of this difference, 58% (46870km2) could be a relatively small net improvement, equivalent to 1444 Tg (or 1.5%) of total non-soil forest CO2 stocks. With increasing attention to accurate monitoring and evaluation of forest area changes for different regions of the globe, our results could facilitate the removal of bias from large-scale estimates based on remote sensors with coarse resolutions.