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Combining forest inventory plot and Landsat Thematic Mapper (TM) data has been widely used for mapping forest carbon. However, uncertainty analysis is a great challenge. This study investigated the uncertainties of mapping and scaling up aboveground forest carbon (AGFC) due to plot location errors in Wu-Yuan of East China. Plot location errors were simulated by randomly perturbing the location of each plot with eleven different distances that varied from 5 to 8000 m. Given a perturbed distance (PD) such as 100 m, a forest carbon map was created by combining and scaling up the plot and TM data from a spatial resolution of 28.5 m × 28.5 m to 969 m × 969 m using a sequential Gaussian block cosimulation algorithm. The maps obtained from the perturbed plot locations were compared with that from the true plot locations. The results showed that, as the plot location PD increased, the accuracy of predicted AGFC values decreased, but their spatial patterns (clustering of high and low values) remained until the PD of 800 m, slightly changed at the PD of 1600 m, looked more different at the PDs of 3000 and 5000 m, and became totally random at the PD of 8000 m. More importantly, it was found that scaling up the spatial data mitigated the impacts of plot location errors on the map accuracy compared to those without the up-scaling.