An accurate, up-to-date, and spatially explicit estimate of the forest aboveground carbon (AGC) density is indispensable for understanding the global carbon cycle and implementing measures to mitigate climate change such as reducing emissions from deforestation and forest degradation (REDD+). Geoscience Laser Altimeter System (GLAS) data have proven to be a powerful candidate for estimating the forest AGC density distribution over large-scale areas because of its global-scale sampling strategy and freely available data. However, the influence of terrain slopes on the accuracy of the GLAS-derived canopy height often limits its utility in mountainous regions. Nevertheless, mountain forests play a key role in carbon storage because of the relatively small influence of human activities, and the carbon sequestration capacities of forests differ according to the region. In this study, we integrated GLAS data, Landsat 8 OLI imagery, and field inventory data to estimate the forest AGC density distribution of Qilian Mountains National Park in northwestern China, which is an essential ecological barrier and carbon sequestration site in western China. We aimed to address the underestimation of carbon storage by mountain forests in previous studies by improving the estimation accuracy of the GLAS-derived canopy height and in turn the forest AGC density in the study area. We first improved upon a physical terrain correction model to derive the canopy height from GLAS data according to five different slope gradient classes. The canopy height could be reliably estimated with a root-mean-squared error (RMSE) of 1.84-3.91 m on the basis of independent validation with forest inventory data. We subsequently calculated the AGC density of GLAS footprints located in different forest types as accurately as possible by using an aboveground biomass estimation model of different forest types and a carbon content conversion factor, as well as the stand density recorded in the forest inventory data. We took a nonparametric approach and applied a MaxEnt model to extrapolate AGC data at the GLAS footprint scale to the whole forest as extracted from Landsat 8 OLI imagery. We also made great efforts to spatiotemporally match all data sources in this study. The results showed that the average forest AGC density in Qilian Mountains National Park was 40.72±6.72 t·hm^-2 in 2018, and the total aboveground carbon storage was 28.58±4.72 Tg. Forest vegetation in areas at 2770-3770 m above sea level had the largest carbon storage, and shaded slopes had significantly greater carbon storage than sunny slopes. The accuracy of the estimation results was independently verified by comparison with the forest resource inventory data and resulted in an RMSE of 18.946 t·hm^-2, which meets the measuring, reporting, and verification guidelines of REDD+. The results of this study can provide a basis for monitoring regional- and even national-scale changes in forest carbon reserves and for formulating sustainable forest management policies. Additionally, the method used in this study can potentially be applied to estimating the carbon storage of forests in mountainous areas.