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Forest carbon density is an important indicator for evaluating forest carbon sink capacities. Accurate carbon density estimation is the basis for studying the response mechanisms of forest ecosystems to global climate change. Airborne light detection and ranging (LiDAR) technology can acquire the vertical structure parameters of forests with a higher precision and penetration ability than traditional optical remote sensing. Combining top of canopy height model (TCH) and allometry models, this paper constructed two prediction models of aboveground carbon density (ACD) with 94 square plots in northwestern China: one model is plot-averaged height-based power model and the other is plot-averaged daisy-chain model. The correlation coefficients (R-2) were 0.6725 and 0.6761, which are significantly higher than the correlation coefficients of the traditional percentile model (R-2 = 0.5910). In addition, the correlation between TCH and ACD was significantly better than that between plot-averaged height (AvgH) and ACD, and Lorey's height (LorH) had no significant correlation with ACD. We also found that plot-level basal area (BA) was a dominant factor in ACD prediction, with a correlation coefficient reaching 0.9182, but this subject requires field investigation. The two models proposed in this study provide a simple and easy approach for estimating ACD in coniferous forests, which can replace the traditional LiDAR percentile method completely.