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Forest canopy cover (CC) directly and indirectly influences various processes of forest ecosystems. Airborne light detection and ranging (LiDAR) can be used to characterize forest spatial structures and further obtain estimates of forest CC. However, nonreturn laser pulses from targets of interest impact the estimation accuracy of forest CC. The objective of this article was to develop a novel method of estimating the nonreturn laser pulses to improve the estimation accuracy of forest CC using LiDAR data. The improved models for estimating forest CC were developed by introducing the loss ratio of laser pulses and a CC coefficient into the original models. The forest CC reference data were collected and used to validate the forest CC estimates. The results show that the loss ratio for forested areas was much higher than that for open ground areas. The range between the sensor and a target was a crucial factor that caused the loss of returns. The relationship between the range and the loss ratio was nonlinear in both open ground and forested areas. Compared with the original models, the improved models combining the loss ratio and the CC coefficient statistically significantly increased the estimation accuracy of the forest CC. Moreover, the forest CC estimates from the canopy height model (CHM) were more accurate than those from the height normalized point cloud (NPC) data. In addition, the simplified models were more generalized than the other models. This article is novel and has great potential to improve mapping of forest CC. [ABSTRACT FROM AUTHOR]