Light Detection and Ranging (LiDAR) is an active remote sensing technology for acqui- ring three-dimensional structure parameters of vegetation canopy with high accuracy over multiple spatial scales, which is greatly important to the promotion of forest disturbance ecology and the ap- plication on gaps. This paper focused on mid-subtropical evergreen broadleaved forest in Hunan Province, and small footprint LiDAR point data were adopted to identify canopy gaps. and measure geomagnetic characteristics of gaps. The optimal grid model resolution and interpolation methods were chosen to generate canopy height model, and the computer graphics processing was adopted to estimate characteristics of gaps which involved gap size, canopy height and gap shape index, then field investigation was utilized to validate the estimation results. The results showed that the gap rec- ognition rate was 94.8%, and the major influencing factors were gap size and gap maker type. Line- ar correlation was observed between LiDAR estimation and field investigation, and the R² values of gap size and canopy height case were 0.962 and 0.878, respectively. Compared with field investiga- tion, the size of mean estimated gap was 19.9% larger and the mean estimated canopy height was 9.9% less. Gap density was 12.8 gaps · hm⁻² and the area of gaps occupied 13.3% of the forest area. The average gap size, canopy height and gap shape index were 85.06 m², 15.33 m and 1.71, respectively. The study site usually contained small gaps in which the edge effect was not obvious.