The formation of canopy gaps alters forest microclimates, influencing understory regeneration, soil organic matter decomposition, and nutrient cycling, thereby playing a crucial role in forest ecology. Traditional methods for detecting canopy gaps typically rely on multispectral imagery or LiDAR data, which are accurate but costly and technically demanding. In recent years, several studies have explored the feasibility of using UAV-based RGB imagery for gap detection. This study utilized UAV RGB imagery to analyze the temporal dynamics of canopy gaps to assess the feasibility of employing RGB-based vegetation indices for canopy gap detection. The Normalized Green–Red Difference Index (NGRDI) combined with DSM differencing was used for analysis. Results show that when NGRDI < 0.03, forest areas can be effectively categorized into two classes: “canopy gaps” and “canopy cover.” The overall classification accuracy reached 93% with a Kappa coefficient of 0.68. However, the omission error was 44.44%, which suggests that the model requires improvement in detecting small or edge gaps. It is recommended that identified threshold be used as a preliminary criterion for “canopy versus non-canopy” classification, supplemented with DSM or CHM data to improve detection accuracy.