Aiming to study the effect of segmentation scale on object based segmentation and classification of forest gap through fusion of aerial orthophoto (DOM) and LiDAR data, the typical natural secondary forest in Maoershan Experimental Forest Farm Donglin Industry Zone of northeastern China was selected as the experimental area. The DOM and airborne LiDAR were used for multiscale segmentation and object-oriented forest gap classification. In the process of image segmentation, three segmentation schemes (segmentation of DOM, segmentation of LiDAR data and segmentation of a fusion of DOM and LiDAR data) were adopted. For each segmentation scheme, 10 segmentation scales were set, then based on the segmentation results, spectral and height features extracted from DOM and LiDAR data were used for object-oriented forest gap classification with the support vector machine (SVM) classifier. The results showed that the classification accuracies of three segmentation and classification schemes showed a decline trend with the increase of scale, which was opposite with trend of ED3 (Modified). Based on the LiDAR data at scale parameter of 10, the best segmentation result was got. At all scale (10~100), the classification accuracy based on LiDAR segmentation and classification was higher than that based on two other data segmentation and classification schemes, and had the more obvious advantage than using only DOM. Based on scheme of LiDAR data segmentation and classification at scale parameter of 10, the highest classification accuracy was got with Kappa coefficient of 80%. The classification accuracies of three segmentation and classification schemes at the optimal scale were significantly higher than these at other scales. The segmentation scale had important effect on the object-oriented forest gaps classification.