Airborne laser data （Light Detection and Ranging， LiDAR） presents challenges in distinguishing between ground and grassland， and visible light vegetation indices are inadequate for differentiating between shrub and tree layers. Therefore， this study proposes the construction of a multi-band information image that integrates LiDAR point cloud data and RGB vegetation indices. The approach integrates multi-band information from LiDAR point cloud data and vegetation indices to create an enhanced image. The fine-grained canopy height model （CHM） is generated using laser point cloud data. Simultaneously， a high-resolution digital orthophoto image is created using unmanned aerial vehicle imagery data. Among the evaluated vegetation indices， the Differential Enhanced Vegetation Index （DEVI） was the most suitable and was fused with the CHM. Subsequently， the CHM+DEVI fused images were reconstructed to eliminate erroneous values. Training samples were constructed， and the classification regression tree algorithm was employed to segment the ground range and adaptively extract vegetation， such as trees， shrubs， and grasslands. Within the tree areas， the local maximum algorithm was applied to detect tree vertices， which served as foreground markers； meanwhile， the non-tree regions were assigned as background markers. The segmentation results were obtained using watershed transformation， and the accuracy of the extracted vegetation information was analyzed by comparing it with ground-truth data. The evaluation results demonstrate the superior performance of the proposed improved algorithm， with the overall recall rate， precision rate， and accuracy F1 score increasing by 3.2%， 3.9%， and 3.5%， respectively. Moreover， the accuracy of tree height measurements exhibited improvements of 1.7%， 6.4%， 1.8%， and 0.3% in the four quadrats. The effectiveness of the improved method was verified， and the higher the degree of vegetation mixing in the region， the better the extraction effect of the improved algorithm.