In our study, we developed a new way to create highly detailed maps of rangeland vegetation to distinguish between grass, shrubs, and trees. Our method combines information from several sources: high-resolution drone cameras, 3D laser scanners (LiDAR) that measure plant height, and satellite imagery. We found that our most accurate maps (95% accuracy) were produced when we fused drone imagery with LiDAR data. Adding the 3D structural information was crucial for helping us tell apart complex vegetation types that often look similar in standard photos. While using only drone data was faster, the results were less accurate. When we scaled up our approach using satellite data, we could map much larger areas, but with lower accuracy and detail. Ultimately, our framework provides ranchers and land managers with precise maps to support better decision-making for rotational grazing, brush control, and monitoring pasture health, promoting more effective stewardship of the land.