Mapping plant species in western rangelands with UAVs

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We tested models for 18 common overstory plant species in different functional groups including shrubs, low sagebrush and rubber rabbitbrush, and the perennial bunchgrass bluebunch wheatgrass. Using Structure-from-Motion photogrammetry and a stacked ensemble learning approach, we achieved a mean classification accuracy of 92.1% (95% CI, 90.9–93.2%) and a weighted F1 score of 91.5% (95% CI, 90.2–92.8%), indicating strong performance despite a highly imbalanced dataset dominated by a few common species. Models trained with low-cost red, green, and blue imagery performed nearly as well as those using multispectral data, with only about a 1% difference in F1 score. However, model transferability was limited: classification accuracy declined sharply at sites where species composition differed from training data, with F1 scores ranging from <0.09 to >0.90 across test sites. These results suggest that although low-cost UAVs can produce accurate and scalable species maps, reliable application across diverse rangelands will require strategic field sampling or shared training datasets. Our findings provide practical guidance for researchers and land managers seeking to incorporate UAV technology into biodiversity monitoring, restoration planning, and invasive species management.