Comparing the abilities of traditional airborne and UAS lidar for mapping low-lying vegetation density across temperate forest types

Accurate mapping of understory vegetation is critical for applications ranging from wildlife habitat and biodiversity monitoring to fire behavior modeling and ecosystem carbon accounting. Lidar offers unique potential for quantifying subcanopy vegetation structure, yet the relative performance of airborne laser scanning (ALS) and uncrewed aerial system (UAS) laser scanning (ULS) for this task remains poorly quantified across different forest types. We compared ALS and ULS estimates of low-lying vegetation density (0.5–2.0 m aboveground), using high-resolution mobile laser scanning (MLS) as reference data, across 26 field plots spanning seven common forest and woodland types in northern Utah, USA. We examined the effects of platform, spatial scale, overstory structure, and vegetation type on model performance, and tested whether predictive accuracy could be improved by incorporating vegetation type and overstory metrics into linear mixed-effects models. Our results show that ALS consistently outperformed ULS despite its lower point density and older acquisition dates, achieving stronger correspondence with MLS-based density across most scales and forest types. ULS was advantageous at very fine spatial resolutions (<2.5 m subplot radii). Across platforms, predictive accuracy was maximized at 4.5 m subplot radii, balancing spatial detail with sufficient point sampling. Vegetation type exerted a strong influence, as those with greater spatial variation in observed density yielded higher predictive performance. Incorporating vegetation type as a random effect and adding overstory density as predictors substantially improved predictive performance, especially for ALS. These findings highlight ALS as a robust and transferable platform for broad-scale mapping of low-lying vegetation density, while ULS provides complementary value for fine-scale, site-specific, and temporally flexible applications. Our results underscore the importance of accounting for overstory conditions and vegetation type when modeling understory structure, and demonstrate the benefits of mixed-effects modeling frameworks. Future research should extend these analyses to additional ecosystems, disturbance contexts, and temporal scales, and explore machine learning approaches that more fully integrate structural and ecological predictors for operational understory mapping.