Mountains of error: UAV LiDAR benchmarking of GEDI and GEDI-derived global canopy height products in steep Himalayan forests

• UAV LiDAR provides a rare canopy-height benchmark in steep Himalayan forests. • GEDI L2A footprint heights and two GEDI-derived global canopy-height maps are evaluated together. • Like-with-like percentile comparisons link footprint metrics to gridded canopy-height products. • Terrain stratification, footprint-shift tests, and multivariate models isolate key error controls. • Coverage gaps and spatially structured discrepancies are quantified for mountain-forest applications. Accurate canopy-height information in steep mountain forests is essential for biomass estimation, habitat characterization, and monitoring climate-sensitive ecotones, yet the reliability of spaceborne LiDAR height products in rugged terrain remains poorly constrained. Here we benchmarked Global Ecosystem Dynamics Investigation (GEDI) footprint canopy-height metrics and two GEDI-derived global canopy-height products (GFCH 2019 and GCH 2020) using a 1 m UAV-LiDAR canopy height model acquired along a ∼ 2,000 m elevational transect in the central Himalayas, Nepal. We show that GEDI RH95, paired with the UAV P95 reference in a like-with-like percentile comparison, reproduces the overall canopy-height distribution with minimal systematic bias but substantial footprint-scale dispersion, with errors that are spatially structured by terrain and sub-footprint heterogeneity. A footprint-shift experiment indicates that plausible horizontal misregistration contributes comparatively little to overall uncertainty reinforcing that topography and within-footprint variability dominate the error budget in this setting. At the map scale, GFCH 2019 captures broad elevational patterns but systematically underestimates tall canopy heights and exhibits terrain-dependent residual structure, whereas GCH 2020 shows widespread positive bias and spatially patchy coverage over the LiDAR-mapped domain. Together, these results provide a rare UAV-LiDAR benchmark for interpreting GEDI footprint heights and GEDI-derived canopy height models in a data-scarce, high-relief mountain forest, and highlight the need for terrain-aware uncertainty characterization and local calibration when these products are used for biomass, habitat, and treeline/upper-montane applications.