Arctic tundra ecosystems are responding to effects of climatic warming via shifts in vegetation composition and increased woody biomass. In this sensitive ecosystem, minor increases in woody plant biomass may induce significant changes in ecosystem structure and function. However, establishing methods for quantifying and potentially scaling woody plant biomass in low-stature biomes is challenging. In this study, we investigated the potential to use terrestrial laser scanning (TLS) to remotely estimate harvested biomass and leaf area of two dominant low-stature (< 1.5 m tall) Arctic shrub species in 0.64 m2 subplots established in northern Alaskan tundra. We explored two biomass estimation approaches (volumetric surface differencing and voxel counting) applied to point clouds obtained from close-range (2 m) and variable-range (< 50 m) terrestrial laser scans. Relationships between harvested biomass and TLS metrics were strong for all combinations of approaches, with voxel counting giving a marginally better result than surface differencing for close-range data (R2 = 0.94 vs 0.92; RMSE = 102 g vs 117 g) and surface differencing proving stronger than voxel counting for variable-range data (R2 = 0.91 vs 0.82; RMSE = 119 g vs 169 g). Strong relationships between total harvested biomass and total leaf dry mass (R2 = 0.93; RMSE = 13.4 g), and between leaf dry mass and leaf wet area (R2 = 0.99; RMSE = 9.01 cm2) justify estimation of shrub leaf area from TLS-derived shrub biomass. Our results show that rapidly acquired, repeatable terrestrial laser scans taken at multiple ranges can be processed using simple biomass estimation approaches to yield aboveground biomass and leaf area estimates for low-stature shrubs at fine spatial scales (sub-meter to ~ 50 meters) with the fidelity required to monitor small but ecologically meaningful changes in tundra structure. Further, these data may be employed as ground reference data for broader scale remote sensing data collection, such as airborne LiDAR scanning, that would enable shrub biomass and leaf-area estimates at fine spatial resolution over large spatial extents.