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. Terrestrial and airborne laser scanning and structure from motiontechniques have emerged as viable methods to map snow depths. While thesesystems have advanced snow hydrology, these techniques have notedlimitations in either horizontal or vertical resolution. Lidar on anunpiloted aerial vehicle (UAV) is another potential method to observe field-and slope-scale variations at the vertical resolutions needed to resolvelocal variations in snowpack depth and to quantify snow depth when snowpacksare shallow. This paper provides some of the earliest snow depth mappingresults on the landscape scale that were measured using lidar on a UAV. Thesystem, which uses modest-cost, commercially available components, wasassessed in a mixed deciduous and coniferous forest and open field for athin snowpack (  20 cm). The lidar-classified point clouds had anaverage of 90 and 364 points/m 2 ground returns in the forest and field,respectively. In the field, in situ and lidar mean snow depths, at 0.4 m horizontal resolution, had a mean absolute difference of 0.96 cm and a rootmean square error of 1.22 cm. At 1 m horizontal resolution, the field snowdepth confidence intervals were consistently less than 1 cm. The forestareas had reduced performance with a mean absolute difference of 9.6 cm, aroot mean square error of 10.5 cm, and an average one-sided confidenceinterval of 3.5 cm. Although the mean lidar snow depths were only 10.3 cm inthe field and 6.0 cm in the forest, a pairwise Steel–Dwass test showed thatsnow depths were significantly different between the coniferous forest, thedeciduous forest, and the field land covers ( p  0.0001). Snowdepths were shallower, and snow depth confidence intervals were higher inareas with steep slopes. Results of this study suggest that performancedepends on both the point cloud density, which can be increased or decreasedby modifying the flight plan over different vegetation types, and the gridcell variability that depends on site surface conditions.

Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States