Mosses, the dominant flora of East Antarctica, show evidence of drying in recent decades, likely due to the regional effects of climate change. Given the relatively small area that such moss beds occupy, new tools are needed to map and monitor these fragile ecosystems in sufficient detail. In this study, we collected low altitude aerial photography with a small multi-rotor Unmanned Aerial Vehicle (UAV). Structure from Motion (SfM) computer vision techniques were applied to derive ultra-high resolution 3D models from multi-view aerial photography. A 2 cm digital surface model (DSM) and 1 cm orthophoto mosaic were derived from the 3D model and aerial photographs, respectively. The geometric accuracy of the orthophoto and DSM was 4 cm. A weighted contributing upstream area was derived with the D-infinity algorithm, based on the DSM and a snow cover map derived from the orthophoto. The contributing upstream area was used as a proxy for water availability from snowmelt, one of the key environmental drivers of moss health. A Monte Carlo simulation with 300 realisations was implemented to model the impact of error in the DSM on runoff direction. Significant correlations were found between these simulated water availability values and field measurements of moss health and water content. In the future ultra-high spatial resolution DSMs acquired with a UAV could thus be used to determine the impact of changing snow cover on the health and spatial distribution of polar vegetation non-destructively.