Spatial and Morphological Analysis of Cryoconite Holes in Kangerlussuaq, Greenland Using Unmanned Aerial Vehicle Imaging and Automated Software Recognition

Thorough analysis of cryoconite holes in glacial regions is essential to understanding the effects of albedo on ice ablation and melt rates. These holes result from built-up debris on ice sheets and are made of a combination of minerals, soot, and dust, as well as in some cases dynamic biological material. The small scale of these holes often causes them to be unrenderable using satellite data and thus unquantifiable. The relatively low albedo of such debris is responsible for the holes' absorption of a much greater quantity of solar radiation than their neighboring environment, thus causing accelerated snow and ice melt around the dark particles. Meltwater allows such particles to coalesce into so-called cryoconite holes (Fig. 1), which continue to expand horizontally and vertically in a positive feedback loop. Enhanced melting in the region where these holes form accelerates this feedback, since the removal of the firn layer from annual snowfall covering the ice often reveals additional previously-buried cryoconite debris. This project investigates and analyzes color images collected from a UAV over the Russell glacier near Kangerlussuaq, Greenland during the summer of 2018 in order to quantify the size — ranging from centimeters to hundreds of meters — and distribution of cryoconite holes. Specifically, we first stitch together more than 100 in-situ visible-wavelength images in order to obtain a single mosaic over the study area ( 1km x 1km). Then, we apply spectral-based filters to identify cryoconite holes and distributed impurities within the region of interest. Lastly, we apply image-recognition software to automatically detect the size and distribution of the cryoconite holes and then compute the fractal dimension of this autocorrelated distribution to quantify the spatial scales of the processes governing the evolution of the holes. The distribution determined from the high-resolution UAV images is then compared to that of satellite imagery and model outputs to quantify (i) the optimal spatial resolution of such remote sensing techniques, and (ii) the extent to which sub-scale surface processes are affecting surface melt in a previously undocumented and significant way. The ability to understand and quantify the impacts of cryoconite is essential to unveiling the secrets of this eluding medium.