Application of low-cost UASs and digital photogrammetry for high-resolution snow depth mapping in the Arctic

The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Cimoli, E, Marcer, M, Vandecrux, B, Boggild, CE, Williams, GD, Simonsen, SB
Format: Article in Journal/Newspaper
Language:English
Published: M D P I AG 2017
Subjects:
Earth Sciences
Oceanography
Physical Oceanography
Arctic
Svalbard
Greenland
Online Access:https://doi.org/10.3390/rs9111144
http://ecite.utas.edu.au/123655
Description
Summary:The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology based on Unmanned Aerial Systems (UASs) and digital photogrammetry was tested over varying surveying conditions in the Arctic employing two diverse and low-cost UAS-camera combinations (500 and 1700 USD, respectively). Six areas, two in Svalbard and four in Greenland, were mapped covering from 1386 to 38,410m 2 . The sites presented diverse snow surface types, underlying topography and light conditions in order to test the method under potentially limiting conditions. The resulting snow depth maps achieved spatial resolutions between 0.06 and 0.09 m. The average difference between UAS-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m. The impact of image pre-processing was explored, improving point cloud density and accuracy for different image qualities and snow/light conditions. Our UAS photogrammetry results are expected to be scalable to larger areal extents. While further validation is needed, with the inclusion of extra validation points, the study showcases the potential of this cost-effective methodology for high-resolution monitoring of snow dynamics in the Arctic and beyond.

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