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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/tc-2020-37
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-37
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Feb 2020

18 Feb 2020

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A revised version of this preprint is currently under review for the journal TC.

Shallow snow depth mapping with unmanned aerial systems lidar observations: A case study in Durham, New Hampshire, United States

Jennifer M. Jacobs1,2, Adam G. Hunsaker1,2, Franklin B. Sullivan2, Michael Palace2,3, Elizabeth A. Burakowski2, Christina Herrick2, and Eunsang Cho1,2 Jennifer M. Jacobs et al.
  • 1Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, 03824, USA
  • 2Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, 03824, USA
  • 3Department of Earth Sciences, University of New Hampshire, Durham, NH, 03824, USA

Abstract. Shallow snowpack conditions, which occur throughout the year in many regions as well as during accumulation and ablation periods in all regions, are important in water resources, agriculture, ecosystems, and winter recreation. Terrestrial and airborne (manned and unmanned) laser scanning and structure from motion (SfM) techniques have emerged as viable methods to map snow depths. Lidar on an unmanned aerial vehicle is also a potential method to observe field and slope scale variations of shallow snowpacks. This paper describes an unmanned aerial lidar system, which uses commercially available components, for snow depth mapping on the landscape scale. The system was assessed in a mixed deciduous and coniferous forest and open field for a shallow snowpack (< 20 cm). The lidar ground point clouds yielded an average of 90 and 364 points/m2 in the forest and field, respectively. Comparisons of snow probe and lidar mean snow depths in the field, at 0.4 m resolution, had a mean absolute difference of 0.96 cm and a root mean squared difference of 1.22 cm. In the forest, the in situ mean snow depth was nearly twice that from the lidar from mean absolute difference of 9.6 cm and root mean squared difference of 10.5 cm. These differences in forests are likely due, in part, to limitations of sampling using a snow probe. At 1 m resolution, the field snow depth precision was consistently less than 1 cm. The forest and heavily vegetated areas had modestly reduced performance with typical values within 4 cm precision. Performance depends on both the point cloud density, which can be increased or decreased by changing the flight plan, and the within cell variability that depends on site surface conditions.

Jennifer M. Jacobs et al.

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Jennifer M. Jacobs et al.

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Short summary
This pilot study describes a proof-of-concept for using a UAV lidar system to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar derived digital terrain models, consistently had 0.5 to 1 cm precision in the field with modestly reduced accuracy in the forest and heavily vegetated areas. Performance depends on the point cloud density and the ground surface variability and vegetation.
This pilot study describes a proof-of-concept for using a UAV lidar system to map shallow...
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