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© 2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Terrestrial and airborne laser scanning and structure from motion techniques have emerged as viable methods to map snow depths. While these systems have advanced snow hydrology, these techniques have noted limitations in either horizontal or vertical resolution. Lidar on an unpiloted aerial vehicle (UAV) is another potential method to observe field- and slope-scale variations at the vertical resolutions needed to resolve local variations in snowpack depth and to quantify snow depth when snowpacks are shallow. This paper provides some of the earliest snow depth mapping results on the landscape scale that were measured using lidar on a UAV. The system, which uses modest-cost, commercially available components, was assessed in a mixed deciduous and coniferous forest and open field for a thin snowpack (< 20 cm). The lidar-classified point clouds had an average of 90 and 364 points/m2 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 root mean square error of 1.22 cm. At 1 m horizontal resolution, the field snow depth confidence intervals were consistently less than 1 cm. The forest areas had reduced performance with a mean absolute difference of 9.6 cm, a root mean square error of 10.5 cm, and an average one-sided confidence interval of 3.5 cm. Although the mean lidar snow depths were only 10.3 cm in the field and 6.0 cm in the forest, a pairwise Steel–Dwass test showed that snow depths were significantly different between the coniferous forest, the deciduous forest, and the field land covers (p < 0.0001). Snow depths were shallower, and snow depth confidence intervals were higher in areas with steep slopes. Results of this study suggest that performance depends on both the point cloud density, which can be increased or decreased by modifying the flight plan over different vegetation types, and the grid cell variability that depends on site surface conditions.

Details

Title
Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States
Author
Jacobs, Jennifer M 1 ; Hunsaker, Adam G 1 ; Sullivan, Franklin B 2 ; Palace, Michael 3   VIAFID ORCID Logo  ; Burakowski, Elizabeth A 2   VIAFID ORCID Logo  ; Herrick, Christina 2   VIAFID ORCID Logo  ; Cho, Eunsang 4   VIAFID ORCID Logo 

 Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA; Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA 
 Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA 
 Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA; Department of Earth Sciences, University of New Hampshire, Durham, NH 03824, USA 
 Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA; Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA; Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA; Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA 
Pages
1485-1500
Publication year
2021
Publication date
2021
Publisher
Copernicus GmbH
ISSN
19940424
e-ISSN
19940416
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2504266595
Copyright
© 2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.