Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 18
Articles | Volume 29, issue 18
https://doi.org/10.5194/hess-29-4539-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-4539-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 18
Research article
 | 
22 Sep 2025
Research article |  | 22 Sep 2025

Characterizing the spatial distribution of field-scale snowpack using unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry

Eunsang Cho, Megan Verfaillie, Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner

Viewed

Total article views: 5,775 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,704 1,168 903 5,775 251 169 233
  • HTML: 3,704
  • PDF: 1,168
  • XML: 903
  • Total: 5,775
  • Supplement: 251
  • BibTeX: 169
  • EndNote: 233
Views and downloads (calculated since 21 Jun 2024)
Cumulative views and downloads (calculated since 21 Jun 2024)

Viewed (geographical distribution)

Total article views: 5,775 (including HTML, PDF, and XML) Thereof 5,775 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 17 May 2026
Download
Short summary
Unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry effectively map high-resolution snow depths. Our study found that UAS lidar outperformed SfM, particularly in capturing stable snow distribution patterns. Vegetation type was the primary factor influencing snow depth across forest and field areas, reflecting soil variables, such as organic matter. When analyzed separately, slope and forest canopy shadowing played key roles.
Read more
Share