16 Undergraduate Research (Posters)

Monday, 18 May 2020
8:30 AM - 5:30 PM
Lake Superior Ballroom KJ

TRACKING THE EVOLUTION OF SEASONAL PRAIRIE SNOW IN EASTERN NORTH DAKOTA WITH UAS-BASED REPEAT AERIAL PHOTOGRAPHY

Paper number: 16-10
Program: Theme Sessions
Time: 8:30 AM – 5:30 PM

Authors

Abstract

The Red River Valley in eastern North Dakota has prolonged seasonal snow cover that varies in depth and density with land type and aspect. Snow-melt runoff is a major contributor to spring floods in low-gradient rivers within the valley, but can be difficult to forecast due to variability in the snowpack and its complex evolution during the cold season. Low-altitude repeat aerial photography collected by uncrewed aerial systems (UAS) can be used to track the evolution of prairie snow across varying land types, although the accuracy of this method is unknown. We test this method at Rabanus Park in Fargo, North Dakota to assess the accuracy and precision of Structure from Motion (SfM) photogrammetry for remote measurement of snow depth. Repeat snow depth measurements were collected in the park in March and April 2019 and UAS-based photos were collected from a height of 60 meters above ground level. A digital terrain model (DTM) of the end-of-harvest, pre-snow surface was created from UAS-based photos collected in October 2018. Digital surface models (DSMs) were created for March and April flights using SfM algorithms available with Pix4D. The October surface was subtracted from March and April snow-covered surfaces to compute the snow depth in each month and the resulting differences were compared with snow depths collected in the field with a snow probe. The March snow surface featured variable depths across the study area, with greater depths at the base of hill slopes and lesser depths along slopes and at hill crests. The SfM-generated snow surface showed the same pattern but the accuracy of modeled snow depth was less where snow depths were shallower. The April snow surface was discontinuous across the study area. The SfM-generated snow surface for April matched the measured snow depth in places with deeper snow, but was less consistent in places where the snow was thinner. While the results of this study suggest that UAS-based photography can be useful for tracking snow depth changes through time, the accuracy of snow measurements diminishes at times when snow depth is thin. We discuss this and other challenges of monitoring seasonal snow in prairie landscapes with close-range remote sensing.

Geological Society of America Abstracts with Programs. Vol. 52, No. 5

doi: 10.1130/abs/2020NC-347974

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