Quantifying the dynamics of microtopography during a snowmelt event

Knowledge of soil microtopography and its changes in space and over time is important to the understanding of how tillage influences infiltration, runoff generation and erosion. In this study, the use of a terrestrial laser scanner (TLS) is assessed for its ability to quantify small changes in the soil surface at high spatial resolutions for a relatively large surface area (100 m2). Changes in soil surface morphology during snow cover and melt are driven by frost heave, slaking, pressure exertion by the snowpack and overland flow (erosion and deposition). An attempt is undertaken to link these processes to observed changes at the soil surface. A new algorithm for soil surface roughness is introduced to make optimal use of the raw point cloud. This algorithm is less scale dependent than several commonly used roughness calculations. The results of this study show that TLSs can be used for multitemporal scanning of large surfaces and that small changes in surface elevation and roughness can be detected. Statistical analysis of the observed changes against terrain indices did not yield significant evidence for process differentiation.