Snow drifts as a driver of alpine plant productivity as observed from weekly multispectral drone imagery

Abstract

Patterns of alpine plant productivity are extremely variable in space and time. Complex topography drives variations in temperature, wind, and solar radiation. Surface and subsurface flow paths route water between landscape patches. Redistribution of snow creates scour zones and deep drifts, which drives variation in water availability and growing season length. Hence, the distribution of snow likely plays a central role in patterns of alpine plant productivity. Given that these processes operate at sub-1 m to sub-10 m spatial scales and are dynamic across daily to weekly time scales, historical studies using manual survey techniques have not afforded a comprehensive assessment of the influence of snow distribution on plant productivity. To address this knowledge gap, we used weekly estimates of normalised difference vegetation index (NDVI), snow extent, and peak snow depth, acquired from drone surveys at 25 cm resolution. We derived six snowpack-related and topographic variables that may influence vegetation productivity and analysed these with respect to the timing and magnitude of peak productivity. Peak NDVI and peak NDVI timing were most highly correlated with maximum snow depth, and snow-off-date. We observed up to a ~30% reduction in peak NDVI for areas with deeper and later snow cover, and a ~11-day delay in the timing of peak NDVI in association with later snow-off-date. Our findings leverage a novel approach to quantify the importance of snow distribution in driving alpine vegetation productivity and provide a space for time proxy of potential changes in a warmer, lower snow future.