Snow process monitoring in montane forests with time-lapse photography

A camera network with hourly resolution was used to monitor the complex snow processes in montane forest environments. We developed a semi-automatic procedure to interpret snow depths from the digital images, which exhibited high consistency with manual measurements and station-based recordings. To extract snow interception dynamics, six binary classification methods were compared. The MaxEntropy classifier demonstrated better performance than the other methods under conditions of varying illumination and was therefore selected as the method used for quantifying snow in tree canopies. Snow accumulation and ablation on the ground, as well as snow loading and unloading in the forest canopies, were investigated using snow parameters derived from the time-lapse photography monitoring. The influences of meteorologic conditions, forest cover, and elevation on the snow processes were also considered. Time-lapse photography proved to be an effective and low-cost approach for collecting useful information on snow processes and facilitating the set-up of hydrological models.