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. Calving is an important process in glacier systems terminating inthe ocean, and more observations are needed to improve our understanding ofthe undergoing processes and parameterize calving in larger-scalemodels. Time-lapse cameras are good tools for monitoring calving fronts ofglaciers and they have been used widely where conditions are favourable.However, automatic image analysis to detect and calculate the size of calvingevents has not been developed so far. Here, we present a method that fillsthis gap using image analysis tools. First, the calving front is segmented.Second, changes between two images are detected and a mask is produced todelimit the calving event. Third, we calculate the area given the front andcamera positions as well as camera characteristics. To illustrate our method,we analyse two image time series from two cameras placed at differentlocations in 2014 and 2015 and compare the automatic detection results to amanual detection. We find a good match when the weather is favourable, but themethod fails with dense fog or high illumination conditions. Furthermore,results show that calving events are more likely to occur (i) close to wheresubglacial meltwater plumes have been observed to rise at the front and (ii)close to one another.

Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard