Abstract
Abstract. Calving is an important process in glacier systems terminating in the ocean, and more observations are needed to improve our understanding of the undergoing processes and parameterize calving in larger-scale models. Time-lapse cameras are good tools for monitoring calving fronts of glaciers and they have been used widely where conditions are favourable. However, automatic image analysis to detect and calculate the size of calving events has not been developed so far. Here, we present a method that fills this gap using image analysis tools. First, the calving front is segmented. Second, changes between two images are detected and a mask is produced to delimit the calving event. Third, we calculate the area given the front and camera positions as well as camera characteristics. To illustrate our method, we analyse two image time series from two cameras placed at different locations in 2014 and 2015 and compare the automatic detection results to a manual detection. We find a good match when the weather is favourable, but the method fails with dense fog or high illumination conditions. Furthermore, results show that calving events are more likely to occur (i) close to where subglacial meltwater plumes have been observed to rise at the front and (ii) close to one another.
Highlights
Tidewater glaciers are one of the main contributors to sea-level rise (Church et al, 2013; Gardner et al, 2013), but the calving process remains difficult to predict and to model
Event size distribution follows a scale-invariant power law that has been further discussed by Åström et al (2014), who classify the termini of calving glaciers as self-organized critical systems such as earthquakes
To determine whether a picture falls into this category, we look at the standard deviation and mean of pixel intensity for each section of the image after calibrating these values for different lightning conditions on a set of images
Summary
Tidewater glaciers are one of the main contributors to sea-level rise (Church et al, 2013; Gardner et al, 2013), but the calving process remains difficult to predict and to model. Event size distribution follows a scale-invariant power law that has been further discussed by Åström et al (2014), who classify the termini of calving glaciers as self-organized critical systems such as earthquakes. They recommended focusing on quantifying the effects of external forcing on the critical state of calving mar-
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