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Discussion papers | Copyright
https://doi.org/10.5194/gi-2018-5
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Jul 2018

Research article | 02 Jul 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Instrumentation, Methods and Data Systems (GI).

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

Dorothée Vallot1, Sigit Adinugroho2,3, Robin Strand3, Penelope How4, Rickard Pettersson1, Douglas I. Benn5, and Nicholas R. J. Hulton6 Dorothée Vallot et al.
  • 1Department of Earth Sciences, Uppsala University, Sweden
  • 2Faculty of Computer Science, Brawijaya University, Indonesia
  • 3Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden
  • 4Institute of Geography, School of GeoSciences, University of Edinburgh, UK
  • 5School of Geography and Geosciences, University St Andrews, Scotland
  • 6Department of Arctic Geology, UNIS, The University Center in Svalbard, Norway

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 be able to parameterise 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 favorable 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 melt water plumes have been observed to rise at the front and (ii) close to one another.

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Short summary
This paper presents a novel method to quantify the sizes and frequency of calving events from time-lapse camera imageries. The calving front of a tidewater glacier experiences different episodes of iceberg deliveries that can be captured by a time-lapse camera situated in front of the glacier. An automatic way of detecting calving events is presented here and compared to manually detected events.
This paper presents a novel method to quantify the sizes and frequency of calving events from...
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