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.

中文翻译：

---

在斯瓦尔巴特群岛图纳布雷恩的延时图像中自动检测产犊事件

产犊是终止于海洋的冰川系统中的重要过程，需要更多的观察以增进我们对正在经历的过程的了解，并在较大规模的模型中参数化产犊。缩时摄影机是监测冰川前缘的好工具，在条件适宜的情况下已广泛使用。然而，到目前为止，尚未开发出用于检测和计算产犊事件大小的自动图像分析。在这里，我们提出一种使用图像分析工具填补这一空白的方法。首先，产犊锋线被分割。其次，检测到两个图像之间的变化，并生成一个遮罩以限制产犊事件。第三，我们根据前摄和相机位置以及相机特性计算面积。为了说明我们的方法，我们分析了2014年和2015年放置在不同位置的两台摄像机的两个图像时间序列，并将自动检测结果与手动检测结果进行了比较。当天气有利时，我们找到了很好的匹配，但是该方法在浓雾或高光照条件下失败。此外，结果表明，产犊事件更有可能发生在（i）靠近冰川下融水羽流在前部上升和（ii）彼此靠近的地方。