Automated Detection of Marine Glacier Calving Fronts Using the 2-D Wavelet Transform Modulus Maxima Segmentation Method

Changes in the calving front position of marine-terminating glaciers strongly influence the mass balance of glaciers, ice caps, and ice sheets. At present, quantification of frontal position change primarily relies on time-consuming and subjective manual mapping techniques, limiting our ability to understand changes to glacier calving fronts. Here we describe a newly developed automated method of mapping glacier calving fronts in satellite imagery using observations from a representative sample of Greenland's peripheral marine-terminating glaciers. Our method is adapted from the 2-D wavelet transform modulus maxima (WTMM) segmentation method, which has been used previously for image segmentation in biomedical and other applied science fields. The gradient-based method places edge detection lines along regions with the greatest intensity gradient in the image, such as the contrast between glacier ice and water or glacier ice and sea ice. The lines corresponding to the calving front are identified using thresholds for length, average gradient value, and orientation that minimize the misfit with respect to a manual validation data set. We demonstrate that the method is capable of mapping glacier calving fronts over a wide range of image conditions (light to intermediate cloud cover, dim or bright, melange presence, etc.). With these time series, we are able to resolve subseasonal to multiyear temporal patterns as well as regional patterns in glacier frontal position change.