Wie detektieren wir die Position der Kalbungsfront?

(die deutsche Version folgt in Kürze)

For extracting frontal positions we teamed up with the “Artificial Intelligence for Cold Regions” (AI-CORE) project. In the following, they explain the efforts in implementing AI methods for monitoring northeast Greenland glaciers.

An accurate parameterization of iceberg calving is essential for understanding glacier dynamics, separating underlying processes, and constraining ice-sheet models. The increasing availability and quality of remote sensing imagery enable us to realize a continuous and precise mapping of relevant parameters such as calving front locations. However, the huge amount of data also accentuates the necessity for automated and scalable analysis strategies.

As part of the “Artificial Intelligence for Cold Regions” project, we develop an automated workflow for extracting calving front positions from multi-spectral Landsat-8 imagery utilizing deep learning methods. This enables us to provide exceedingly dense calving front datasets for most of the Greenlandic outlet glaciers like the Zachariae Isstrøm and Nioghalvfjerdsbrae in northeast Greenland. Robust predictions are especially challenging in this area since calving, ocean, as well as illumination conditions, change drastically throughout the year. The two animations present preliminary results of our work showcasing over 350 frontal positions for each of the two glaciers from 2013 to 2021. Along with other geodetic measurements, the derived frontal positions will be part of the GROCE subproject 6 and support to separate various effects regarding ice dynamics and surface processes of the Greenland Ice Sheet.

More information about automated calving front delineation: https://doi.org/10.5194/egusphere-egu21-4528.