Automatic Glacier Surface Analysis from Airborne Laser Scanning

Glaciers are interesting phenomena to scientists, mountaineers and tourists. Glaciers have a great impact on the local economy, power generation and water supply. Furthermore, the behaviour of glaciers is influenced by climate variations, such as changes in temperature. Monitoring glaciers can therefore give valuable insight to glaciologists. Two aspects of glaciers that can be monitored are the delineation of a glacier and the crevasses within a glacier. In this paper it is presented how these two aspects can be detected automatically from Airborne Laser Scanning (ALS) data. The delineation of a glacier can be derived from ALS data by setting up a classification of the elevation model into the classes glacier and non-glacier surface. The smoothness, which is calculated from the ALS data, is used as classification criterion. Crevasses within the glacier can be detected by assuming that they are deviations from a regular glacier surface without any crevasses. Such a surface can be calculated with techniques from Mathematical Morphology. Given the assumption that crevasses have a V-like shape, the bottom of the crevasse and the two edges can be reconstructed from the point data. ALS data that was acquired at the Hintereisferner in Tyrol, Austria was used for testing the algorithms. Both the delineation of the glacier and the detection of crevasses give good results in the presented approach. However, the delineation of the glacier might fail if many crevasses cause exceptions to the smoothness criterion. Crevasses are sometimes not detected due to snow bridges. The quality of the reconstruction of crevasses is hard to assess due to the lack of reference data at the test location. Data acquisition with a higher point density and the acquisition of reference data for crevasses with Terrestrial Laser Scanning are recommended to independently check the result.