Combining satellite multispectral image data and a digital elevation model for mapping debris-covered glaciers

Abstract

Automated glacier mapping from satellite multispectral image data is hampered by debris cover on glacier surfaces. Supraglacial debris exhibits the same spectral properties as lateral and terminal moraines, fluvioglacial deposits, and bedrock outside the glacier margin, and is thus not detectable by means of multispectral classification alone. Based on the observation of low slope angles for debris-covered glacier tongues, we developed a multisource method for mapping supraglacial debris. The method combines the advantages of automated multispectral classification for clean glacier ice and vegetation with slope information derived from a digital elevation model (DEM). Neighbourhood analysis and change detection is applied for further improvement of the resulting glacier/debris map. A significant percentage of the processing can be done automatically. In order to test the sensitivity of our method against different DEM qualities, it was also applied to a DEM obtained from ASTER stereo data. Additionally, we compared our multisource approach to an artificial neural network (ANN) classification of debris, using only multispectral data. While the combination with an ASTER-derived DEM revealed promising results, the ANN classification without DEM data does not.