Remote sensing of glaciers

Background and study area

It is widely known that alpine glaciers are considered as sensitive climatic indicators to assess the impact of recent global warming. In a few parts of the world some glaciers have been monitored for more than a century after their latest extent at the end of the Little Ice Age (around 1850).

The current research activities find their roots in the EU-funded OMEGA project ( that dealt in particular with glaciology and climate change in a framework of remote sensing, GIS and visualisation methods. The study site at hand is the glacier Hintereisferner located near the border of Austria and Italy (46° 48´ N, 10° 46´ E) in the Tyrolean Alps (Figure 1). This glacier area was chosen as it meets the main requirements for a good study site; i.e. old terrestrial and/or aerial photography is available and a long series of glaciological and meteorological records exist. A little more than 100 years ago, studies were initiated on the motion of Hintereisferner (Blümcke & Hess 1899) and a survey of velocity and ice thicknesses already started in 1894. Climatic records for the region are available since 1860 (Kuhn et al. 1997). Nowadays Hintereisferner is well monitored in terms of mass balance and velocity measurements (Kuhn et al. 1999). Figure 1: The glacier Hintereisferner in August 2002 (Photograph taken by Norbert Span, University of Innsbruck, Austria).

Aims of the research

Within this project remote sensing techniques are applied and developed to conduct research on the temporal behaviour of the glacier Hintereisferner in terms of glacier dynamics, snow/ice distributions and reflective properties using optical remote sensing datasets. A key role is in store to make evaluations on the status of glacial areas more automatic. The outcome of these studies should make a contribution to the current research on glaciology and add a better understanding on how to rapidly assess glacier dynamics using optical remote sensing techniques.

Recent results

From all the optical datasets available, Landsat Thematic Mapper (Figure 2) and ASTER images are used as they achieve almost global coverage. From these images, satellite-derived surface reflectances are computed after performing radiometric, atmospheric and topographic corrections. Parameterisations of specific reflectance intervals have shown that different glacial surface types such as snow, firn and ice (Figure 3) can be distinguished using only a single equation (Hendriks & Pellikka 2004).
Figure 2: Landsat ETM+ image from 13.09.1999 over the study area

An effort has been made recently to produce an interactive masking scheme that semi-automatically delineates glacier outlines. This procedure only requires a single raw (E)TM scene. Clouds, shadows and water areas that are often miss-classified, are effectively filtered away due to a combination of TM5 thresholding and band rationing. The modelling procedure was already tested on 8 Landsat (E)TM scenes and an accuracy of 85% was achieved compared to manual delineations from topographic maps (Hendriks & Pellikka in progress). Accurate glacier masking is an important tool to assess glacier change or filter out non-glacier areas for spectral studies. And example of some intermediate model outputs is depicted in Figure 4.

Figure 3: Relation between modelled spectrometer averages and modelled average satellite reflectance using weighing functions for different glacier surfaces.

Figure 4: Glacier masking procedure with intermediate modelling steps (created in Erdas Imagine)

Next to space-borne remote sensing techniques, more detailed aerial photography techniques are being applied to assess parameters concerning glacier dynamics. Digital camera data is used to study glacier topography and surface characteristics. Image mosaics (Figure 5) and elevation models are created from high-resolution false colour digital camera imagery (CIR) by using an iterative mathematical process called bundle block adjustment. The main advantages of developing these methods are to increase our understanding of changing glacier (micro-) topography, supra-glacial debris mapping and glacio-fluvial characteristics.

Figure 5: Image mosaic created from CIR imagery and locations of sample areas for multi-angular reflectance analyses




Blümcke, A., & H. Hess. 1899. Untersuchungen am Hintereisferner . Wissenschaftliche Ergänzungshefte zur Zeitschrift des Deutschen und Österreichischen Alpenvereins , Band 1, Heft 2 (pp. 1-87). München: Bruckmann AG.

Hendriks, J.P.M. & P. Pellikka, 2004. Estimation of reflectance from a glacier surface by comparing spectrometer measurements with satellite-derived reflectances. Zeitschrift für Gletscherkunde und Glazialgeologie 38(2) : 139-154.

Hendriks , J.P.M. & P. Pellikka in progress. Preliminary title: The dynamics of Hintereisferner by satellite-derived glacier masking.

Kuhn, M., Dreiseitl, E., Hofinger, S., Markl, G., Span, N. & Kaser, G. 1999. Measurements and models of the mass balance of Hintereisferner. Geografiska Annaler , 81 A (4), 659 – 670.

Kuhn, M., Schlosser, E. & Span, N. 1997. Eastern alpine glacier activity and climatic records since 1860. Annals of Glaciology, 24, 164-168.

Cooperation partners

  • Department of Geography, University of Innsbruck
  • Department of Meteorology and Geophysics, University of Innsbruck
  • Department of Geography, University of Turku