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Evaluating the potential of high-resolution airborne remote sensing for glaciology

Evaluating the potential of high-resolution airborne remote sensing for glaciology

One of the fundamental requirements for the creation of accurate models of glacier mass-balance is the provision of accurate topographic data in the form of Digital Elevation Models (DEMs). Such data have typically been derived from either traditional topographic maps, or more recently, from spaceborne altimetry data. These data sources, however, typically have a low spatial resolution; where high resolution data are required, they are derived by interpolation of the low resolution data, and hence produce inherently 'smooth' DEMs. In recent years, however, the performance of airborne, high resolution altimeters has improved markedly. In this project we investigate the potential of a high resolution LiDAR (Light Detection And Ranging) system both as a tool for the creation of accurate, high resolution DEMs, but also to evaluate the potential of such data in other areas of glaciology such as measurement of glacier velocity through feature tracking.

LiDAR systems are analogous to conventional Radar systems, but instead of radio waves, use a pulsed, near-infrared laser to measure the distance from the aircraft to the surface. They are capable of very high spatial resolutions, which depend on the aircraft altitude, as well as the capabilities of the system itself. For this project, we have used data gathered with the Cambridge University Unit for Landscape Modelling's (part of the Department of Geography) Optech ALT 3033 LiDAR system, which was carried by the UK Natural Environment Research Council Airborne Remote Sensing Facility Dornier Do228 Research Aircraft. Data were collected in the summers of 2003 and 2005, from Midre Lovenbreen, a valley glacier in north-west Spitsbergen, near the Ny Alesund international research base.

The data collected have a spatial resolution of around 1.5 metres, and an accuracy of better than 0.1 m. These data reveal a wealth of surface detail; meltwater channels on the glacier surface are clearly visible, as are crevassed regions. Individual meltwater channels can be tracked downstream along their length, for instance; their depth increases downstream in the image, as would be expected given the increase in water discharge, which leads to greater incision (though melting) into the glacier surface. The ease with which these features can be seen in the imagery should allow their movement to be tracked, giving an estimate of the glacier velocity. We will be testing this once the data collected during 2005 have been through post-collection processing to correct for the movement of the aircraft.

Figure 1. High resolution DEM of Midre Lovenbreen derived from the LiDAR data collected during the summer of 2003. The data are shaded according to slope angle and orientation to highlight the detail in the image. The relatively smooth surface of the glacier itself is easily distinguishable from the rough, unglaciated terrain. Surface meltwater streams on the glacier are highlighted in blue; crevasses in green. The area shown is approximately 5km by 3km, and consists of over 12.5 million individual data points.

Image as described adjacent

The LiDAR data also include information on the intensity (strength) of the reflected pulse. These data allow us to distinguish different types of surface in the image; in particular, the area of the glacier which is still covered with snow can be easily distinguished from areas where all the snow has melted. The edge of the glacier itself can also be easily seem.

Figure 2. Elevation corrected intensity image. The remaining snow-covered areas appear as white/very light grey; bare glacier ice areas as dark/mid grey, and the surrounding topography as mid/light grey.

Image as described adjacent

We are also investigating the use of these high-resolution data to help determine the surface roughness of glaciers, which is an important control on the fluxes of heat into or out of the surface from the atmosphere, and hence a control on glacier melt rates, and which also affect the reflectance of the glacier surface in satellite radar imagery.


  • Rees, W.G. and Arnold, N.S. 2007. Mass balance and dynamics of a valley glacier measured by high-resolution LiDAR. Polar Record 43(227), 311-319.
  • Arnold, N.S., Rees, W.G., Hodson, A.J. and Kohler, J. 2006 Topographic controls on the energy balance of a high Arctic glacier. Journal of Geophysical Research. 111 F02011, doi 10.1029/2005JF000426 (15 pp).
  • Rees, W.G. and Arnold, N.S. 2006 Scale-dependent roughness of a glacier surface: implications for radar backscatter and aerodynamic roughness modelling. Journal of Glaciology. 52, 177, 214-222.
  • Arnold, N.S., Rees, W.G., Devereaux, B.J. and Amable, G. 2006. Evaluating the potential of high-resolution airborne LiDAR in glaciology. International Journal of Remote Sensing 27 (5-6), 1233-1251.
  • Arnold, N. and Rees, W.G. 2003. Self-similarity in glacier surface characteristics. Journal of Glaciology 49(147), 547-554.