The characteristics of snow pack surfaces are spatially and temporally highly variable. Today, in situ field observations provide mostly isolated information on characteristics such as grain size, grain shape, free water content or surface roughness. Remote sensing instruments are promising tools for systematic and wide-area mapping of several snow surface parameters. This study presents
a methodology for an automated, systematic and wide-area detection and mapping of rough snow surfaces including avalanche deposits using optical remote sensing data of high spatial and radiometric resolution. A processing chain integrating directional, textural and spectral information was developed using ADS40 airborne scanner data acquired in spring 2008 and 2009 over a test site near Davos, Switzerland. Though certain limitations exist, encouraging detection and mapping accuracies can be reported. The presented approach is a promising addition to existing field observation methods for remote regions, and can be applied in inaccessible areas.