Snow surface roughness from mobile laser scanning data

• We propose a processing chain for snow surface roughness estimation with MLS data.
• The results were validated against roughness values by plate photography method.
• Modelling of snow surface shape and roughness using MLS data is possible in mm scale.
• Spatial resolution of roughness computation is limited the most by profile spacing.
• MLS is a new fast method for large area surface characterization and monitoring.

Mobile laser scanning is a rapid and flexible method for acquisition of high resolution three-dimensional topographic data. Lidar based mobile mapping system produces three-dimensional point cloud from the surrounding objects. Typically, a two-dimensional profiling scanner is mounted on the system and the third dimension is achieved by the movement of the vehicle. The characteristics of the obtained point cloud depend largely on the sensor arrangement and the sensor properties.In this paper we discuss an application of mobile laser scanning for producing snow surface roughness information for climate data validation. The ROAMER, a single-scanner mobile laser scanning system, was deployed for the survey of three dimensional snow surface data.Relatively large areas could be reached with mobile laser scanning, which improves the output of surface roughness measurements and increases the statistical validity.The accuracy and precision of the mobile scanning system used in the study are almost at the same level as those of terrestrial laser scanners. The relative point precision for the system is estimated to be a few millimetres with centimetre level absolute positioning. The results show that the roughness produced from the data is in agreement with the validation data obtained from the plate photography process. This means that mobile laser scanning can be successfully used in snow surface roughness determination from large areas. The major challenge is related to direct georeferencing of mapping sensor data with global satellite navigation and inertial positioning. However, computation of surface roughness is a local operation, where the absolute accuracy is of little significance, but good relative precision is essential. The dense sampling of the surface enabled us to study multi-scale approach for surface roughness modelling, which is discussed more in this paper.We believe that even in the near future, mobile laser scanning will be considerably exploited in many applications in the environmental modelling and monitoring e.g. in forestry, hydrology, glaciology and climate sciences.