Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.240
IF 5-year value: 5.768
IF 5-year
CiteScore value: 8.9
SNIP value: 1.713
IPP value: 5.53
SJR value: 3.18
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
h5-index value: 51
Volume 6, issue 3
Geosci. Model Dev., 6, 837–848, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 6, 837–848, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model description paper 22 Jun 2013

Model description paper | 22 Jun 2013

PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.1.0)

S. Härer1, M. Bernhardt1, J. G. Corripio2, and K. Schulz1,3 S. Härer et al.
  • 1Department of Geography, LMU Munich, Munich, Germany
  •, Innsbruck, Austria
  • 3Institute of Water Management, Hydrology and Hydraulic Engineering (IWHW), University of Natural Resources and Life Sciences (BOKU), Vienna, Austria

Abstract. Terrestrial photography is a cost-effective and easy-to-use method for measuring and monitoring spatially distributed land surface variables. It can be used to continuously investigate remote and often inaccessible terrain. We focus on the observation of snow cover patterns in high mountainous areas. The high temporal and spatial resolution of the photographs have various applications, for example validating spatially distributed snow hydrological models. However, the analysis of a photograph requires a preceding georectification of the digital camera image. To accelerate and simplify the analysis, we have developed the "Photo Rectification And ClassificaTIon SoftwarE" (PRACTISE) that is available as a Matlab code. The routine requires a digital camera image, the camera location and its orientation, as well as a digital elevation model (DEM) as input. If the viewing orientation and position of the camera are not precisely known, an optional optimisation routine using ground control points (GCPs) helps to identify the missing parameters. PRACTISE also calculates a viewshed using the DEM and the camera position. The visible DEM pixels are utilised to georeference the photograph which is subsequently classified. The resulting georeferenced and classified image can be directly compared to other georeferenced data and can be used within any geoinformation system. The Matlab routine was tested using observations of the north-eastern slope of the Schneefernerkopf, Zugspitze, Germany. The results obtained show that PRACTISE is a fast and user-friendly tool, able to derive the microscale variability of snow cover extent in high alpine terrain, but can also easily be adapted to other land surface applications.

Publications Copernicus