Snow grooming using machine guidance for piste management processes: Case study: Ormberget ski piste
2013 (English)Report (Other academic)
The snow is an increasingly precious commodity to the ski industry's production of pistes. To preserve this commodity is the basis for economic production. The wear on the groomed snow consists primarily of two reasons: skiers and melting. A slope that is optimally processed will last longer and require less maintenance. In addition to this obvious that maintenance must be planned based on where wear has taken place there is an additional planning variable, namely time. Timing is of utmost importance when it comes to snow as a material. A process model will be presented covering a systematic approaches how road construction technologies can be adapted to the snow grooming process. The expected results are a process model which can be used in order to optimize the snow grooming management in order to extend the skiing season. There is possibly two ways of doing this. Firstly to optimize the snow mass haul management process during the skiing season. Secondly re-build the different piste off season to create better environmental conditions during season. This study consists of four kinds of data. Laser scanned data for 3D terrain modeling of ski piste (ground) and collected data by snow groomer for 3D terrain modeling of ski piste during the objective period of investigation. The differences between 3D terrain models by laser scanning and snow groomer is representative of snow depth for corresponding day. Furthermore, in order to verify the method, snow depth and snow surface are measured by group of students from Luleå University of Technology.
Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2013. , 20 p.
Snow grooming, machine guidance, terrain models, GNSS, piste management, Civil engineering and architecture - Geoengineering and mining engineering
Samhällsbyggnadsteknik och arkitektur - Geoteknik och gruvteknik
Research subject Soil Mechanics; Construction Engineering and Management
IdentifiersURN: urn:nbn:se:ltu:diva-22075Local ID: 167623c0-d76a-4e20-a44d-c3a589678ac6OAI: oai:DiVA.org:ltu-22075DiVA: diva2:995123
Godkänd; 2013; 20130705 (amizei)2016-09-292016-09-29Bibliographically approved