The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1009–1016, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1009-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1009–1016, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1009-2020

  12 Aug 2020

12 Aug 2020

GEOMATIC TECHNIQUES FOR THE OPTIMIZATION OF SKI RESOURCES

I. Aicardi1,2, S. Angeli1, N. Grasso1, A. M. Lingua1,2, and P. Maschio1 I. Aicardi et al.
  • 1DIATI, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10124 Torino, Italy
  • 2PIC4SeR, Politecnico di Torino Interdepartmental Centre for Service Robotics, Torino, Italy

Keywords: aerial photogrammetry, unmanned aerial vehicle (UAV), GNSS, ski, snow depth

Abstract. Climate change is already affecting the entire world, with extreme weather conditions such as drought, heat waves, heavy rain, floods and landslides becoming more frequent, including Europe. In according to Paris agreement and relative European announcement of Carbon neutrality (by 2050), the saving of water and energy supplies is a fundamental aspect in the management of resources in production, sports, hospitality facilities and so on. Some methodologies for the optimization of the consumption of natural resources are required. This article describes an activity aimed at measuring, monitoring and analysing the thickness of the snowpack on the ski slopes during the winter season to permit a sustainable approach of snowmaking in alpine ski areas . The authors propose a methodology based on the integration of multitemporal surface (ground/snow) survey by Autonomous Aerial Vehicle (AAV) and low cost GNSS receivers mounted on snow groomers for a RTK (Real Time Kinematic) solution. To obtain a complete snow surface digital models with poor detailed images on ski slopes, some pre-processing techniques have been analysed to locally improve contrast and details with a local high pass filtering. The methodology has been employed in two study areas (Limone Piemonte, Prato Nevoso) located in the province of Cuneo, in the southern alpine area of Piedmont.