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As the Earth is becoming more exposed to heat, ski resorts are depending more and more on large snowmaking activities to maintain their hills. Climate change began to have an immense impact on the planet’s ecosystems several decades ago, causing a rapid temperature increase in the 2000s (Marcello et al., 2020). According to research, even several degrees of warming may result in fewer cold winter days with snow and, as a result, shorter seasons for ski resorts. In this sense, for several decades, global warming has been impacting winter sports in a negative way, making these kinds of sports more dependent on human intervention and temperature maintenance. Consequently, the business’ income declines, and its expenses rise (Marcello et al., 2020). Customers who will express their desire to use the services of ski resorts will be directly impacted by such expenditures, which are transferred to visitors in the form of more expensive lift passes and resort rates. In this sense, already costly winter activities run the risk of becoming increasingly restricted and less varied as resort expenses climb.
After considering climate change, ski resort snow administration is essential for ensuring proper functioning and the optimum winter weather for skiing-related events. For ski operators to organize snowmaking capacity, snow mobility by equipment, as well as other regular managerial functions, they need to have a basic understanding of the volume and geographical extent of snow (Bodmer et al., 2000). Snow management accounts for a considerable proportion of staff time and associated expenses in ski resorts, particularly for snowmaking and maintenance.
To quantify snow level and geographic dispersion, a number of remote sensing tools have been tried and developed recently. Studies analyzed snow thickness and the distribution pattern of the snow across distances ranging from 3000 meters using a long-range terrestrial-laser scanner (LIDAR) (Harder et al., 2020). It could be difficult for locations with a thin snowpack, but the highest and average absolute snow level errors discovered were 0.5-0.6 and 0.2-0.3m (Harder et al., 2020). However, it is precise enough to identify snow distribution patterns in regions with heavy snow cover. Researchers additionally showed that snow thickness assessments with 0.30m RMSE may be performed in high-alpine catchment areas using commercially accessible aerial pictures taken with the Leica ADS80 (Harder et al., 2020). Thus, such a device is among the most precise cameras.
Previously, the application of stereo satellite pictures or helicopter-borne Ground Penetrating Radars was investigated, and snow accumulation’s seasonal variation was measured. Unmanned aerial vehicles with a wide range of sensing devices have recently unveiled new possibilities for more flexible and economically calculated digital surface models (Harder et al., 2020). Researchers looked into the use of unmanned aerial systems to measure snow depth distribution (Harder et al., 2020). When opposed to terrestrial imaging systems, such devices have the benefit of being more adaptable to encompass areas that would otherwise be unreachable (Joiner et al., 2022). Nevertheless, when sub-metric measurements are required, photogrammetry’s precision and clarity, particularly with fresh snow where it is challenging to record variations between pixels, might be challenging (Harder et al., 2020). Despite the fact that this technology is evolving quickly, the outcomes in these particular cases are improving quickly as devices increase their efficiency.
Data on the comprehensive accumulating and melting dynamics until this point in time may be obtained by capturing the geographical distribution of snowfalls and snow cover at a certain moment. To comprehend snow dynamics, test systems, look at the geographical scalability of process interactions, and initialize forecasting accuracy, precise estimation of snow accumulation and ablation is required (Trenson et al., 2018). Space-borne hyper-spectral imaging systems are devices with spectrum coverage from the visual to near-infrared (NIR), occasionally additionally including ultraviolet (UV) and infrared frequencies (Marcello et al., 2020). It was outlined that there is a number of objectives for hyper-spectral sensors, such as the observation of the physiology of ecosystems as well as snow and ice reflectivity, accumulating, and melting.
In this situation, a conceptual system that improves snow management, resort navigation, and avalanche prediction using a personal remote sensing system can be the solution. Remote sensing conceptual systems consist of multispectral, hyperspectral, and infrared satellites. Among the requirements for this solution is the Leica ADS80 camera, which is among the leading devices with geographical data and precise images (Pons et al., 2018). With its cutting-edge line sensing technologies, Leica Geosystems can analyze and acquire data faster than any other camera. As for the goals, it involves the training of employees on the use and analysis of the sensor. Such methods provide an outstanding opportunity to demonstrate an understanding of the geographic variation of snow thickness on ski slopes (Transon et al., 2018). The objectives of such an approach will be to evaluate the viability and prospects of using such distant location tools to enhance the knowledge ski resort management teams have about the spatial variability of snow complexity, which will aid in decision-making for tasks like snowmaking, maintenance, and avalanche risk mitigation.
However, while among the attributes that may be retrieved through multispectral and hyperspectral remote sensing are snow-covered region, reflectivity, particle size, liquid extremely close to the surface, and temperature, there might be constraints. Among the challenges might be the detection of snow accumulation in forested areas and the adaptation of the techniques to frequent, large-scale computation. In this sense, extended employee training or recruitment of specialists might be required.
References
Bodmer, H. C., Seidel, K., & Aerts, J. (2000). Remote sensing based management information system for ski resort planning. In M.F. Buchroithner (Ed.). A decade of trans-European remote sensing cooperation, pp.319-325.
Harder, P., Pomeroy, J. W., & Helgason, W. D. (2020). Improving sub-canopy snow depth mapping with unmanned aerial vehicles: Lidar versus structure-from-motion techniques. The Cryosphere, 14(6), 1919-1935. Web.
Joiner, J., Fasnacht, Z., Qin, W., Yoshida, Y., Vasilkov, A. P., Li, C.,… & Krotkov, N. (2022). Use of hyper-spectral visible and near-infrared satellite data for timely estimates of the Earth’s surface reflectance in cloudy and aerosol loaded conditions: Part 1–application to RGB image restoration over land with GOME-2. Frontiers in Remote Sensing, 2, 1-10. Web.
Marcello, J., Eugenio, F., Gonzalo-Martin, C., Rodriguez-Esparragon, D., & Marques, F. (2020). Advanced processing of multiplatform remote sensing imagery for the monitoring of coastal and mountain ecosystems. IEEE Access, 9, 6536-6549. Web.
Pons, M., Lopez, J.I., Revuelto, J., Alonso, E., Vilella, M., Travesset, Apodaka, J., Pesado, C., Margalef, A., & Iravani, P. (2018). Remote sensing techniques for helping decision-making in the management of ski areas. International Snow Science Workshop Proceedings. Web.
Transon, J., d’Andrimont, R., Maugnard, A., & Defourny, P. (2018). Survey of hyperspectral earth observation applications from space in the sentinel-2 context. Remote Sensing, 10(2), 1-32. Web.
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