The Hyperspectral Lidar Satellite

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Table of Contents

Analysis and Findings
Cost, Materials, Technical and Environmental Constraints
Trades Made to Achieve the Selected Optimum
Final System Performance Effectiveness Evaluation, Strengths, and Weaknesses
Lessons Learned
Synthesis and Interpretation
References

Analysis and Findings

Hyperspectral remote sensing provides geophysical data and uses imagery to analyze the current snow on slopes in real-time. LIDAR data allows terrain identification with a spatial resolution of 1 m. In laser scanning, absolute errors of snow level did not exceed 0.6 m (Harder et al., 2020). Hyperspectral sensors allow tracking accumulation and melting of snow and ice and thereby classifying ground cover in areas of ski resorts. LIDAR satellites aid in assessing objects spectral and spatial movements, which are critical for moving systems such as snow cover (Kahraman & Bacher, 2021). The three-dimensional imaging capability allows the classification of object elevation information, which is a significant component of snow level monitoring and applicability in its analysis.

Cost, Materials, Technical and Environmental Constraints

Hyperspectral imaging has a high cost associated with launching satellites, maintaining them, and transmitting information with subsequent analysis. Facilities require laboratories that will analyze the images and validate them. The main technical requirement is the need for high-capacity memory systems and their protection, which ski resorts will not always be able to guarantee. It also leads to limitations, such as the need to create a system of evaluating good and poor-quality satellite images that can be applied to assess the snow cover (Joiner et al., 2022). The environmental limitations of LIDAR satellites can be the consequences of the satellite launch itself, as combustible fuel is used, and its oxidized products are released into the atmosphere, affecting the climate.

Trades Made to Achieve the Selected Optimum

Multiple scattering using LIDAR technology allows for a relatively accurate determination of the snow depth. High resolution, one of the advantages, can make satellite operations difficult (Lu et al., 2022). However, compared to other methods, it is the most optimized and comfortable tool (Norton et al., 2022). Ski resorts need to switch to satellite systems and cooperate with space programs if they want to stay in business. The ability to navigate and respond quickly to change will pay for the cost of LIDAR materials and technology in the long run.

Final System Performance Effectiveness Evaluation, Strengths, and Weaknesses

Implementing remote sensing technology has advantages over other technologies. It is justified primarily by the ability to track the condition of the snow cover continuously. In addition, the ability to measure the height of the cover rather than analyze its particles is the advantage of LIDAR satellites. The weaknesses are difficulty predicting errors and recognizing false and true images (Fair, 2021). Mullen et al. (2022) point out that satellites should be replaced by unscrewed aerial vehicles because they can cover large areas and minimize harm. Currently, no fully automated systems support such satellites and therefore, analyze them.

Lessons Learned

LIDAR systems can be tools in organizing geophysical assessment of snow conditions, which plays a crucial role in winter sports. Li et al. (2022) point out that the capabilities of these systems are necessary for a comprehensive study of the cryosphere and, therefore, for decision-making in climate conservation and maintenance. Earth climate monitoring programs are a source of forecasting the overall path of ecology and allow for a new perspective on any snow-related activities. The best way is to integrate hyperspectral satellite technology into winter sports management processes.

Synthesis and Interpretation

Climate change is one of the critical challenges for ski sports, as the recorded gradual decrease in snow cover thickness leads to difficulties in implementing the Olympic Games. Skiing is highly dependent on snow conditions, and Orr & Inoue (2019) point out that climate effects could severely affect the games. Guzman (2022) points out that the Olympic Committee is concerned about the technology used at the Beijing Games. The efficiency of artificial snow is not high: the presence of waste and energy costs violates the environmental friendliness of such snow. Consequently, the present situation with the natural snow climate is an advantage that cannot be guaranteed under the current warming and melting of snow and ice on the mountain slopes.

References

Fair, Z. (2021). Application of lidar altimetry and hyperspectral imaging to ice sheet and snow monitoring. Dissertation. The University of Michigan. Web.

Guzman, C. d. (2022). What artificial snow at the 2022 Olympics means for the future of winter games. Time. Web.

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., Lamsal, L., & Krotkov, N. (2022). Use of hyper-spectral visible and near-infrared satellite data for timely estimates of the Earths surface reflectance in cloudy and aerosol loaded conditions: Part 1application to RGB image restoration over land with GOME-2. Frontiers in Remote Sensing, 2, 1-10. Web.

Kahraman, S., & Bacher, R. (2021). A comprehensive review of hyperspectral data fusion with lidar and sar data. Annual Reviews in Control, 51, 236-253. Web.

Lu, X., Hu, Y., Zeng, X., Stamnes, S. A., Neuman, T. A., Kurtz, N. T., Yang, Y., Zhai, P.W., Gao, M., Sun, W., Xu, K., Liu, Z., Omar, A. H., Baize, R. R., Rogers, L. J., Mitchell, B. O., Stamnes, K., Huang, Y., Chen, N., & Fair, Z. (2022). Deriving snow depth from ICESat-2 Lidar multiple scattering measurements: Uncertainty analyses. Frontiers in Remote Sensing. Web.

Mullen, A., Sproles, E. A., Hendrikx, J., Shaw, J. A., & Gatebe, C. K. (2022). An operational methodology for validating satellite-based snow albedo measurements using a UAV. Frontiers in Remote Sensing. Web.

Norton, C.L., Hartfield, K., Collins, C.D.H., van Leeuwen, W.J.D., & Metz, L.J. (2022). Multi-temporal LiDAR and hyperspectral data fusion for classification of semi-arid woody cover species. Remote Sensing, 14(2896). Web.

Orr, M., & Inoue, Y. (2019). Sport versus climate: Introducing the climate vulnerability of sport organizations framework. Sport Management Review, 22(4), 452-463. Web.

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