Most of Tianshan Highway is located in the high and cold mountain areas over 2,000 m above the sea level in Xinjiang. Highway distress in late stage is exacerbated and frequently occurs especially avalanche, wind blowing snow, geohazard, flood damage, and other disasters along the highway, which seriously affects the traffic. Restricted by the climate and topography and other factors, conventional ground monitoring is hard to be applied into dealing with the emergency situation. In contrast, remote sensing technology can monitor snows in large-scale and real-time. Based on the remote sensing data sources and studied areas, MODIS data is selected to reversely derive snow depth and analyze the temporal-spatial snow distribution in Dushanzi-Kuqa region of Tianshan Highway of northern Xinjiang in recent years. Analysis revealed that the snowfall was mainly concentrated on late October to the early April in the next year. The snow area in July is the least in summer, and reaches largest in winter, accounting for about 35.7% of the entire studied area. Most of the snow depth is at 12 to 15 cm, and the maximum snow cover depth is about 23 cm in the Duzishan region of Wusu County. The study conclusion is of great theoretical and practical significance for distress monitoring and controlling.


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    Titel :

    Snow Depth Inversion and Analysis of Temporal-Spatial Snow Distribution Along Tianshan Highway Based on MODIS Data


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Wuhong (Herausgeber:in) / Wu, Jianping (Herausgeber:in) / Jiang, Xiaobei (Herausgeber:in) / Li, Ruimin (Herausgeber:in) / Zhang, Haodong (Herausgeber:in) / Yin, Bo (Autor:in)

    Kongress:

    International Conference on Green Intelligent Transportation System and Safety ; 2021 November 19, 2021 - November 21, 2021



    Erscheinungsdatum :

    28.10.2022


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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