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Night Time Satellite Imagery for Assessment the Dynamics of Touristic Zones in Tashkent Region of Uzbekistan

Lola GulyamovaTashkent State Technical University (TSTU), Geology-prospecting and mining - metallurgy faculty, Tashkent, UzbekistanRisolat NizomovaTashkent State Technical University (TSTU), Geology-prospecting and mining - metallurgy faculty, Tashkent, UzbekistanSunatilla GaibnazarovTashkent State Technical University (TSTU), Geology-prospecting and mining - metallurgy faculty, Tashkent, UzbekistanNasiba ShamsiyevaTashkent State Technical University (TSTU), Geology-prospecting and mining - metallurgy faculty, Tashkent, UzbekistanNargiza AbdullaevaTashkent State Technical University (TSTU), Geology-prospecting and mining - metallurgy faculty, Tashkent, UzbekistanMuxlisa RaximovaTashkent State Technical University (TSTU), Geology-prospecting and mining - metallurgy faculty, Tashkent, UzbekistanDoston MaxmadiyevTashkent State Technical University (TSTU), Geology-prospecting and mining - metallurgy faculty, Tashkent, UzbekistanI.G. AbdullaevNational University of Uzbekistan (NUU), Geography and geoformation systems faculty, Tashkent, Uzbekistan
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Abstract. The assessment of tourism dynamics is essential for sustainable regional development, particularly in emerging tourism markets like Uzbekistan. This study applies night time satellite imagery from the Suomi National Polar Partnership (Suomi NPP) and the VIIRS radiometer to evaluate spatial and temporal changes between 2012 and 2023 in 17 touristic zones comprising 1,008 touristic objects of the Tashkent region. 2,448 cases of Radiance growth, frequency, and intensity distributions were analyzed to understand seasonal variations and categorize zones by tourism capacity. Results indicate strong correlations between radiance values and touristic activity, enabling classification into low, moderate, high, and very high tourism capacity categories. This method offers a cost-effective, scalable, and reliable tool for assessing tourism potential, particularly in areas where official statistics are limited. The findings provide a foundation for integrating remote sensing data with socio-economic indicators, supporting improved decision-making in tourism planning, infrastructure development, and resource management.

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