Skip to main navigation menu Skip to main content Skip to site footer

Vol. 13 No. 4 (2026)
International Multidisciplinary Journal for Research & Development

Articles

LANDSCAPE-BASED SPATIAL ZONING FOR ECOTOURISM DEVELOPMENT IN BOSTANLIQ DISTRICT, UZBEKISTAN

Published 2026-04-29

  • Abduraximov Sarvar Mirodilovich

Abduraximov Sarvar Mirodilovich
Department of Natural Geography, Faculty of Geography and Geoinformation, National University of Uzbekistan

Abstract

This study develops a landscape-based spatial zoning approach to support sustainable ecotourism development in Bostanliq District, Uzbekistan. The research integrates remote sensing data and GIS-based spatial analysis to assess the distribution of key natural and climatic factors influencing tourism suitability. ERA5-Land reanalysis data were used to derive air temperature, precipitation, wind speed, and solar radiation, which were combined into a simplified Tourism Climate Suitability Index (sTCI). This index was integrated with landscape components such as vegetation cover, terrain slope and elevation, proximity to water bodies, and anthropogenic pressure to delineate ecotourism suitability zones. The results indicate that areas with moderate elevation, dense vegetation, proximity to water resources, and relatively low human disturbance exhibit the highest potential for ecotourism development. The proposed framework provides a scientific basis for spatial planning, sustainable tourism management, and conservation-oriented land-use decision-making.

Keywords

ecotourism, landscape approach, spatial zoning, remote sensing, geographic information systems, tourism climate index, Bostanliq District.

PDF

References

  1. L. Sh. Akhmedova, Z. I. Soltanmuradova, R. G. Dandamaeva, and G. I. Idziev, “Analysis of current state and development trends of the tourism sector in the regions of the North Caucasus, Russia,” South Russ. Ecol. Dev., vol. 19, no. 2, pp. 104–116, 2024, doi: 10.18470/1992-1098-2024-2-0.
  2. S. Kondrateva and E. Shlapeko, “INTERNATIONAL PROJECT ACTIVITIES AS A TOOL FOR TOURISM DEVELOPMENT IN THE RUSSIAN ARCTIC,” Geogr. Environ. Living Syst., vol. 2024, no. 2, pp. 24–44, 2024, doi: 10.18384/2712-7621-2024-2-24-44.
  3. E. G. Kropinova et al., “Strategic Master Planning Of Territories for Promising Tourism Development (Case for the Macro-Region ‘Russian Baltic’),” Geogr. Environ. Living Syst., vol. 2024, no. 4, pp. 84–112, 2024, doi: 10.18384/2712-7621-2024-4-84-112.
  4. I. А. Savchenko, M. I. Zolotarev, E. V. Kremnyov, and O. V. Kuznetsova, “Chinese experience of mountain tourism management in the context of urbanization: case of Guizhou province,” Sustain. Dev. Mt. Territ., vol. 16, no. 2, pp. 620–629, 2024, doi: 10.21177/1998-4502-2024-16-2-620-629.
  5. A. Grigoryev, “SACRED PLACES AS HERITAGE MONUMENTS AND OBJECTS OF TOURISM: GEOGRAPHICAL ASPECTS,” Geogr. Environ. Living Syst., vol. 2024, no. 1, pp. 137–153, 2024, doi: 10.18384/2712-7621-2024-1-137-153.
  6. N. Yu. Svyatokha, A. A. Chibilyov, and D. A. Grudinin, “Spatial organization of medical and health tourism in the Russia‐Kazakhstan tranboundary region,” South Russ. Ecol. Dev., vol. 19, no. 4, pp. 191–201, 2024, doi: 10.18470/1992-1098-2024-4-16.
  7. K. Sh. Zaburaeva, C. Sh. Zaburaev, and M. B. Sedieva, “Ethnocultural tourism in the system of recreational nature management of the North Eastern Caucasus,” Sustain. Dev. Mt. Territ., vol. 16, no. 2, pp. 570–579, 2024, doi: 10.21177/1998-4502-2024-16-2-570-579.
  8. J. Aryal, C. Sitaula, and A. C. Frery, “Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia,” Sci. Rep., vol. 13, no. 1, p. 13510, Aug. 2023, doi: 10.1038/s41598-023-40564-0.
  9. S. Shcherbakova and E. Shvets, “Tourism in the Russian-Belarusian Border Area: Current State and New Points Of Development,” Geogr. Environ. Living Syst., vol. 2024, no. 4, pp. 113–138, 2024, doi: 10.18384/2712-7621-2024-4-113-138.
  10. V. T. Pham, T. A. T. Do, H. D. Tran, and A. N. T. Do, “Classifying forest cover and mapping forest fire susceptibility in Dak Nong province, Vietnam utilizing remote sensing and machine learning,” Ecol. Inform., vol. 79, 2024, doi: 10.1016/j.ecoinf.2023.102392.
  11. B. W. Heumann, S. J. Walsh, and P. M. McDaniel, “Assessing the application of a geographic presence-only model for land suitability mapping,” Ecol. Inform., vol. 6, no. 5, pp. 257–269, 2011, doi: 10.1016/j.ecoinf.2011.04.004.
  12. B. Alikhanov, B. Pulatov, and S. Luqmon, “The Detection of Past and Future Land Use and Land Cover Change in Ugam Chatkal National Park, Uzbekistan, Using CA-Markov and Random Forest Machine Learning Algorithms,” Forum Geogr., vol. 38, pp. 121–137, May 2024, doi: 10.23917/forgeo.v38i2.4221.
  13. S. D. Suchi, A. Menon, A. Malik, J. Hu, and J. Gao, “Crop Identification Based on Remote Sensing Data using Machine Learning Approaches for Fresno County, California,” Proceedings - IEEE 7th International Conference on Big Data Computing Service and Applications, BigDataService 2021. Institute of Electrical and Electronics Engineers Inc., pp. 115–124, 2021. doi: 10.1109/BigDataService52369.2021.00019.
  14. K. Sh. Zaburaeva, C. Sh. Zaburaev, M. B. Sedieva, and A. A. Shaipova, “Assessment of transport network provision in the regions of the North-Eastern Caucasus for recreation and tourism purposes*,” Vestn. St. Petersburg Univ. Earth Sci., vol. 69, no. 4, pp. 627–645, 2024, doi: 10.21638/spbu07.2024.402.

Downloads

Download data is not yet available.