IQLIM O‘ZGARISHI SHAROITIDA MARKAZIY OSIYO VA TYAN-SHAN TOG‘LARI EKOTIZIMLARINI MASOFADAN ZONDLASH ASOSIDA O‘RGANISH: SCOPUS NASHRLARINING BIBLIOMETRIK TAHLILI
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Ushbu maqolada Markaziy Osiyo va Tyan-Shan tog‘lari ekotizimlari, iqlim o‘zgarishi, o‘simlik qoplamasi dinamikasi va
masofadan zondlash usullari (NDVI, GAT) mavzulqurg‘oqchilagi Scopus bazasidagi ilmiy nashrlar bibliometrik tahlil qilindi.
2010-2025 yillar oralig‘idagi 657 ta nashr tahlil etilib, yillik dinamika, nashr turlari, fan yo‘nalishlari, hamkor muassasalar va
mamlakatlar bo‘yicha iqtiboslanish ko‘rsatkichlari o‘rganildi. Natijalar shuni ko‘rsatdiki, so‘nggi o‘n yilda nashrlar soni keskin
o‘sib, 2025-yilda 100 taga yaqinlashgan. Tadqiqotlarda Xitoy ilmiy muassasalari yetakchilik qilmoqda. Mavzu global iqlim
o‘zgarishi va tog‘ ekotizimlarining barqarorligi bilan bog‘liq bo‘lib, mintaqaning ekologik monitoringi uchun muhim ilmiy asos
yaratadi.
1. Jiang, L., Jiapaer, G., Bao, A., Guo, H., Ndayisaba, F. (2017). Vegetation dynamics and responses to climate change and
human activities in Central Asia. Science of the Total Environment, 599-600, 967-980.
https://doi.org/10.1016/j.scitotenv.2017.05.012
2. Yin, G., Hu, Z., Chen, X., et al. (2016). Vegetation dynamics and its response to climate change in Central Asia. Journal of
Arid Land, 8(3), 375–388. https://doi.org/10.1007/s40333-016-0043-6
3. Saidaliyeva, et al. (2024). Adaptation to climate change in the mountain regions of Central Asia: A systematic literature
review. WIREs Climate Change. https://doi.org/10.1002/wcc.891
4. Chen, T., Bao, A., Jiapaer, G., et al. (2019). Disentangling the relative impacts of climate change and human activities on
arid and semiarid grasslands in Central Asia during 1982-2015. Science of the Total Environment, 653, 1311–1325.
https://doi.org/10.1016/j.scitotenv.2018.11.058
5. Li, Y., Chen, Y., Sun, F., Li, Z. (2021). Recent vegetation browning and its drivers on Tianshan Mountain, Central Asia.
Ecological Indicators, 129, 107912. https://doi.org/10.1016/j.ecolind.2021.107912
6. Gao, L., Deng, H., Lei, X., et al. (2021). Evidence of elevation-dependent warming from the Chinese Tian Shan. The
Cryosphere, 15, 5765–5783. https://doi.org/10.5194/tc-15-5765-2021
7. Liu, Q., Yang, Z., Han, F., et al. (2016). NDVI-based vegetation dynamics and their response to recent climate change: a
case study in the Tianshan Mountains, China. Environmental Earth Sciences, 75, 1–12. https://doi.org/10.1007/s12665-016-
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8. Zhang, H., Li, L., Zhao, X., et al. (2024). Changes in Vegetation NDVI and Its Response to Climate Change and Human
Activities in the Ferghana Basin from 1982 to 2015. Remote Sensing, 16(7), 1296. https://doi.org/10.3390/rs16071296
9. Li, Q., Cheng, J., Yan, J., et al. (2025). Comparison of Satellite-Derived Vegetation Indices for Assessing Vegetation
Dynamics in Central Asia. Water, 17(5), 684. https://doi.org/10.3390/w17050684
10. Yan, K., et al. (2025). A global systematic review of the remote sensing vegetation indices. International Journal of Applied
Earth Observation and Geoinformation.
11. Nasirova, M., et al. (2024). Bibliometric analyses of climate change study in Central Asia between 1990–2022.
International Journal of Global Warming. https://doi.org/10.1504/IJGW.2024.135357
12. Dong, D., et al. (2025). Research Trends in Vegetation Spatiotemporal Dynamics and Their Driving Forces: A Bibliometric
Analysis. Forests, 16(4), 588. https://doi.org/10.3390/f16040588
13. Sweileh, W.M. (2020). Bibliometric analysis of peer-reviewed literature on climate change and human health.
Globalization and Health. https://doi.org/10.1186/s12992-020-00576-3.
14. Pobedinsky, V., Shestak, V. (2020). Improving Environmental Legislation in Central Asia. Environmental Policy and Law,
50(2), 123–135. https://doi.org/10.3233/EPL-200205
15. Song, S., et al. (2025). Integrated spatial priority assessment in Central Asia. Sustainable Cities and Society.
https://doi.org/10.1016/j.scs.2024.105686
16. van Eck, N.J., Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping.
Scientometrics, 84, 523–538. https://doi.org/10.1007/s11192-009-0146-3
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