Vegetation Cover Change in Ugam Chatkal National Park, Uzbekistan, in Relation to Climate Variables During the Post-Soviet Period (1991-2022)
DOI:
https://doi.org/10.23917/forgeo.v38i1.3824Keywords:
NDVI, SAVI, Climate change, Uzbekistan, BoʻstonliqAbstract
This paper presents a comprehensive study relating to the vegetation cover change in Ugam Chatkal National Park (Uzbekistan) and its relation to climate change during the post-Soviet period (1991-2022). The study utilises remote sensing technology, specifically the Normalised Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI), to monitor spatiotemporal changes in vegetation. Landsat satellite imagery and meteorological data, including temperature and precipitation records form the basis of the analysis. The research aims to understand the impact of climatic factors, such as air temperature, soil temperature, and precipitation on vegetation cover. Statistical methods, such as Pearson's correlation analysis, are employed to determine the strength and direction of relationships between these variables. The study reveals that both NDVI and SAVI are strongly correlated with air and soil temperatures, indicating the significant influence of these climatic factors on vegetation health and growth. The findings suggest that changes in vegetation cover in the Ugam Chatkal National Park are closely tied to climate change, with air temperature revealing a substantial correlation with time, indicating a trend toward increasing temperatures. The study also forecasts future climatic and vegetation trends, predicting an increase in air temperature, precipitation, and vegetation cover over the next four decades. In particular, the research highlights the magnitude of monitoring and understanding the complex interactions between climate change and vegetation dynamics, which are crucial for environmental management and regional policy-making.
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Adepoju, K., Adelabu, S., & Fashae, O. (2019). Vegetation Response to Recent Trends in Climate and Lan-duse Dynamics in a Typ-ical Humid and Dry Tropical Region under Global Change. Advances in Me-teorology, 2019, 1–15. doi: 10.1155/2019/4946127. [Google Scholar] [CrossRef]
Alikhanov, B., Juliev, M., Alikhanova, S., & Mondal, I. (2021). Assessment of influencing factor method for delineation of groundwater potential zones with geospatial techniques. Case study of Bostanlik district, Uzbekistan. Groundwater for Sustainable Development, 12, 100548. doi : 10.1016/j.gsd.2021.100548. [Google Scholar] [CrossRef]
Chang, C. T., Lin, T. C., Wang, S. F., & Vadeboncoeur, M. A. (2011). Assessing growing season beginning and end dates and their relation to climate in Taiwan using satellite data. International Journal of Re-mote Sensing, 32(18), 5035–5058. doi: 10.1080/01431161.2010.494635. [Google Scholar] [CrossRef]
Chen, T., Tang, G., Yuan, Y., Guo, H., Xu, Z., Jiang, G., & Chen, X. (2020). Unraveling the relative impacts of climate change and human activities on grassland productivity in Central Asia over last three decades. Science of The Total Environment, 743, 140649. doi: 10.1016/j.scitotenv.2020.140649. [Google Scholar] [CrossRef]
Dech, S., Holzwarth, S., Asam, S., Andresen, T., Bachmann, M., Boettcher, M., Dietz, A., Eisfelder, C., Frey, C., Gesell, G., Gessner, U., Hirner, A., Hofmann, M., Kirches, G., Klein, D., Klein, I., Kraus, T., Krause, D., Plank, S., & Kuenzer, C. (2021). Poten-tial and Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience. Remote Sensing, 13(18), 3618. doi: 10.3390/rs13183618. [Google Scholar] [CrossRef]
Dong, J., Li, L., Li, Y., & Yu, Q. (2022). Inter-comparisons of mean, trend and interannual variability of glo-bal terrestrial gross pri-mary production retrieved from remote sensing approach. Science of The Total Environment, 822, 153343. doi: 10.1016/j.scitotenv.2022.153343. [Google Scholar] [CrossRef]
Eisfelder, C., Asam, S., Hirner, A., Reiners, P., Holzwarth, S., Bachmann, M., Gessner, U., Dietz, A., Huth, J., Bachofer, F., & Kuen-zer, C. (2023). Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation In-dex (NDVI) Time-Series—The TIMELINE NDVI Product. Remote Sensing, 15(14), 3616. doi: 10.3390/rs15143616. [Google Scholar] [CrossRef]
Faour, G., Mhawej, M., & Nasrallah, A. (2018). Global trends analysis of the main vegetation types throughout the past four dec-ades. Applied Geography, 97, 184–195. doi: 10.1016/j.apgeog.2018.05.020. [Google Scholar] [CrossRef]
Feng, X., Li, J., Cheng, W., Fu, B., Wang, Y., Lü, Y., & Shao, M. (2017). Evaluation of AMSR-E retrieval by detecting soil moisture decrease following massive dryland re-vegetation in the Loess Plateau, China. Remote Sensing of Environment, 196, 253–264. doi: 10.1016/j.rse.2017.05.012. [Google Scholar] [CrossRef]
Fu, Y. H., Piao, S., Opde, Beeck, M., Cong, N., Zhao, H., Zhang, Y., Menzel, A., & Janssens, I. A. (2014). Re-cent spring phenology shifts in western C entral E urope based on multiscale observations. Global Ecology and Biogeography, 23(11), 1255–1263. doi: 10.1111/geb.12210. [Google Scholar] [CrossRef]
Gabban, A., Liberta, G., San-Miguel-Ayanz, J., & Barbosa, P. (2004). Forest fire risk estimation from time se-ries analisys of NOAA NDVI data. Remote Sensing for Agriculture, Ecosystems, and Hydrology, 5232, 587-595. doi: 10.1117/12.511003. [Google Scholar] [CrossRef]
Godde, C. M., Boone, R. B., Ash, A. J., Waha, K., Sloat, L. L., Thornton, P. K., and Herrero, M. (2020). Glo-bal rangeland production systems and livelihoods at threat under climate change and variability. Envi-ronmental Research Letters, 15(4), 044021. doi : 10.1088/1748-9326/ab7395. [Google Scholar] [CrossRef]
Hatfield, J. L., & Prueger, J. H. (2015). Temperature extremes: Effect on plant growth and development. Wea-ther and Climate Ex-tremes, 10, 4–10. doi: 10.1016/j.wace.2015.08.001. [Google Scholar] [CrossRef]
Helldén, U., & Tottrup, C. (2008). Regional desertification: A global synthesis. Global and Planetary Change, 64(3–4), 169–176. doi: 10.1016/j.gloplacha.2008.10.006.. [Google Scholar] [CrossRef]
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. doi: 10.1016/0034-4257(88)90106-X [Google Scholar] [CrossRef]
Hussien, K., Kebede, A., Mekuriaw, A., Beza, S. A., & Erena, S. H. (2023). Spatiotemporal trends of NDVI and its response to cli-mate variability in the Abbay River Basin, Ethiopia. Heliyon, 9(3), e14113. doi : 10.1016/j.heliyon.2023.e14113. [Google Scholar] [CrossRef]
Kalisa, W., Igbawua, T., Henchiri, M., Ali, S., Zhang, S., Bai, Y., & Zhang, J. (2019). Assessment of climate impact on vegetation dynamics over East Africa from 1982 to 2015. Scientific Reports, 9(1), 16865. doi : 10.1038/s41598-019-53150-0. [Google Scholar] [CrossRef]
Lieberherr, G., & Wunderle, S. (2018). Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for Euro-pean Lakes. Remote Sensing, 10(7), 990. doi: 10.3390/rs10070990. [Google Scholar] [CrossRef]
Lioubimtseva, E., Cole, R., Adams, J. M., & Kapustin, G. (2005). Impacts of climate and land-cover changes in arid lands of Cen-tral Asia. Journal of Arid Environments, 62(2), 285–308. doi: 10.1016/j.jaridenv.2004.11.005. [Google Scholar] [CrossRef]
Lioubimtseva, E., & Henebry, G. M. (2009). Climate and environmental change in arid Central Asia: Impacts, vulnerability, and adaptations. Journal of Arid Environments, 73(11), 963–977. doi: 10.1016/j.jaridenv.2009.04.022. [Google Scholar] [CrossRef]
Méndez-Barroso, L. A., Vivoni, E. R., Watts, C. J., & Rodríguez, J. C. (2009). Seasonal and interannual rela-tions between precipita-tion, surface soil moisture and vegetation dynamics in the North American monsoon region. Journal of Hydrology, 377(1–2), 59–70. doi: 10.1016/j.jhydrol.2009.08.009. [Google Scholar] [CrossRef]
Michael, Y., Helman, D., Glickman, O., Gabay, D., Brenner, S., & Lensky, I. M. (2021). Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series. Science of The Total Environment, 764, 142844. doi: 10.1016/j.scitotenv.2020.142844. [Google Scholar] [CrossRef]
Momm, H. G., Elkadiri, R., & Porter, W. (2020). Crop-Type Classification for Long-Term Modeling: An Inte-grated Remote Sensing and Machine Learning Approach. Remote Sensing, 12(3), 449. doi: 10.3390/rs12030449. [Google Scholar] [CrossRef]
Mutanga, O., & Kumar, L. (2019). Google Earth Engine Applications. Remote Sensing, 11(5), 591. doi : 10.3390/rs11050591. [Google Scholar] [CrossRef]
Na, L., Na, R., Bao, Y., & Zhang, J. (2021). Time-Lagged Correlation between Soil Moisture and Intra-Annual Dynamics of Vegeta-tion on the Mongolian Plateau. Remote Sensing, 13(8), 1527. doi: 10.3390/rs13081527. [Google Scholar] [CrossRef]
Onwuka, B. (2018). Effects of Soil Temperature on Some Soil Properties and Plant Growth. Advances in Plants & Agriculture Re-search, 8(1). doi: 10.15406/apar.2018.08.00288. [Google Scholar] [CrossRef]
Paudel, K. P., & Andersen, P. (2010). Assessing rangeland degradation using multi temporal satellite images and grazing pressure surface model in Upper Mustang, Trans Himalaya, Nepal. Remote Sensing of En-vironment, 114(8), 1845–1855. doi: 10.1016/j.rse.2010.03.011. [Google Scholar] [CrossRef]
Pielke Sr., Adegoke, R. A. J., Beltrán-Przekurat, A., Hiemstra, C. A., Lin, J., Nair, U. S., Niyogi, D., & Nobis, T. E. (2007). An over-view of regional land-use and land-cover impacts on rainfall. Tellus B: Chemical and Physical Meteorology, 59(3), 587. doi: 10.1111/j.1600-0889.2007.00251.x. [Google Scholar] [CrossRef]
Pouliot, D., Latifovic, R., & Olthof, I. (2009). Trends in vegetation NDVI from 1 km AVHRR data over Cana-da for the period 1985–2006. International Journal of Remote Sensing, 30(1), 149–168. doi: 10.1080/01431160802302090. [Google Scholar] [CrossRef]
Ranjana, R. Y. M., Weiqang, Ma, B. B., & Barjeece Bashir. (2020). Interannual Variation of NDVI, Precipita-tion and Tempera-ture during the Growing Season in Langtang National Park, Central Himalaya, Nepal. Applied Ecology and Environmen-tal Sciences, 8(5), 218–228. doi: 10.12691/aees-8-5-5. [Google Scholar] [CrossRef]
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia & Analge-sia, 126(5), 1763–1768. doi: 10.1213/ANE.0000000000002864. [Google Scholar] [CrossRef]
Seim, A., Omurova, G., Azisov, E., Musuraliev, K., Aliev, K., Tulyaganov, T., Nikolyai, L., Botman, E., Helle, G., Dorado Liñan, I., Jivcov, S., & Linderholm, H. W. (2016). Climate Change Increases Drought Stress of Juniper Trees in the Mountains of Central Asia. PLOS ONE, 11(4), e0153888. doi: 10.1371/journal.pone.0153888. [Google Scholar] [CrossRef]
Soudani, K., François, C., Le Maire, G., Le Dantec, V., & Dufrêne, E. (2006). Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment, 102(1–2), 161–175. doi: 10.1016/j.rse.2006.02.004. [Google Scholar] [CrossRef]
Sun, H., Wang, J., Xiong, J., Bian, J., Jin, H., Cheng, W., & Li, A. (2021). Vegetation Change and Its Response to Climate Change in Yunnan Province, China. Advances in Meteorology, 2021, 1–20. doi: 10.1155/2021/8857589. [Google Scholar] [CrossRef]
Tian, F., Fensholt, R., Verbesselt, J., Grogan, K., Horion, S., & Wang, Y. (2015). Evaluating temporal consis-tency of long-term global NDVI datasets for trend analysis. Remote Sensing of Environment, 163, 326–340. doi: 10.1016/j.rse.2015.03.031. [Google Scholar] [CrossRef]
Tottrup, C., & Rasmussen, M. S. (2004). Mapping long-term changes in savannah crop productivity in Senegal through trend anal-ysis of time series of remote sensing data. Agriculture, Ecosystems & Environment, 103(3), 545–560. doi: 10.1016/j.agee.2003.11.009. [Google Scholar] [CrossRef]
Walker, J. J., & Soulard, C. E. (2019). Phenology Patterns Indicate Recovery Trajectories of Ponderosa Pine Forests After High-Severity Fires. Remote Sensing, 11(23), 2782. doi: 10.3390/rs11232782. [Google Scholar] [CrossRef]
Wang, J., Rich, P. M., & Price, K. P. (2003). Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. International Journal of Remote Sensing, 24(11), 2345–2364. doi: 10.1080/01431160210154812. [Google Scholar] [CrossRef]
Wang, J., Wang, K., Zhang, M., & Zhang, C. (2015). Impacts of climate change and human activities on vege-tation cover in hilly southern China. Ecological Engineering, 81, 451–461. doi: 10.1016/j.ecoleng.2015.04.022. [Google Scholar] [CrossRef]
Zhao, J., Huang, S., Huang, Q., Wang, H., Leng, G., Peng, J., & Dong, H. (2019). Copula-Based Abrupt Varia-tions Detection in the Relationship of Seasonal Vegetation-Climate in the Jing River Basin, China. Re-mote Sensing, 11(13), 1628. doi: 10.3390/rs11131628. [Google Scholar] [CrossRef]
Zhao, X., Yu, M., Pan, S., Jin, F., Zou, D., & Zhang, L. (2023). Spatio-temporal distribution and trends moni-toring of land desertifi-cation based on time-series remote sensing data in northern China. Environ-mental Earth Sciences, 82(11), 263. doi: 10.1007/s12665-023-10950-y. [Google Scholar] [CrossRef]
Zong, X., Tian, X., & Yin, Y. (2020). Impacts of Climate Change on Wildfires in Central Asia. Forests, 11(8), 802. doi: 10.3390/f11080802. [Google Scholar] [CrossRef]
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Copyright (c) 2024 Bokhir Alikhanov, Bakhtiyor Pulatov , Luqmon Samiev
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