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Assessment of Vegetation Cover Changes and its Relationship with Temperature and Precipitation (Case Study: Urmia Lake Basin)

Document Type : Original Article

Authors
Ahmad Mazidi 1
Hamideh Dehghan 2
Hajar Toofani Kopai 2
1 Associate Professor of Climatology and Faculty Member, Department of Geography, Yazd University, Yazd, Iran
2 PhD student in Climatology, Yazd University, Yazd, Iran
10.30467/nivar.2025.504638.1322
Abstract
Vegetation cover is an essential factor in the structure and function of terrestrial ecosystems and one of the fundamental links in the vital water-soil-plant-atmosphere chain. This research was conducted to investigate vegetation cover changes and the factors affecting it in the Urmia Lake Basin. In this regard, precipitation images from the CHRIPS database and LST and NDVI images from the MODIS sensor were used during a 23-year statistical period (2001-2023). The results of the seasonal rainfall rate show that rainfall in this basin has a decreasing trend from the periphery of the basin towards the center of the basin. The highest rainfall in this basin is related to the winter season in 2018 and 2019, and the lowest rainfall is related to the summer season in 2003. The land surface temperature has increased due to changes in the vegetation cover of the Urmia Lake Basin. It was also found that most seasons during this period had a temperature between 20 and 40 degrees Celsius, which covered an area of about 25 to 35 thousand square kilometers. The study of vegetation cover in the Urmia Lake Basin showed that the largest area of vegetation cover occurs in the spring season, which has a decreasing trend (R2 = -0.151), and the smallest area of vegetation cover occurs in the winter season with a decreasing trend (R2 = -0.44). The results of the correlation between rainfall and vegetation cover showed that there is a positive and significant correlation in the winter and autumn seasons, and a positive and non-significant correlation in the spring and summer seasons at the 0.05 confidence level. The correlation between temperature and vegetation cover showed that there is a positive correlation and a strong and significant relationship in the winter season at the 0.01 confidence level.
Keywords
Land Surface Temperature
Vegetation Cover
Urmia Lake Basin
MODIS Sensor
Subjects
1.       Khorani, A., Alimoradi, S., & Esmailpour, Y. (2017). Vegetation dynamics in relation to temperature and precipitation in the rangelands of the Karun Basin, Khuzestan Province. Journal of Applied Research in Geographical Sciences, 17(44), 155–176. (In Persian)
2.       Khorshiddoust, A. M., Panahi, A., Khorramabadi, F., & Imanipour, H. (2022). Effects of climatic parameters on vegetation distribution in central Iran. Journal of Spatial Analysis of Environmental Hazards, 9(2), 73–86. (In Persian)
3.       Roosta, E., Mahmoud, A., & Mazidi, A. (2022). Assessing the stability of vegetation cover change trends using remote sensing (Case study: Northern Afghanistan watershed). Geography and Environmental Sustainability, 12(2), 17–35. (In Persian)
4.       Zarghani, H., Mofidi, A., & Shafieinia, M. (2018). Climate change analysis and its impacts: A case study of sea level rise. In Proceedings of the 2nd National Conference on Iranian Climatology. (In Persian)
5.       Shamsipour, A. A., Azizi, Q., Karimi-Ahmadabad, M., & Moghbel, M. (2013). Behavioral analysis of land surface temperature patterns in urban environments (Case study: Tehran). Geography and Environmental Sustainability, (6), 67–86. (In Persian)
6.       Taheri Ghasemabadi, J., Karami, M., & Asadollahi, A. (2018). Quantitative and qualitative assessment of vegetation cover changes using satellite imagery and their relationship with climatic parameters (Case study: Bojnord County). Journal of Modern Research in Geographical Sciences, Architecture and Urban Planning, (13), 1–12. (In Persian)
7.       Kohnehpooshi, S. H., Shayan, H., & Baradaran, S. (2013). Environmental degradation of Lake Urmia: Status, causes, and consequences. In Proceedings of the 2nd International Conference on Environmental Hazards. (In Persian)
8.       Karami, M., Taheri Ghasemabadi, J., & Asadollahi, A. (2018). Quantitative and qualitative vegetation cover changes using satellite imagery and climatic parameters (Case study: Bojnord County). Noor Journal, 2(13). (In Persian)
9.       Urmia Lake Restoration Program, Economic and Social Committee. (n.d.). Report on the economic and social conditions of the Urmia Lake Basin. (In Persian)
10.   Noorian, A. M. (2002). National report on natural disaster risk management. Iran Meteorological Organization. (In Persian)
11.   Hadizadeh, H., & Al-Hosseini, S. A. (2017). Monitoring vegetation cover changes using Landsat satellite imagery (Case study: Jiroft County). In Proceedings of the 5th National Conference on Geomorphology and Environmental Challenges. (In Persian)
12.   Hadian, F., Hosseini, Z., & Hosseini, M. (2014). Monitoring vegetation cover changes using precipitation data and satellite imagery in northwest Iran. Iranian Journal of Range and Desert Research, 21(4). (In Persian)
13.   Barbosa, H. A., Kumar, T. V. L., & Silva, L. R. M. (2015). Recent trends in vegetation dynamics in South America and their relationship to rainfall. Natural Hazards, 77, 883–899. https://doi.org/10.1007/s11069-015-1635-8
14.   Rimkus, E., Stonevicius, E., Kilpys, J., Maciulyte, V., & Valiukas, D. (2017). Drought identification in the eastern Baltic region using NDVI. Earth System Dynamics, 8, 627–637. https://doi.org/10.5194/esd-8-627-2017
15.   Gillespie, T. W., Ostermann-Kelm, S., Dong, C., Willis, K. S., Okin, G. S., & MacDonald, G. M. (2018). Monitoring changes of NDVI in protected areas of southern California. Ecological Indicators, 88, 485–494. https://doi.org/10.1016/j.ecolind.2018.01.031
16.   Liu, S., Huang, S., Xie, Y., Wang, H., Huang, Q., Leng, G., Li, P., & Wang, L. (2019). Spatiotemporal changes in vegetation cover in a typical semi-humid and semi-arid region of China. Ecological Indicators, 98, 462–475. https://doi.org/10.1016/j.ecolind.2018.11.022
17.   Liu, S., Liu, Y., Wang, C., & Dang, X. (2022). Distribution characteristics and human health risks of high-fluorine groundwater in a coastal plain: A case study in southern Laizhou Bay, China. Frontiers in Environmental Science. https://doi.org/10.3389/fenvs.2022.888968
18.   Krakauer, N. Y., Lakhankar, T., & Anadón, J. D. (2017). Mapping and attributing normalized difference vegetation index trends for Nepal. Remote Sensing, 9, 1–15. https://doi.org/10.3390/rs9030292
19.   Magee, T. K., Ringold, P. L., & Bellmore, J. R. (2008). Alien species importance in native vegetation along wadeable streams. Plant Ecology, 195, 287–307. https://doi.org/10.1007/s11258-007-9326-1
20.   Maselli, F., & Chiesi, M. (2006). Integration of multi-source NDVI data for the estimation of Mediterranean forest productivity. International Journal of Remote Sensing, 27, 55–72. https://doi.org/10.1080/01431160500168611
21.   Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. Journal of the Indian Geophysical Union, 12(3), 131–140. (DOI not available)
22.   Maidment, R. I., Allan, R. P., & Black, E. (2015). Recent observed and simulated changes in precipitation over Africa. Geophysical Research Letters, 42, 8155–8164. https://doi.org/10.1002/2015GL065765
23.   Nanzad, L., Zhang, J., Tuvdendorj, B., Nabil, M., Zhang, S., & Bai, Y. (2019). NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia. Journal of Arid Environments, 164, 69–77. https://doi.org/10.1016/j.jaridenv.2019.01.019
24.   Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., & Stenseth, N. C. (2005). Using satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20, 503–510. https://doi.org/10.1016/j.tree.2005.05.011
25.   Reutter, H., Olesen, F. S., & Fischer, H. (1994). Distribution of land surface brightness temperature derived from AVHRR data. Remote Sensing of Environment, 15, 95–104. https://doi.org/10.1016/0034-4257(94)90023-9
26.   Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107. https://doi.org/10.1016/0034-4257(95)00186-7
27.   Shi, S., Yu, J., Wang, F., Wang, P., Zhang, Y., & Jin, K. (2021). Quantitative contributions of climate change and human activities to vegetation changes on the Loess Plateau. Science of the Total Environment, 755, 142419. https://doi.org/10.1016/j.scitotenv.2020.142419
28.   Tian, H., Wang, Y., Chen, T., Zhang, L., & Qin, Y. (2021). Early-season mapping of winter crops using Sentinel-2 optical imagery. Remote Sensing, 13, 3822. https://doi.org/10.3390/rs13193822
29.   Tucker, C. J. (1996). History of the use of AVHRR data for land applications. In G. D’Souza, A. S. Belward, & J. P. Malingreau (Eds.), Advances in the use of NOAA AVHRR data for land applications (pp. 1–19). Kluwer Academic Publishers. (DOI not available)
30.   Wen, Z., Wu, S., Chen, J., & Lu, M. (2017). NDVI-based long-term vegetation changes and their responses to climatic and anthropogenic factors in the Three Gorges Reservoir Region, China. Science of the Total Environment, 574, 947–959. https://doi.org/10.1016/j.scitotenv.2016.09.172
31.   Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106–115. https://doi.org/10.1016/j.rse.2009.08.014
 
Nivar
Volume 49, 130-131 - Serial Number 130
October 2025
Pages 177-194

  • Receive Date 04 February 2025
  • Revise Date 22 June 2025
  • Accept Date 12 July 2025
  • Publish Date 23 September 2025