Extended Abstract
Introduction
Vegetation is one of the most sensitive environmental components to changes in climatic variables, and any fluctuation in precipitation, temperature, and humidity can result in rapid and observable responses in its growth and dynamics. Teleconnection patterns, particularly ENSO (El Niño–Southern Oscillation), are recognized as major drivers of climatic conditions at regional and global scales and can substantially influence vegetation cover by altering precipitation and temperature patterns. Monitoring and modeling vegetation cover can therefore help in tracking regional climate-change trends (Jiao et al., 2018; Ding et al., 2020; Jien et al., 2023). Identifying climate-induced fluctuations in vegetation conditions is particularly important, especially given recent climate change and the role of vegetation in mitigating its impacts. The most widely used parameter for evaluating vegetation responses to climate variability is the Normalized Difference Vegetation Index (NDVI), derived from satellite remote-sensing data (Adole et al., 2016; Huang et al., 2021; Suberi et al., 2021; Buras et al., 2020; Barbosa et al., 2019). Teleconnection patterns represent persistent, large-scale atmospheric circulation regimes that influence distant regions by altering temperature, precipitation, and pressure patterns. These patterns are statistically defined and explain variations in local and regional climatic variables in response to different phases of large-scale climate modes. Among these, ENSO (El Niño–Southern Oscillation) and NAO (North Atlantic Oscillation) are the most influential in climate–biosphere studies. Jien et al. (2025), in a study titled Impact of the El Niño–Southern Oscillation on Global Vegetation, demonstrated that ENSO, through its influence on precipitation and temperature patterns, is one of the most important drivers of interannual vegetation variability worldwide. Their findings suggest that ENSO impacts differ across regions and that the type of ENSO event—whether the Eastern Pacific (EP) or Central Pacific (CP) pattern—can induce distinct vegetation responses. Nevertheless, the precise interactions between vegetation and ENSO require further investigation. A review of previous research shows that only a limited number of studies in Iran have examined the influence of teleconnection patterns on vegetation dynamics. By focusing on the Maroon Basin in the southern Zagros region and employing key teleconnection indices, the present study addresses a major research gap in this field.
Materials and Methods
In this study, to investigate the relationship between teleconnection indices (NINO4, NINO3, NINO3.4, NINO1+2, and SOI) and winter vegetation changes in the Maroon watershed, the Normalized Difference Vegetation Index (NDVI) from the MODIS sensor was used for the period 2001–2023. MODIS data, with a spatial resolution of 250 m and a 16-day temporal interval, were obtained after atmospheric correction and processed on the Google Earth Engine platform. NDVI was calculated based on the ratio (NIR − Red)/(NIR + Red). To assess the relationship, Pearson correlation coefficients were calculated between the teleconnection indices during winter and winter NDVI over the 23-year period. Following this, the locations with the highest and lowest correlation coefficients were identified. The coefficient indicates both the strength and direction of the relationship, with values close to ±1 representing a strong dependency. Teleconnection indices reflect the synchronization of climate fluctuations in a given region with changes in sea-level pressure and temperature in other regions, and their data were obtained from the NCEP/NCAR database. Additionally, the mean NDVI for each winter season was calculated in a GIS environment, as winter represents the main rainy season in the watershed. The low vegetation density during this season facilitates the detection of changes induced by climate variability and human activities. To examine the relationships, Pearson correlation coefficients were calculated between the winter teleconnection indices and the winter NDVI values over a 23-year period. After computing the coefficients, the locations with the highest and lowest correlation values were identified.
Results and Discussion
The winter NDVI time-series analysis for the Maroon Basin from 2001 to 2023 showed that the southern and southwestern parts of the basin consistently exhibited the highest vegetation density, while the northern and central areas had the lowest. NDVI values displayed considerable interannual variability, with years such as 2023 recording the highest and years like 2008 and 2012 the lowest vegetation levels. Correlation analysis with ENSO indices revealed that areas with very dense vegetation were most sensitive to the warm phase of ENSO, showing a strong negative correlation with NINO3.4. In contrast, moderate vegetation classes showed a positive correlation with NINO4. Sparse vegetation exhibited weaker responses to ENSO fluctuations. The positive correlation with SOI further indicated that the cold phase of ENSO is generally associated with slight improvements in vegetation conditions. Overall, the findings demonstrate that vegetation in the Maroon Basin is highly responsive to ENSO variability, and the magnitude of this response depends on vegetation type and density. These results are consistent with previous studies conducted in semi-arid regions both within Iran and internationally.
Conclusion
Winter vegetation in the Maroon watershed was analyzed over a 23-year period (2001–2023) to assess the influence of ENSO indices. The results showed that the southern and southwestern parts of the basin had the highest vegetation density, while the northern and northeastern areas exhibited the lowest density. The strongest negative correlation with ENSO was observed between the NINO3.4 index and the very dense vegetation class (r = -0.68), whereas sparse vegetation classes showed weak responses. The SOI index exhibited a weak positive correlation with dense vegetation. Overall, ENSO had a moderate to weak impact on winter NDVI, while local and ecological factors played a more decisive role in vegetation changes. These findings highlight the importance of long-term monitoring and the concurrent consideration of both local and teleconnection factors for effective natural resource management in semi-arid regions.