چكيده لاتين :
Vegetation plays an important role in the cycle of energy, carbon, hydrology and bio-geochemistry. The climate and vegetation have a mutual effect on each other. For example, the surface vegetation affects atmospheric patterns by affecting the surface albedo (which determines the amount of radiation available for global warming, low atmosphere and evaporation as well). Therfore, the long-term study of the effect of the remot linking patterns on the varibility of vegetation is essential. So far, no study has been done on the effect of remote linking patterns on the varibility of vegetions.Therefore, the main objective of this study is to detect the vegetation changes in the month of May in Iran in relation to the remote linking patterns of the North Atlantic Oscillation. In this regard, remote linking patterns, such as El Nino have a significant effect on the surface climate with their periodic oscillations (Glantz, 1991). Many studies have been carried out in relation to the remote linking patterns and climatic elements on regional scale, but the role of remote linking patterns in the vegetation changes is a new topic which has been brought up lately (Wang et al., 2004). The normalized difference vegetation index (NDVI) obtained from the remote sensing satellite data is widely used to examine the vegetation features. Vicent Serrano et al. (2004) identified the positive and negative trends between NDVI and NAO in the Northern and Southern parts of Iberian Peninsula, respectively, by investigating the relation of NDVI, the North Atlantic Oscillation index (NAO) and the precipitation. Gouveia et al (2008) extracted the NAO correlation in the winter with vegetation activity in the spring and summer seasons by the combination of NDVI and luminosity temperature. Cook et al. (2004), Stockli and Vidale (2004), Sarkar and Kafatos (2004), Mennis, (2001), Erasmiet et al., (2009) also showed that there was a relationship between the remote linking patterns and vegetation in different parts of the world. Lu et al. (2012), showed that the vegetation impressibility in china in El Nino phase is greater than that of La Nino phase.
Materials & Methods
In order to investigate the relationship between the North Atlantic Oscillation and vegetation changes in the month of May in Iran, the normalized vegetation index products of MODIS sensor (MOD13A3) were used during the statistical period of 2001-2014. By applying the NDVI 0.2 threshold on the average long-term map of the vegetation index for the month of May in Iran, the area with larger and equal vegetation of the desired threshold was separated. Then, due to the severity and weakness of the NDVI values, the aforementioned area was divided into 3 areas based on the values of NDVI in order to assess the sensitivity of each area with regard to the remote linking patterns of the North Atlantic Oscillation which, helps identify the relationship between each vegetation category (namely, thinned, medium and dense vegetation) and the North Atlantic Oscillation index.
Results & Discussion
Due to the existence of vegetation-free deserts in Iran, an area susceptible to vegetation was first separated based on the threshold of at least 0.2 of the NDVI values. This region has about 38.2% of the country’s total area. Due to the high spatial variations in the NDVI values, the area was divided into 3 classes of thinned, medium and dense vegetation based on 0.2 to 0.5, 0.5 to 0.7 and higher than 0.7 ranges. It was assumed that the area with thinned and dense vegetation had the highest and lowest sensitivity respectively, with regard to the changes of the remote linking patterns. The positive and negative phases of the North Atlantic Oscillation (NAO) have significant effects on the climate of Iran. For example, the amount of vegetation, precipitation and humidity advection in many parts of the West, Northwest, and Northeast of Iran in the February 2010 (as a negative phase), were much higher than that in the February 2014 (as a positive phase). A 14-year time series was prepared from the NDVI values of the May for 18363 points in Iran and, each point was calculated with the variations in the values of the NAO index of January to May in a Pearson correlation coefficient matrix (assuming that the NAO changes in January influence the vegetation of May in Iran). The results showed that the positive and negative correlation values in terms of spatiality can be observed in all regions without a regular spatial pattern however, the maps showed that negative correlation values have covered a wider range of Iran in January and February. This indicates that, in the positive phase of the pattern, the higher values of sea level pressure in the Azore region, coinciding with the poor moisture transfer and precipitation systems, have caused less vegetation in a few months later (May) in Iran.
Conclusion
Given the highest coefficient of determination obtained in February(0.77) in East Azerbaijan province, the vegetation values of May can be estimated for the index points located in the Northwest and western provinces using the state of NAO in the months of winter.
