Assessment of the Effect of Climatic Factors on the Growth of Dense Pastures of Iran, Using AVHRR Images

IntroductionClimate is among the most important factors affecting vegetation condition. AVHRR-NDVI data are used to evaluate climatic and environmental changes at regional – as well as global - scales. Since 1983, Advanced Very High Resolution Radiometer (AVHRR) of the NOAA satellites gave a continuous spatial cover on a regular time scale of the photosynthetic activity, which can be expressed by indices such as the Normalized Difference Vegetation Index (NDVI). The NDVI, defined as the ratio (NIR-VIS)/(NIR+VIS), represents the absorption of photosynthetic active radiation and hence act as a kind of measurement of the photosynthetic capacity of the canopy. Previous studies indicated that there was a significant relationship between AVHRR-NDVI and precipitation or temperature. For example, in the northern Great Plains, many researchers have found close relationships between AVHRR-NDVI and climate, especially precipitation. Various studies (Yang et al 1998, Richard & Poccard 1998, Wang et al. 2001, Ji & Peters 2004, Li et al 2004) found significant correlations between NDVI and rainfall in different regions, including arid and semi-arid environments.In Iran, researchers assessed only relation between drought and NDVI. For example, Taherzadeh (2007) has been studied relation between NDVI and Standardized Precipitation Index (SPI) in the Minab basin. The results of that study showed that there was a good relationship between SPI and NDVI, but there was a negative relation between Land Surface Temperature (LST) and SPI, as well (Taherzadeh, 2007, 173).Shamsipour (2007) has studied drought for the Kashan area using NDVI and VCI. The results of that study showed that there were almost suitable relations between NDVI and VCI with meteorological methods. According to product of utilization NDVI and VCI, the years 2000 and 2001 were with drought condition, and the years 2002 & 2004 were with wetness (Shamsipour, 2007, 1).MethodIn this research, the authors analyzed the relation between vegetation density and the monthly climatic variables of rain, relative humidity (Mean, Max., and Min.), and temperature (Mean, Max., and Min.) in the dense pastures (75-100 percent) of Iran. The climatic data were obtained from the Meteorological Organization of Iran for 134 stations (October2005- October 2006) and vegetation density was extracted from AVHRR-NOAA satellite as the NDVI index for January to October 2006. The vegetation layers (forest and pasture) were obtained from the Forest Organization of Iran, than divided to layers based on density. The authors analyzed AVHRR-NDVI and seven climate variables, using a Multivariate Ordinary Least Squares regression (MOLS) technique. The maximum value composite (MVC) is calculated from a multi-temporal series of geometrically corrected NDVI images. The common maximum NDVI value composite (MVC) method was used to compile monthly NDVI dataset. The maximum value composite method could minimize atmospheric effects, scan angle effects, cloud contamination and solar zenith angle effects.Results and Discussion For interpolation the climatic variables, the researchers applied geostatistic methods such as Inverse Distance Weighting, Kriging, and Co-Kriging. In most of the variables, Co-kriging method showed the lowest of errors; but the mean temperature showed reliable results with IDW method. On the over all, in interpolation the climatic factors with CO-Kriging method results showed that best R2 between observed and predicted values for precipitation, maximum, minimum temperature, maximum, mean and minimum relative humid are 0.436, 0.93, 0.863, 0.672, 0.741 and 0.703; but for mean temperature R2 is 0.881 in IDW method. Previous studies using both observation and simulation models showed that there was a complicated lag effect between vegetation and climatic variables. Lag time was an affect of climatic factors on the vegetation growth, which the lag time varied from several days to 1 year - or even longer. For example, obtained results by researchers i.e. Potter and Brooks (1998) showed lag times of 1 to 2 months for maximum and minimum temperature and rainfall. Conclusion In this paper, the authors obtained the lag time for the vegetation growth response to the climatic factors, two months for rainfall and one month for the other variables.In most of the variables, Co-Kriging method showed the lowest errors; but the mean temperature showed reliable results with IDW method. The results showed that the relation between NDVI was higher relative humidity (mean and max) and maximum temperature, but lower for the rain and minimum temperature. The effect of the warm season climate was higher than that of the cold season. The highest relation of 0.78 was experienced in October, and the lower 0.23 value was computed for January.