ارزیابی تاثیر عوامل اقلیمی بر رشد پوشش گیاهی در مراتع متراکم ایران با استفاده از تصاویر AVHRR

Introduction Climate is amang 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 ۱۹۸۳, 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 ۱۹۹۸, Richard & Poccard ۱۹۹۸, Wang et al. ۲۰۰۱, Ji & Peters ۲۰۰۴, Li et al ۲۰۰۴) 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 (۲۰۰۷) 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, ۲۰۰۷, ۱۷۳). Shamsipour (۲۰۰۷) 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 ۲۰۰۰ and ۲۰۰۱ were with drought condition, and the years ۲۰۰۲ & ۲۰۰۴ were with wetness (shamsipour, ۲۰۰۷, ۱). Method In this research, the authars 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 (۷۵-۱۰۰ percent) of Iran. The climatic data were obtained from the Meteorological Organization of Iran for ۱۳۴ stations (October۲۰۰۵- October ۲۰۰۶) and vegetation density was extracted from AVHRR-NOAA satellite as the NDVI index for January to October ۲۰۰۶. The vegetation layers (forest and pasture) were obtained from the Forest Organization of Iran, than divided to layers based on density. The authars analyzed AVHRR-NDVI and seven climate variables, using a Multivariate Ordinary Least Squares regression (MOLS) technique. The maximmum value composite (MVC) is calculated from a multi-temporal series of geometrically corrected NDVI images. The common maximmum NDVI value composite (MVC) method was used to compile monthly NDVI dataset. The maximmum 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 researcgers 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 R۲ between observed and predicted values for precipitation, maximmum, minimum temperature, maximmum, mean and minimum relative humid are ۰.۴۳۶, ۰.۹۳, ۰.۸۶۳, ۰.۶۷۲, ۰.۷۴۱ and ۰.۷۰۳; but for mean temperature R۲ is ۰.۸۸۱ 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 ۱ year - or even longer. For example, obtained results by researchers i.e. Potter and Brooks (۱۹۹۸) showed lag times of ۱ to ۲ months for maximmum and minimum temperature and rainfall. Conclusion In this paper, the authars 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 ۰.۷۸ was experienced in October, and the lower ۰.۲۳ value was computed for January.