بررسی اثر خشکسالی بر روی پوشش گیاهی با استفاده از تکنیک های سنجش از دور، مطالعه موردی: حوضه آبریز سد درودزن

Extended Abstract
Introduction
Drought is one of the characteristics of the climate system that occurs every year without any warning and regardless of geographical boundaries and with economic and political differences (Niazi et al., 1396: 83). Drought is a threat in most parts of the world, including arid and semi-arid regions, and increases restrictions on agricultural production, accessible water, desertification, and the loss of natural pastures and vegetation. Iran is a vast country that has a different climate due to its special location and topographic feature (Alijani et al., 2007: 161). The average annual rainfall is about 251 mm (Eskandari et al., 1399: 116). This is less than 25% of the average annual rainfall on the planet and about 33.33 average annual rainfall on the land surface, so most of Iran is located in the arid climate of the world (Nabizadeh et al., 1397: 133). Therefore, the need for further research and study in this field in different regions of Iran in order to identify temporal and spatial changes of this climate phenomenon and provide appropriate management solutions to deal with and manage it is very necessary, but unfortunately due to the importance of less important issue Most studies in this field have been based either on limited terrestrial data or on specific time and limited areas. As a result, there is a need to develop new methods and use new remote sensing techniques and satellite imagery to assess the time and place of drought (Firoozi et al., 1398: 171).
 
Methodology
MODIS images were used to monitor vegetation changes in the catchment area of ​​Dorodzan Dam. The reason for using these images is long-term coverage and the possibility of accessing these images so that vegetation changes can be well followed. In this research, the drought situation in recent decades is first investigated and determined using the statistics of stations in access and near or near the catchment area of ​​Dorodzan Dam. The method of identifying droughts is using SPI method. This method is one of the most widely used techniques for estimating drought. After identifying the dry years, the vegetation surface in different years is obtained using MODIS satellite images and their extent is compared. A series of indicators were used to assess the drought situation in the region. Examples of indices used in the analysis of rainfall data in this study are the Chinese ZSI and CZI indices and the normal percentage index is the standardized precipitation index. In the tables, the intensity of drought is determined based on the results of the indices. (Roozgar et al., 2012: 6). Then, the best of these indicators is determined.
 
Results and discussion
The output chart of the drought index was prepared based on periods of 1 and 3 months for Dorodzan station and two other stations to check the accuracy and performance of work. The dry area is estimated in October 2001 according to the chart. Also, in 2004, 2010 and 2013, the months (1 and 10) have been estimated according to the one-month index of the arid region. Paying attention to the value of the standard precipitation index tables shows the value close to zero as close to normal and normal situation in each region, and the higher the number of negative numbers, the higher the drought intensity is estimated.
Based on the standard precipitation index (SPI), the drought threshold can be determined for each time period. Therefore, based on this index, in addition to calculating the severity of drought, we can also determine its duration. The standardized precipitation index is based on the probability of precipitation for each time period and is very important for early warning and drought monitoring.
According to the results obtained from indicators and graphs during the statistical period in general and in all three stations in 2001, 2003, 2005, 2007 and 2013, the dry period has been estimated that the results show The basin typically has moderate to severe droughts, and satellite imagery was obtained to calculate the NDVI index during these years for three months (October, August, September, and January).
Correlation test between meteorological and vegetation indices was taken during the statistical period for dry and wet years. According to the results, the NDVI index value for each station for the short period of one month has shown the highest response and shows a high correlation value at the level of 95%. According to the statistical results, the vegetation index has the highest correlation with Dorodzan station in relation to drought and climate changes and in 2001 to 2014 for both dry periods in the years (2001, 2003, 2005, 2007 and 2013) and for The wet year in 2009 and 2014 was above 0.8 and this was due to the high correlation and high impact of rainfall in dry and wet periods on vegetation in the region.
Conclusion
A closer look at the Dorodzan station in 2001, 2003, and 2005 in the dry season according to the table of impact values ​​was very high and the correlation value was above 0.9. Also, in the wet season of 2009, the index value is higher than in 2014, when the wet season occurred, and the high value in this year also shows that the wet season also had a positive effect on vegetation in the region. But previously it was said that two other stations were used for accuracy. Shiraz station was closer to Eghlid. It is equal to 0.925 and in 2014 for the wet period shows the value of 0.981. However, Eghlid station, which is further away from Shiraz station than the basin, has a one-month standard rainfall index and in 2009 and 2014 for the wet season of the region shows a value above 0.9, which is due to the strong effect of average rainfall. It occurs monthly during the growing and maintenance period of the region's vegetation, and in 2007 shows a high correlation with the rest for the dry period. The practical conclusion of this study is: Drought causes damage to agricultural and agricultural sectors, so it is better to prevent the expansion of orchards in areas with low rainfall and lake shores. Use satellite imagery Use other sensors to increase the accuracy of the changes.