Investigating the Relationship Between Topography and Drought in Southwestern Iran Using Remote Sensing

Human beings have faced one of the most important problems in recent years: the water crisis and the occurrence of drought. Due to this, it is important to study the drought situation when managing water resources. In any climate, drought is caused by a lack of rainfall. However, unfortunately, defining drought and how it relates to hydrological phenomena is very difficult. First of all, drought may not affect all components of the hydrological system simultaneously. The second point is that drought does not refer to an absolute lack of moisture, but to a relative one. As a result of climate change and reduced rainfall and increased evapotranspiration in recent years, drought has become a major problem in the world, in general in arid and semi-arid regions such as Iran. Therefore, drought monitoring and management are essential. Most traditional methods rely on observations from meteorological stations and emphasize droughts. However, researchers and experts have considered the use of remote sensing techniques and satellite images as a useful tool for spatial and temporal monitoring of agricultural drought. A variety of meteorological and remote sensing indicators influence the study of droughts. Standard Index-Evapotranspiration Index (SPEI) and RDI Drought Index  are two such methods.Due to the importance of the topic of this study, the study examined drought status in the study area using remote sensing indicators (EVI, NDVI), drought prediction using Markov and Kumarkov chains, and the connection between them and drought conditions.

EVI index Hewitt and Liu introduced the EVI in 1994. As defined below, improved vegetation indices minimize atmospheric effects and differences in blue and red reflections. NIR, RED, Blue are amount of reflections in the blue, red and infrared bands.   L is the aerosol penetration coefficient and soil separation parameter which was considered equal to 1 in this equation. The coefficients C1, C2, G were considered equal to 6, 7.5, 2.5, respectively.NDVI index Normalized difference vegetation index (NDVI) is an indirect measure of photosynthetic activity. The range of this index is -1 for the minimum and +1 for the maximum amount of photosynthetic activity. NDVI is defined as follows (Tucker et al., 2010):  (2)Pnear and Pred are the reflectance values of the near infrared and red wavelengths, respectively, for pixel i during the months j and year k, respectively.

According to the results, southern regions have lower values for all vegetation indices, which indicates a lack of vegetation in these regions and the existence of drought. There is no drought in the northern areas and parts of the east of the region. Also, the results show that the region often falls into the middle class of drought based on EVI and NDVI indices. The results indicate that in 2000, 96% and 78% of the region were in the middle classes of drought, and in 2020, 98% and 93% of the region were in the middle classes of drought.
The results for the EVI index showed that 47% class 1 to class 3, 20% class 2 to class 4, 12% class 4 to class 5, 29% class 5 to class 6, 27% class 6 to class 7, 21% class 7 to Class 8, 14% to Class 8 to Class 9, and about 8% to Class 9 to Class 10, indicating an increase in drought in the area. NDVI index 49% Class 1 to Class 7, 32% Class 4 to Class 5, 19, Class 5 to Class 6, 14, Class 6 to Class 7, 11% Class 7 to Class 8 and 27% Class 8 to Class 9 has done.

Then, in order to extract the landform map of the study area, the topographic position index (TPI) was used. The results showed that high areas such as ridges and hills, near zero codes indicate flat areas or areas with low slope changes and negative codes indicate low areas such as valleys and waterways. The results showed that the highest landform in the middle drainage and plain area (about 14) percent and the lowest area is related to narrow valleys (about 1 percent). The results showed that the NDVI and EVI index in low altitude areas is lower (dry condition) and the index in high altitude areas is the highest (wet condition). Thus, the topographic situation can be used to predict the drought situation.