Introduction
Dust storms are meteorological phenomena that occur in arid and semi-arid regions with annual rainfall of less than 200 to 250 mm in wind speeds exceeding the threshold. The most important conditions for creating dust along the unstable air is a damp air. So, if unstable air is damp, the precipitation and lightning phenomenon will occur and if it is dry, it creates a dust phenomenon. The incidence of this phenomenon has increased in the Middle East in recent years. Studies also show that the central holes of Iran with more than 150 days and then the southwest and western regions of the neighboring countries of Iraq, Saudi Arabia and Syria, which are the source of dust phenomena in the country, have the largest frequency of dusty days.
Materials and Methods
In the present study, three False Color Combinations (FCCs) were used as RGB to determine the best picture that could reveal dusty areas. The present study was conducted in two separate sections. First, with the help of satellite imagery and remote sensing techniques, which form the basis of the present study, dusty days were detected in the study area. Then, the weather conditions of the study area were analyzed using synoptic maps. Also, in order to detect the dust phenomenon of the area under study on satellite imagery, of the 10 satellite images received from NASA's website at the first level of the Terra satellite (which records data only during the day) related to selected dust days have been used since 2008. In this study, a horizontal view factor of less than or equal to 1000 meters was used to detect dust storms (local and transverse).
Discussion and Results
The results indicate that in the NDDI index, the numerical values of the earth and dust are in the same range. Therefore, this index is not capable of detecting dust from the earth. The results obtained from the application of the BTD and BTDI index on the study area showed that the dust and clouds cannot be separated and that there is a significant number of spots with a numerical value of dust on the cloud. It was also expected that the cloud would be well separated by applying the LRDI index; while, as with the above indicators, the dust and clouds have the same digital numbers and then overlap, which causes the lack of accurate identification of dust on satellite imagery. Therefore, the results showed that the performance of the mentioned indicators on the study area is not satisfactory and cannot distinguish and identify the dust from other complications. The results of the synoptic analysis showed that on July 1, 2008, there is a deep trough on Iran, which is centered on the southwest of Iran on the Persian Gulf and the Mediterranean Sea, which increases the divergence of the upper levels. This trough reflects the increasing instability, climb and cyclogenesis over the region, which, of course, is at 12 o'clock in Greenwich.
At 500 hp, on July 1, which is the peak dusty day in Iran, the trough axis has moved to westward, and the strengthening of the western and northwest winds has caused the dust to climb from the surface of the deserts of Iraq to the west Iran. It should be noted that the area is at the center of instability.
Conclusions
The results showed that all three-color patterns have revealed the dust mass, and all three patterns have been able to properly disassemble the dust pixels from dust-free pixels and help the researcher to better distribute the dust from the cloud and other earth-surface complications. The visual comparison of color images in all cases showed that this method has a better ability to detect the areas of dust than other methods and effectively distinguish the dusty areas from other complications. Therefore, the use of multidimensional image data, the combination of dust indicators and the creation of a false color image in such a way that it can directly reveal dust-covered areas, has a good ability to detect Iran's dust on the Modis image.