جستجوی مقالات مرتبط با کلیدواژه « Modis Aqua » در نشریات گروه « جغرافیا »

In urban regions, thermal islands are exacerbated by the heat waves (HWS) effect, and it has the potential to negatively influence the health and welfare of urban residents. Scientists predict that heat waves will intensify and become more persistent in the coming years due to climate change. As a result, the likelihood of these two phenomena occurring simultaneously will increase in the coming years, even in small, non-industrial cities. Therefore, the purpose of this study is to compare the impact rate of thermal islands from the occurrence of heat waves in Kermanshah and Ilam cities from 2003 to 2018 and It is tried to determine under the conditions of heat waves, Which of the studied cities and at what time of the day the intensity of urban heat islands has increased?

 In order to identify and extract heat waves, the maximum daily temperature data of Kermanshah and Iilam stations, from 2003 to 2018, by using Fumiaki Index and MATLAB software, days whit temperature above +2 standard deviation or above the mean Normalized Thermal Deviation (NTD) that lasted at least two days, were identified as the day with HWs and calculated by equation 1:                                                                                                                                                                     (1)
Where T (i, j, n) temperature of day i th from month j th in year n th,  the average temperature of day i from month j. To eliminate the noise in the mean, a 9-day moving average filter was performed on these data three times and calculated by equation 2:                                                                                                                                                                 (2)
Where ∆T= (i, j, n) absolute deviation of temperature from the long-term average on day j th of the month i th, in year n th compared to the long-term average temperature of the same day. In order to the values of temperature deviation of different times and places to be comparable at a certain time and place, it is necessary to standardize these absolute values of temperature deviation by means of temperature diffraction. Like day-to-day changes, diffuse T∆ at 31 days for each day is calculated by equation 3, then the 9-day moving average was performed three times
                                                                                                                                                           (3)
The value  is the average temperature deviation in 31 days that is calculated by equation 4:                                                                                                                                                          (4)
Finally, Normalized Thermal Deviation (NTD) is calculated by the following equation:                                                                                                                                                        (5)
Where .Then in MATLAB software, days with temperatures +2 above average (NTD) and lasting at least two days, were selected as the day with the HW (Equation 6).
                                                                                                                                                             (6)
Then the thermal island was calculated in Kermanshah and Iilam cities using Equation 7:                                                                                                                                                              (7)
Where SUHI, is the surface heat island, MLSTurban is the average surface temperature in the urban area and FLSTrural is the surface temperature with the highest frequency of occurrence in the rural area.

The purpose of this study was Comparison the Impact rate of thermal islands from the occurrence of heat waves in Kermanshah and Ilam cities. Which has had an increasing trend in Kermanshah and no trend in Ilam. Also, the highest monthly frequency of heat waves in both cities was in March. Also the maximum duration of this risk was 4 days and short-term in Kermanshah and 6 days and long-term in Ilam. The results also showed that in both the heat wave and no heat wave condition, most of the day the cold island and at night sometimes the heat island (although weak) is formed in the Kermanshah and Ilam city centers, but in the heat wave conditions, especially in daytime and in Kermanshah city, the intensity of thermal islands was higher than normal. Studies also showed that the persistence of heat waves did not play a significant role in the intensification of thermal islands because the impact of thermal islands from the occurrence of two-day and four-day heat waves was almost the same. In the studied cities, in both Heat wave and no heat wave condition, a cold island has been formed in the city center, but in each heat wave, the intensity of the cold island has been more than a normal day for at least one day. In Ilam, even at night, mostly the cold islands have been created in the city center, although its intensity has been less compared to the daily cold islands. However, the most intense heat island in Kermanshah was in normal conditions, which was 3.5 degrees Celsius, but in Ilam, the most intense heat island occurred in heat wave conditions, which was 1.6 degrees Celsius. According to the explanations provided, the occurrence of heat waves did not have an effect on the intensification of thermal islands, especially during the day. In heat wave condition, in both cities the percentage of relative humidity was lower than normal, but the maximum wind speed in both cities was sometimes higher than normal days. to some extent indicates the open development horizons of the progress of the study area.

According to the results, thermal islands in both cities, especially in the daytime, even in the absence of heat waves in the center of the cities under study and have been affected by the occurrence of heat waves. Therefore, because according to scientists, climate change will increase climate risks such as heat waves, and the current small and non-industrial cities will experience more heat waves in the coming years, and will certainly grow and develop. Therefore, in order to prevent the negative consequences of the interaction of heat waves and heat islands in the future, further research is necessary. Also, in order to reduce the intensity of thermal islands and reduce the surface temperature in the center of these cities in the coming years, solutions such as: covering surfaces and buildings with materials with high heat capacity, protection of green spaces and creating green roofs, especially in urban centers, paying attention to the direction of the wind in the constructions so that there is a possibility of wind canalization and heat discharge between the buildings. Adjusting the density of buildings and their decentralized construction in the center of these cities seems necessary.

Urban development and industrial activities have led to dramatic changes in the physical characteristics of the land surface, energy balance and, as a result, local climate change in big cities. To investigate heat/cold island intensity, Modis Terra and Aqua data were used to obtain land surface temperature from 2002 to 2016 and all cities with a population of more than 500,000 were selected. Modis land cover data were used to extract urban and non-urban areas. And so the difference between the maximum and minimum latitude and longitude of the urban area was obtained and on every side, the city was expanded to its size To specify the non-urban area. In the next step, a representative temperature of the non-urban had to be selected from non-urban cells. For this purpose, for each day the temperature that had the highest frequency among all cells of the non-urban was selected as the representative temperature of the non-urban. And the urban heat/cold island intensity was obtained from the difference between the Representative temperature of non-urban and all urban cells. The result shows that at the day in all metropolises except Rasht from a cold island. At night, except Zahedan, which has a weak cold island, in the other city form a heat island.

Vegetation is the mirror of climate. So getting information regarding the status of crop such as extent and distribution of it is of great importance. As collecting data about the continuous changes of vegetation is a tedious and hard work and take lots of costs. So in like a condition remote sensing is a very efficient way which provide a good view of a region. The goal of this study is to considering the long term mean of Iran’s vegetation using NDVI index. In this paper, the 16 days data of NDVI data of MODIS Aqua from 1381.4.13 to 1393.12.23 were exploited from MODIS web page and then in the base of 10 billion of codes, the long term mean of 16 days NDVI of Iran during the years was calculated. As the NDVI of above 0.2 represents vegetation the long term mean of vegetation was calculated for each of the 16 days. The findings showed that Iran’s vegetation in the period of December 26th to January 9th is minimum and from April 15th to May 1th is maximum.

As vegetation and its variations have an effective influence, it is indispensable to be aware of the vegetation changes for each of the given geographic territory. The aim of this study is to examine trends in the NDVI index over Iran's elevation class. For this purpose, the MODIS Aqua Satellite NDVI data were obtained from July 4th 2002 to March 14th 2015. The 16 days long term mean of NDVI was computed based on the analysis of 10 billion pixels and, at the next step, a matrix was constructed in 111*293, which the rows were the representatives of the elevation class from -50 to 5500m in 50 meter steps and the columns showed the long term mean of the NDVI in the 16 days temporal resolution. The trend investigation using Mann-Kendall trend test revealed a positive trend for 400-450, 450-500, 500-550, 700-750 and 1250-1300 m elevation class at 0.95 level of significance, the other remaining elevation class showed no significant trend.

The aim of this study is to explore the role of land surface temperature on distribution of snow-covered days in Iran. Firstly, the land surface temperature data were obtained from NASA for 2003 to 2014 in the spatial resolution of 1×1 km. MODIS Terra and MODIS Aqua data were also downloaded from 2003 to 2014 in the spatial resolution of 500 * 500. After preparation of the data in MATLAB software, Digital Elevation Model of the country was applied to calculate the annual LST for each of the elevations grouped by 1 meter. The findings of this study revealed that in the extents of the country which annul LST is below 30 º C the environmental condition is suitable for the accumulation of snow cover. And it was also found that the LST is 30 º C for the elevations above 1700 m. findings also revealed that there is no snow-covered day for the extents that LST is 37 º C and the most number of snow-covered days can be seen in the regions that LST is 0 º C.

At the high elevations of river basins, precipitations are mainly in the form of snow and its accumulation provides water of the rivers in warm seasons. Having accurate and on time information is of great importance for flood controlling, estimation of snow water equivalent. The extent of snow cover and its variations are important parameters in hydrologic and climatic systems. Suitable and accurate evaluation of snow cover on both small and large scales is very crucial. Lack of information on the high elevations is an issue which cause increasing concerns due to climate change as a great number of large rivers originate from these highlands. In the last few decades many researches have been carried out on the study of snow cover using remote sensing data. For instance Maskey et al (2011) used MODIS Terra data to examine seasonal snow cover in Nepal for the period from 2000 to 2008. His findings revealed that snow cover is more in the elevation zone of 3000 to 4000 meters compared to the elevation zone of 4000 to 5000 and 5000 to 6000. Khadka et al (2014) evaluated snow cover in different seasons in Tamakoshi in the highlands of Himalaya using MODIS data for the time coverage from 2000 to 2009. The results indicated that snow cover below the elevation of 4500 meters above sea level is not much significant. In winter and spring at the elevation above 4500 meters the snow cover areas are very noticeable. However in summer the elevation zones above 5500 meters have significant extent of snow cover.
In the present paper MODIS Terra and MODIS Aqua data were used to detect snow reservoirs of the country. The selected study period covers the years from 1382 to 1393. As MODIS Aqua data are missing before the year 1382, we had to limit the study period only to the aforementioned years. Before the analysis of the data, we applied two different algorithms to minimize cloud contamination that is a big obstacle against snow cover monitoring. One of the applied algorithms is based on three days filtering and the second is made on the combination of the two products. By merging the two products we managed to develop a regional snow cover data set over Iran. We also used a Digital Elevation Model that was exactly like the snow cover data both on the special resolution and projection system.
The findings of the present study revealed that there are three principle snow reservoirs that are very suitable for the accumulation of snow cover. The snow reservoir is an area which is snow-covered in long period of time in a year. The three main snow reservoirs of the country are Alborz, North-west and Zagrous and the most number of snow covered days on the heart of these snow reservoir is 153, 132 and 127 days respectively. The analysis revealed that the heart of Alborz snow reservoir is a point in Alamkooh which has a north facing slopes that is suitable for snow cover accumulation. The findings also revealed that in the snow reservoir of Zagrous the relation between snow covered days and elevation is not very matched in west to east direction. And this is due to decrease of precipitation from west to east in this area.
In this study the daily time series of MODIS Terra and MODIS Aqua data were applied to detect snow reservoirs of the country. Before using the daily data, some cloud removal technics were applied on the raw daily data to minimize cloud cover effects. The findings revealed that in Iran there are three main snow reservoirs which are Alborz, North-west and Zagrous. The most number of snow covered days was detected to be on Alborz snow reservoirs. It has been detected in this study that in eastern Zagrous the changes of snow cover with elevation is not a positive direct relation and it tends to be reduced as elevation increase. We also concluded that the most number of snow covered days are not necessarily seen on the highest mountains in Iran but in lower elevations. It was discovered that role of topographic conditions is of great importance for the accumulation of the snow cover. The eastern and northern aspects are suitable for the persistence of snow cover days and the highest number of snow covered days was detected in these aspects in the country.

Snow-covered areas and its variations are important parameters in hydrological and climatic systems. For the regions that snowmelts supply needed water, snow cover trend analysis is of great importance. Many big rivers of Iran originate from snow glaciers in mountains. So, investigation of snow cover changes is actually important. In the present study, the changes of snow-covered days in different elevation zones in Iran have been calculated via using MODIS Terra and MODIS Aqua data. The highest resolution data of these products that are available at 500m resolution were applied from 1382 to 1393. The Digital Elevation Model (Dem) of Iran that is in accordance to snow data both in resolution and projection system was exploited from NASA web site. In the first step, the frequency of snow covered days was calculated for each of the elevation zones from 1500 to 5500 in the intervals of 100 m and, as the second step, the trend of each zone was analyzed using Mann-Kendall test. The results indicated that in the months of Farvardin, Tir, Amordad, Shahrivar, Azar, Bahman, and Esfand no significant trend exists in any of the elevation zones. However in the months of Ordibehest and Khorad, there were negative trend in twenty one and ten elevation zones, respectively. It was also noticed that some positive trend exist in some of the individual elevation zones in the months of Mehr, Aban and Dey. In these aforementioned months, the numbers of elevation zones with positive trend were 2, 1 and 1 respectively.

The aim of this study is to examine the changes of snow-covered days in the elevation levels of Zayanderoud River Basin. The MODIS Terra and MODIS Aqua data were applied in the resolution of 500 × 500 m in the daily time scale from 2003 to 2014. A Digital Elevation Model of the Basin that was the same as snow data both in spatial resolution and projection system was extracted over the Basin. To explore the trend for snow-covered days for each of the elevation levels, first the frequency of snow-covered days was calculated over each of the elevations from 1500 to 3850 meters at the intervals of 50 meter and then the trend was examined using Mann-Kendall trend test. The investigations showed that in April and May the number of snow-covered days indicates a decreasing tendency in the high elevation levels of the Basin. In the months of November and December, the number of snow-covered days has shown a positive tendency over many of the elevation levels. The examinations of changes in precipitation regimes show that the shares of precipitation in the fall months have increased while the shares of winter months have decreased. It seems that the variation of precipitation shares have influenced the snow cover regime of the Basin, and snow cover decreasing trend accompanied by changes in the regime could have adverse effects for the Basin.

In this paper daily MODIS Terra and MODIS Aqua data were applied to calculate seasonal clustering of snow-covered days in Iran. These data are available in 500 * 500 meters. The applied data in this study are the finest available data for MODIS products. As the first step, numerical snow data were collected in Matlab software and then some algorithms were exploited to reduce cloud cover. After preparation of data the mean matrix of solar months from Farvardin to Esfand was prepared in Matlab. The dimension of the Matrix was 12 * 7541502 that rows represent each month and the columns depict spatial pixels. In the second step, the Euclidean distance was then calculated using ward method in Matlab. The findings of this study indicated that there are four snow-covered seasons in Iran. No snow season includes the months of Ordibehesht to Khordad, the transient months that are Farvardin and Aban, the shell of snow-covered season includes the months of Azar and Esfand. And the core of snow-covered season that incorporates the months of Dey and Bahman. And we also calculated for each of the climate season and each of solar months the number of snow-covered days and their areas in Iran. Investigations revealed that the highest area of one day snow-covered day is in the month of Dey. In this month the extent of one day snow-covered days covers 15 percent of the country. In the last step, the relation between snow-covered days and altitude was explored using Iran’s Digital Elevation Model. The results confirmed that in each of season this relation is different.

Glaciers and snow covers in the mountains play an important role in the water budget of many areas of the world (Ramage and Isacks, 2003). In high altitudes and mountainous regions, snowmelt is a great contributor to the yearly runoff. The snow melt supplies 1/6 of people's needed water but due to global warming these glaciers may be at risk (Barnett, et al. 2005). For the regions of the world that snowmelt provides the needed water, trend analysis of snow cover is of great importance. Therefore, using data and information that is limited to stations cannot be satisfactory as in many high lands no station exists and the density of them is not enough to make us able to monitor snow cover changes. But snow cover information based on remote sensing data is an alternative way to obtain necessary information in both regional and global scale (hall et al. 2005; Brown and Armstrong, 2010). For this purpose remote sensing products have been introduced to scientific community based on geosynchronous and pole orbiting satellites(Romanov, et al. 2003; de Ruyter, et al. 2006; Zhao and Fernandes , 2009; Hall et al. 2010). In this way a lot of research has been conducted to analyze snow cover changes that have been noticed as follow: Maskey et al. (2011) investigated snow cover trend in Nepal and nearby areas using MODIS terra data from the years 2000 to 2008. The analysis indicated that in elevations below 6000 m in January there is a downward trend (Maskey et al. 2011, 391). Ke and Liu (2014) applied MODIS Terra and MODIS Aqua data from 2000 to 2012 in order to analyze snow cover in Xingzan in china. The findings showed that in winter for the elevations below 2000 m and above 4000 m negative trend exists (Ke and Liu, 2014, 22). Akyurek et al. (2011) investigated snow cover area in Karasu basin as the headwater of Uphrate River from 2000 to 2009. Therefore MODIS data were applied for this purpose. The results indicated that in the study period no negative trend is detected (Akyurek, et al. 2011, 3647 and 3637). Sonmez et al. (2014) applied IMS data over Turkey from 2004 to 2012 to analyze snow cover trend. Using Mann-kendall trend test revealed that in general a decreasing trend can be seen in the country but in fall a positive trend and in spring and summer a negative trend was detected.
Study area: Iran is located between 25° and 40°N and 44° and 64°E and is a mountainous country bordering the Gulf of Oman, the Persian Gulf, and the Caspian Sea. Overall, sixty percent of Iran is covered by mountains, with the central part of the country consisting of two dry deserts: the Dasht-e-Kavir and the Dasht-e-Lut. The Alborz range in the north, close to the Caspian Sea, extends in an east–west direction with a maximum elevation of approximately 5000 m. The Zagros Mountains are aligned in a northwest to southeast direction and reach a maximum elevation of approximately 3500 m. These two ranges play a significant role in determining the non-uniform spatial and temporal distribution of precipitation across the entire country (Javanmard et al., 2010).
Material and In the present paper MODIS Terra and MODIS Aqua data were used to identify the trend of snow-covered days across Iran. The selected study period covers the years from 1382 to 1393. As MODIS Aqua data are missing before the year 1382, we had to limit the study period only to the aforementioned years. The data of these products were downloaded in daily time scale. Before the analysis of the data, we applied two different algorithms to minimize cloud contamination that is a big hindrance against snow cover monitoring. One of the applied algorithms is based on three days filtering and the second is made on the combination of the two products. By exploiting these algorithms we managed to reduce cloud cover considerably. In the second step we started analyzing the data by creating different codes in Matlab. Application of cloud removal methods have been suggested by many researchers (Dietz et al. 2014, Ke and Liu 2014, Wang et al. 2009). In the numerical format of remote sensing data a especial code has been introduced for each feature, for instance the code 200 represent snow, the code 50 represent cloud and etc. As the spatial resolution of the data was in 500 meters, we needed a Digital Elevation Model to be consistent with snow data both in spatial resolution and projection system. Thus a DEM with the aforementioned attributes was provided from NASA. By using this DEM we also were able to calculate the mean altitude of regions that have had trend whether positive or negative. To examine the trend of snow-covered days the monthly frequency of snow-covered days were calculated for the period from 1382 to 1393 and in the next step the monthly matrices were converted to seasonal ones and then the slope of regression equations were calculated for each of the pixels and finally the slopes that had the same signs were considered as the regions with significant trend and these pixels were converted to maps.
The results of this study indicated the presence of trend in different seasons of Iran both positive and negative. The findings showed that in spring 1.1 and 0.32 percent of Iran’s overall territory has experienced negative and positive trends, respectively. In summer only 0.001 percent of Iran’s extent has had trend whether positive or negative in the number of snow-covered days. In the season of fall 3.8 and 3.2 percent of Iran’s was proved to have negative and positive trend respectively. In this season the areas having trend showed counterpart patterns of changes in the number of snow-covered days. For instance eastern regions of Iran indicates downward trend but conversely western counterparts have experienced upward trend in the number of snow-covered days. This similar pattern was noticed in some other parts of the country. In the season of winter the highest rate of trend was noticed. In this season some areas of mountainous regions especially those located in western Zagros have had the most downward trend in the number of snow-covered days. Some of these areas have significant trend of decrease equal 4 days or more. In this season the mean elevation of regions having decreasing trend was 1790 meters from sea level while the mean elevation of areas having upward trend was 2030 meters from sea level. In this season nearly 50 percent of the areas having trend have had the rate of trend equals -1 to 0 days annually. And the rate of decrease in over 30 percent of other areas was -2 to -1 days annually.
In this study MODIS Terra and MODIS Aqua data were applied to examine the trend of snow-covered days across the country. Before taking the daily data in to the analyses some pre-processing analyses were applied on the raw data to minimize cloud cover effects. The findings of the recent paper confirmed the existence of both downward and upward trend in all of the seasons in the country with the greatest rate of trend in winter. In this season nearly 22 percent of Iran’s territories were proved to have a significant decreasing trend in the number of snow-covered days. These areas are mainly located along Zagros and Alborz ranges that are considered to be Iran’s water supply. But only almost 2.6 percent of Iran has had an increasing trend in the number of snow-covered days and most are positioned in lower altitudes. It seems that the recent droughts in Iran stem from the noticeable decrease in the number of snow-covered days. And accordingly crucial steps should be taken and needed policies must be applied to mitigate the adverse effects of this phenomenon across the country.

Snow is a kind of precipitation that is formed by the condensation of moist air mass and in the condition that temperature is below freezing. Although small areas of the world are mountainous regions but these small territories play an important role in the hydrological context of river basins. And in many areas snow covers and glaciers supply drinking water. Monitoring and forecasting of snow cover areas is essential for the promotion of climatic predictions and water-related decisions particularly in mountainous regions that a great share of needed water is provided. Some works have been conducted regarding the influence of altitude, slope and aspect on the distribution of snow-covered days. In this part some of these works have been reviewed here as follow. Endrizzi et al (2006) have indicated that there is a relation between snow water equivalent, altitude and aspect. They found that the dependence of snow water equivalent to altitude is weaker in fall and stronger in the spring. Gurung et al (2011) have shown that in Bhutan the accumulation of snow is varied based on aspect. North-east and north-west facing slopes are favored to be accumulated by snow in the seasons of winter, summer and fall.
In the present paper MODIS Terra and MODIS Aqua data were used to explore the relation of snow-covered days with altitude, slope and aspect. The selected study period covers the years from 1382 to 1393. As MODIS Aqua data are missing before the year 1382, we had to limit the study period only to the aforementioned years. The data of these products were downloaded in daily time scale. Before the analysis of the data, we applied two different algorithms to minimize cloud contamination that is a big hindrance against snow cover monitoring. One of the applied algorithms is based on three days filtering and the second is made on the combination of the two products. By exploiting these algorithms we managed to reduce cloud cover considerably. In the second step we started analyzing the data by creating different codes in Matlab. As the spatial resolution of the data was in 500 meters, we needed a Digital Elevation Model to be consistent with snow data both in spatial resolution and projection system. So a DEM with the aforementioned attributes was provided from NASA. In the applied Digital Elevation Model the information of aspect and slope for each of the grids was available. In the next step we developed some codes in Matlab to explore the relation between altitude, aspect and slope.
The considered relation between the number of snow-covered days and altitude indicated that there are three patterns in this way. Up to the elevations of 700 meters the number of snow-covered days does not increase by the increase of elevations. In the elevations between 700-1700 the number of snow-covered days shows a gradual increase, but in the elevations between 1700-3200 the number of snow-covered days experiences a significant increase by the changes of the elevations. Above the elevation of 3200 meters the behavior of snow-covered days does not show a clear pattern. So it can be concluded that the number of snow-covered days does not show a linear pattern. The analysis of slope indicated that the snow-covered is the most frequent in the slope of 25 degree. And above this slope the snow-covered days become less frequent. And the analysis of aspect indicated that the numbers of snow-covered days are observed frequently in the north facing slopes and in the south and south-west facing slopes the snow-covered days become less frequent. In another part of this paper, the profile of snow-covered days and other parameters like aspect and altitude was investigated over three mountains of Sahand, Karkas and Lale zar. The obtained results confirmed that in the eastern and northern facing slopes the numbers of snow-covered days are more frequent than their western and southern counterparts respectively.
In this study MODIS Terra and MODIS Aqua data were exploited in order to investigate the relation between snow-covered days with altitude, slope and aspect. The study period of the present study covers the period from 1382 to 1393. Before using the daily raw data, two fundamental algorithms were performed on the initial data to minimize cloud cover. After reducing cloud cover in the raw data, we started analyzing by creating some codes in Matlab. The obtained results show that in Iran the relation between snow-covered days and altitude does not depict a linear relation and in each of the elevation zones the behaviors are completely different. The most direct direction of snow-covered days and altitude was seen in the elevations between 1700 to 3200. The analysis of aspect show that north facing slopes has a good potential to be snow covered during the year. The analysis of slope indicated that in slope of 25 degree, the numbers of snow-covered days are the most frequent in comparison to the other slopes. So the slope of 25 degree is indeed the critical slope in this country.

In this research has been used from Dust hourly data (Horizontal View and meteorological code) 87 synoptic stations in 2008 year. After extraction of dusty days، 1 July 2008 selected for case study Because of the horizontal visibility lower than 1000 m (Dust Storms diagnostic criteria in this study) in most stations in the western half of Iran. Also MODIS Images of Aqua and Terra in 1 July 2008 times 8:55، 7:20 and 7:15 has been used for detection Dust Storm on Satellite images. Visual indexes of true color image and NDDI، BTD، BTDI and LBTD digital Indexes have been used for detection of Dust Storm on MODIS images. Results indicated that LRDI digital index and false color composing have the higher resolution than the other indexes for detection of dust storms. According studied satellite images and other studies، Dust Storms source of western Iran is external that their mainis clouding deserts of Syria، Arabian، Northern Africa and Iraq dried beds.