جستجوی مقالات مرتبط با کلیدواژه « سنجنده مادیس » در نشریات گروه « جغرافیا »

Vegetation is one of the most important components of the ecological life of the environment due to its great impact on various ecosystems and also due to its direct impact on human livelihood and life, is a fundamental and important factor. Therefore, the purpose of this research is to investigate vegetation changes in the central Zagros using satellite numerical data. In this regard, vegetation data of MODIS sensor with a spatial resolution of 250 m in the period 2001 to 2018 were obtained. These data were analyzed using Python coding language in relation to topographic measurements of height, the amount and slope direction by moving window raster processing method and cell -to -cell analysis method. The results showed that the maximum vegetation density in the central Zagros is observed in April to June and its minimum in January. The heights of the central Zagros show the highest rate of vegetation index in spring and the lowest rate in the winter. The maximum value of vegetation index occurs at an altitude of 1500 m. Vegetation index in the central Zagros has an upward trend up to a slope of 95% and then decreases slowly. The minimum vegetation index rate is observed in the southern direction from 150° - 200° of North azimuth and its maximum in the North direction is from 300° - 360° of North azimuth. The maximum vegetation altitude (M.V.A) descends to lower altitudes in cold months and ascends to higher altitudes in the warm months of the year. In general, the trend of changes in vegetation index shows an increase in vegetation index in this mountainous unit.

Drought is a creepy phenomenon, reversible climate and an unavoidable reality in different climates that is caused by a lack of rainfall over a period of time. The purpose of this research is to monitor in Bushehr province based on SPI and VCI indices using MODIS sensors. In this regard, using 54 time-series images of the Normalized Vegetation Difference Index (NDVI) of MODIS satellite products from the growing seasons (January, February and March) between 2001 and 2018, the VCI, Vegetation Status Index, which is a normalized index to assess drought is calculated. Then, the standard SPI precipitation index for 5 synoptic stations (Borazjan, Bushehr, Deylam, Dayyer and Jam) was calculated during an 18-year statistical period, and according to the SPI classification table, very severe drought, severe drought, drought Moderate, almost normal, moderate wet, very wet and very severe wet were divided. Then the correlation coefficient of these two indices was calculated and it was found that the two indices have a significant correlation coefficient. The results of drought monitoring showed that during this 18-year period, in some years, the VCI index of severe drought (2009) shows that less rainfall occurred in the same year, and in 2014 it shows a very severe wet season where significant rainfall has occurred. Also, the results of the correlation between these two indices showed that the six-month SPI correlation coefficient has a higher correlation with the average VCI index than the quarter. In general, it was found that in the absence of meteorological indicators, a satellite index can be used to monitor drought.

Aerosol optical depth in 550 nm and angstrom exponent measurements with MODIS have been studied with 1-degree resolution for the period 2006-2017 in the middle east. Moreover, tropospheric aerosol optical depth and depolarization ratios measured at 532 nm with CALIOP have been studied for same area and same period of time too. These parameters have been classified seasonally. Optical depth results show high values for the region especially in spring and summer seasons. During the cold seasons, optical depth values are much less compared with their values at warm seasons. At spring, dust sources located in northern Iraq and those located in central and northern parts of Arabian Peninsula are much more active. Sources located in southern parts of Arabian Peninsula get more active by summer. Angstrom exponent results show that in arid and semi-arid parts of middle east, aerosol sizes are mainly in coarse mode. In arid parts of Iraq and Arabian Peninsula coarse mode particles are dominant during 4 seasons, but for arid parts inside Iran coarse mode is dominant during warm seasons and a modification in suspended particle sizes can be seen during cold seasons. Depolarization measurements of CALIOP show that almost in all seasons, non-spherical particles are ready in middle east atmosphere which is usual for an area inside the dust belt.

Human interventions in natural areas as a change in land use have led to a domino effect of anomalies and then environmental hazards. These extensive and cumulative changes in land cover and land use have manifested themselves in the form of anomalies such as the formation of severe runoff, soil erosion, the spread of desertification, and salinization of the soil. The main purpose of this study is to reveal the temperature inductions of the land cover structure of Lorestan province and to analyze the effect of land use changes on the temperature structure of the province. In this regard, the data of land cover classes of MCD12Q2 composite product and ground temperature of MOD11A2 product of MODIS sensor were used. Also, in order to detect the temperature inductions of each land cover during the hot and cold seasons, cross-analysis matrix (CTM) technique was used. The results showed that in general in Lorestan province 5 cover classes including: forest lands, pastures, agricultural lands, constructed lands and barren lands could be detected. The results of cross-matrix analysis showed that in hot and cold seasons, forest cover (IGBP code 5) with a temperature of 48 ° C and urban and residential land cover (IGBP code 13) with a temperature of 16 ° C as the hottest land use, respectively. They count. In addition, it was observed that the thermal inductions of land cover in the warm season are minimized and there is no significant difference between the temperature structure of land cover classes; But in the cold season, the thermal impulses of land cover are more pronounced. The results of analysis of variance test showed that in the cold period of the year, unlike the warm period of the year, different land cover classes; Significantly (Sig = 0.026) has created different thermal impressions in the province. Scheffe's post hoc analysis indicated that this was the difference between rangeland cover classes and billet up cover.                                                                                                              

materials and Method:                                                                                                                 

 In this study, to reveal the relationship between land cover levels and different land use classes, cross-information matrix analysis was used in the ARC-GIS software platform. Since one of the main objectives of the study was to investigate and reveal the albedo inductions of land cover classes in Lorestan province, so the relationship between these two factors was investigated by cross-matrix analysis technique. In this regard, two sets of data were used. The first set of data was related to land cover classes of MODIS sensor composite product with a spatial resolution of 1 km and hierarchical data format (MCD 12(Q2 (MCD product) which was obtained from the database of this sensor

 Land cover classes or perhaps it can be said that land use is one of the most important shapers and determinants of climate near the earth. In this study, it was observed that in general, 5 major land cover classes in the province are separable, among which rangeland and forest lands account for 85% of the total land cover of the province. On the other hand, it was seen in this study that the average spatial albedo of the province in spring, autumn and winter is about 0.2, which is very close to the global value of this component, but in winter the average value of this index in the province reaches 0.3, which can be increased Shows attention. The five land cover classes in the province had their own unique albido induction in winter, which was separable and distinct from each other, but in spring, summer and autumn, no significant distinction of albido induction of these land cover was revealed. 

The study of snow cover as one of the most important sources of freshwater supply is of great importance. Due to the mountainous conditions of Iran, it is not possible to measure the area of snow cover. Accordingly, the use of satellite imagery to identify snow storage is of great importance. In this study, the spatio-temporal changes of Iran snow cover for the cold period of the year were evaluated using the snow cover product of MODIS Terra satellite during the period of 2003-2018. The trend and slope of the snow cover were investigated using Man-Kendall non-parametric tests and the Sen's slope estimator and change-point of snow cover using Buishand test. The results showed that in January, the highest amount of snow cover is 16.6 percent, and the lowest amount of snow cover was computed in October, which is less than 1 percent. The main center of Iran's snow cover in the cold period of the year in the highlands is above 4000 meters. The snow cover trend is negative in all studied months and the maximum decrease in snow cover was calculated in January and the change-point was calculated in 2008 January, which is statistically significant at the level of 0.05. The significant decrease in snow cover during the cold period of the year which is a major threat to Iran's water resources.

This research tries to investigate the nighttime land surface temperature (NLST) (22:30) in Tehran City and its suburbs by using MODIS satellite images from 2000 to 2021. In this regard, NLST characteristics in the urban and suburbs of Tehran were obtained through the MOD11A2 Version 6 product. These data provide an average 8-day per-pixel land surface temperature with a 1-kilometer spatial resolution in a 1200 by 1200 km grid. The NLST time series were obtained for the urban and suburbs, through which the nighttime surface urban heat island (NSUHI) time series were calculated. The results showed that the trend of NLST in the urban is completely different from suburbs. Although NLST shows a significant declining trend in the suburbs, it does not show any significant trend in the urban, Hence the further increase of NLST in the suburbs has caused the downward trend of NSUHI. The results of Mann-Kendall showed that the NSUHI time series had experienced a decreasing trend seasonally and annually, which is significant at the 99% confidence level. Although the earth has become warmer in recent years, the rate of warming in the urban and suburbs has not been the same, so NSUHI in Tehran has recorded a significant downward trend seasonally and annually.

The use of remote sensing is particularly important in identifying burned areas due to its extensive spatial coverage and the provision of information at different times. Today, Modis fire products are widely used for this purpose. The purpose of this study is to evaluate the capability of Modis MOD14, MOD14A2 (Terra), and MYD14, MYD14A2 (Aqua) fire detector products and to identify fire areas in Golestan state. First, a point map of all the images was generated, then to evaluate the accuracy of the fire products, the prepared point map for the products was compared with terrestrial reality data. If the location of each of the reported fires is consistent with the fires identified by the products, that location was correctly identified as the fire. Landsat images were used as a ground accuracy map to evaluate the accuracy of Modis images. The results showed that six regions identified by level 2 fire products and eight regions were detected by level 3 fire products were identified. The results show the accuracy of the images with a coefficient of R ^ 2 of 0.94 and a coefficient of RMSE of 426.12 ha. The studies conducted in this study show that to improve the performance of the text fire detection algorithm, this algorithm is proposed for the forests of Golestan province and following the conditions and characteristics of the fire area, its intensity, and area. Be developed to provide better results.

Drought evaluation is important in terms of spatial and temporal for planning to reduce damages in the Kordestan province. In this research, Standardized Precipitation Index and the Enhanced Vegetation Index have been used from the extracted satellite images for determinants of drought. so, the statistical data of Meteorological stations including maximum monthly temperatures, total annual precipitation and the images of MODIS sensor have been employed. By comparing meteorological parameters such as average annual temperature, rainfall and the comparison of maps of the Standard Precipitation Index and Enhance Vegetation Index, the condition of drought has been investigated in the region in a 17-year period. The results of the two SPI and EVI indices indicate that the drought is due to rain changes have in the west-to-east direction. This phenomenon is more severe in the eastern regions whereas vegetation sensitivity and the fluctuation of its variations have been affected by precipitation changes in the north-to-south direction over the region. In this way, the southern regions have shown higher sensitivity. Southern regions are generally more vulnerable to droughts, especially in the south-east of the province. Regions with high drought sensitivity make up about 10 percent of the area regarding the regions in the province, whereas 91 percent of the area of regions with very high drought sensitivity is places where the landuse involves growing wheat with rain water.

Snow reservoirs are one of the most important sources of water supply in Iran. This study aims to investigate the changes in snow cover in Sefidrood basin and its sub-basins. To achieve this goal, snow cover images of MODIS's MOD10cm product were used over a period of 19 years (2000 to 2019). After analyzing the images in ENVI software, the snow area of each image was calculated and, consequently, monthly values for all years of the study period were extracted and transferred to ArcGIS software. The modified Mann-Kendall test was applied to examine the annual and seasonal trends of snow cover. Then, the Sen’s slope estimator test was used to determine the rate of change. Based on the findings, it can be seen that the time of beginning and end of snow in Sefidrood basin and its sub-basins has undergone very little change. The snow cover of the basin starts in October and reaches its minimum in April after increasing towards the cold period of the year. January is the month with maximum snow cover in all sub-basins. During the period under review, the range of snow cover was maximum in the water year 2006-2007 and minimum in 2009-2010. Findings from the fit of the modified non-parametric Mann-Kendall test on the annual time series of the snow cover of the basin indicate a decrease and of course insignificant trend in most of the basin area, although sometimes slight increasing trends are also seen. In terms of spatial distribution, in the central and eastern regions of the basin, there is a significant decreasing trend of snow cover; its annual decreasing rate in some areas is 17.77% per decade. Seasonal trends are also decreasing. The highest rate of decrease is related to winter, which in some areas reaches about 33% per decade.

The area covered by snow (SC) and land surface temperature (LST) and their fluctuations in different altitudes of a mountain unit are important in climatic, hydrological and water and ecological resources management. In this study, the relationship between SC and LST in this mountainous unit was examined in monthly, seasonal and annual intervals. For this purpose, Terra and Aqua Satellite image data which are carrying Modis sensor, used in temporal range of 2003-2018.In all time periods studied, a clear relationship between elevation and SC, was observed in the Central Alborz highlands. The relationship between these two environmental indicators are direct, although the rate of change varies on different altitudes. Two specific height thresholds were observed in Central Alborz, the first threshold being at an altitude of 1000 meters and the other at 4000 meters. So that the SC rises to a height of 1000 meters with increasing altitude. After an altitude of 4,000 meters, the slope changes again and starts to decrease. LST variations are the opposite of SC. In general, increasing the height leads to a decrease in LST, but, up to 1000m is an exception to this rule, and increasing the height will increase the LST.

The area covered by snow (SC) and land surface temperature (LST) and their fluctuations in different altitudes of a mountain unit are important in climatic, hydrological and water and ecological resources management. Snow cover and land surface tempratue distribuations on different elevational class would be important from the view point of environmental systems and ecosystems observations and management. One of the major mountainous unit in Iran, which is supplying many human population, is the Central Alborz mountain, located in the northern boundary of Iran.

In this study, the relationship between SC and LST in this mountainous unit was examined in monthly, seasonal and annual intervals. For this purpose, Terra and Aqua Satellite image data with spatial resolusion of 50m which are carrying Modis sensor, used in temporal range of 2003-2018. Digital Surface Model released by the Japan Aerospace Exploration Agency (JAXA) deployed the Advanced Land Observing Satellite (ALOS) between January 2006 and May 2011, used in this research. This data have spatial resolusion of 1 arc secound (~30m) and a vertical RMSE of 4.4 m. and now is one of the most accurate dataset with global coverage and free of charge.

In all time periods studied, a clear relationship between elevation and SC, was observed in the central Alborz highlands. The relationship between these two environmental indicators are direct, although the rate of change varies on different altitudes. Two specific height thresholds were observed in Central Alborz, the first threshold being at an altitude of 1000 meters and the other at 4000 meters. So that the SC rises to a height of 1000 meters with increasing altitude. After an altitude of 4,000 meters, the slope changes again and starts to decrease. LST changes are the opposite of SC changes, in general, increasing the height leads to a decrease in LST of course, up to 1000 meters is an exception to this rule, and increasing the height will increase the LST. This is due to the cooling effect of the Caspian Sea and high humidity at altitudes below 1000 meters and also decreasing vegatation coveabdr density up to 1000m, which mainly includes the northern slopes of Alborz. Forests, forest-steppes and grasslands, are absorbing the sunrays energy and consume it in the process of photosynthesis, and so they prevents, the land surface temperature to be increased. In the highland of central Alborz (the elevation up to almost 1000m) lower humidity and vegetation cover in addition to the rocky surfaces, leads to the higher LSTs. From an altitude of 1000 meters and above, the general trend of increasing altitude leads to a decrease in LST in Central Alborz. Another environmental indicator was defined in this study, which was called the Equilibrium Line Altitude of Land surface Temperature and Snow Cover (ELALS). ELALS is a height at which LST and SC reach equilibrium. The annual average of this environmental index is in the hight of 2,800 meters during the study period in the Central Alborz highlands. The minimum level of ELALS in winter is 1740 meters above sea level. This environmental index tends to reach low altitudes in cold seasons and months and tends to higher altitudes in warmer periods of the year.

Finally, this environmental index can be used in geomorphological studies of glaciers, climates, water resources, hydrological management of basins and ecological studies of mountainous landscapes.

Climate variables and their fluctuations dramatically affect terrestrial ecosystems and their variations. Vegetation indices have been used in numerous studies to investigate the relationship between ecosystem changes and climate parameters. In this study, GIS based spatiotemporal analyses were applied to model the relationship between vegetation variations based on the EVI-MODIS and its response to land surface temperature (LST) and rainfall in Mazandaran province during the period of 2000-2016. The LST parameter was derived from the MODIS data and rainfall parameter was achieved via meteorological station data in the region. Correlation and linear regression analyses were used to study the relationship between spatiotemporal enhanced vegetation index (EVI) and two climatic parameters. The findings indicated that the EVI had a rising trend over the study period which was mostly due to the increase in paddy fields. There was also a significant spatial correlation between EVI and LST which was significant and direct in the winter months and reversed during summer. The tabulate area analysis showed that throughout the winter months the spatial distribution of the highest EVI pixels matched to the maximum temperature pixels (20 to 27 ° C), while during June to September, the maximum EVI values were related to the areas in which the LST was less than 25 °C. Although there was no significant relationship between EVI/MODIS and rainfall in studied area, they reached a peak with a lag time of 1/5 to 2/5 months in the spring. The final results showed that the temperature is the main EVI climate factor in region and MODIS products have high potential to reveal the spatiotemporal dynamics of vegetation, the impact of human factors and its relation with the climatic factors of temperature and rainfall in the region.

According to the Intergovernmental Panel on Climate Change (IPCC) in 2012, globally, a large number of climatic events have increased in recent decades such as extreme temperatures, floods and etc. That’s the number of warm days and nights has increased, and climate models predict extreme temperature by the end of the 21st century (IPCC, 2012). Ecosystems, the global economy and public health are highly vulnerable to these extreme events, especially extreme temperatures (Kunkel et al., 1999). Generally, in Iran, the regionalization of extreme temperatures has been studied. For example, Rezaei et al. (2015) examined the extreme temperatures in two months with extreme temperature and identified different areas for Iran. Masoudian and Darand(2008) also studied extreme cold temperature in Iran and regionalized six areas for Iran. Considering the studies that indicates the occurrence of extreme temperatures for different parts of the world, it is interesting to note the role of these extreme temperatures on evapotranspiration difference between extreme cold and warm temperatures. Evapotranspiration is the water loss from the ground to the atmosphere and defined as a key process in the water cycle (Wang and Dikeson, 2012), which is related to plant growth (Alberto et al., 2014), drought (Anderson et al, 2011), greenhouse gas (Balogh et al., 2015) and climate change (Abtew and Melesse, 2012). The purpose of this study is to answer the question of what is the changes in evapotranspiration under extreme temperature conditions in Iran.

For answer the research’s question it found clearly that January 2008 and July 2010 had recorded extreme cold and warm temperatures during the period of 30 years. For these two months, 55- air temperature stations data, soil temperature from NCEP / NCAR reanalysis database, land surface temperature (LST), vegetation cover, and evapotranspiration from Moderate Resolution Imaging Spectroradiometer (MODIS) were utilized in five kilometer or 0.05 degree resolution. At first, the risk of occurrence of the extreme temperatures was determined by the distribution of the cumulative risk and the Gumbel distribution during these two months. The land surface temperature data product (LST) namely MOD11C3, which has 0.05 degrees (approximately 5 kilometers or 5600 meters) and with a monthly and global time scale was used. To investigate the changes in evapotranspiration, the MODIS evapotranspiration product namely MOD16 was utilized (Mu et al., 2012). The data is available on an annual, eight-day and monthly basis. In this process, evapotranspiration is provided globally and with a resolution of one kilometer covering 109 million square kilometers of the land’s surface. The algorithm used the Penman-Monteith equation to produce this product (Monteith, 1965). Then for the analysis Pearson’s correlation and coefficient of determination were used. 

The results showed that the occurrence of extreme temperatures above 50 degrees Celsius is 0.06 in July and temperatures higher than 22 degrees Celsius is 0.008 in January. Also, the probability of temperature higher than 5 degrees Celsius is 0.50 in January. Correlations results indicated that the two factors of energy (air temperature) and soil moisture are the main controller of the relationship between these parameters (LST and evapotranspiration), so that when the air temperature was above 5 degrees Celsius, a significant negative correlation was observed (-0.24 in January and -0.64 In July) and when the air temperature is below than 5 degrees, it will be positive (0.23 in January). Generally, regardless of the threshold, a negative correlation was obtained for every two months, but a weakest negative correlation (close to zero) was observed in January, due to the recording of temperatures exceeding 5 ° C with an incidence of 50%. The humidity factor shows that every two months have suffered from a certain moisture threshold due to extreme cold and warm temperatures, and if there is a moisture limit, this relation will be negative, thus it’s a determination factor for the overall negative relationship (regardless of the temperature threshold) in January.

The extreme temperatures showed the highest impact on evapotranspiration so that air temperature was identified as a trigger for the relationship between LST and evapotranspiration.

 Frost causes a lot of damage to the agricultural sector every year.From the meteorological point of view, when the temperature drops below a certain value, frost occurs. This threshold may vary from one crop to the other. Not much research has been done to predict frost using remote sensing technology. Most of the models used to predict frost have been provided by climatologists, geographers and meteorologists based on data collected at meteorological stations.The measurements at meteorological stations are at a point and the number of these stations are limited. Therefore, depending on the surface coverage and texture around the station, the air temperature would only be valid in certain and limited distance from the stations. On the other hand, satellite images have relatively acceptable spatial resolution specially for using in the environmental studies.This indicates the necessity of using remote sensing data in many occasions including frost prediction.This work tried to predict areas at risk of frost using the NEAT method in the state of Georgia, USA. For this purpose, the MODIS satellite data and the data collected in meteorological stations of AEMN network are used.  

The State of Georgia, in the southern part of the United States between latitude of 30o31’ to 35o north, and longitude of 81o to 85o53’ west with an area of 154077 square kilometers, was chosen for this case study.The reason for choosing this region was merely because of accessibility and availability of surface collected data mostly in cultivating and agricultural zones. In this study, data collected in 10 AEMN stations from 2005 to 2015 were used for modeling and evaluation. Also, data collected in 68 stations of AEMN were used for evaluation of model for two different periods. The satellite images used in this study is collected by Moderate Resolution Imaging Spectroradiometer (MODIS) on board of Terra and Aqua platforms. The MODIS products used in this study consist of LST (MOD11 and MYD11), lifted index (MOD07 and MYD07), total precipitable water (MOD05 and MYD05), and normalized differential vegetation index (MOD13). Also, in this study, to estimate air temperature in each 1 by 1 km grid box, the method developed by Mobashari et al. (2018) was used. The method offered an accuracy of 2.33 °C and a correlation coefficient of 0.94. Khesali and Mobasheri, 2019 presented Near-surface Estimated Air Temperature (NEAT) model in which extrapolation coefficients for air temperature to the next hours are calculated. To increase the accuracy of the NEAT model, it was recalculated using AEMN data at Aqua and Tera passing times. The methodology in this study consists of the following steps. •  Selection of study area and collecting temperature data from AEMN meteorological stations, •  Reproducing NEAT model coefficients  usinga set of AEMN data, • Evaluating NEAT equation using another set of AEMN data, •  Receiving and preparation of MODIS products and calculation of air temperature at the passing time of Terra and Aqua, • Applying NEAT to the MODIS images, • Producing Frost map using temperatures estimated by NEAT • Evaluation of frost prediction accuracy   Results and Discussion In order to implement the model, Two periods were selected: 3–9 December 2006 and 3–11 April 2007 in which severe crop damage across the southeastern United States has happened (Prabha and Hoogenboom, 2008). First, the NEAT model coefficients are calculated using the AEMN network data, and evaluated for air temperature extrapolation to the next hours.  Then, the air temperature was extracted using MODIS products for Aqua and Terra night time sensors. Finally, the NEAT model was applied to the air temperature extracted from satellite images, and the nighttime temperature was predicted from approximately 22:30 pm to 7:30 am of next day at 15 minute intervals. Then in the extracted images the air temperature was classified into two degreeintervals. Areas with temperatures below zero degrees Celsius are considered frost zones. Data from 68 AEMN network stations were used for evaluation. Statistical parameters like RMSE and variations of User Accuracy and Overall Accuracy were analyzed over the night. The RMSE value for all data, which is 13,840, is estimated to be 2.5 degrees. This parameter has an increasing trend from the satellite passing time to 6 hours and varies from 0.1 to 2.5 degrees Celsius. The results show the effectiveness of the proposed model in frost prediction.

In this study, AEMN meteorological data and MODIS satellite images were used for frost prediction. The study area is located in the Georgia state in the southeast of the US. Using the Neat model, air temperature is extrapolated during night in 15 minute intervals. Air temperature maps for two periods of time are produced. The results and accuracy assessment parameters show the ability of the proposed model in air temperature prediction and its effectivenessin frost prediction

Not only have clouds and precipitation (snow, rain and hail) made up an important part of the subsystem atmospheric phenomena of terrestrial climate, but are also considered as the main components of the water cycle. Aerosols play an important role in cloudiness and precipitation. In warm clouds with the same liquid water path, increase in aerosols concentration results in the reduction of effective radius of cloud droplets and increase of cloud albedo, which is referred to as “first indirect effect” or “Twomey effects” (Twomey, 1977;Twomey et al., 1984). Variation in concentration of aerosols causes variation in the thickness, life period and precipitation rate of the clouds, referred to as “second indirect effect” or “Albrecht effect”, (Albrecht, 1989; Hansen et al. 1997; Ackerman et al. 2000).
The city of Tehran, the capital of I. R. of Iran, is a mega-polis with a population of more than 10 million. The city is located in the central part of the Alborz Mountain Range and characterized by its complex topography and diverse climate in an area of about 700 Km2. The city's long term annual average precipitation is 300mm. The growth of the city during the last 30 years has turned the city to one of the most polluted cities in the country, particularly during autumn and winter. The geographical conditions of the city are the other most important factors affecting the city's environmental condition and the nature of the particulate matters that may be found in its atmosphere.
In this paper, the influence of aerosols on microphysics of clouds over Tehran during the years 2003 to 2012 has been investigated.
Material and In this paper, the influence of aerosols on microphysics of clouds over Tehran throughout the years 2003 to 2012 was studied. Correlations between microphysics of clouds and aerosols from the Moderate Resolution Imaging Spectroradiometer (MODIS) were examined over Tehran. These are both from the collection 5.1 level 3 daily data, at 10×10 resolution. Daily AOD, CER, COT, CWP, CTT and CTP observed by MODIS on board the Aqua satellite from 2003 to 2012 on 10×10 grids are used in this work. In this study, Aerosol Index (AI) and Aerosol Optical Depth (AOD) were used as a proxy for the cloud condensation nuclei (CCN). To reduce errors, water phase cloud is defined as having a cloud top pressure greater than 800hPa.
Pearson and Spearman correlation coefficients were calculated and written for all cloud microphysical quantities with AOD as well as AI by software SPSS-V13. For the study, scatter plot diagrams between CER, CWP, CF, CTT, CTP and COT with AI and AOD were drawn. Furthermore, scatter plot diagrams between cloud microphysics quantities were drawn. We used the AI and AOD as our aerosol products, as AI includes size dependence and has been shown to correlate better with CCN. Pearson and Spearman correlation coefficients and scatter plot diagrams showed that there are positive correlation coefficients between the aerosol index and cloud top temperature and cloud top pressure. However, there are negative correlation coefficients between aerosol index and cloud fraction, cloud optical thickness and cloud water path. There are no significant negative correlation coefficients between cloud effective radius and AI over Tehran. These correlations suggest the increase of aerosols in Tehran in the last 10 years, because of over seeding. In these 10 years, there was reduced cloudiness over Tehran. The correlations between quantities of cloud microphysics fully match with theoretical studies.
At first step the scatter plots of CER, CWP, CF (cloud fraction), CTT, CTP and COT versus AI and AOD, which were considered as the representative of aerosols in our study, were created to provide an early picture of the possible interrelationship between those cloud microphysics and aerosols characteristics. The same diagrams were plotted to provide a picture of the way microphysical characteristics of clouds vary with each other. Study on the relationship between the AOD and AI with CCN showed that AI is a better representative of CCN .Then, correlations between clouds microphysical properties and both AOD and AI were examined by means of Pearson and Spearman correlation coefficients which were calculated usingSPSS-V13. The results showed that there are positive correlation between the AI and CTT and CTP. However, there are negative correlation between aerosol index and CF, COT and CWP. Negative but insignificant correlation was observed between CER and AI over Tehran. The correlations suggest the increase in aerosols concentration over Tehran during the duration of study might be regarded as one of the main reasons for the reduced cloudiness and precipitation over Tehran. The correlations between quantities of cloud microphysics fully match the theoretical studies.

Article 5، Volume 23، Issue 91، Autumn 2014، Page 69-80 XML PDF (1566 K) Document Type: Research Paper Abstract Dust haze phenomenon is one of the atmospheric phenomena which leaves negative environmental impacts and it is one of the most important environmental challenges in West and South-West of Asia، particularly in Iran. This phenomenon related to desertification processes and occurs in arid and semi-arid regions of the world. Remote sensing is science and technique to obtain information from geographic phenomena without any contact with them. To detect the phenomenon of dust haze the images with wide coverage and high frequency are needed therefore the images related to MODIS sensor is suitable for study of dust haze phenomenon due to high spectral bands. The main objectives of this research was identification the sources generating dust haze entering the West and South West of Iran and monitoring the movement of dust haze. In this research the source of two occurrence of dust haze phenomenon related to 2011 which has occurred in areas of West and South West of Iran was identified with the help of satellite images and using thermal and visible bands of MODIS sensor and Ackerman index. Then monitoring was carried out and synoptic analysis to identify the movement of dust haze from its source to Iran was done By using500 hectopascal geopotential height synoptic data، sea level pressure and 500and 100 hectopascal wind direction maps. Finally، its movement from the source to Iran was monitored by using GIS and Spatial Analysis Tools.