Mehdii Jafari; Gholamreza Zehtabian; Hasan Ahmadi; Tayebeh Mesbahzadeh; Ali Akbar Norouzi
Abstract
Introduction: The phenomenon of dust in Iran is a serious risk that caused major problems for the environment and human health. Research has shown that the frequency and severity of these storms have increased in recent years. In addition, numerical weather models alone are not capable of storm detection, ...
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Introduction: The phenomenon of dust in Iran is a serious risk that caused major problems for the environment and human health. Research has shown that the frequency and severity of these storms have increased in recent years. In addition, numerical weather models alone are not capable of storm detection, which requires the use of dust detection methods based on data remote sensing. The purpose of this research was to analyze the statistical data and identify the days with dust, the source of dust entering the Isfahan area, and identify the route of its movement. Material and methods: For data analysis of dust, data from dust collecting data was collected from the selected station in the study area with a suitable statistical period from the meteorological organization on a daily basis for 8 hours, in the form of special codes for the period of 2010 to 2013, which were processed and analyzed using statistical methods. In order to zoning the dust abundance in the province, the IDW method was used to interpolate and transform point-to-area data. The dispersion zonation map for the 2010-2013 period was mapped using the GIS software. In order to visualize the dust phenomenon, after the geometric correction of the images, the radian values of the images were converted to brightness temperature using the Planck function. Corrections and processing of images were done in ENVI software. Due to the large influence of dust particles on the reflection and brightness temperature of the 31 and 32 bands of the material, the difference between the two bands' brightness was used to represent the dust mass. Also, to prevent the detection of cloud areas, the threshold of 290 K was applied to the 12-micron range. HYSPLIT Lagrangian model was used to trace the dust particles' path. Results and disscussion: The results of this study indicated that a total of 1467 days of dust phenomena were reported for selected stations from 2010 to 2013, in which the Naein station was the largest with 634 days and the Kashan station had the least frequency of dust with 50 days. The results also indicated the greatest frequency percentage occurred in May, June and April and the lowest frequency percentages in December and January. The frequency of monthly dust data showed that 44.57% of the total dust incidence occurred in April, May and June and 32.4% in December and January. Accordingly, it can be concluded that monthly dust changes occur frequently in the region consistent with the regional climate during all months of the year. The zoning results showed that during the study period, the southeast of the province, especially Naien station, had the highest incidence of dust. The application of brightness temperature and temperature threshold in order to separate the dust from the terrain, especially in areas with backlight, was helpful in the detection of dust. The results of the tracking showed that the main route for the transfer of dust to the studied area was southwest-northeast. Conclusion: In general, according to satellite images on the first and the peak day of dust, the main sources of dust in the region were northern Arabian deserts. Based on the results obtained from image processing and the output of the model, the northern part of Saudi Arabia and the southwest-northeast route were the origin and main route of dust entering Isfahan.
Mahmoodreza Tabatabaei,; Karim Solaimani; Ali Akbar Noroozi
Volume 10, Issue 1 , October 2012
Abstract
A watershed stream network consists of a collection of rivers and streams that drain surface water flows within a watershed. These spatial data are key in calculating various aspects of a watershed, such as physiography, hydrology, soil erosion, sediment, etc. One of physical parameters in a watershed ...
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A watershed stream network consists of a collection of rivers and streams that drain surface water flows within a watershed. These spatial data are key in calculating various aspects of a watershed, such as physiography, hydrology, soil erosion, sediment, etc. One of physical parameters in a watershed is the “bifurcation ratio”, which shows the level of roundness or elongation of a watershed related to a stream network. The bifurcation ratio is calculated on the basis of an ordered stream network and it is one of the main criteria used to evaluate watershed flood hydrograph patterns. The main problems in ordering watershed stream networks are the discontinuity in stream networks of topography maps and differences with water flow model maps. These deficiencies create problems in calculating other watershed parameters such as length, ordering, and density of streams. As current GIS software is not able to compensate for these shortcomings, the present research used a previously designed GIS model (ArcGIS environment, using ESRI ArcObjects), applying a new approach for ordering watershed stream networks. The results of this study showed that this methodology could be applied to conduct a more accurate ordering of stream network (based on Strahler’s Algorithm) where there is no discontinuity between streams in a network, and to gain better harmony with water flow model of a watershed.
Ali Akbar Noroozi,; Mehdi Homaee; Abbas Farshad
Volume 9, Issue 1 , October 2011
Abstract
Soil salinity expansion is an environmental challenge particularly in arid and semi arid regions. In order to evaluate the progressing extent of soil salinity in relation with natural and human-induced conditions, a study was conducted using the Landsat TM imagery. The present study was conducted in ...
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Soil salinity expansion is an environmental challenge particularly in arid and semi arid regions. In order to evaluate the progressing extent of soil salinity in relation with natural and human-induced conditions, a study was conducted using the Landsat TM imagery. The present study was conducted in the Garmsar area to the East of Tehran. A total of 288 soil samples were analyzed to determine the relationship between the spectral reflectance and Electrical Conductivity (EC), as salinity indicator. Multiple regression analysis and Ordinary Least Square regression (OLS) were used to examine the relationships between EC and derived spectral to generate several models. In the case of derived spectral, mid-infrared band (TM Band-7), visible band (Band-1), Tasseled cap3 (Wetness index) and PCA2 (Principal Component Analysis) were found to be most correlated with the observed EC values of the surface layer of the soil, at 99% confidence level. The accuracy of the prediction model was tested using a validation set of 52 soil samples in Eyvanekey plain, close to study area where the environmental circumstance consist of similar properties. RMSE and MAE were used to evaluate the performance of the map prediction quality. Results showed that the appropriate model could predict the soil salinity with precision of 4.1 and 0.49 dS m-1, respectively. The predicted salinity ranged from 0dS/m to 110dS/m. Therefore, the EC estimations were suitable to generate soil salinity map. Sensitivity analysis was tested on applied parameters that showed Band-1 and Band-7 were 3 and 2 times more than sensitive rather than other parameters respectively. The results are promising and certainly useful for soil salinity prediction.
