E Khodabandehloo; A Alimohamdadi; A Sadeghi-Niaraki; A Darvishi Boloorani; A.A Alesheikh
Volume 8, Issue 1 , November 2016, , Pages 1-18
Abstract
Dust storm has been one of the most important challenges of western Asia. This phenomenon has been intensified due to the drought and has many negative effects on people's lives. Since this region located in a dust belt in the world, it is necessary to explore different aspects of this phenomenon ...
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Dust storm has been one of the most important challenges of western Asia. This phenomenon has been intensified due to the drought and has many negative effects on people's lives. Since this region located in a dust belt in the world, it is necessary to explore different aspects of this phenomenon is well. Predictive and modeling of this phenomenon can be prevented of jeopardizing the lives of millions of people. So present a Regional Model to assess different aspects of this phenomenon is necessary. Since climate and weather elements are constantly changing, the spatiotemporal model should be used for modeling and visualization. Hence, a model for estimating dust emission has been designed and developed and Geographic Information System (GIS) spatial modeling capabilities and remote sensing (RS) data (wind speed, soil moisture, soil texture and digital elevation model) are used. The model which is called DustEM calculates horizontal dust emission. In this study, modeling is done for 2001 to 2007 and model’s output is evaluated by MODIS AOD and for dictating hot spot area output is clustered in 3 categories contain high, medium and low with threshold 0.3 and 0.6 for AOD. Accuracy index mean for the study period was 73.6% and show high precision of model in detecting hot spot area.
F Shafiei; A Darvishi Boloorani; S Pourmanafi; A Shahsavani
Volume 8, Issue 2 , November 2016, , Pages 1-16
Abstract
Dust storms are atmospheric phenomena with negative environmental effects and especially for human health. Sampling and analysis of physical and chemical composition of recent dusts show that they are not merely composed of dust, gravel, sand and salt particles, rather they are of complex combination ...
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Dust storms are atmospheric phenomena with negative environmental effects and especially for human health. Sampling and analysis of physical and chemical composition of recent dusts show that they are not merely composed of dust, gravel, sand and salt particles, rather they are of complex combination of chemical elements. Metals such as soil alkaline metals, carbon, silica, aluminum, potassium, calcium and other organic components are observed that all these elements can have harmful effects on public health. In this study, Ahwaz where during last decades has seen several storms was studied. Using collected samples in the ground station and laboratory analyses dust contents were determined for seven dust events. For use of remote sensing technology using satellite images in order to identify the elements of dust, MODIS satellite images were used. Using MODIS images, the least squares method and cross-validation modeling, the relationship between MODIS bands and the results and measured chemical contents of dusts were created. Results show that silica, can be estimated from the ratio of 21 band to 26 with RMS 1.28. For aluminum, the ratio bands 25 to 26 with RMSE 2.08, for calcium the ratio of bands 24 to 25 with RMSE 2.3, for sodium of the ratio of bands 23 to 27 with RMSE 0.48 and finally for Magnesium the ratio of bands 15 to 24 with 0.78 RMSE are useful indexes to identify these elements using MODIS satellite images. According to the results MODIS images are useful for dust storms chemical elements estimation. Also using CALIPSO data, the rate of concentration and intensity of dust particles at the height of 6 km/asl are
M Shakeri; F Mirzapour; A Darvishi Boloorani; S.K Alavi Panah
Volume 8, Issue 1 , November 2016, , Pages 55-70
Abstract
Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the ...
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Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the produced data. The optimum utilization of fused images relies, ultimately, on the precision of the employed fusion method. Evaluation of this important aspect requires selection of an optimum assessment metric which is appropriate for the objectives and application areas of fused images. Different application areas such as, natural resources, civil areas and etc. have different preferences with regard to maintaining the spectral and spatial data. Therefore, selection of the best fusion method, that is appropriate for the application area of the image, through image quality assessment metrics is one of the users’ challenges in this field. The present paper, thus, attempts to provide an analysis and assessment of 20 common image quality assessment methods so as to identify and introduce the most optimum metrics based on the area of application of fused images. It also tries to introduce the factors causing differences in the way quality is assessed by the metrics. And then present a model for identifying the capabilities of each metric for displaying the distortions that occur in the spectral and spatial aspects of data. To this end, two metrics of high-pass filter and spectral angle mapper are taken into consideration as spectral and spatial data comparison bases, and the performance of metrics with regard to their assessment of the quality of simulated data, that contain images with controlled spectral and spatial distortions, is evaluated. Spectral distortions were introduced by high-pass filter effect, band displacement and changing color tone. Low-pass filter and attrition filters with structural elements of different dimensions were also used for introducing spatial distortions. Due to offering different spectral and spatial resolutions, images from Landsat8, EO-1, and Worldview satellites were used. Pieces with different land applications were cropped from these images to serve as test images. The assessment of the metrics tested on these images resulted in the categorization of metrics into three groups as per their capability for displaying spectral and spatial distortions. The first group included methods that functioned on the basis of noise for overall assessment of images with respect to their noise; these methods included ERGAS, MSE, PSNR, WSNR, and SNR indices. The second group were those aligned with Spectral Angular Mapper method that are suitable for assessment of images with sensitive applications as they display the spectral distortions with greater precision; These methods include BIAS, RASE, Q, MSSIM, NQM, FSIM, SRSIM, and SAM indices. The third group is also compatible with high-pass filter of HPF, RFSIM and MAD that are of a greater capability for displaying spatial distortions.
H.A Bahrami; S Mirzaei; A Darvishi Boloorani
Volume 7, Issue 4 , November 2015, , Pages 13-26
Abstract
In recent years, dust storm has become a common phenomenon in West Asia and especially Iran. This phenomenon is affecting almost all aspects of life including fauna and flora as well as human life. This research aimed to investigate the effects of dust storms on the wheat canopy, that are the most important ...
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In recent years, dust storm has become a common phenomenon in West Asia and especially Iran. This phenomenon is affecting almost all aspects of life including fauna and flora as well as human life. This research aimed to investigate the effects of dust storms on the wheat canopy, that are the most important agricultural species, reflectance and best band for selected narrow band indices to discriminating wheat canopies which are under dust stress in different growing stages. Two wheat (Triticum aestivum L.) varieties, Aflak and Pishtaz, were grown in pots under controlled conditions. The treated samples were exposed to simulated dust storm, in the wind tunnel, at two growth stages including Tillering and Heading stages. In each stage the treatments were exposed in 2, 4 and 6 days. Field spectroscopy measurements were carried out at canopy level using a full range spectro-radiometer Fieldspec-3-ASD. New narrow-band vegetation indices from NDVI, RVI, PVI and SAVI2 indices were computed from the all measured canopy spectra, Tillering and Heading stageseparately. To assess the performance of the indices, the RMSE, R2 and cross-validation method were used. For most indices, the selected optimum narrow bands are very close to one another and located in visible and NIR spectral domains. The result showed that the PVI index performed the best for considering the dust effect on wheat crops. The result also show that the selected indices have better performance in the Tillering stage ( 0.77; 0.63 0.80)for estimating the dusty days, compared with Heading stage ( 0.91; 0.62 0.71). Therefore, determining the dusty days by narrow band indices could be done precisely in the early stage of wheat growing.
Ehsan Tamassoki1; Asadollah Khoorani; Ali Dervishi Bolorany; Ahmad noheghar
Volume 7, Issue 4 , November 2015, , Pages 27-44
Abstract
Wind erosion and dust storms are of major environmental hazards all around the world. Because of The extent of arid and semiarid regions in South and South- East of Iran and the successive incidence of this phenomenon in this region, it is important to study these phenomena. The aim of this study is ...
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Wind erosion and dust storms are of major environmental hazards all around the world. Because of The extent of arid and semiarid regions in South and South- East of Iran and the successive incidence of this phenomenon in this region, it is important to study these phenomena. The aim of this study is monitoring and predicting dust storms in south and south-east of Iran. For this purpose 92 Images of MODIS sensor as well as weather data of 18 stations are used. Dusty days (originating in outside and around the station) were extracted. After monthly and annually monitoring of storms, in order to predicting the frequency of dust storms based on spatial regression, climatic factors and NDVI are used. The results show that the number of storm are high in the beginning year and is decreasing in Jun and July. More than 78 percent of dust storms are of near station type. Spatial regression equations could predict amount of storms. Based on the origin of dust storms in this study combating desertification and wind erosion program could reduce frequency of this storms.
