جستجوی مقالات مرتبط با کلیدواژه "طوفان گرد و غبار" در نشریات گروه "آبخیزداری، بیابان، محیط زیست، مرتع"

Dust storms is one of the most important environmental hazards, especially in arid and semi-arid climates, which causes irreparable damages to the various dimensions of the environment. The aim of this study was to identify dust sources in south-east Ahwaz, using a remote sensing technique and fuzzy logic. For this purpose, the satellite images of dust events of Ahwaz City were investigated and the wind characteristic of the study area were studied by plotting the storm rose of Ahvaz Synoptic Station. In the next step, the criteria were categorized into two categories: soil moisture, land surface temperature and vegetation constitute the main layers and land use, spatial data of soil, climate, slope and sedimentology were used as constraint layers. The main layers were extracted from Landsat OLI-TIRS satellite images. The Boolean method were used to standardize the constraint layers, and the fuzzy membership functions were used to standardize the main layers. Also, main layers weighting was done using fuzzy analytic hierarchy process. Finally, the dust sources map was obtained by overlapping the layer. To validate the results, 44 ground control point were selected from the study area that showed high accuracy (81.8%) for identified areas. Based on investigating of satellite image and storm rose of Ahvaz Synoptic Station, the south-east of Ahwaz was recognized as dust-generating source and 145162.01 ha of lands are dust generating sources from which 69343/01 ha (47.77%) belongs to destroyed rangelands and 335.99 ha (0.23%) belongs to Canebrake, contains the highest and lowest level of sources of dust production in the study area, respectively.

Iran is frequently exposed to local and synoptically dust storm due to the geographical location of Iran. In recent years, dust storm frequencies and intensities have been increased significantly in Iran and especially in Isfahan Province, seriously disrupting human life and affecting the quality of life. This phenomenon is particularly increased in the spring and summer. Climate factors play an important role in dust storms. In this research, spatiotemporal changes of climate factors and dust storms were studied. Therefore, we analyzed climate factors (precipitation, temperature, wind speed and humidity) and dust storms frequency during 1992 to 2016. Poisson regression model was used for statistical modeling of temporal and spatial variations of dust and climatic parameters. According to the models, there was conformity between the results and the predicted values throughout the months. In addition, the results showed that wind speed played a major role in the occurrence of dust storms and had the highest coefficient. The results also showed that most of the dusty days are in the spring and then in the eastern part of the province, which is related to the local centers in the eastern part of the province and summer winds.

Generally, satellite images and climatological and environmental data are used simultaneously in dust storm studies. Most of the processing techniques are limited to a few indices such as brightness temperature differences, normalized dust density index, aerosol optical depth, false color composite and visual image interpretation. Since the reliability of the above-mentioned methods are varied, so an attempt was made to increase the reliability of automatic spatial image clustering by contributing the expert knowledge via visual image interpretation. Therefore, the required satellite images were collected for a period of 2005 to 2008, in Mesopotamian, Syria and south-western provinces of Iran. For doing this, the MODIS color composite images were decomposed into their ordinary bands and then used for further analysis. In this research, spatial image clustering followed by visual image interpretation (hybrid classification) was applied for accurate mapping of geographical extents of dust storm with different intensities. The sources of dust storms were also identified through visual image interpretation by considering the interpretation criteria such as shapes and patterns. Comparison of the previous results and obtained by this research, indicated critical conditions in terms of dust storm occurrence in the region. Image clustering and hybrid classification led to daily dust persistence over the region and then those images compiled in order to prepare annual total heavy dust persistence map. The result of using brightness temperature difference method verified similarity and reliability of obtained results. Therefore, this simple method could be proposed for identification of dust sources, related plumes and their affecting areas, when raw image data are not available.

The aim of this study was to determine some factors affecting dust storms phenomenon using different methods. In order to determine the best-input combination, variable reduction techniques such as factor analysis (maximum likelihood, principal component analysis), Gama test, and multivariate forward regression analysis were used. Each of these methods presented different combinations used by feedforward neural network model, with Levenberg–Marquardt algorithm and multivariate forward regression with R²=0.87 and RMSE=0.04 was selected as the best suitable combination of neural network model. In addition, monthly and seasonal data were applied by neural network using the best-input combination, and the simulation of dust storm phenomenon was done in summer and spring during the months of April, May, June, July, August and September with a higher correlation coefficient and lower mean square error, due to the good distribution of the dust storm data. The results showed that based on these methods used in this study, dominant wind speed, horizontal visibility, continuity and average of wind speed were the most important factors affecting dust storm phenomenon in Yazd province.

One of the major natural disasters that caused tremendous damages each year in many areas of the country is desert، especially in Yazd، Iran. Strong winds and the formation of dust storms will take place several times each year. In this study، data from meteorological stations in Yazd (thunder storms، wind magnitude (size، amount)، wind duration، visibility، fastest wind speed، average wind speed، prevailing winds and dust storms) in the period 1953-2005 were used on a monthly basis. In order to determine the most appropriate combination of neural network and input parameters (inputs) that influence the phenomenon of dust storms from Variable reduction technique of factor analysis (maximum likelihood، principal component)، principal component analysis، stepwise progressive and gamma test were used. Each of the methods presented، each with a different combination of these compounds neural network feed Forward back propagation with the algorithm of Levenberg-Marquardt have been used. The results showed that the stepwise progressive R² = 0. 87 and RMSE = 0. 04 provides the most suitable combination for a neural network. Comparison of simulated dust storm phenomenon in seasons and months in different years showed that the phenomenon of dust storm in summer and spring seasons and months of April، May، June، July، August and September are different. In comparing the neural network feed forward back propagation models with algorithm of Levenberg-Marquardt and decision tree with algorithm CART، Neural networks model with a correlation coefficient of 0. 87 and the root mean square error of 0. 04، the decision tree method with a correlation coefficient of 0. 86 and the root mean square error of 0. 06 has more carefully in order to simulate dust storm.