تحليل اپتيكي و پايش پديده گردوغبار با استفاده از تكنيك‌هاي سنجش از دور، سيستم اطلاعات جغرافيايي و داده‌هاي زميني (مطالعه موردي: استان اصفهان)

Optical Analysis an‎d Monitoring of Dust Phenomenon Using Remote Sensing Techniques, Geographic Information System an‎d Ground Data (Case Study: Isfahan Province)

جغرافياي طبيعي

Dust phenomenon as one of the most important environmental hazards in arid an‎d semi - arid regions has increased in recent years in isfahan province an‎d has many consequences for human health, environment an‎d economic activities. The purpose of this study is to monitor an‎d analyze the spatial _ temporal patterns of dust in isfahan province using remote sensing data an‎d eva‎luation of the performance of different dust indices. For this purpose, AOD, NDDI an‎d EDI indices were used. The required data were extracted by google earth engine from modis sensor an‎d after processing were analyzed as seasonal an‎d annual averages over the period from march 20, 2020 to march 20, 2025. The AOD data were extracted by the MAIAC algorithm from the product MCD19A2, colection6.1 modis satellite. This product directly correlates with the MAIAC algorithm, without requiring other surface reflectance data, provides AOD with high accuracy for each day an‎d every 1 km pixel. NDDI index was used after preparing AOD MAIAC index maps. This index is a spectral index based on earth surface reflectance which is designed to detect an‎d monitor dust accumulation in optical satellite images. This index uses spectral reflectance differences between 3 an‎d 7 modis sensor bonds for analsing an‎d monitoring dust. Finally, EDI as an composite optical index of AOD MAIAC an‎d NDDI were used to improve the results of both indices. In EDI, there is an index called alpha, which is introduced to improve the results of dust monitoring an‎d eliminate the shortcomings of other indicators. The results showed that EDI as an composite optical index with linear regression coefficient 0.881 has a high ability to detect seasonal an‎d anual variations of dust. After that, AOD MAIAC with the rate of 0.606 regression had acceptable accuracy in dust monitoring but it was not enough to monitor this phenomenon by itself .Further, the NDDI index with the coefficient of determination 0.473 has been associated with error due to the influence of lan‎d - surface reflectance properties in some regions an‎d shows a high error in dust monitoring. Comparison of the results showed that integrative use of optical an‎d atmospheric indices can significantly increase the accuracy of dust monitoring .

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