The dust storm is one of the severe natural disasters that has been recently threatening the Middle East region due to climate changes and human activities. This phenomenon has become a national crisis in some countries in this region in previous years, especially in spring and summer. This research aims to detect and monitor the areas covered by the seasonal and occasional dust storm from (Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. MODIS imagery possesses impressive spectral and temporal characteristics that are essential for such an environmental application of Earth observations. An efficient algorithm, based on the spectral and statistical analysis of both thermal and reflectance bands of MODIS data, was developed through a decision tree method. To this end, an index was proposed to detect the dust over the land using the brightness temperature of thermal bands. The results of the proposed algorithm were assessed utilizing ground-based observation of synoptic stations. The proposed method showed high reliability and performance as well as the automatic capability of dust detection in land and sea areas of the image simultaneously. The evaluation of results showed that the proposed algorithm could detect thin and thick dust storms with an overall accuracy of about 80%. Moreover, the dust monitoring results visually agreed well with the Ozone Monitoring Instrument aerosol index dust products.