evaluating the effectiveness of ackerman's algorithm in monitoring dust storms: a case study of ilam province, iran

Determining the spatial distribution of dust storms in sedimentary areas is essential for forecasting and controlling these natural-manmade hazards. therefore, this study sought to investigate the efficiency of ackerman’s dust detection technique and the normalized difference dust index (nddi) in identifying dust storms in ilam province using the moderate resolution imaging spectroradiometer (modis) sensor images taken on 12/08/2015 and 02/09/2015. to this end, the data regarding the dusty days in five meteorological stations were collected and analyzed to examine the status of dust in ilam province using climate data and remote sensing. moreover, an output dust map was used for the images based on numerical values. on the other hand, dust areas were identified by applying thresholds related to each algorithm. finally, the accuracy of ackerman’s technique and the nddi was determined using pm10 pollution monitoring stations in the five stations mentioned above.the results showed that the algorithms used in this study could detect particulate matter: the ackerman algorithm was more efficient in detecting dust, while the nddi algorithm was only applicable for separating clouds from the ground. furthermore, a low correlation was found between the nddi and the terrestrial data (0.15). in other words, it was found that from among the two techniques, ackerman’s dust detection technique obtained a higher correlation (0.35) with terrestrial data than did the nddi (0.15), indicating the high capability of the algorithm in detecting the dust phenomenon. therefore, it could be argued that dust storms can be modeled and simulated with high accuracy via ackerman’s algorithm.