Detection of Dust Storms in Jazmoriyan Drainage Basin Using Multispectral Techniques and MODIS Image

Document Type : Full length article


1 PhD Candidate in Water Structures, Shahid Bahonar University of Kerman, Iran

2 Associate Professor of Water Structures, Shahid Bahonar University of Kerman, Kerman, Iran


Based on the importance of dust storm phenomenon, negative effects of the dusts on human health and social and economic consequences, it is essential to identify the dust source locations for planning and to eliminate the production factors of the dust storms. The last improvement in remote sensing makes a situation for using the satellite image for exploration of the dust sources. In this study, the MODIS image data were used for detection of the sources of dust storm in Jazmoriyan seasonal wetland and their corresponding watersheds. In order to achieve this target, we used three methods including Xie (2009), Zhao et al. (2010) and Liu (2011). The performance of the methods was investigated by AOD and horizontal visibility. In order to simulate the path of dust aerosol, we used HYSPLIT model Lagrangian approach for forward trajectory.
Materials and methods
Jazmurian is a dried wetland in a closed drainage basin in south-east Iran. Population growth, irrigation in surrounding farmland, dam building on feeding river, climate change and drought made the wetland to dry. The Jazmoriyan wetland, 300 km2 in area, is located between Sistan-va-Baluchistan and Kerman provinces, 58° 39' to 59° 14' E and 27° 10' to 27° 38' N. 
In this research, we have used field data (horizontal visibility), satellite data (MODIS level1B and Level 2 products), and meteorological data and Hysplit model output data.
The Xie (2009) method is based on decision tree through several indexes. Zhao et al. (2010) method was developed for dust detection on earth and ocean in daytime. Liu and Liu (2011) suggested the Thermal Infrared Integrated Dust Index (TIIDI) for separating the dust, sand surface and cloud. Representing the intensity of dust storm is the main advantage of this method. The most important feature of this method is to show the intensity of the dust storm.
Statistical analysis was conducted using Excel (Microsoft). Image processing was done with ENVI 5.3 software. Afterward the appropriate band for dust detection was identified. Then, some image was selected for extracting the thresholds.
Finally, based on extracted thresholds, dust storm over the Jazmoriyan watershed by MODIS images data on January 4, 2017, to January 7, 2017, was detected. The intensity of the dust was classified by TIIDI method, and dust source was introduced based on the region with the highest dust intensity. Three critical points of dust were identified with this method.  
Results and discussion
The results have demonstrated that these methods are useful for dust detection. The results of dust detection show that, there aren't any dust storms in Jazmoriyan on January 4, 2017. The dust storm began at 6:40 on January 5, 2017, in center of swamp Jazmotiyan and it had increasing trend until 9:55 at that time. Following of this process, the dust storm reaches to the highest txtent on January 6, 2017, at 7:25 and decreasing trend was started at 9 AM at the same day. The dust storm was finished in Jazmoriyan watershed in next day (January 7, 2017). Furthermore, the two-days forward air-mass trajectories with HYSPLIT model show that the dusty air masses at all altitudes are moved to the south-east part of Iran and will affect Oman Sea and Makran Mountain. The analysis of meteorological maps showed that a jet with a speed more than 30 m/s has covered all study area. It increased the dust storm possibility in the region. Based on the results, the extracted bands and thresholds in Jazmoriyan watershed is in agreement with other researchers. The results of dust detection obtained from MODIS confirm the results from obtained myd04 products and horizontal visibility.
Unsuitable distribution of synoptic station and lack of ground monitoring stations around the Jazmoriyan swamp are the issues in dust monitoring. MODIS image data can be used for dust storm detection. Performance of Xie (2009), Zhao 2010, et al. and TIIDI methods were investigated. The results of these methods using MODIS image data on January 4, 2017, to January 7, 2017, showed that the dust storm that began on January 5, 2017, was approximating at 6:40 AM. The dust had an increasing trend until the next day. The dust was spreading in a vast area on January 6, 2017, at 7:25 and completely was disappeared on January 7, 2017. In addition, the results of path tracing of aerosols of dust source represent the aerosol movement to the south-east Iran, Makran Coasts, and Persian Gulf. This is same as the results of other researcher.  


Main Subjects

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Volume 50, Issue 3
October 2018
Pages 545-562
  • Receive Date: 19 December 2017
  • Revise Date: 09 May 2018
  • Accept Date: 27 May 2018
  • First Publish Date: 23 September 2018