Evaluating the Effectiveness of Ackerman's Algorithm in Monitoring Dust Storms: A Case Study of Ilam Province, Iran

Document Type : Original Article

Authors

1 1Assistant Professor in Remote Sensing and Geographic Information System, Firouzabad Institute of Higher Education, Firouzabad, Iran

2 Graduated from Larestan Azad University, Master's degree

Abstract

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.

Keywords

References

  1. Abdelwaheb, M., Jebali, K., Dhaouadi, H. and Dridi-Dhaouadi, S. (2019). Adsorption of nitrate, phosphate, nickel and lead on soils: Risk of groundwater contamination. Ecotoxicology and environmental safety, 179, 182-187.‏
  2. Ackerman, P. L. and Heggestad, E. D. (1997). Intelligence, personality, and interests: evidence for overlapping traits. Psychological bulletin, 121(2), 219.‏
  3. Azizi Qasim, Miri Morteza, Nabavi,Seyyed Omid. Detection of dust in the west of Iran. Geographical studies of arid regions. 2012. No, 2 (7).pp.63-81.5.
  4. Baddock, M. C., J. E. Bullard and R. G. Bryant. (2009). Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia. Remote Sensing of Environment 113 (7): 1511-1528.
  5. Eyring, V., Isaksen, I. S., Berntsen, T., Collins, W. J., Corbett, J. J., Endresen, O. and Stevenson, D. S. (2010). Transport impacts on atmosphere and climate: Shipping. Atmospheric Environment, 44(37), 4735-4771.‏
  6. Fayazi, Mohammad Ali. (2014). Survey and evaluation of dust detection algorithms on MODIS satellite images (a case study of southwestern Iran). Master Thesis, Supervisor: Mohammad Hossein Rezaei Moghadam, University of Tabriz.
  7. Hoslett, J., Ghazal, H., Mohamad, N. and Jouhara, H. (2020). Removal of methylene blue from aqueous solutions by biochar prepared from the pyrolysis of mixed municipal discarded material. Science of the Total Environment, 714, 136832.‏
  8. Huang, J., J. Ge and F. Weng. (2007). Detection of Asia dust storms using multisensor satellite measurements. Remote Sensing of Environment 110 (2): 186-191.
  9. Karimi-Jafari, M., Padrela, L., Walker, G. M. and Croker, D. M. (2018). Creating cocrystals: A review of pharmaceutical cocrystal preparation routes and applications. Crystal Growth & Design, 18(10), 6370-6387.‏
  10. R.c., Munchak. L. a., Mattoo. S., Patadia. F., Remer. L. A., and Kolz. R. E. (2015). Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance. Journal of Atmosphere Measurement Tech, (8): 4083–4110.
  11. Miri, Morteza. 2011. Statistical analysis - synoptic of dust phenomenon in the west of Iran, Master Thesis. Geography College, Tehran University.
  12. Omidvar, Kamal; Nekounam, Zari. (2009). Applying wind rose, and dust rose to analyze the dust phenomenon and determine the seasonal wind regime associated with this phenomenon: a case study (Sabzevar city), Natural Geography Research. No. 76: pp. 85-104.2. Taghavi, Farahnaz. Olad, Elahe.Safarzade, Taher. Irannejad, Parviz. 2013. Detection and monitoring of dust storms in western Iran using remote sensing methods. Earth and Space Physics.No 39 (3), pp.96-83.
  13. Patel, A., Taghavi, M., Bakhtiyari, K. and Júnior, J. C. (2013). An intrusion detection and prevention system in cloud computing: A systematic review. Journal of network and computer applications, 36(1), 25-41.‏
  14. Qian, W. Chen, Y. Jiang, M. Hu, Q. (2015). An Anomaly-Based Method for identifying Signals of Spring and Autumn Low-Temperature Events in the Yangtze River Valley, China Journal of Applied Meteorology and Climatology 54 (6), 1216-1233.
  15. Shamsipoor, Ali Akbar; Safarrad, Taher (2012). Synoptic analysis of dust phenomenon satellites (dust July 2009). Natural Geography Research, No.44 (79). pp. 126-111.
  16. Zeng, D., Li, M., Zhou, R., Zhang, J., Sun, H., Shi, M. and Liao, W. (2019). Tumor microenvironment characterization in gastric cancer identifies prognostic and immunotherapeutically relevant gene SignaturesCellular landscape of gastric cancer TME and relevant signatures. Cancer immunology research, 7(5), 737-750.‏
  17. Zhang, P., Lu, N.M., Hu, X.Q., Dong, C.H. (2006). Identification and Physical Retrieval of Dust Storm Using Three MODIS Thermal IR Channels, Global and Planetary Change Vol. 52, PP. 197-206.
Desert Ecosystem Engineering
Volume 10, Issue 8 in English
June 2023
Pages 36-27

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