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دمی زاده, محمود, مهدوی, رسول, نوروزی, علی‌اکبر, حلی‌ساز, ارشک, غلامی, حمید. (1400). آشکارسازی و واکاوی گرد و غبار در استان هرمزگان. سامانه مدیریت نشریات علمی, 13(1), 111-124. doi: 10.22092/ijwmse.2020.128059.1730
محمود دمی زاده; رسول مهدوی; علی‌اکبر نوروزی; ارشک حلی‌ساز; حمید غلامی. "آشکارسازی و واکاوی گرد و غبار در استان هرمزگان". سامانه مدیریت نشریات علمی, 13, 1, 1400, 111-124. doi: 10.22092/ijwmse.2020.128059.1730
دمی زاده, محمود, مهدوی, رسول, نوروزی, علی‌اکبر, حلی‌ساز, ارشک, غلامی, حمید. (1400). 'آشکارسازی و واکاوی گرد و غبار در استان هرمزگان', سامانه مدیریت نشریات علمی, 13(1), pp. 111-124. doi: 10.22092/ijwmse.2020.128059.1730
دمی زاده, محمود, مهدوی, رسول, نوروزی, علی‌اکبر, حلی‌ساز, ارشک, غلامی, حمید. آشکارسازی و واکاوی گرد و غبار در استان هرمزگان. سامانه مدیریت نشریات علمی, 1400; 13(1): 111-124. doi: 10.22092/ijwmse.2020.128059.1730

آشکارسازی و واکاوی گرد و غبار در استان هرمزگان

مهندسی و مدیریت آبخیز
مقاله 9، دوره 13، شماره 1، فروردین 1400، صفحه 111-124    اصل مقاله (1.03 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22092/ijwmse.2020.128059.1730
نویسندگان
محمود دمی زاده1؛ رسول مهدوی* 2؛ علی‌اکبر نوروزی3؛ ارشک حلی‌ساز4؛ حمید غلامی4
1پزوهشکده حفاظت خاک و ابخیزداری
2دانشیار، دانشکده منابع طبیعی و کشاورزی، دانشگاه هرمزگان
3دانشیار، پژوهشکده حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران
4استادیار، دانشکده منابع طبیعی و کشاورزی، دانشگاه هرمزگان
چکیده
قرارگیری استان هرمزگان در کمربند خشک و نیمه‌خشک جهان و نزدیکی با کشورهایی چون عربستان، پاکستان و افغانستان و همچنین شرایط بیابانی، فقر پوشش گیاهی، ناپایداری هوا و جریان بادهای شدید در این منطقه باعث وقوع طوفان‌های گرد و غبار در مناطق مختلف این استان ‌شده است که امروزه به‌عنوان یکی از چالش‌های زیست‌محیطی در جنوب کشور شناخته می‌شود.
به‌منظور بررسی پدیده گرد و غبار در استان هرمزگان ابتدا کلیه داده‌های هواشناسی 12 ایستگاه سینوپتیک منطقه بین سال‌های 2000 تا 2018 میلادی موردبررسی قرار گرفتند و پس از مشخص شدن تاریخ وقوع طوفان‌های گرد و غباری، تصاویر ماهواره‌ای موردنیاز برای رخداد‌های فوق تهیه شد.
بر اساس نتایج مشخص شد که ایستگاه قشم با 2762 روز بیشترین و ایستگاه میناب با 356 روز کمترین فراوانی وقوع گرد و غباری در استان را ثبت نموده‌اند. همچنین سال‌های 2007، 2008 و 2003 بیشترین رخداد این پدیده را در بین سال‌های موردمطالعه دارا می‌باشند.
بر اساس داده‌های بررسی‌شده از مجموع 35716 روز همراه با پدیده گرد و غبار، 84 درصد رخدادهای گرد و غبار در ایستگاه‌های موردمطالعه دارای منشأ محلی (کد 07) و 16 درصد دارای منشأ فرامحلی (06) می‌باشد.
همچنین بر اساس نتایج حاصل از پردازش داده‌های فوق، بیشترین فراوانی وقوع گرد و غبار مربوط به سه ماه می، آوریل و ژوئیه می‌باشد و ماه‌های نوامبر، دسامبر و اکتبر دارای کمترین وقوع پدیده گرد و غبار در استان بوده است.
برای بارزسازی و پایش توده‌های گرد و غبار تعداد 48 تصویر سنجنده مودیس که در آن دید افقی کمتر از 1000 متر بوده و کمینه چهار ایستگاه هواشناسی استان وقوع طوفان گرد و غبار را در آن ثبت نموده‌اند، با استفاده از چهار الگوریتم Ackerman, TDI, TIIDI, NDDI مورد پردازش و تحلیل قرار گرفتند.
نتایج بیانگر کارایی بهتر الگوریتم TDI برای بارزسازی توده‌های گرد و غبار در منطقه می‌باشد، ضمن اینکه مناطق شرقی استان هرمزگان، تالاب جازموریان، شرق سیستان و بلوچستان، مناطق غربی افغانستان و پاکستان و نیز مناطق مرکزی و جنوبی عربستان از مهم‌ترین کانو‌ن‌های تولید گرد و غبار در منطقه شناخته شدند.
کلیدواژه‌ها
الگوریتم آشکارسازی؛ مودیس؛ گرد و غبار؛ عمق نوری؛ واکاوی؛ هرمزگان
عنوان مقاله [English]
Dust Storm Analysis and Detection in Hormozgan Province
نویسندگان [English]
Mahmood Damizadeh1؛ Rasool Mahdavi2؛ Ali Akbar Noroozi3؛ Arshk Hollisaz4؛ Hamid Gholami4
1Soil Consrvation and Watershed Management Research Institude
2Associate Professor, Faculty of Agricultural and Natural Resources, Hormozgan University
3Associate Professor Soil Conservation and Watershed Management Research Institute, AREEO, Tehran, Iran
4Assistant Professor, Faculty of Agricultural and Natural Resources, Hormozgan University Associate Professor, SCWMRI, AREEO, Tehran, Iran
چکیده [English]
Geographical location and proximity to countries such as Saudi Arabia, Pakistan and Afghanistan as well as desert conditions and poor vegetation cover, weather instability and high winds have caused dust storms in different parts of Hormozgan province. This phenomenon is nowadays recognized as one of the environmental challenges in southern Iran. In order to study the dust phenomena in Hormozgan province, were first analyzed the dust data of twelve synoptic stations in the region between 2000 and 2018 Ackerman’s model, Normalized Difference Dust Index (NDDI), Thermal-infrared Dust Index (TDI) and Thermal Infrared Integrated Dust Index (TIIDI) were four Algorithm methods for dust source and plume identification using MODIS data MODIS Level 1B and MODIS Level 2 aerosol data to delineate and compares. Results showed that Qeshm station with 2762 days had the most and Minab station with 356 days had the least frequency of dust occurrence in the province. Also, 2007, 2008 and 2003 have the highest occurrence among the studied years. According to the survey data from 35716 days associated with dust phenomenon, 84% of dust events in the stations were locally originated (code 07) and 16% were of external source (06). The results also showed that the most occurrence of dust occurred in May, April and July, and the least occurrence of dust occurred in November, December and October. The results are shows all of the techniques except NDDI were successful in detecting dust plumes, but the most effective algorithm for plumes identification varied from event to event. In addition, TDI is the best algorithm comparing 3 evidence and eastern regions of Hormozgan, Jazmoorian area, Sistan and Baluchestan, and the western part of Afghanistan and Pakistan and south of Saudi Arabia are most important for dust source in Hormozgan Province.
کلیدواژه‌ها [English]
Algorithm detector, Modis, Dust Storm, Analysis, Hormozgan
مراجع
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  2. Ataei, S., A. Mohammadzadeh and A.A. Abkar. 2015. Using decision tree method for dust detection from MODIS satellite image. Geomatics Science and Technology, 4(4): 151- 160 (in Persian).
  3. Azizi, Gh., Miri and S.O. Nabavi. 2013. Detection of dust in the Midwest of Iran. Journal of Arid Regions Geographic Studies, 7: 63-83 (in Persian).
  4. Azizi, Q., M. Miri and S.O. Nabavi. 2012. Tracking of dust storm in western part of Iran. Journal of Geographic Studies at Arid Regions, 2(7): 12-27.
  5. 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.
  6. Bahak, B. 2019. Spatial analysis of dust occurrence process in Sistan and Baluchestan Province using statistical methods. Quarterly of Geography (Regional Planning), 8: 97-109 (in Persian).
  7. Dargahian, F., S. Lotfinasab Asl, M. Khosroshahi and A. Gohardoust. 2018. Determining the share of internal and external resources of dust in Khuzestan Province. Iran Nature, 2: 36-41 (in Persian).
  8. Darmenov, A. and I.N. Sokolik. 2005. Identifying the regional thermal-IR radiative signature of mineral dust with MODIS. Geophysical Research Letters, 32(16): 32-49.
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  13. Jebali, A., Z. Zare, M.R. Ekhtesasi and R. Jafari. 2019. Performance evaluation of detector algorithms of dust storms in arid lands, case study: Yazd Province. Desert Ecosystem Engineering Journal, 8(23): 85-105 (in Persian).
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  38. Ataei, S., A. Mohammadzadeh and A.A. Abkar. 2015. Using decision tree method for dust detection from MODIS satellite image. Geomatics Science and Technology, 4(4): 151- 160 (in Persian).
  39. Azizi, Gh., Miri and S.O. Nabavi. 2013. Detection of dust in the Midwest of Iran. Journal of Arid Regions Geographic Studies, 7: 63-83 (in Persian).
  40. Azizi, Q., M. Miri and S.O. Nabavi. 2012. Tracking of dust storm in western part of Iran. Journal of Geographic Studies at Arid Regions, 2(7): 12-27.
  41. 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.
  42. Bahak, B. 2019. Spatial analysis of dust occurrence process in Sistan and Baluchestan Province using statistical methods. Quarterly of Geography (Regional Planning), 8: 97-109 (in Persian).
  43. Dargahian, F., S. Lotfinasab Asl, M. Khosroshahi and A. Gohardoust. 2018. Determining the share of internal and external resources of dust in Khuzestan Province. Iran Nature, 2: 36-41 (in Persian).
  44. Darmenov, A. and I.N. Sokolik. 2005. Identifying the regional thermal-IR radiative signature of mineral dust with MODIS. Geophysical Research Letters, 32(16): 32-49.
  45. Furman, H.K.H. 2003. Dust storms in the Middle East: sources of origin and their temporal characteristics. Indoor and Built Environment, 12(6): 419-426.
  46. Hao, X. and J.J. Qu. 2007. Saharan dust storm detection using moderate resolution imaging spectroradiometer thermal infrared bands. Journal of Applied Remote Sensing, 1(1): 16-39
  47. Hsu, N.C., T. Si-Chee, M.D. King and J.R. Herman. 2004. Aerosol properties over bright reflecting source regions. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 557-569.
  48. Jalali, N., F. Iranmanesh and M. Davoodi. 2017. Identification on dust storm sources and their affecting areas in south-west provinces of Iran, using MODIS image. Watershed Engineering and Management, 9(3): 318-331 (in Persian).
  49. Jebali, A., Z. Zare, M.R. Ekhtesasi and R. Jafari. 2019. Performance evaluation of detector algorithms of dust storms in arid lands, case study: Yazd Province. Desert Ecosystem Engineering Journal, 8(23): 85-105 (in Persian).
  50. Karimi, K., A. Moridnejad, S. Golian, J. Mohammad Vali Samani, D. Karimi and S. Javadi. 20012. Comparison of dust source identification techniques over land in the Middle East region using MODIS data. Canadian Journal of Remote Sensing, 38(5): 586-599
  51. Kheirandish, Z., J.B. Jamali and B. Rayegani. 2018. Identification of the best algorithm for dust detection using MODIS data. Natural Environmental Hazards, 7: 205-219 (in Persian).
  52. Liu, Y. and R. Liu. 2011. A thermal index from MODIS data for dust detection. International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.
  53. Middleton, N.J. 2017. Desert dust hazards: a global review. Aeolian Research, 24: 53-63.
  54. Miller, S.D. 2003. A consolidated technique for enhancing desert dust storms with MODIS. Geophysical Research Letters, 30: 20-36.
  55. Noroozi, A.A. 2016. Evaluation of matched filter method for wind erosion mapping Landsat 8 OLI imagery, central and north West province of Khuzestan. Quarterly Journal of Environmental Erosion Research, 61(21): 89-104 (in Persian).
  56. Ogren, J.A. 1995. A systematic approach to in situ observations of aerosol properties. Aerosol Forcing of Climate: Report of the Dahlem Workshop on Aerosol Forcing of Climate, Berlin 1994, April 24-29.
  57. Qaderi Nasab, F. and M. Rahnama. 2018. Detection of dust storms in Jazmoriyan Drainage Basin using multispectral techniques and MODIS Image. Physical Geography Research Quarterly, 50: 545-562 (in Persian).
  58. Qu, J.J., X. Hao, M. Kafatos and L. Wang. 2006. Asian dust storm monitoring combining Terra and Aqua MODIS SRB measurements. IEEE Geoscience and Remote Sensing Letters, 3(4): 484-486.
  59. Rashki, A., P.G. Eriksson, C.J.d.W. Rautenbach, D.G. Kaskaoutis, W. Grote and J. Dykstra. 2013. Assessment of chemical and mineralogical characteristics of airborne dust in the Sistan region, Iran. Chemosphere, 90(2): 227-236.
  60. Raygani, B., Z. Kheirandish, F. Kermani, M. Mohammdi Miyab and A. Torabinia. 2017. Dentification of active dust sources using remote sensing data and air flow simulation, case study: Alborz Province. Desert Management, 4(8): 15-26 (in Persian).
  61. Roskovensky, J.K. and K.N. Liou. 2003. Detection of thin cirrus from 1.38 μm/0.65 μm reflectance ratio combined with 8.6–11 μm brightness temperature difference. Geophysical Research Letters, 30(19): 10-26.
  62. Roskovensky, J.K. and K.N. Liou. 2005. Differentiating airborne dust from cirrus clouds using MODIS data. Geophysical Research Letters, 32(12): 12-35.
  63. San-Chao, L., L. Qinhuo, G. Maofang and C. Liangfu. 2006. Detection of dust storms by using daytime and nighttime multi-spectral MODIS images. 2006 IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, USA.
  64. Shahsavani, A., M. Yarahmadi, N. Jafarzade Haghighifard, A. Naimabadie, M. Mahmoudian and H. Saki. 2011. Dust storms: environmental and health impacts. Journal of North Khorasan University of Medical Sciences, 2(4): 45-56.
  65. Shamshiri, S., R. Jafari, S. Soltani and N. Ramazani. 2014. Identification and zonation of dust storms in Kermanshah Province by using MODIS images. Applied Ecology, 3(8): 23-35.
  66. Shamsipur, A.A. and Safar rad. 2011. Satellite and synoptic analysis of dust storm in western half of Iran, case study: July 2009. Physical Geography Research Quarterly Journal, 79: 111-126.
  67. Singh, J., Y.J. Noh, S. Agrawal and B. Tyagi. 2018. Dust detection and aerosol properties over Arabian Sea using MODIS data. Earth Systems and Environment, 3(1): 139-152.
  68. Taghavi, F., E. Owlad and S.A. Ackerman. 2017. Enhancement and identification of dust events in the south-west region of Iran using satellite observations. Journal of Earth System Science, 126: 28-46.
  69. Tao, M., L. Chen, Z. Wang, J. Wang, H. Che, X. Xu, W. Wang, J. Tao, H. Zhu and C. Hou. 2017. Evaluation of MODIS deep blue aerosol algorithm in desert region of East Asia: ground validation and intercomparison. Journal of Geophysical Research Atmospheres, 122(19): 10357-10368.
  70. Wong, M.S., F. Xiao, J. Nichol, J. Fung, J. Kim, J. Campbell and P.W. Chan. 2015. A multi-scale hybrid neural network retrieval model for dust storm detection, a study in Asia. Atmospheric Research, 158–159: 89-106.
  71. Zhang, P., N.M. Lu, X. Hu and C.H. Dong. 2006. Identification and physical retrieval of dust storm using three MODIS thermal IR channels. Global and Planetary Change, 52(1- 4): 197-206.
  72. Zhao, T.X.P., S. Ackerman and W. Guo. 2010. Dust and smoke detection for multi-channel imagers. Remote Sensing, 2(10): 23-47.
آمار
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