• صفحه اصلی
  • ارزیابی حساسیت اراضی جهت تعیین مناطق مستعد تولید گردو غبار (مطالعه موردی: استان البرز)

اشتراک گذاری

آدرس مقاله


کد مقاله : JEST-2010-5184 (R1) بازدید : 93 صفحه: 151 - 164

10.30495/jest.2021.55807.5184

نوع مقاله: پژوهشی

ارزیابی حساسیت اراضی جهت تعیین مناطق مستعد تولید گردو غبار (مطالعه موردی: استان البرز)

محورهای موضوعی : ارزیابی پی آمدهای محیط زیستی

کتایون حجتی 1 (دانشجوی دکتری مدیریت محیط زیست – اقتصاد محیط زیست، دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران،ایران)
زهرا عابدی 2 (عضو هیات علمی دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران. * (مسوول مکاتبات))
بهزاد رایگانی 3 (عضو هیات علمی گروه ارزیابی و مخاطرات محیط زیست، پژوهشکده محیط زیست و توسعه پایدار، سازمان حفاظت محیط زیست، تهران، ایران.)
مصطفی پناهی 4 (عضو هیات علمی دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی.)

تاریخ دریافت : 1399/07/19 تاریخ پذیرش : 1399/09/23 تاریخ انتشار : 1400/11/01

کلید واژه: حساسیت به فرسایش, فرسایش بادی, مدل‌منطقه ای, عضویت فازی,

چکیده مقاله :

زمینه و هدف: وسعت بسیار زیاد مناطق خشک و فراوانی پدیده های گرد و غبار در کشور باعث شده است، شناسایی دقیق کانون های تولید گرد و غبار همواره یکی از اهداف اصلی پژوهش ها در زمینه گرد و غبار به شمار آید. هدف اصلی این مطالعه شناسایی منبع طوفان گرد و غبار در استان البرز است.روش بررسی: در این پژوهش از شاخص حساسیت زمین در برابر فرسایش بادی (ILSWE) برای مکان یابی منابع تولید گرد و غبار استفاده شد. شاخص ILSWE از ترکیب پنج فاکتور موثر در فرسایش بادی شامل فرسایش دهندگی اقلیم، فرسایش پذیری خاک، سله خاک، پوشش گیاهی و زبری سطح ایجاد شده است. برای محاسبه این فاکتورها از نقشه های دما، بارش، سرعت باد، درصد شن، سیلت، رس، کربنات کلسیم، EVI و کاربری اراضی استفاده شد. بعد از محاسبه هریک از فاکتورها، با ضرب آنها در هم، شاخص ILSWE محاسبه شد. درنهایت با طبقه بندی این شاخص در نرم افزار Arc GIS مناطق حساس شناسایی شد.یافته ها: نقشه نهایی شاخص ILSWE نشان داد که به طورکلی مناطق جنوبی استان البرز نسبت به دیگر نواحی، به فرسایش بادی حساس تر هستند. نقشه طبقه بندی ILSWE نشان داد که 5/34٪ از منطقه مورد مطالعه در کلاس خیلی کم، 8/26 ٪ در کلاس کم، 3/18٪ در کلاس متوسط، 6/12٪ در کلاس زیاد و 8/7٪ در کلاس خیلی زیاد حساسیت به فرسایش بادی قرار دارد. کلاس حساسیت زیاد به عنوان کانون ایجاد گرد و غبار در نظر گرفته شد که عمدتا در نواحی جنوبی استان البرز قرار دارد. اکثر نواحی کانون ایجاد گرد و غبار، اراضی بایر هستند.بحث و نتیجه گیری: با توجه به نتایج این پژوهش، اراضی بایر نقش مهمی در تولید گرد و غبار استان البرز دارند درنتیجه عملیات تثبیت خاک در این نواحی برای کاهش گرد و غبار لازم است. به طور کلی نتایج این پژهش نشان داد که شاخص ILSWE، یک مدل منطقه ای مناسب جهت تعیین مناطق مستعد و کانون های تولید گرد و غبار است.

چکیده انگلیسی:

Background and Objective: The vastness of arid areas and the abundance of dust storms in the country have made the accurate identification of dust production centers always one of the main goals of research in the field of dust. The primary objective of this study is dust storm source identification in Alborz ProvinceMaterial and Methodology: In this study, the Index of Land Susceptibility to Wind Erosion (ILSWE) was used to locate dust sources. The ILSWE was created by combining five influential wind erosion factors including climatic erosivity, soil erodibility, soil crust, vegetation cover & surface roughness. Temperature, precipitation, wind speed, sand percentage, silt, clay, calcium carbonate, EVI and land use maps were used to calculate these factors. After calculating each of the factors, by multiplying them together, the ILSWE index was calculated. Finally, by classifying this index in Arc GIS software, sensitive areas were identified.Findings: The final map of ILSWE index showed that in general, the southern regions of Alborz province are more sensitive to wind erosion than other regions. The ILSWE classification map showed that 34.5% of the studied area falls within the very low class, 26.8% in the low class, 18.3% in the medium class, 12.6% in the high class and 7.8% in very high sensitivity to wind erosion class. Very high sensitivity class was considered as dust sources, which is mainly located in the southern parts of Alborz province. Most sources of dust are barren lands.Discussion and Conclusion: According to the results of this study, barren lands have an important role in dust production in Alborz province; therefore soil stabilization operations in these areas are necessary to reduce dust. In general, the results of this study showed that the ILSWE index is a suitable regional model for determining susceptible areas and centers of dust production.

منابع و مأخذ:


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  1. Boloorani, A. D., Samany, N. N., Mirzaei, S., Bahrami, H. A., & Alavipanah, S. K., 2020. Remote Sensing and GIS for Dust Storm Studies in Iraq. In Environmental Remote Sensing and GIS in Iraq. Springer, Cham, 2020. 333-375.
    2.
  2. Waggoner, D.G., Sokolik, I. N., 2010. Seasonal dynamics and regional features of MODIS-derived land surface characteristics in dust source regions of East Asia. Remote Sensing of Environment. 114(10), 2126-2136.
    3.
  3. Huang, J., Li, Y., Fu, C., Chen, F., Fu, Q., Dai, A., ... & Zhang, L., 2017. Dryland climate change: Recent progress and challenges. Reviews of Geophysics, 55(3), 719-778.‏
    4.
  4. Alizadeh‐Choobari, O., Ghafarian, P., & Owlad, E., 2016. Temporal variations in the frequency and concentration of dust events over Iran based on surface observations. International Journal of Climatology, 36(4), 2050-2062.‏
    5.
  5. Zolfaghari, H., Masoumpour Samakosh, J., Shaygan Mehr, Sh., Ahmdi, M., 2010. A Synoptic Investigation of Dust Storms in Western Regions of Iran during 2005- 2010 (A Case Study of Widespread Wave in July 2009). Geography and Environmental Planning, 22(3), 17-34. (In Persian)
    6.
  6. Beyranvand, A., Azizi, G., Alizadeh-Choobari, O., & Boloorani, A. D., 2019. Spatial and temporal variations in the incidence of dust events over Iran. Natural Hazards, 1-13.
    7.
  7. Mei, D., Xiushan, L., Lin, S., & Ping, W. A. N. G., 2008. A dust-storm process dynamic monitoring with multi-temporal MODIS data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37.‏
    8.
  8. Huang, M., Peng, G., Zhang, J., & Zhang, S., 2006. Application of artificial neural networks to the prediction of dust storms in Northwest China. Global and Planetary change, 52(1-4), 216-224.‏
    9.
  9. Effati, M., Bahrami, H. A., Gohardoust, M., Babaeian, E., & Tuller, M., 2019. Application of Satellite Remote Sensing for Estimation of Dust Emission Probability in the Urmia Lake Basin in Iran. Soil Science Society of America Journal, 83(4), 993-1002.
    10.
  10. Qu, J. J., Hao, X., Kafatos, M., & Wang, L., 2006. Asian dust storm monitoring combining Terra and Aqua MODIS SRB measurements. IEEE Geoscience and remote sensing letters, 3(4), 484-486.
    11.
  11. Middleton, N., & Kang, U., 2017. Sand and dust storms: impact mitigation. Sustainability, 9(6), 1053.‏
    12.
  12. Rayegani, B., kheirandish, Z., Kermani, F., Mohammdi Miyab, M., Torabinia, A., 2017. Identification Of Active Dust Sources Using Remote Sensing Data And Air Flow Simulation (Case Study: Alborz Province). Desert Management, 4(8), 15-26. (In Persian)
    13.
  13. Fenta, A. A., Tsunekawa, A., Haregeweyn, N., Poesen, J., Tsubo, M., Borrelli, P., ... & Kawai, T., 2020. Land susceptibility to water and wind erosion risks in the East Africa region. Science of the Total Environment, 703, 135016.
    14.
  14. Funk, R., Reuter, H.I., 2006. Wind erosion. In: Boardman, J., Poesen, J. (Eds.), Soil erosion in Europe. Wiley, Chichester, UK, pp. 563–582.
    15.
  15. Mehrabi S, Soltani S, Jafari R., 2015. Analyzing the Relationship Between Dust Storm Occurrence and Climatic Parameters. Journal of Water and Soil Science. 19 (71) :69-81
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  16. Rayegani, B., Barati Ghahfarokhi, S., Khoshnava, A., 2019. Dust & Sand Source Identification Using Remotely Sensed Data: a comprehensive Approach. Journal of Range and Watershed Managment, 72(1), 83-105. (In Persian)
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  17. Rayegani, B., Barati, S., Goshtasb, H., Gachpaz, S., Ramezani, J., & Sarkheil, H., 2020. Sand and dust storm sources identification: A remote sensing approach. Ecological Indicators, 112, 106099.‏
    18.
  18. Feuerstein, S., & Schepanski, K., 2019. Identification of Dust Sources in a Saharan Dust Hot-Spot and Their Implementation in a Dust-Emission Model. Remote Sensing, 11(1), 4.
    19.
  19. Borrelli, P., Panagos, P., & Montanarella, L., 2015. New insights into the geography and modelling of wind erosion in the european agricultural land. Application of a spatially explicit indicator of land susceptibility to wind erosion. Sustainability, 7(7), 8823-8836.
    20.
  20. Borrelli, P., Panagos, P., Ballabio, C., Lugato, E., Weynants, M., & Montanarella, L., 2016. Towards a pan‐European assessment of land susceptibility to wind erosion. Land Degradation & Development, 27(4), 1093-1105.‏
    21.
  21. Rayegani, B., Khirandish, Z., 2018. Utilization of time series of satellite data in order to validate the identified dust storm sources in Alborz province. Jsaeh. 2018; 4 (4) :1-18 . (In Persian)
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  27. Fryrear, D.W., Bilbro, J.D., Saleh, A., Schomberg, H.M., Stout, J.E., Zobeck, T.M., 2000. RWEQ: improved wind erosion technology. J. Soil Water Conserv. 55, 183–189.
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    29.
  29. Borrelli, P., Ballabio, C., Panagos, P., Montanarella, L., 2014. Wind erosion susceptibility of European soils. Geoderma 232, 471–478.
    30.
  30. Fryrear, D.W., Saleh, A., Bilbro, J.D., Schomberg, H.M., Stout, J.E., Zobeck, T.M., 1998. Revised Wind Erosion Equation (RWEQ). Wind Erosion and Water Conservation Research Unit, USDA-ARS, Southern Plains Area Cropping Systems Research Laboratory. Technical Bulletin No. 1. pp. 185. (accessed 2019). https://www.csrl.ars.usda.gov/wewc/rweq/rweq.pdf.
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    36.

 

 

 

 

 




 




 

 

 

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