دوره 4، شماره 4 - ( 10-1396 )                   جلد 4 شماره 4 صفحات 1-18 | برگشت به فهرست نسخه ها

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1- استادیار ساطمان حفاظت محیط زیست ، bhz.ray@gamil.com
2- کارشناسی ارشد ساطمان حفاظت محیط زیست
چکیده:   (713 مشاهده)
 
وسعت بسیار زیاد مناطق خشک و بیابانی در کشور و فرکانس بالای پدیده­های گرد و غبار در آن باعث شده است، شناسایی دقیق کانون­های تولید گرد و غبار همواره یکی از اهداف اصلی پیش­نیاز عملیاتهای احیائی و بیابان­زدایی به شمار آید. هدف از مطالعه حاضر، اعتبارسنجی کانون­های شناسایی­شده تولید گرد و غبار در استان البرز  با استفاده از سری زمانی داده­های ماهواره­ای و داده­های ایستگاه­های هواشناسی می­باشد. بدین­منظور داده­های TRMM سنجنده TMI، داده­ی 16 روزه پوشش گیاهی، داده­ی 8 روزه درجه حرارت سطح زمین و عمق اپتیکی هواویز مودیس و همچنین اطلاعات زمینی گرد و غبار ایستگاه­های سینوپتیک و پایش آلودگی هوا دریافت شدند. تجزیه و تحلیل روند تغییرات رطوبت خاک، درجه حرارت و پوشش گیاهی در یک دوره زمانی 15 ساله صورت پذیرفت. همچنین عمق اپتیکی هواویز در رویدادهای ریزگرد با غلظت بالا برای کانون­های محتمل مورد بررسی قرار گرفت. علاوه بر این مناطقی که در طی دوره زمانی، عمق اپتیکی گرد و غبار بالاتری نسبت به نواحی دیگر داشتند، مشخص شدند. درنهایت با استفاده از اطلاعات زمینی گرد و غبار، عمل واسنجی برای کانون­های شناسایی­شده انجام گرفت. نتایج تجزیه و تحلیل روند تغییرات، نشان­دهنده کاهش معنی­دار پوشش گیاهی، رطوبت خاک و دمای سطح زمین در محل کانون­های محتمل تولید ریزگرد در طی دوره زمانی مورد مطالعه بود. کاهش درجه حرارت در بخش جنوبی استان البرز و غرب تهران با فرکانس بالای غبار در ناحیه در ارتباط بود که این تکرار رویداد گرد و غبار در بررسی سری زمانی داده­های عمق اپتیکی هواویز نیز نشان داده شد. بررسی سری زمانی عمق اپتیکی هواویز نشان داد که تمرکز ریزگرد در نزدیکی یا بر روی کانون­های شناسایی­شده وجود دارد و بالا بودن مقدار غلظت در این نواحی، نشان­دهنده صحت کانون­های شناسایی­شده گرد و غبار می­باشد. همچنین بررسی عمق اپتیکی در رویدادهای با غلظت بالا و بررسی همزمان جهت حرکت هوا نشان داد کانون­های شناسایی­شده به درستی انتخاب گردیده است. تلفیق اطلاعات زمینی گرد و غبار با جهت حرکت باد نیز صحت کانون­های ریزگرد شناسایی­شده را تایید نمود. در کل یافته­های تحقیق نشان­دهنده قابلیت بالای سری­های زمانی داده­های سنجش از دور در اعتبارسنجی کانون­های شناسایی­شده تولید ریزگرد می­باشد. نتایج تحلیل سری­های زمانی داده های ماهواره­ای نشان داد که درجه حرارت سطح زمین به عنوان یک پارامتر اقلیمی مهم در شناسایی و اعتبارسنجی کانون­های گرد و غبار به شمار می­رود. بر اساس نتایج تحلیل در جایی که فرکانس وقوع گرد و غبار بالا است، کاهش معنی­دار درجه حرارت سطح زمین مشاهده می­شود.
متن کامل [PDF 691 kb]   (365 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: تخصصي
دریافت: ۱۳۹۶/۳/۳۰ | پذیرش: ۱۳۹۶/۱۱/۱۹ | انتشار: ۱۳۹۶/۱۲/۲۶

فهرست منابع
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23. Levy, R., and C. Hsu 2015. MODIS Atmosphere L2 Aerosol Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA. In
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26. Owe, M.; R. de Jeu, and T. Holmes. 2008. Multisensor historical climatology of satellite‐derived global land surface moisture. Journal of Geophysical Research: Earth Surface, 113
27. Palmer, M.A.; J.B. Zedler, and D.A. Falk. 2016. Foundations of restoration ecology. Island Press,
28. Parinussa, R.M.; A.G. Meesters; Y.Y. Liu; W. Dorigo; W. Wagner, and R.A. de Jeu. 2011. Error estimates for near-real-time satellite soil moisture as derived from the land parameter retrieval model. IEEE Geoscience and Remote Sensing Letters, 8: 779-783.
29. Pozzer, A.; A. de Meij; J. Yoon; H. Tost; A. Georgoulias, and M. Astitha. 2015. AOD trends during 2001–2010 from observations and model simulations. Atmospheric Chemistry and Physics, 15: 5521-5535.
30. Quintano, C.; A. Fernández-Manso; A. Stein, and W. Bijker. 2011. Estimation of area burned by forest fires in Mediterranean countries: A remote sensing data mining perspective. Forest Ecology and Management, 262: 1597-1607.
31. Samadi, M.; A.D. Boloorani; S.K. Alavipanah; H. Mohamadi, and M.S. Najafi. 2014. Global dust Detection Index (GDDI); a new remotely sensed methodology for dust storms detection. Journal of environmental health science and engineering, 12: 20.
32. Schatzel, S.J. 2009. Identifying sources of respirable quartz and silica dust in underground coal mines in southern West Virginia, western Virginia, and eastern Kentucky. International Journal of Coal Geology, 78: 110-118.
33. Sobrino, J.A., and Y. Julien. 2013. Trend analysis of global MODIS-Terra vegetation indices and land surface temperature between 2000 and 2011. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6: 2139-2145.
34. Sokolik, I.; K. Darmenova; A. Darmenov; X. Xi; Y. Shao; B. Marticorena, and G. Bergametti 2009. Understanding the impact of changes in land-use/land-cover and atmospheric dust loading and their coupling upon climate change in the NEESPI study domain drylands. In, EGU General Assembly Conference Abstracts (p. 7419)
35. Sorek-Hamer, M.; I. Kloog; P. Koutrakis; A.W. Strawa; R. Chatfield; A. Cohen; W.L. Ridgway, and D.M. Broday. 2015. Assessment of PM 2.5 concentrations over bright surfaces using MODIS satellite observations. Remote Sensing of Environment, 163: 180-185.
36. Sun, L.; X. Zhou; J. Lu; Y.-P. Kim, and Y.-S. Chung. 2003. Climatology, trend analysis and prediction of sandstorms and their associated dustfall in China. Water, Air, & Soil Pollution: Focus, 3: 41-50.
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38. Verbesselt, J.; R. Hyndman; G. Newnham, and D. Culvenor. 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114: 106-115.
39. Wang, H.; Q. Li; Z. Gao; B. Sun, and X. Du 2014. Assessment of land degradation using time series trends analysis of vegetation indictors in Beijing-Tianjin dust and sandstorm source region. In, Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International (pp. 753-756): IEEE
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42. Zhao, S.; D. Yin, and J. Qu. 2015. Identifying sources of dust based on CALIPSO, MODIS satellite data and backward trajectory model. Atmospheric Pollution Research, 6: 36-44.
43. Alkhatib, M.Q.; S.D. Cabrera, and T.E. Gill 2012. Automated detection of dust clouds and sources in NOAA-AVHRR satellite imagery. In, Image Analysis and Interpretation (SSIAI), 2012 IEEE Southwest Symposium on (pp. 97-100): IEEE
44. Ashrafi, K.; M. Shafiepour-Motlagh; A. Aslemand, and S. Ghader. 2014. Dust storm simulation over Iran using HYSPLIT. Journal of environmental health science and engineering, 12: 9.
45. Boardman, J. 2006. Soil erosion science: Reflections on the limitations of current approaches. Catena, 68: 73-86.
46. Cao, H.; F. Amiraslani; J. Liu, and N. Zhou. 2015. Identification of dust storm source areas in West Asia using multiple environmental datasets. Science of the Total Environment, 502: 224-235.
47. Clark, M.L.; T.M. Aide; H.R. Grau, and G. Riner. 2010. A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America. Remote Sensing of Environment, 114: 2816-2832.
48. De Jeu, R.; W. Wagner; T. Holmes; A. Dolman; N. Van De Giesen, and J. Friesen. 2008. Global soil moisture patterns observed by space borne microwave radiometers and scatterometers. Surveys in Geophysics, 29: 399-420.
49. Dong, Z.; X. Yu; X. Li, and J. Dai. 2013. Analysis of variation trends and causes of aerosol optical depth in Shaanxi Province using MODIS data. Meteorological Institute of Shaanxi Province-China
50. Dubovyk, O.; T. Landmann; B.F. Erasmus; A. Tewes, and J. Schellberg. 2015. Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa. International Journal of Applied Earth Observation and Geoinformation, 38: 175-183.
51. Eastman, J. 2015a. TerrSet Tutorial. Clark Labs, Clark University: Worcester, MA, United States
52. Eastman, J.R. 2015b. TerrSet manual. Accessed in TerrSet version, 18: 1-390.
53. Fu, G.; Z. Shen; X. Zhang; P. Shi; Y. Zhang, and J. Wu. 2011. Estimating air temperature of an alpine meadow on the Northern Tibetan Plateau using MODIS land surface temperature. Acta Ecologica Sinica, 31: 8-13.
54. Gerivani, H.; G.R. Lashkaripour; M. Ghafoori, and N. Jalali. 2011. The source of dust storm in Iran: a case study based on geological information and rainfall data. Carpathian Journal of Earth and Environmental Sciences, 6
55. Gruhier, C.; P.d. Rosnay; S. Hasenauer; T. Holmes; R.d. Jeu; Y. Kerr; E. Mougin; E. Njoku; F. Timouk, and W. Wagner. 2010. Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site. Hydrology and Earth System Sciences, 14: 141-156.
56. Holmes, T.; R. De Jeu; M. Owe, and A. Dolman. 2009. Land surface temperature from Ka band (37 GHz) passive microwave observations. Journal of Geophysical Research: Atmospheres, 114
57. Ibrahim, Y.Z.; H. Balzter; J. Kaduk, and C.J. Tucker. 2015. Land degradation assessment using residual trend analysis of GIMMS NDVI3g, soil moisture and rainfall in Sub-Saharan West Africa from 1982 to 2012. Remote Sensing, 7: 5471-5494.
58. Jackson, T.J.; R. Bindlish; L. SSAI; M.E. Wood, and H. Gao 2002. Soil moisture mapping the southern US with the TRMM microwave imager: pathfinder study. In, Proceedings of the Hydrology Conference
59. Kim, D.; M. Chin; H. Bian; Q. Tan; M.E. Brown; T. Zheng; R. You; T. Diehl; P. Ginoux, and T. Kucsera. 2013. The effect of the dynamic surface bareness on dust source function, emission, and distribution. Journal of Geophysical Research: Atmospheres, 118: 871-886.
60. Kimura, R. 2012. Effect of the strong wind and land cover in dust source regions on the Asian dust event over Japan from 2000 to 2011. SOLA, 8: 77-80.
61. Klingmüller, K.; A. Pozzer; S. Metzger; G.L. Stenchikov, and J. Lelieveld. 2016. Aerosol optical depth trend over the Middle East. Atmospheric Chemistry and Physics, 16: 5063-5073.
62. Kuenzer, C.; Z. Bartalis; M. Schmidt; D. Zhao, and W. Wagner. 2008. Trend analyses of a global soil moisture time series derived from ERS-1/-2 scatterometer data: floods, droughts and long term changes. Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci, 37: 13.
63. Levy, R., and C. Hsu 2015. MODIS Atmosphere L2 Aerosol Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA. In
64. Lhermitte, S.; J. Verbesselt; W.W. Verstraeten, and P. Coppin. 2011. A comparison of time series similarity measures for classification and change detection of ecosystem dynamics. Remote Sensing of Environment, 115: 3129-3152.
65. Muhs, D.R.; J.M. Prospero; M.C. Baddock, and T.E. Gill. (2014). Identifying sources of aeolian mineral dust: Present and past. Mineral Dust (pp. 51-74): Springer.
66. Owe, M.; R. de Jeu, and T. Holmes. 2008. Multisensor historical climatology of satellite‐derived global land surface moisture. Journal of Geophysical Research: Earth Surface, 113
67. Palmer, M.A.; J.B. Zedler, and D.A. Falk. 2016. Foundations of restoration ecology. Island Press,
68. Parinussa, R.M.; A.G. Meesters; Y.Y. Liu; W. Dorigo; W. Wagner, and R.A. de Jeu. 2011. Error estimates for near-real-time satellite soil moisture as derived from the land parameter retrieval model. IEEE Geoscience and Remote Sensing Letters, 8: 779-783.
69. Pozzer, A.; A. de Meij; J. Yoon; H. Tost; A. Georgoulias, and M. Astitha. 2015. AOD trends during 2001–2010 from observations and model simulations. Atmospheric Chemistry and Physics, 15: 5521-5535.
70. Quintano, C.; A. Fernández-Manso; A. Stein, and W. Bijker. 2011. Estimation of area burned by forest fires in Mediterranean countries: A remote sensing data mining perspective. Forest Ecology and Management, 262: 1597-1607.
71. Samadi, M.; A.D. Boloorani; S.K. Alavipanah; H. Mohamadi, and M.S. Najafi. 2014. Global dust Detection Index (GDDI); a new remotely sensed methodology for dust storms detection. Journal of environmental health science and engineering, 12: 20.
72. Schatzel, S.J. 2009. Identifying sources of respirable quartz and silica dust in underground coal mines in southern West Virginia, western Virginia, and eastern Kentucky. International Journal of Coal Geology, 78: 110-118.
73. Sobrino, J.A., and Y. Julien. 2013. Trend analysis of global MODIS-Terra vegetation indices and land surface temperature between 2000 and 2011. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6: 2139-2145.
74. Sokolik, I.; K. Darmenova; A. Darmenov; X. Xi; Y. Shao; B. Marticorena, and G. Bergametti 2009. Understanding the impact of changes in land-use/land-cover and atmospheric dust loading and their coupling upon climate change in the NEESPI study domain drylands. In, EGU General Assembly Conference Abstracts (p. 7419)
75. Sorek-Hamer, M.; I. Kloog; P. Koutrakis; A.W. Strawa; R. Chatfield; A. Cohen; W.L. Ridgway, and D.M. Broday. 2015. Assessment of PM 2.5 concentrations over bright surfaces using MODIS satellite observations. Remote Sensing of Environment, 163: 180-185.
76. Sun, L.; X. Zhou; J. Lu; Y.-P. Kim, and Y.-S. Chung. 2003. Climatology, trend analysis and prediction of sandstorms and their associated dustfall in China. Water, Air, & Soil Pollution: Focus, 3: 41-50.
77. Taramelli, A.; M. Pasqui; J. Barbour; D. Kirschbaum; L. Bottai; C. Busillo; F. Calastrini; F. Guarnieri, and C. Small. 2013. Spatial and temporal dust source variability in northern China identified using advanced remote sensing analysis. Earth Surface Processes and Landforms, 38: 793-809.
78. Verbesselt, J.; R. Hyndman; G. Newnham, and D. Culvenor. 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114: 106-115.
79. Wang, H.; Q. Li; Z. Gao; B. Sun, and X. Du 2014. Assessment of land degradation using time series trends analysis of vegetation indictors in Beijing-Tianjin dust and sandstorm source region. In, Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International (pp. 753-756): IEEE
80. Wang, S.; X. Mo; S. Liu; Z. Lin, and S. Hu. 2016. Validation and trend analysis of ECV soil moisture data on cropland in North China Plain during 1981–2010. International Journal of Applied Earth Observation and Geoinformation, 48: 110-121.
81. Yerramilli, A.; V.B.R. Dodla; V.S. Challa; L. Myles; W.R. Pendergrass; C.A. Vogel; H.P. Dasari; F. Tuluri; J.M. Baham, and R.L. Hughes. 2012. An integrated WRF/HYSPLIT modeling approach for the assessment of PM2. 5 source regions over the Mississippi Gulf Coast region. Air Quality, Atmosphere & Health, 5: 401-412.
82. Zhao, S.; D. Yin, and J. Qu. 2015. Identifying sources of dust based on CALIPSO, MODIS satellite data and backward trajectory model. Atmospheric Pollution Research, 6: 36-44.