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Utilization of time series of satellite data in order to validate the identified dust storm sources in Alborz province. Journal of Spatial Analysis Environmental Hazards 2018; 4 (4) :1-18
URL: http://jsaeh.khu.ac.ir/article-1-2683-fa.html
URL: http://jsaeh.khu.ac.ir/article-1-2683-fa.html
رایگانی بهزاد، خیراندیش زهرا. بهره گیری از سری زمانی داده های ماهواره ای به منظور اعتبارسنجی کانون های شناسایی شده تولید گرد و غبار استان البرز. تحلیل فضایی مخاطرات محیطی. 1396; 4 (4) :1-18
1- ساطمان حفاظت محیط زیست ، bhz.ray@gamil.com
2- ساطمان حفاظت محیط زیست
2- ساطمان حفاظت محیط زیست
چکیده: (7434 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.
واژه‌های کلیدی: سنجنده تی ام آی، سنجنده مودیس، درجه حرارت سطح زمین، عمق اپتیکی هواویز مودیس، روند تغییرات سریهای زمانی دادههای دور سنجی، ترست، مدلساز روند تغییرات سطح زمین
فهرست منابع
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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.
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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)
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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.
37. 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.
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.
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41. 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.
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.
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