تحلیل فضایی مخاطرات محیطی

تحلیل فضایی مخاطرات محیطی

بهره گیری از سری زمانی داده های ماهواره ای به منظور اعتبارسنجی کانون های شناسایی شده تولید گرد و غبار استان البرز

نویسندگان
بهزاد رایگانی
زهرا خیراندیش
ساطمان حفاظت محیط زیست
چکیده
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.
کلیدواژه‌ها
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

عنوان مقاله English

Utilization of time series of satellite data in order to validate the identified dust storm sources in Alborz province

چکیده English

Dust is one of the common processes of arid and semiarid regions that its occurrence frequencies has increased in recent years in Iran. The proper identification of sand and dust storms (SDS) is particular importance due to its impact on the environment and human health. So far, several methods for identifying these sources have been proposed such as methods based on field studies and geomorphologic studies, as well as methods on the basis of a numerical model of air flow simulation. Therefore, identifying the process of land cover changes and changes in suspended particles in the air can help to identify the correct sources of sand and dust. Also, to manage the reduction of dust, it will be very useful to analyze the trend of changes in sand and dust sources. This data can provide some useful information to the decision makers about the future occurrence of sand and dust storm and control it. Satellite-based remote sensing is an appropriate tool for examining changes in the surface conditions of the earth over time. Satellite sensors are well suited for this purpose because of the fact that constant measurements can be repeated on a fix spatial scale. Therefore, in this research, we have tried to test different remotely sensed data time series for validation of the identified SDS sources using the latest remote sensing techniques and their integration with other information.

The aim of this study is to validate the identified dust generation sources in Alborz province using time series of satellite data and meteorological stations data. In first step, OLI data of Landsat 8 during the years 2013 through 2015 were used to make maps of vegetation cover, soil moisture and land cover sensibility to wind erosion. These maps were combined with geology and roughness indices by multi-criteria evaluation method to obtain a map of sand & dust source potential areas. Also, based on the location of the intersection of the air flow with the surface of the earth and the application of masks of non-wind erodible areas on them, probable sand and dust sources were identified. These regions were integrated with the map of sand & dust source potential areas using the MCE method (WLC) and based on a stratified random sampling plan, susceptible sites of sand & dust sources were identified. Then in this research, the time series of satellite data and weather stations data were used and the trend of vegetation, soil moisture and surface temperature at the location of identified areas during a 15-year period were monitored. Product of LPRM_TMI_DY_SOILM3 from TMI sensor, data of 16-day vegetation, 8-day land surface temperature and data of aerosol optical depth from MODIS sensor were received. Also ground- based data of dust from synoptic and air pollution monitoring stations were received. Changes Trend analysis of soil moisture, temperature and vegetation cover was done during the period. Also aerosol optical depth in dust events with high concentration was evaluated for possible sources. In addition, the areas with higher dust optical depth than other areas were identified during the period. Finally, identified sources was validated using ground- based data of dust.

The result of trend analysis showed a significant decrease in vegetation, soil moisture and land surface temperature at the place of possible dust sources during the studied period. Decreasing temperature in the southern part of Alborz Province and west of Tehran province was associated with higher frequency of dust in the area that shows why dust events has high frequency. Study of time series of aerosol optical depth data showed that concentration of dust is at or near the detected sources and the high concentration in this area is indicating identified areas are accurate. Checking optical depth in the event of high concentration and checking concurrent of air direction showed the detected sources has been correctly identified. Also Integration of dust information of synoptic and air pollution monitoring stations with the wind direction confirmed the high accuracy of identified dust sources.

Overall, findings showed the ability time series of remote sensing data to validate dust storm sources. The results of the analysis of the time series of the satellite remote sensing data showed that the surface temperature as an important climatic parameter can be well used in the identification and validation of sand & dust sources. Based on the results of this analysis in areas where the frequency of sand & dust storm events is high, there is a significant decrease in the surface temperature. This is particularly evident in the annual maximum surface temperature in the southwestern part of Iran, an area that is considered to be the predominant trajectory of sand & dust storm.


کلیدواژه‌ها English

TMI
MODIS
AOD
Trend of Remotely Sensed Time Series Data
TerrSet
Earth Trend Modeler
اداره کل حفاظت محیط زیست استان البرز، وضعیت محیط زیست استان البرز، تهدیدها، فرصت‌ها و راهکارهای پیشنهادی. 1394، قابل دسترس از:http://alborz.doe.ir/portal/File/ShowFile.aspx?ID=0d70ec13-80c2-48f7-ac7a-3e3027f5c2e9
اداره کل هواشناسی استان البرز، نگرشی بر ویژگی‌های اقلیمی استان البرز. 1394، قابل دسترس از: http://www.alborz-met.ir/Dorsapax/Data/Sub_0/File/pahnehbandy.p%20df.pdf
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Eastman, J. 2015a. TerrSet Tutorial. Clark Labs, Clark University: Worcester, MA, United States
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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
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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.
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.
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.
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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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.
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
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.
Boardman, J. 2006. Soil erosion science: Reflections on the limitations of current approaches. Catena, 68: 73-86.
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.
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.
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.
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
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.
Eastman, J. 2015a. TerrSet Tutorial. Clark Labs, Clark University: Worcester, MA, United States
Eastman, J.R. 2015b. TerrSet manual. Accessed in TerrSet version, 18: 1-390.
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.
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
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.
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
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.
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
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.
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.
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.
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.
Levy, R., and C. Hsu 2015. MODIS Atmosphere L2 Aerosol Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA. In
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.
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.
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
Palmer, M.A.; J.B. Zedler, and D.A. Falk. 2016. Foundations of restoration ecology. Island Press,
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.
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.
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.
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.
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.
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.
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)
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.
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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