بررسی نوسانات درجه حرارت سطح زمین به کمک سری‌های زمانی تصاویر سنجنده مودیس از ماهواره Terra و Aqua تحت شرایط اقلیمی (منطقه مورد مطالعه: استان تهران)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، گروه ارزیابی و مخاطرات محیط زیست، پژوهشکده محیط زیست و توسعه پایدار، تهران

2 فارغ التحصیل کارشناسی ارشد علوم و مهندسی محیط زیست، ارزیابی و آمایش سرزمین، دانشکده محیط زیست، کرج

3 دانشیار، گروه محیط زیست طبیعی و تنوع زیستی، دانشکده محیط زیست، کرج

4 دانشیار، گروه تنوع زیستی و ایمنی زیستی، پژوهشکده محیط زیست و توسعه پایدار، تهران

چکیده

تجزیه و تحلیل داده‌های سری زمانی درجه حرارت سطح زمین (LST)، به طور قابل توجهی درک ما را از تغییرات بلندمدت اقلیمی بهبود می‌بخشد. هدف از تحقیق حاضر بررسی روند تغییرات دما در استان تهران با استفاده از تصاویر سری زمانی سنجنده MODIS ماهواره Terra (از سال 2002) و ماهواره Aqua (از سال 2003) تا پایان سال 2018 می‌باشد. روند تغییرات دمایی تصاویر دریافت شده با استفاده از تحلیلگر ETM نرم‌افزار TerrSET و معنی‌داری آنها با روش‌های پارامتری ضریب همبستگی و ناپارامتری آزمون معنی‌داری من-کندال (در سطح 1%) مشخص گردید. این روش‌ها برای شناسایی روند تغییرات متغیر‌های حداکثر، میانه و حداقل دما به صورت ماهانه و سالانه استفاده شد. سپس اعتبارسنجی تصاویر ماهواره‌ای به کمک تحلیل روند داده‌های ایستگاه‌های سینوپتیک استان تهران با استفاده از روش‌های رگرسیونی صورت پذیرفت. همچنین روند تغییرات دمایی بدست آمده از ماهواره ترا با روند تغییرات دمایی بدست آمده از ماهواره آکوآ توسط شاخص کاپا و سری‌های زمانی این دو ماهواره توسط آنالیز Linear Modeling و ضریب همبستگی R مقایسه شد. براساس نتایج، نشانه‌های تغییر اقلیم در استان تهران، به‌ ویژه از نظر دما، قابل مشاهده است. نتایج اعتبارسنجی نشان داد روند تغییرات تصاویر ماهواره‌ای حداقل دما شباهت 98.3 درصدی با روند تغییرات داده‌های حداقل دمای ایستگاه‌های سینوپتیک استان تهران دارد بر این اساس می‌توان گفت بین روند تغییرات تصاویر ماهواره‌ای و روند تغییرات داده‌های زمینی هم‌خوانی قابل قبولی وجود دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Investigation of Land Surface Temperature Fluctuations Using Time Series of MODIS Sensor Images from Terra and Aqua Satellites Under Climatic Conditions (Study Area: Tehran Province)

نویسندگان [English]

  • Behzad Rayegani 1
  • Armaghan Ardalani 2
  • Hamid Goshtasb 3
  • Bagher Nezami 4
  • Ali Jahani 1
1 College of Environment
2 College of Environment
3 college of Environment
4 College of Environment
چکیده [English]

Analysis of Land Surface Temperature (LST) time series data significantly improves our understanding of long-term climate change. Since about two-thirds of Iran is covered by arid and semi-arid climates; Therefore, it is important to study temperature fluctuations in the country in order to determine and predict the resulting crises. The purpose of this study was to investigate the trend of temperature changes in Tehran province because in recent years we are facing with overpopulation, which is one of the issues affecting climate change in Tehran province because overpopulation increases consumption of fossil fuels, greenhouse gases emissions and As a result, the temperature rises. In this essay, the trend of temperature changes was investigated using the time series of MODIS sensor images of Terra satellite (since 2002) and the time series of MODIS sensor images of Aqua satellite (since 2003) until the end of 2018. The trend of temperature changes of the received images was determined using TerrSET software ETM analyzer and their significance was determined by parametric methods of correlation coefficient and non-parametric Mann-Kendall significance test (at 1% level). These methods were used to identify the trend of changes in maximum, medium and minimum temperature variables on a monthly and annual basis. The trend estimate of these changes was converted to degrees Celsius by ordinary least squares analysis (OLS). In addition, the trend of minimum temperature data changes of synoptic stations in Tehran province was obtained using regression methods and was compared and validated with trend maps of minimum temperature changes obtained from satellite images. In order to compare the performance of MODIS sensor in Terra and Aqua satellites, the time series of MODIS sensor images of Terra satellite were compared with the time series of MODIS sensor images of Aqua satellite by Linear Modeling analysis and R correlation coefficient. Also, the classified areas of significant increase, decrease and no significant trend obtained from the MODIS satellite sensor were compared with the classified areas obtained from the Aqua satellite sensor by the Kappa index. In addition, in order to identify the ecological effects of the trend of surface temperature changes, the shape file of protected areas and national parks of Tehran province was added to the maps of the trend of temperature changes in Arc Gis software. According to the results, the maximum, average and minimum monthly temperatures during the years under study were almost without a significant trend However, a significant increase in surface temperature was observed in the study of annual variables in most parts of Tehran province, especially in Tehran city, desert and plain areas. Also, the irrational or sudden increase in temperature in an area has reasons such as uncontrolled construction, proximity to industrial towns and drying of rivers, lakes, wetlands, etc. Comparison of the minimum temperature trend of satellite images with the minimum temperature trend of synoptic stations datas in Tehran province showed 98.3% similarity. Based on this, it can be said that there is an acceptable agreement between the trend of changes in satellite images and the trend of changes in terrestrial data, and because the degree of concordance of the results obtained from the Aqua satellite is more similar to the data of the synoptic stations than the results obtained from the Terra satellite, it is therefore recommended to study the temperature changes of the Aqua satellite. Comparison of MODIS sensor images time series of Terra satellite with Aqua MODIS sensor time series in most parts of Tehran province showed a high correlation coefficient (99% similarity). Except Tehran city and the east of Tehran province (Firoozkooh city) which can be the difference due to housing, human interference, pollution due to higher population density and agricultural use of land and harvest. Also, kappa index is below 0.7, which indicates the lack of similarity in the classification of significant areas in the whole province by the MODIS sensor between the two satellites. Based on this, it can be said that although MODIS sensor is recommended for long-term changes and better understanding of the change process but there seems to be uncertainty in the interpretation of the data obtained from the classification of MODIS sensor images. Therefore, care must be taken in choosing the type of satellite for the MODIS sensor. Based on the results obtained from identifying the ecological effects of temperature changes, Jajroud Protected Area has a trend of more temperature changes than other protected areas in Tehran province. Considering the trend of rising land surface temperature and its role in increasing evapotranspiration, we should look for solutions to better manage water resources and improve its exploitation methods, especially in agriculture and industry in Tehran province.

کلیدواژه‌ها [English]

  • Mann-Kendall significance test
  • TerrSET Software
  • Synoptic Station
  • Kappa Index
  • Correlation Coefficient R
  1. منابع

    1. Ansari, M., Noori, G., & Fotohi, S. (2017). Investigation of temperature precipitation and flow trend using nonparametric mankendall (Case Study: Kaju river in Sistan and Baluchestan). Journal of Watershed Management Research, 7(14), 152-158. https://www.sid.ir/en/journal/ViewPaper.aspx?id=550980
    2. Asfaw, A. (2018). "Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia (case study: Woleka sub-basin)." Weather and Climate Extremes 19: 29-41.
    3. Azizi, G., Roushani, M. (2008). Using Mann-Kendall Test to Recognise of Climate Change in Caspian Sea Southern Coasts. Geographical Research Quarterly, 40(64), -. https://www.sid.ir/en/journal/ViewPaper.aspx?ID=113380
    4. T.S. (2003), Regional Climate Change: T rends Analysis of Temperature and Precipitation Series at Canadian Sites, Canadian Journal of Agricultural Economics, and 48(1): 27-38.
    5. Dettinger, M.D., Cayan, D.R., Meyer, M., & Jeton A.E. (2004). Simulated hydrologic responses to climate variations and change in the Merced, Carson and American River basins, Sierra Nevada, California, 1900- 2099. Climate Change, 62: 283-31
    6. Domonkos, P. (2003). Long term changes in observed temperature and precipitation series 1901 - 1998 from Hungary and their relations to larger scale changes.Theoretical and Applied Climatology, Volume 75, Numbers 3-4 / September, pp 131-147.
    7. Domroes, M., A. El-Tantawi. (2005). Recent temporal and spatial temperature changes in Egypt. International Journal of Climatology, 25(1), January, pp 51-63.
    8. Dracup, J.A., Vicuna, S. (2005). An Overview of Hydrology and Water Resources Studies on Climat Change: the California Experience. Proc EWRI2005 Impacts of Global Climate Change.
    9. Eastman J.R., (2015). TerrSet TUTORIAL Clark University
    10. Ghasami, S. (2017). Analysis of changes Using the method of Mann-Kendall (Case Study of Four townships of Chaharmahal and Bakhtiari Province). , 10(37), 149-166.
    11. Heydari Alamdarloo, E., Zehtabian, G., Khosravi, H., Raygani, B., Khalighi, S., & Taghizadeh, R. (2019). Investigation on the Climatic Parameters Fluctuation Using Data from the The European Centre for Medium-Range Weather Forecasts (Case study: Shirkouh Region - Yazd Province), Iranian Journal of Watershed Management Science and Engineering, 13(46), 22-31. magiran.com/p2046178
    12. Hansen je., & lebedoff. (1990). Sun and dust versus the greenhouse gases: an assessment of their relative roles in global climate.
    13. Intergovernmental Panel on Climate Change (IPCC). 2001. In: Houghton, J.T. et al. (Eds.), The Third Assessment Report of Working
    14. Kendall M, 1975. Rank Correlation Methods. London: Charles Griffin.
    15. Kermani, F., Rayegani, B., Nezami, B., Goshtasb, H., & Khosravi, H. (2018). Assessing the vegetation trends in arid and semi-arid regions (Case study: Touran Protected Area), Desert Ecosystem Engineering Journal, 6(17), 1-14. magiran.com/p1804084
    16. Khoshravesh, M., Mirnaseri, M., & Mahsa Pesarakloo, M. (2018). Change detection of precipitation trend of northern part of Iran using mann- kendall non-parametric test, Journal of Watershed Management Research, 8(16), 223-231. magiran.com/p1801187
    17. Knight, JH. Minasny, B. McBratney, AB., Koen, TB., & Murphy, BW. 2018. Soil temperature increase in eastern Australia for the past 50years. Geoderma. 313: 241-249.
    18. Kumar, P. V., Bindi, M., Crisci, A., & Maracchi, G. ( 2005), Detection of variations in air temperature at different time scales the period 1889-1998 at Firenze, Italy. Climatic change, 72, 1-2 / September, pp 123-150.
    19. Lettenmaier, D. P., Wood, E. F., & Wallis, J. R. (1994). Hydro –Climatological Trends in the Continental United States, 1948-88. Journal of Climate, 7(4), April, pp 586-607.
    20. Mahdavi, M. (2013). Applied Hydrology (2 ed., Vol. 1). University of Tehran press.
    21. Mousavi, A., Farahpour, M., Shokri, M., Solaimani, K., & Godarzi, M. (2006). Vegetation cover change during 25 years, Case of Lar Dam Basin, Iranian Journal of Range and Desert Research, 13(3), 186. magiran.com/p445067
    22. Rayegani, B. (2019). Identification of potential dust sources using remote sensing data (Case Study: Alborz Province), Journal of Natural environment hazards, 8(20), 1-19. magiran.com/p1978200
    23. Rayegani,B., Barati Ghahfarokhi, S., & Khoshnava, A. (2019). Dust & sand source identification using remotely sensed data: a comprehensive approach. Journal of Range and Watershed Management, 72(1), 83-105. magiran.com/p1997842
    24. Rayegani, B., Arzani, H., Moghadami, M., & Heydari, E. (2019). Application of remote sensing to assess climate change effects on plant productivity and phenology (Case study area: Tehran Province), Journal of Rangeland, 13(3), 450-460. magiran.com/p2040485
    25. Rayegani, B., Barati, S., Goshtasb, H., Sarkheil, H., & Ramezani, J. (2019). An effective approach to selecting the appropriate pan-sharpening method in digital change detection of natural ecosystems. Ecological Informatics, 53, 100984.
    26. Rayegani, B., Jahani, A., Satari Rad, A., & Shoghi, N. (2018). Predicting of land use changes for 2030 using remote sensing and Landsat multi-temporal images (case study: Mashhad). Town and Country Planning, 10(2), 249-269.
    27. Seleshi, Y., & Zanke, U. ( 2004). Recent changes in rainfall and rainy days in Ethiopia International Journal of Climatology, 24(8), June, pp 973-983.
    28. Sheikh, V.B., Bahremand, A., & Mooshakhian, Y. (2012). A comparison of trends in hydrologic variables in the Atrak river basin using non-parametric trend analysis tests. water and soil conservation. 2, 1-22.
    29. Tayanç, M., Im, U., Doğruel, M., & Karaca, M. (2009). Climate change in Turkey for the last half century. Climatic change, 94, 3-4/June, pp 483-502.

     

     

     

     

     

     

     

     

     

     

     

    1. Teferi, E., Uhlenbrook, S., & Bewket, W. (2015). Inter-annual and seasonal trends of vegetation condition in the Upper Blue Nile (Abay) Basin: dual-scale time series analysis. Earth System Dynamics, 6 (2).
    2. Willis, K.S. (2015). Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation 182, 233-242.
    3. Zhang, H., Wang, E., Zhou, D., Luo, Z., & Zhang, Z. (2016). Rising soil temperature in China and its potential ecological impact. Scientific reports. 6:
    4. Zohrabi, N., Massah Bavani, A., Goodarzi, E., & Heidarnejad, M. (2016). Identify trend in the annual temperature and precipitation in Karkheh river basin, Journal of Wetland Ecobiology, 8(2), 5-22. magiran.com/p1661329 1-8.