تحلیل و پراکنش سالانه و فصلی دمای سطح زمین در طبقات ارتفاعی ایران با استفاده از داده‌های MODIS

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه جغرافیا، دانشکده علوم انسانی و اجتماعی، دانشگاه یزد، یزد، ایران.

چکیده

پس از انقلاب صنعتی، به‌دلیل استفاده بیش از حد از سوخت‌های فسیلی، تغییر کاربری اراضی و افزایش جمعیت و به‌تبع آن گسترش روزافزون فعالیت‌های صنعتی برای تأمین نیاز و رفاه جمعیت کره زمین، تغییرات قابل‌توجهی در اقلیم کره زمین به‌وجود آمده ‌است که بارزترین آن افزایش متوسط دمای کره‌زمین، افزایش سطح آب و دمای سطح اقیانوس‌ها می‌باشد. از آنجایی‌که این تغییرات می‌توانند تهدید جدی برای پایداری محیط‌زیست به شمار روند، لذا پژوهش حاضر به بررسی سری‌ زمانی (2022-2001) دمای روز سطح زمین (LST Day) در طبقات ارتفاعی ایران (5600-40-متر) با استفاده از محصولات 8 روزه سنجنده MODIS. MOD11A2.061 پرداخته است. نتایج نشان داد طبقه ارتفاعی 2 (800-400 متر) با میانگین دمای 4/38 درجه سلسیوس به‌عنوان گرم‌ترین و طبقه 10 (5600-3600 متر) با میانگین دمای 8/9 درجه سلسیوس به‌عنوان سردترین طبقه ارتفاعی طی دوره موردمطالعه شناخته شده است. همچنین کویر لوت مرکزی، که کمترین ارتفاع را به خود اختصاص داده است، در همه فصول به‌عنوان گرم‌ترین منطقه کشور شناخته شده‌است. بالاترین میانگین دما در سال 2021 با دمای 28 درجه سلسیوس و کمترین میانگین دما در سال‌های 2007، 2009، 2011 و 2012 با میانگین دمای 25 درجه سلسیوس ثبت شد. روند کلی میانگین دما، افزایش دما را در مقیاس سالانه و فصلی به استثنای فصل پاییز نشان می‌دهد. همچنین در همه طبقات افزایش حداقل دما و کاهش تنوع فضایی دما در منطقه موردمطالعه مشاهده می‌شود که عوامل طبیعی و انسانی می‌توانند نقش داشته باشند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Analysis and distribution of annual and seasonal land surface temperature in Iran's elevation floors using MODIS data

نویسندگان [English]

  • Fatemeh Shakiba
  • Iman Rousta
  • Ahmad Mazidi
Department of Geography, Faculty of Humanities and Social Sciences, Yazd University, Yazd, Iran.
چکیده [English]

Climate change is the term used to describe the significant and ongoing variations occurring in the Earth's climate, such as changes in temperature, precipitation, and wind patterns. These changes have been increasingly observed in recent decades, and they are mainly caused by human activities, particularly the emission of greenhouse gases that trap heat in the atmosphere, leading to global warming. An increase in the Earth's surface temperature is a crucial factor in climate change because it affects the exchange of energy between the Earth's surface and the atmosphere, and this, in turn, impacts weather patterns and systems. To monitor and study temperature changes, scientists often use thermal infrared remote sensing data, which enables the remote measurement of the Earth's surface temperature and provides valuable information for understanding interactions between the hydrosphere, biosphere, and atmosphere. The consequences of climate change are far-reaching and pose significant challenges to human security worldwide. Changes in temperature and rainfall patterns can lead to crop loss, threaten food security, exacerbate water scarcity, and contribute to the frequency and severity of natural disasters. To mitigate the harmful effects of climate change, it is necessary to take measures to reduce greenhouse gas emissions and adapt to climate change, such as adopting new agricultural technologies and practices, improving water management, and so on.
To begin with, the elevation floors data were extracted from the digital model map of Iran. In the next step, the seasonal and annual time series of land surface temperature day were analyzed for the entire study area, as well as for each elevation floors individually. These analyses were conducted using MODIS images, for the statistical period of 2001-2022.In order to analyze temperature changes in each altitude class and to achieve more accurate and reliable results, the trends of parameters such as Min, Max, Mean, STD, Variety, Majority and Minority were calculated. The 8-day MODIS.MOD11A2.061 images are the main data source of the present study and are obtained for free from the LP DAAC system (https://lpdaac.usgs.gov) and they include a total of 966 images, each year having 46 images. All calculations and preparation of maps have been done through Arc GIS, GIS Pro Arc and Excel software.
Studies on the climate of Iran have revealed that the highest average temperature is during spring and summer. This study, which was conducted based on statistical surveys of temperature on an annual and seasonal time scale, showed an increasing trend of temperature during these periods. Conversely, the observed temperature has decreased in the autumn season. Moreover, the minimum temperature has increased and the difference between the minimum and maximum temperature has decreased, leading to a reduction in spatial temperature variation in the area under study. The southwestern regions of Iran, including the Khuzestan province, and the central Lut desert, are the hottest parts of the country during the summer season. During other months of the year, the hottest areas are found in the Lut desert, coasts of the Oman Sea, the provinces of Sistan and Baluchistan and Hormozgan, which have the lowest altitude above sea level. On the other hand, higher altitudes such as Damavand and Zagros mountains have been observed to have the lowest average temperatures. Elevation floor 2 has an annual average temperature of 38.4 degrees Celsius, while elevation floor 10 has the lowest annual average temperature of 9.8 degrees Celsius. In general, areas with higher altitudes have lower temperatures than those with lower altitudes in all seasons of the year.
The highest annual average was recorded in 2021 with a temperature of 28 degrees Celsius. While the years 2007, 2009, 2011 and 2012 have the lowest average temperature with a temperature of 25 degrees Celsius. The 2nd floor with a height of 400-800 meters has the highest temperature. This class is mostly focused on the catchment area of the Central Plateau, which, including the vast deserts of Dasht Kavir and Kavir Lut, has a hot and dry climate with the highest average temperature in spring and summer. In parts of the first floor of Koirlu and parts of the coastal areas of the Oman Sea and the Persian Gulf, due to the role of the specific heat of water, the highest average temperature has been allocated in the autumn and winter seasons. On the other hand, the 10th floor, which includes the heights of Alborz and Zagros, has the lowest average temperature.

کلیدواژه‌ها [English]

  • Elevation Floors
  • Land Surface Temperature
  • MODIS
  • Temperature anomaly

مراجع

احمدی، م.؛ داداشی رودباری، ع. ع. و احمدی، ح. (1397). واکاوی دمای روزهنگام سطح زمین ایران مبتنی‌بر برون‌داد سنجندهMODIS ، فصلنامه علوم محیطی، 16 (1)، 47-68.
احمدی، م.؛ میرزایی، ز. و داداشی رودباری، ع. ع. (1400). بررسی پراکنش فصلی و روند بی‏‌هنجاری دمای سطح زمین روز و شب ایران با استفاده از داده‏‌های سنجنده MODIS، مجله پژوهش‌های جغرافیای طبیعی، 53(3)، 351-364.
علیجانی، ب. (1374). آب‌وهوای ایران، تهران: انتشارات دانشگاه پیام نور.
قادری، آ.؛ عزیزی، ز. و عبدالهی، خ. (1401). ارزیابی اثرات زیست محیطی احداث سد کارون 4 بر پارامترهای اقلیمی منطقه با استفاده از سنجش از دور، فصلنامه سد و نیروگاه برق آبی ایران، 9 (32)، 1-10.
کاکه ممی، آ.؛ قربانی، ا.؛ اصغری سراسکانرود، ص.؛ قلعه، ا. و غفاری، س. (1399). بررسی رابطه تغییرات کاربری اراضی و پوشش گیاهی با دمای سطح زمین در شهرستان نمین، مجله سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 11 (2)، 27-48.
کاویانی، ع.؛ سهرابی، ت. و دانش‌کارآراسته، پ. (1392). تخمین دمای سطح زمین با استفاده از شاخص اختلاف نرمال شده (NDVI) در تصاویر سنجنده‌های MODIS وLandsat ETM+، مجله هواشناسی کشاورزی، 1(1)، 14-25.
مرادی، م.؛ صلاحی، ب. و مسعودیان، س. ا. (1395)، بررسی شیب دمای سطح زمین در ایران با داده‌های روزهنگام MODIS، مجله پژوهش‌های جغرافیای طبیعی، 48 (4)، 517-532.
Aw, J., & Kleeman, M. J. (2003). Evaluating the first‐order effect of intraannual temperature variability on urban air pollution. Journal of Geophysical Research: Atmospheres, 108(D12).
Bounoua, L., Nigro, J., Thome, K., Zhang, P., Fathi, N., & Lachir, A. (2018). A method for mapping future urbanization in the United States. Urban Science, 2(2), 40.
Bartkowiak, P., Castelli, M., & Notarnicola, C. (2019). Downscaling land surface temperature from MODIS dataset with random forest approach over alpine vegetated areas. Remote Sensing, 11(11), 1319.
Czajkowski, K. P., Goward, S. N., Stadler, S. J., & Walz, A. (2000). Thermal remote sensing of near surface environmental variables: application over the Oklahoma Mesonet. The Professional Geographer, 52(2), 345-357.
Chopping, M., Moisen, G. G., Su, L., Laliberte, A., Rango, A., Martonchik, J. V., & Peters, D. P. (2008). Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer. Remote sensing of Environment, 112(5), 2051-2063.
DeVisser, M. H., Messina, J. P., Moore, N. J., Lusch, D. P., & Maitima, J. (2010). A dynamic species distribution model of Glossina subgenus Morsitans: The identification of tsetse reservoirs and refugia. Ecosphere, 1(1), 1-21.
Duan, S.-B., Li, Z.-L., Tang, B.-H., Wu, H., & Tang, R. (2014). Generation of a time-consistent land surface temperature product from MODIS data. Remote sensing of Environment, 140, 339-349.
Ebrahimi Khusfi, Z., Roustaei, F., Ebrahimi Khusfi, M., & Naghavi, S. (2020). Investigation of the relationship between dust storm index, climatic parameters, and normalized difference vegetation index using the ridge regression method in arid regions of Central Iran. Arid land research and management, 34(3), 239-263.
Ebrahimi-Khusfi, Z., & Soleimani Sardoo, M. (2021). Recent changes in physical properties of the land surface and their effects on dust events in different climatic regions of Iran. Arabian Journal of Geosciences, 14(4), 287.
Futcher, J. A., Kershaw, T., & Mills, G. (2013). Urban form and function as building performance parameters. Building and environment, 62, 112-123.
Gulersoy, A. (2014). Seferihisar’da arazi kullanımının zamansal değişimi (1984-2010) ve ideal arazi kullanımı için öneriler. Süleyman Demirel Üniversitesi Fen-Edebiyat Fakültesi Sosyal Bilimler Dergisi, (31), 155-180.
Ghafarian Malamiri, H. R., Rousta, I., Olafsson, H., Zare, H., & Zhang, H. (2018). Gap-filling of MODIS time series land surface temperature (LST) products using singular spectrum analysis (SSA). Atmosphere, 9(9), 334.
Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J., Dai, X., Maskell, K., & Johnson, C. A. (2001). Climate change 2001: the scientific basis (Vol. 881). Cambridge university press Cambridge.
Huynen, M.-M., Martens, P., Schram, D., Weijenberg, M. P., & Kunst, A. E. (2001). The impact of heat waves and cold spells on mortality rates in the Dutch population. Environmental health perspectives, 109(5), 463-470.
Hereher, M. E. (2019). Estimation of monthly surface air temperatures from MODIS LST time series data: application to the deserts in the Sultanate of Oman. Environmental monitoring and assessment, 191(9), 592.
Jin, M., & Dickinson, R. E. (2010). Land surface skin temperature climatology: Benefitting from the strengths of satellite observations. Environmental research letters, 5(4), 044004.
Karl, T. R., Jones, P. D., Knight, R. W., Kukla, G., Plummer, N., Razuvayev, V., Gallo, K. P., Lindseay, J., Charlson, R. J., & Peterson, T. C. (1993). A New Perspective on Recent Global Warming: Asymmetric Trends of Daily Maximum and Minimum Temperature. Bulletin of the American Meteorological Society, 74(6), 1007-1024.
Key, J. R., Collins, J. B., Fowler, C., & Stone, R. S. (1997). High-latitude surface temperature estimates from thermal satellite data. Remote sensing of Environment, 61(2), 302-309.
Kalma, J. D., McVicar, T. R., & McCabe, M. F. (2008). Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surveys in Geophysics, 29, 421-469.
Kayet, N., Pathak, K., Chakrabarty, A., & Sahoo, S. (2016). Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Modeling Earth Systems and Environment, 2, 1-10.
Kok, J. F., Ward, D. S., Mahowald, N. M., & Evan, A. T. (2018). Global and regional importance of the direct dust-climate feedback. Nature Communications, 9(1), 241.
Lin, I., Chen, J.-P., Wong, G. T., Huang, C.-W., & Lien, C.-C. (2007). Aerosol input to the South China Sea: Results from the MODerate resolution imaging spectro-radiometer, the quick scatterometer, and the measurements of pollution in the troposphere sensor. Deep Sea Research Part II: Topical Studies in Oceanography, 54(14-15), 1589-1601.
Li, Z., Liu, X., Ma, T., Kejia, D., Zhou, Q., Yao, B., & Niu, T. (2013). Retrieval of the surface evapotranspiration patterns in the alpine grassland–wetland ecosystem applying SEBAL model in the source region of the Yellow River, China. Ecological Modelling, 270, 64-75.
Li, Z., Duan, S., Tang, B., Wu, H., Ren, H., Yan, G., Tang, R., & Leng, P. (2016). Review of methods for land surface temperature derived from thermal infrared remotely sensed data. Journal of remote sensing, 20(5), 899-920.
Lu, L., Zhang, T., Wang, T., & Zhou, X. (2018). Evaluation of collection-6 MODIS land surface temperature product using multi-year ground measurements in an arid area of Northwest China. Remote Sensing, 10(11), 1852.
Lin, L., Di, L., Zhang, C., & Guo, L. (2024). The Global Land Surface Temperature Change in the 21st Century—A Satellite Remote Sensing Based Assessment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 1756-1764.
Makowski, K., Wild, M., & Ohmura, A. (2008). Diurnal temperature range over Europe between 1950 and 2005. Atmospheric Chemistry and Physics, 8(21), 6483-6498.
Mattar, C., Franch, B., Sobrino, J., Corbari, C., Jiménez-Muñoz, J., Olivera-Guerra, L., Skokovic, D., Sória, G., Oltra-Carriò, R., & Julien, Y. (2014). Impacts of the broadband albedo on actual evapotranspiration estimated by S-SEBI model over an agricultural area. Remote sensing of Environment, 147, 23-42.
Naudts, K., Chen, Y., McGrath, M. J., Ryder, J., Valade, A., Otto, J., & Luyssaert, S. (2016). Europe’s forest management did not mitigate climate warming. Science, 351(6273), 597-600.
Nabizada, A. F., Rousta, I., Dalvi, M., Olafsson, H., Siedliska, A., Baranowski, P., & Krzyszczak, J. (2022). Spatial and temporal assessment of remotely sensed land surface temperature variability in Afghanistan during 2000–2021. Climate, 10(7), 111.
Oku, Y., Ishikawa, H., Haginoya, S., & Ma, Y. (2006). Recent trends in land surface temperature on the Tibetan Plateau. Journal of climate, 19(12), 2995-3003.
Olsson, L., Barbosa, H., Bhadwal, S., Cowie, A., Delusca, K., Flores-Renteria, D., Hermans, K., Jobbagy, E., Kurz, W., & Li, D. (2019). Land degradation: IPCC special report on climate change, desertification, land 5 degradation, sustainable land management, food security, and 6 greenhouse gas fluxes in terrestrial ecosystems. In IPCC special report on climate change, desertification, land 5 degradation, sustainable land management, food security, and 6 greenhouse gas fluxes in terrestrial ecosystems (pp. 1). Intergovernmental Panel on Climate Change (IPCC).
Öztürk, M. A., Mermut, A. R., & Celik, A. (2013). Urbanisation, Land Use, Land Degradation, and Environment. Daya Publishing House.
Peón, J., Recondo, C., & Calleja, J. F. (2014). Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature. International Journal of Remote Sensing, 35(13), 5148-5166.
Peng, X., Wu, W., Zheng, Y., Sun, J., Hu, T., & Wang, P. (2020). Correlation analysis of land surface temperature and topographic elements in Hangzhou, China. Scientific reports, 10(1), 10451.
Rousta, I., Olafsson, H., Nasserzadeh, M. H., Zhang, H., Krzyszczak, J., & Baranowski, P. (2021). Dynamics of daytime land surface temperature (LST) variabilities in the Middle East countries during 2001–2018. Pure and Applied Geophysics, 178(6), 2357-2377.
Southworth, J. (2004). An assessment of Landsat TM band 6 thermal data for analysing land cover in tropical dry forest regions. International Journal of Remote Sensing, 25(4), 689-706.
Seto, K. C., Sánchez-Rodríguez, R., & Fragkias, M. (2010). The new geography of contemporary urbanization and the environment. Annual review of environment and resources, 35, 167-194.
Stone, B., Vargo, J., & Habeeb, D. (2012). Managing climate change in cities: Will climate action plans work? Landscape and urban planning, 107(3), 263-271.
Soltani, M., Laux, P., Kunstmann, H., Stan, K., Sohrabi, M., Molanejad, M., Sabziparvar, A., Ranjbar SaadatAbadi, A., Ranjbar, F., & Rousta, I. (2016). Assessment of climate variations in temperature and precipitation extreme events over Iran. Theoretical and Applied Climatology, 126, 775-795.
Solangi, G. S., Siyal, A. A., & Siyal, P. (2019). Spatiotemporal dynamics of land surface temperature and its impact on the vegetation. Civil Engineering Journal, 5(8), 1753-1763.
Shakiba, F., Rousta, I., Mazidi, A., & Olafsson, H. (2024). Spatial and temporal variation of day and night time land surface temperature and its drivers over Iran’s watersheds using remote sensing. Earth Science Informatics, 1-21.‏
Ustrnul, Z., Woyciechowska, J., & Wypych, A. (2023). Relationships between Temperature at Surface Level and in the Troposphere over the Northern Hemisphere. Atmosphere.
Van De Kerchove, R., Lhermitte, S., Veraverbeke, S., & Goossens, R. (2013). Spatio-temporal variability in remotely sensed land surface temperature, and its relationship with physiographic variables in the Russian Altay Mountains. International Journal of Applied Earth Observation and Geoinformation, 20, 4-19.
Wan, Z. (2014). New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote sensing of Environment, 140, 36-45.
Weng, Q., Fu, P., & Gao, F. (2014). Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote sensing of Environment, 145, 55-67.
Yang, J., El-Kassaby, Y. A., & Guan, W. (2020). The effect of slope aspect on vegetation attributes in a mountainous dry valley, Southwest China. Scientific reports, 10(1), 16465.
Zhao, T., Guo, W., & Fu, C. (2008). Calibrating and evaluating reanalysis surface temperature error by topographic correction. Journal of climate, 21(6), 1440-1446.
Zhou, W., Peng, B., Shi, J., Wang, T., Dhital, Y. P., Yao, R., Yu, Y., Lei, Z., & Zhao, R. (2017). Estimating high resolution daily air temperature based on remote sensing products and climate reanalysis datasets over glacierized basins: a case study in the Langtang Valley, Nepal. Remote Sensing, 9(9), 959.
Zhang, X., Estoque, R. C., & Murayama, Y. (2017). An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables. Sustainable cities and society, 32, 557-568.
Zhang, Q., Feng, T., Wang, M., Yang, G., Lu, H., & Sun, W. (2023). A Twenty-Year Assessment of Spatiotemporal Variation of Surface Temperature in the Yangtze River Delta, China. Remote Sensing, 15(9), 2274.
فیزیک زمین و فضا
دوره 51، شماره 1
تاریخ انتشار الکترونیکی: 20 خرداد 1404
خرداد 1404
صفحه 87-106

فایل ها

هم رسانی

ارجاع به این مقاله

آمار