واکاوی جزیرة گرمایی رویة زمین در شهرهای ارومیه و تبریز و ارتباط آن با وردش‏ های پهنة آبی دریاچة ارومیه

نوع مقاله : مقاله کامل

نویسندگان

1 استاد گروه آب‏ و هواشناسی، دانشکدة منابع طبیعی، دانشگاه کردستان

2 پژوهشگر پسادکتری گروه آب‏ و هواشناسی، دانشکدة منابع طبیعی، دانشگاه کردستان

چکیده

پایش مکانی و زمانی جزیرة گرمایی شهری با داده‏ های دورسنجی رو به افزایش است و یکی از مهم ‏ترین دلایل آن ارائة اطلاعات مکانی بیشتر از دمای شهرها نسبت به داده‏ های زمینی است. هدف از این پژوهش واکاوی اثر نوع پوشش زمین در منطقة خارج از شهر بر جزیرة گرمایی شهری است. به این منظور، از دمای رویة زمین مودیس در چهار برداشت روزانه برای برآورد شدت جزیرة گرمایی شهرهای تبریز و ارومیه بهره گرفته شده است. به منظور بررسی اثر نوع پوشش حومة شهر بر شدت جزیرة گرمایی، گونه ‏های غالب پوشش زمین در اطراف این دو شهر جداگانه کندوکاو شد. همچنین، بررسی وردش‏ های زمانی گسترة دریاچة ارومیه و ارتباط آن با شدت جزیرة گرمایی از دیگر اهداف این پژوهش است. یافته‏ها نشان داد نوع پوشش حومه اثر چشم‏گیری در شدت جزیرة گرمایی و سرمایی رویه دارد. هر کدام از دو شهر مورد واکاوی با توجه به نوع و تراکم پوشش اطراف آن رفتار متفاوتی را، به ‏ویژه در وردش‏های درون‏ سالانة جزیرة گرمایی، نشان می ‏دهند. تغییر نوع پوشش حومه بر جزیرة گرمایی ارومیه اثر بیشتری نسبت به تبریز دارد. این شرایط در پیوند با تغییرات گسترة دریاچة ارومیه نیز در شهر ارومیه چشم‏گیرتر است. یافته ‏ها همچنین گویای این است که افتوخیز بیشتر دمای رویة زمین نسبت به دمای هوا جزیرة گرمایی شدیدتری را نیز برای شهرها به دست می‏ دهد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Analysis of surface urban heat island in Urmia and Tabriz cities and the relationship with water extent variation of Urmia Lake

نویسندگان [English]

  • Mohammad Darand 1
  • Masood Moradi 2
1 Department of Climatology. Faculty of Natural Resources, University of Kurdistan, Iran
2 Postdoc Researcher
چکیده [English]

Introduction:
Urban heat island monitoring with remote sensing data is increasing and one of the most important reasons is to provide more spatial information of urban temperature than terrestrial data. The heat island resulting from this data is called the surface urban heat island. Different methods can show the intensity of Surface urban heat island in a city differently. Furthermore, consider of the temporal and spatial variations in temperature can cause error in calculating urban heat island. The relationship between factors such as vegetation index, land use, altitude and meteorological factors with urban heat island has been investigated and proven in previous researches. In this regard, predicting the land surface temperature in and around the cities to simulate the intensity of the urban heat island in the coming years has been of interest to researchers because reliable predictions of the difference between urban and rural areas are essential for planning about cities. Different cities may affected by different factors depending on the climate in which they are established. Therefore, in the study of the heat island of Tabriz and Urmia, land use is investigated as a determining factor. In addition, temporal variations in the area of Lake Urmia will be studied to assess the relationship between the extent changes of this water body and the intensity of surface urban heat island in Tabriz and Urmia.
Material and Methods:
In this study, MODIS land surface temperature data (MOD11A1) in tile No. h21, v06 has been used to investigate the urban heat island. This tile covers northwestern Iran. The common time series used in MODIS Terra and Aqua is from 2003 to 2019. Terra and Aqua each monitor the entire earth twice a day. In this study, all four observations have been used in order to evaluate the diurnal variations of the urban heat island. Second MODIS data that used in this study is the land cover type (MCD12Q1). To identify the types of land cover, the FAO Land Cover Classification System has been selected among the existing classification layers, which has been generated by applying the supervised classification method to the MODIS reflectance data. Another MODIS product has been used to study the changes in the area of Lake Urmia. This product provides a time series of the world's lakes extent, depth and reservoir variations. The data were obtained from the detection of water and land pixels using a machine-learning algorithm.
Urban area and type of pervasive land cover around the city has been obtained using MODIS land cover type data. The pixels that belonged to a specific land cover in more than 75% of the study period (temporal frequency of land use species) were considered as the representative pixels of that land cover. To determine the urban and rural area, an area equal to the size of the urban extent around it has been selected as rural area. The land covers were examined among the rural pixels. The pixels that cover more than two thirds of the rural area have been identified (spatial frequency of land covers). The intensity of the heat island has been estimated according to each of the dominant land covers. Then, the intensity of surface urban heat island in relation to each of the land covers of rural district has been compared. This process also has been done once for the whole area of the rural pixels.
Results and Discussion:
Evaluations of land cover type in rural area of Urmia and Tabriz cities showed that land cover type of cropland and natural herbaceous has the largest area with more than 75% of land cover frequency. The surface urban heat island in Tabriz has annual cycles. In the warm period of the year, the cropland shows a more intense heat island rather than natural herbaceous and all rural area. In addition, at this time of the year, the estimate of urban heat island in relation to the area of natural herbaceous in most cases indicates the cold heat island in Tabriz. These conditions are inverted during the cold period of the year and the urban heat island in cropland and natural herbaceous shows the cold and heat island, respectively. The intensity of the urban heat island of Urmia in the land cover of cropland and natural herbaceous is well separated and show completely different annual cycles, but its annual variations is the same as in Tabriz. The use of natural herbaceous as a rural land cover in Urmia shows a more severe cold island in the warm period of the year than Tabriz.
The urban heat island of Urmia at night shows obvious differences compared to Tabriz. First, the annual in the Urmia heat island cycles are well seen, which indicates the increase of the night heat island in the warm period of the year and its decrease in the cold period. The second major difference is the urban heat island values related to different rural land cover type. The heat island of Urmia, although in smaller numbers, often shows more intensity than Tabriz. It may be due to the smaller size of Urmia city compared to Tabriz and its shorter distance from the lake that cause to more affect by water extent variation of the Urmia Lake. Because of this condition, the daytime urban heat island in Urmia occurred more frequently. In addition, there is a significant difference between urban heat island of rural land covers in Urmia. While this difference is less in Tabriz and less in nighttime than daytime.
Conclusion:
Calculation of the surface urban heat island with MODIS data showed that in some cases, especially during the daytime cold island occurs in some parts of the two cities of Tabriz and Urmia. The calculated heat or cold island was determined by selected type of land covers in rural area. In addition, the selected type of land cover in rural area has a great effect on estimating the intensity of the urban heat island. Cropland as a rural area during the night shows more intense heat island than natural herbaceous while during the daytime the opposite condition was happened. The use of all type land covers as rural area shows the intensity of the heat island between cropland and natural herbaceous as rural area.
Due to the large effect of heterogeneous surfaces on the measurement of surface temperature during the daytime, measurements at nighttime can provide the intensity of the urban heat island with better accuracy. In this regard, nighttime observations of MODIS land surface temperature, especially in Aqua, which is the closest observation to minimum temperatures, can be useful in monitoring the intensity of the urban heat island and its temporal-spatial changes, especially in warm period of the year.

کلیدواژه‌ها [English]

  • urban heat island
  • Urmia Lake
  • Land surface temperature
  • MODIS
رمضانی، ب. و دخت‏محمد، س. م. (1389). شناخت محدودة مکانی تشکیل جزیرة گرمایی در شهر رشت، مجلة پژوهش و برنامه‏ریزی شهری، ۱(۱): ۴۹-64.
شمسی‏پور، ع.؛ مهدیان ماه‏فروزی، م.؛ اخوان، ه. و حسین‏پور، ز. (1391). واکاوی رفتار روزانة جزیرة گرمایی شهر تهران، مجلة محیط‏شناسی، ۳۸(4): ۴۵-56.
عزیزی، ق.؛ شمسی‏پور، ع.؛ مهدیان ماه‏فروزی، م. و میری، م. (1392). تأثیرپذیری شدت جزیرة گرمایی شهری تهران از الگوهای همدیدی جو، مجلة محیط‏شناسی، 39(4): ۵۵-66.
کارکن سیستانی، م. و دوستان، ر. (1394). جزیرۀ گرمایی کلان‏شهر مشهد، مجلة جغرافیا و توسعة فضای شهری، ۲(۲):  ۱۲۳-138.
مجرد، ف.؛ ناصریه، م. و هاشمی، س. (1397). بررسی تغییرات دوره‏ای و فصلی جزیرة گرمایی شهر کرمانشاه در شب و روز با استفاده از تصاویر ماهواره‏ای، مجلة فیزیک زمین و فضا، 44(2): ۴۷۹-494.
مسعودیان، س. ا. و ترکی، م. (1398). واکاوی تغییرات زمانی و مکانی جزیرة گرمایی کلان‏شهر اهواز به کمک داده‏های مودیس، مجلة جغرافیا و برنامه‏ریزی محیطی، ۳۰(73): ۷۵-92.
مسعودیان، س.ا. و منتظری، م. (1399). رفتار زمانی- مکانی جزیرة گرمایی کلان‏شهر اصفهان، مجلة مخاطرات محیط طبیعی، ۹(24): ۳۵-46.
مزیدی، ا. و حسینی ف. (1394). تأثیر تغییر کاربری و پوشش زمین بر جزیرة گرمایی در منطقة شهری یزد با استفاده از داده‏های سنجش از دور، مجلة جغرافیا و توسعه، ۱۳(38): ۱-12.
منصوری س.؛ خالدی ش.؛ برنا ر. و اسدیان ف. (1398). اثر تغییرات کاربری و کاهش فضای سبز شهری بر تشدید جزیرة گرمایی و آلودگی شهر تهران (مطالعة موردی: منطقة یک)، جغرافیا (فصل‏نامة علمی- پژوهشی و بین‏المللی انجمن جغرافیای ایران)، ۱۷(63): ۱۱۴-129.
Azizi, G.; Shamsipour, A.; Mahdian Mahfrouzi, M. and Miri, M. (2014). Intensities of the Urban Heat Island of Tehran under the Influence of Atmospheric Synoptic Patterns, Journal of Environmental Studies, 39(4): 55-66.
Karkon Sistani, M. and Doostan, R. (2015). Heat Island of Mashhad Metropolis, Geography and Urban Space Development, 2(3): 123-138.
Masoodian, S.A. and Torky, M. (2019). Climatology of Surface Urban Heat Island of Ahwaz Metropolis, Geography and Environmental Planning, No. 73, PP. 75-92.
Masoodian, S.A. and Montazeri, M. (2020). Tempo-spatial behavior of Surface Urban Heat Island of Isfahan Metropolitan Area, Journal of Natural Environmental Hazards, No. 24, PP. 35-46.
Mansouri, S.; Khaledi, Sh.; Borna R. and Asadian, F. (2020). Effect of Land Use Change and Urban Green Space Reduction on Intensification of Heat Island and Pollution in Tehran (Case Study: Region One), Geography, No. 63, PP. 114-129.
Mazidi, A. and Hoseini, F.S. (2015). Effects of Changing Land Use and Land Cover on the Heat Island in Urban Area of Yazd Using Remote Sensing Data, Geography and Development Iranian Journal, No. 38, PP. 1-12.
Mojarrad, F.; Naserieh, M. and Hashemi, S. (2018). Investigation of Periodic and Seasonal Variations of Urban Heat Island (UHI) at Night and Day Using Satellite Imagery in Kermanshah City, Journal of the Earth and Space Physics, 44(2): 479-494.
Ramezani, B. and Dokht Mohammad, S.M. (2010). Recognition of the spatial boundary of heat island formation in Rasht city, Journal of Research and Urban Planning, No. 1, pp. 49-64.
Shamsipour, A.; Mahdian Mahfrouzi, M.; Akhavan, H. and Hoseinpour, Z. (2013). An Analysis on Diurnal Actions of the Urban Heat Island of Tehran, Journal of Environmental Studies, 38(4): 45-56.
Chen, M.; Zhou, Y.; Hu, M. and Zhou, Y. (2020). Influence of Urban Scale and Urban Expansion on the Urban Heat Island Effect in Metropolitan Areas: Case Study of Beijing–Tianjin–Hebei Urban Agglomeration, Remote Sensing, No. 12, PP. 1-19.
Cosgrove, A. and Berkelhammer, M. (2018). Downwind footprint of an urban heat island on air and lake temperatures, Climate and Atmospheric Sciences, Climate and Atmospheric Science, No. 2, PP. 1-10.
Dewan, A.; Kiselev, G.; Botje, D.; Iftekhar Mahmud, G.; Bhuian, M.H. and Hassan, Q.K. (2021). Surface urban heat island intensity in five major cities of Bangladesh: Patterns, drivers and trends. Sustainable Cities and Society, No. 71, PP. 1-12.
Friedl, M. and Sulla-Menashe, D. (2019). MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC, Sioux Falls, South Dakota.
Gallo, K.P. and Owen, K.W. (1999). Satellite-Based Adjustments for the Urban Heat Island Temperature Bias. Journal of Applied Meteorology, No. 38, PP. 806-813.
Kataoka K.; Matsumoto F.; Ichinose T. ans Taniguchi M. (2009). Urban warming trends in several large Asian cities over the last 100 years, Science of the total environment, No. 407, PP. 3112-3119.
Khandelwal, A.; Karpatne, A.; Marlier, M.; Kim, J.; Lettenmaier, D.P. and Kumar, V. (2017). An approach for global monitoring of surface water extent variations in reservoirs using MODIS data, Remote Sensing of Environment, No. 202, PP. 113-128.
Li, K.; Chen, Y.; Wang, M. and Gong, A. (2019). Spatial-temporal variations of surface urban heat island intensity induced by different definitions of rural extents in China, Science of the Total Environment, No. 669, PP. 229-247
Li, L.; Chen, Zha, Y. and Zhang, J. (2020). Spatially non-stationary effect of underlying driving factors on surface urban heat islands in global major cities, International Journal of Applied Earth Observation and Geoinformation, No. 90, PP. 1-12.
Mathew, A.; Sreekumar, S.; Khandelwal, S. and Kumar, R. (2019). Prediction of land surface temperatures for surface urban heat island assessment over Chandigarh city using support vector regression model, Solar Energy, No. 186, PP. 404-415.
Morabito, M.; Crisci, A.; Guerri, G.; Messeri, A.; Congedo, L. and Munafò, M. (2021). Surface urban heat islands in Italian metropolitan cities: Tree cover and impervious surface influences. Science of the Total Environment, No. 751, PP. 1-19.
Oke, T.R. (1982). The energetic basis of the urban heat island. Quarterly Journal of Royal Meteorological Society, No. 108, PP. 1-24.
Palou, F.S. and Mahalov, A. (2019). Summer- and Wintertime Variations of the Surface and Near-Surface Urban Heat Island in a Semiarid Environment. Weather and Forecasting, No. 34, PP. 1849-1865.
Rizwan, A.M.; Dennis, L.Y.C. and Liu, C. (2008). A review on the generation, determination and mitigation of Urban Heat Island, Journal of Environmental Sciences, 20(1): 120-128.
Sekertekin, A. and Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area, Ecological Indicators, No. 122, PP. 1-11.
Song, Y. and Wu, C. (2016). Examining the impact of urban biophysical composition and neighboring environment on surface urban heat island effect, Advances in Space Research, No. 57, PP. 96-109.
Strahler, A.; Muchoney, D.; Borak, J.; Friedl, M.; Gopal, S.; Lambin, E. and Moody, A. (1999). MODIS Land Cover Product Algorithm Theoretical Basis Document (ATBD). Boston University, Boston.
Tepanosyan, G.; Muradyan, V.; Hovsepyan, A.; Pinigin, G.; Medvedev, A. and Asmaryan, S. (2021). Studying spatial-temporal changes and relationship of Land Cover and Surface Urban Heat Island derived through remote sensing in Yerevan, Armenia. Building and Environment, No. 187, PP. 107390.
Voogt, J.A. and Oke, T.R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, No. 86, PP. 370-384.
Wan, Z. (1999). MODIS Land-Surface Temperature Algorithm Theoretical Basis Document, University of California, Santa Barbara.
Wan, Z. (2013). Collection-6 MODIS Land Surface Temperature products users’ guide, ERI. University of California, Santa Barbara.
Wang, Z.; Meng, Q.; Allam, M.; Hu, D.; Zhang, L. and Menenti, M. (2021). Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China. Ecological Indicators, No. 128, PP. 1-14.
Yang B.; Meng F.; Xinli, K. and Ma C. (2015). The Impact Analysis of Water Body Landscape Pattern on Urban Heat Island: A Case Study of Wuhan City, Advances in Meteorology, No. 2015 (ID:416728), PP. 1-7.
Yao, R.; Wang, L.; Huang, X.; Niu, Y.; Chen, Y. and Niu, Z. (2018). The influence of different data and method on estimating the surface urban heat island intensity, Ecological Indicators, No. 89, PP. 45-55.
Zhou, B.; Lauwaet, D.; Hooyberghs, H.; Ridder, KD.; Kropp, JP. and Rybski, D. (2016). Assessing Seasonality in the Surface Urban Heat Island of London. Journal of Applied Meteorology and Climatology, No. 55, PP. 493-505.‏