برآورد تغییرات فضایی- زمانی شدت جزیره حرارتی کلانشهر تهران با استفاده از تصاویر ماهوارهای LANDSAT8 و ASTER ASTER
محورهای موضوعی :
فصلنامه علمی برنامه ریزی منطقه ای
هادی رضایی راد
1
,
مجتبی رفیعیان
2
1 - دانشآموخته دکترای شهرسازی، دانشگاه تربیت مدرس، تهران، ایران
2 - دانشیار گروه شهرسازی، دانشگاه تربیت مدرس، تهران، ایران
تاریخ دریافت : 1395/08/17
تاریخ پذیرش : 1396/05/31
تاریخ انتشار : 1396/08/01
کلید واژه:
کلانشهر تهران,
جزیره حرارتی,
حرارت سطوح شهری,
مصرف انرژی شهری,
آزمون من- کندال,
چکیده مقاله :
جزیره حرارتی شهر یکی از بارزترین مضاهر آب و هوایی شهرنشینی در شهرهای امروزی است. افزایش دمای شهری به شدت باعث افزایش تقاضای برق برای تهویه هوای داخل ساختمانها، میزان غلظت هوا و افزایش انتشار آلودگیهای نیروگاه برق از جمله دی اکسید گوگرد، مونواکسید کربن، اکسید نیتروژن و ذرات معلق میشود. بدین ترتیب تحلیل و درک پویایی حرارت شهری و شناسایی ارتباط آن با تغییرات منشاء انسانی برای مدلسازی، پیشبینی تغییرات محیطی و نهایتا سیاستگذاری شهری الزامی به نظر میرسد. بنابراین هدف پژوهش حاضر برآورد فضایی- زمانی جزیره حرارتی مناطق بیست و دوگانهی شهر تهران بین سالهای 94-1382 در اثر تحولات توسعهی کالبدی شهر است. در فرآیند دستیابی به هدف مورد نظر تصاویر ماهوارهای بدون پوشش ابری و صاف کلانشهر تهران توسط ماهوارهی Landsat8 برای مرداد ماه سال 1394 و ماهوارهی Aster برای مرداد ماه سال 1382 تهیه شده است. این تصاویر از طریق الگوریتمهای طراحی شده و در محیط Envi به الگوهای فضایی جزیره حرارتی مناطق 22گانه شهر تهران تبدیل شده است. مقایسه و تحلیل الگوهای فضایی جزایر حرارتی در سیر زمانی 1394-1384 با استفاده از آزمون من- کندال نشان از 0.6 همبستگی فضایی داشته است، این بدان معناست که در 40% از سطح شهر تهران طی تقریبا یک دههی اخیر به دلایل اثرات توسعهی کالبدی شهر الگوی فضایی جزیره حرارتی تغییر یافته است. همچنین سایر نتایج نشان از کاهش کمینهی حرارت سطح (c̊ 3.67) و کاهش میانگین حرارت سطح (c̊ 0.47) طی یک دههی اخیر شهر تهران دارد. البته شایان ذکر است روند تحولات الگوی فضایی جزیره حرارتی که ناشی از تغییرات سیاستهای کالبدی- عملکردی و فعالیتهای انسانی است، در حوزهی غربی شهر بویژه در مناطق 5 ، 22 و قسمت شرقی منطقهی 21 بیشترین تحولات را به خود اختصاص دادهاند.
چکیده انگلیسی:
The simplest definition of urbanization is that urbanization is the process of becoming urban. Urban climate is defined by specific climate conditions which differ from surrounding rural areas. Urban areas, for example, have higher temperatures than surrounding rural areas and weaker winds. Land Surface Temperature is an important phenomenon in global climate change. As the green house gases in the atmosphere increases, the LST will also increase. Energy and water exchanges at the biosphere–atmosphere interface have major influences on the Earth's weather and climate. Numerical models ranging from local to global scales must represent and predict effects of surface fluxes. In this study, LST for Tehran Metropolitan, was derived using SW algorithm with the use of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution. SW algorithm needs spectral radiance and emissivity of two TIR bands as input for deriving LST. The spectral radiance was estimated using TIR bands 10 and 11. Emissivity was derived with the help of land cover threshold technique for which OLI bands 2, 3, 4 and 5 were used. The output revealed that LST was high in the barren regions whereas it was low in the hilly regions because of vegetative cover. As the SW algorithm uses both the TIR bands (10 and 11) and OLI bands 2, 3, 4 and 5, the LST generated using them were more reliable and accurate.
منابع و مأخذ:
Abrams, Michael., Simon, Hook. (2005). ASTER User Handbook, Version2, Jet Propulsion Laboratory.
André, C., et al. (2015). Land surface temperature retrieval over circumpolar Arctic using SSM/I–SSMIS and MODIS data. Remote Sensing of Environment, 162, 1-10.
Bhang, K.J., et al. (2009). Evaluation of the Surface Temperature Variation With Surface Settings on the Urban Heat Island in Seoul, Korea, Using Landsat-7 ETM+ and SPOT. Geoscience and Remote Sensing Letters, IEEE, Volume: 6 , Issue: 4, Page(s): 708- 712.
Bobrinskaya, Maria. (2012). “Remote Sensing for Analysis of Rela- Tionships between Land Cover and Land Surface Temperature in Ten Megacities.” (December).
Chander, G., et al. (2009), Summary of current radiometric, Remote sensing of environmental, 113(5): 893-903.
Collatz, G.J., et al. (2000). A mechanism for the influence of vegetation on the response of the diurnal temperature range to changing climate, Geophys. Res. Lett., 27, 3381-3384.
Gartland, Lisa. (2008). HEAT ISLANDS UNDERSTANDING AND MITIGATING HEAT IN URBAN AREAS. Earthscan in the UK and USA in: Typeset by MapSet Ltd, Gateshead,UK.
Guillevic, Pierre., et al. (2012). Land Surface Temperature product validation using NOAA's surface climate observation networks—Scaling methodology for the Visible Infrared Imager Radiometer Suite (VIIRS), Remote Sensing of Environment, 124.
Huang, C., et al. (2010). An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens. Environ. 114, 183–198.
José, A., et al. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90, 434 – 440.
Kerr, Y., et al. (2004). Land surface temperature retrieval: Techniques and applications: Case of the AVHRR. In D. A. Quattrochi, & J. C. Luwall (Eds.), Thermal remote sensing in land surface processes (pp. 33–109). Boca Raton Fl.: CRC Press.
Kotroni, J., et al. (2009). Analyses of summer lightning activity and precipitation in the Central and Eastern Mediterranean. Atmospheric Research, 91, 453-458.
Li, Hui. (2016). Pavement Materials for Heat Island Mitigation: Design and Management Strategies, Oxford, UK: Elsevier.
Markham, B.L., et al. (2004). Landsat sensor performance: History and current status. IEEE Trans. Geosci. Remote Sens. 42, 2691–2694.
Meng, Q.Y., et al. (2009). Determinants of indoor and personal exposure to PM2.5 of indoor and outdoor origin during the RIOPA study. Atmos Environ 43(36):5750–5758.
Moran, M., et al. (2009). Partitioning evapotranspiration in semiarid grassland and shrubland ecosystems using time series of soil surface temperature. Agricultural and Forest Meteorology, 149, 59–72.
Niu, C. Y., et al. (2015). Analysis of soil moisture condition under different land uses in the arid region of Horqin sandy land, northern China. Solid Earth, 6, 1157 1167.
Oke,TR. (2006). Initial guidance to obtain representative meteorological observations at urban sites.
Owen, T.W., et al. (1998). Remotely sensed surface parameters governing urban climate change, Internal Journal of Remote Sensing, 19, 1663-1681.
Pitman, A., et al. (2011). Importance of background climate in determining impact of land-cover change on regional climate, Nature Climate Change, 1, 472–475, 2011.
Rajeshwari,A., Mani, N,D. (2014). ESTIMATION OF LAND SURFACE TEMPERATURE OF DINDIGUL DISTRICT USING LANDSAT 8 DATA, International Journal of Research in Engineering and Technology, Volume 03, Issue 05.
Rezaei Rad, Hadi. (2017). Analysis of Physical Planning Effects on Energy Consumption Balance In Tehran Metropolitan Sub Regions, Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (Ph.D.) in Urban and Regional Planning and Design, Tarbiat Modares University, Tehran.
Rezaei Rad, Hadi., et al. (2013). Assessment the impact of policies on building density 'heat island using SVF methods in GIS (Case Study: Tehran), Second of Conference on Planning and Environmental Management, Tehran University, Tehran.
Rezaei Rad, Hadi., et al. (2017). Evaluating the effects of increasing of building height on land surface temperature, International Journal Urban Manage Energy Sustainability, 1(1): 11-16.
Rezaei Rad, Hadi., Rafieian, Mojtaba. (2014). Assessing the effects of architectural residential complexes in the concentration of air pollution using Envi-met (A case study: Ghytariyeh neighborhood Tehran), Conference on Clean Air, Beheshti University, Tehran.
Rezaei Rad, Hadi., Rafieian, Mojtaba. (2016). Evaluating The Effects of High rise building On Urban Heat Island by Sky View Factor (A case study: Narmak neighborhood Tehran), Basic Studies and New Technologies of Architecture and Planning Naqshejahan, Tatbiat Modares University, Tehran.
Rezaei Rad, Hadi.. (2012). The Evaluation of High Rise Building Policies in Tehran Detailed Plan with the Emphasis on Functional-Spatial Organizations, Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Art (M.A.) in Urban and Regional Planning, Tarbiat Modares University, Tehran.
Roy, D.P., et al. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154–172.
Sajadiyan, Nahid. (2015). Forcasting of pollution due to city transport in great Tehran by using GIS, LUR Model and artificial neural network, Sepehr Journal, Tehran, No (95)24.
Santamouris, M. (2014). On the energy impact of urban heat island and global warming on buildings. Energy and Buildings 82, 100–113.
Santamouris, M., Cartalis, C., Synnefa, A., and Kolokotsa, D. (2015). On the impact of urban heat island and global warming on the power demand and electricity consump tion of buildings – a review. Energy and Buildings.
Santamouris, Mat., Kolokotsa, Denia. (2016). “URBAN CLIMATE MITIGATION”, First published 2016 by Routledge, New York.
Shukla, J., Mintz, Y. (1982). The influence of land-surface-evapotranspiration on the earth’s climate. Science, 247, 1322–1325.
Skelhorn, Cynthia Pamela. (2013). “A Fine Scale Assessment of Urban Greenspace Impacts on Microclimate and Building Energy in Manchester.”
Sobrino, J.A., et al. (1993). Caselles, V.; Coll, C. Theoretical split-window algorithms for determining the actual surface temperature. Il Nuovo Cimento, 16, 219–236.
Srivanit, Manat., Hokao, Kazunori. (2012). Thermal Infrared Remote Sensing for Urban Climate and Environmental Studies: An Application for the City of Bangkok, Thailand, JARS, 9(1).
Sun, J., et al. (2011). Parameter estimation of coupled water and energy balance models based on stationari constraints of surface state, Water Resour. Res., 47,W02515.
Svensson, M. K., Eliasson, I. (2002). Diurnal air temperatures in built‐up areas in relation to urban planning, Landsc. Urban Plan., vol. 61, no. 1, pp. 37–54.
Tran, N., et al. (2009). Strategies for Design and Construction of High‐Reflectance Asphalt Pavements. Transportation Research Record: Journal of the Transportation Research Board, No. 2098, Transportation Research Board of the National Academies, Washington, D.C., 124–130.
Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing 64, 335–344.
Weng, Q., et al. (2014). Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sens. Environ. 145, 55–67.
Yang, X., et al. (2013). Evaluation of a microclimate model for predicting the thermal behavior of different ground surfaces, Build. Environ., vol. 60, pp. 93–104.
Yuan, Fei., et al. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery, Remote Sensing of Environment, 106.
Zareie, S., et al. (2016). Derivation of land surface temperature from Landsat Thematic Mapper ( TM ) sensor data and analyzing relation between land use changes and surface temperature, Manuscript under review for journal Solid Earth.
Zhou, Y., Ren, G. (2011).Change in extreme temperature event frequency over mainland China, 1961–2008, Clim. Res., 50, 125–139.
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