دوره 4، شماره 3 - ( 6-1396 )                   جلد 4 شماره 3 صفحات 1-16 | برگشت به فهرست نسخه ها

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rafiean M, rezai rad H. Measuring the Impact of Vegetation Greenness on Spatial Changes of Heat Island Intensity in Tehran Metropolitan by Using ASTER and Landsat8 Satellite Images. Jsaeh. 2017; 4 (3) :1-16
URL: http://jsaeh.khu.ac.ir/article-1-2746-fa.html
رفیعیان مجتبی، رضایی راد هادی. سنجش اثرات سبزینگی گیاهی در تحولات فضایی شدت جزیره حرارتی سطح کلانشهر تهران با استفاده از تصاویر ماهواره‌ای LANDSAT8 و ASTER . تحلیل فضایی مخاطرات محیطی. 1396; 4 (3) :1-16

URL: http://jsaeh.khu.ac.ir/article-1-2746-fa.html


1- دانشیار ، Mrafiyan@gmail.com
2- استادیار
چکیده:   (1940 مشاهده)
حرارت سطح شهری (LST) متغیر کلیدی برای کنترل ارتباط بین شار حرارت تابشی، نهفته و محسوس می‌باشد. بدین ترتیب تحلیل و درک پویایی LST و شناسایی ارتباط آن با تغییرات منشاء انسانی برای مدلسازی، پیش‌بینی تغییرات محیطی و نهایتا سیاستگذاری شهری لازم است. از سمتی هم افزایش مقدار پوشش گیاهی یکی از کاراترین استراتژیهای کاهش اثرات خرده اقلیم شهری می‌باشد. در همین راستا جهت تحلیل روندیابی تغییرات حرارتی سطوح و میزان همبستگی فضایی سبزینگی پوشش گیاهی با این پدیده در اثر تحولات شهرنشینی و شهرسازی شهر تهران بین سالهای 94-1382 مورد پژوهش واقع شده است. 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 به کمک نرم‌افزار Envi و از طریق الگوریتم‌های مختلف در سنجش از دور به الگوهای فضایی میزان حرارت سطوح و شاخص پوشش گیاهی نرمال شده (NDVI) کلانشهر تهران تبدیل شده است. خروجی‌های فضایی این پژوهش نشان می‌دهند در طی تقریبا یک دهه‌ی اخیر کمینه‌ی و میانگین حرارت سطوح کلانشهری تهران به ترتیب c̊ 3.67 و c̊ 0.47 کاهش یافته است. همچنین میانگین شاخص پوشش گیاهی نرمال شده از0.06- به 0.10 افزایش یافته است. در همین بازه زمانی برآورد همبستگی فضایی بین NDVI با LST در مناطق 22گانه شهر هم حاکی از کاهش 2% است. این کاهش همبستگی به معنای افزایش نقش فعالیت‌های انسانی بر میزان شدت جزیره حرارتی شهر است. بنابراین توجه به برنامه‌ریزی فعالیت‌های انسانی در شهر در راستای جلوگیری از تغییرات اقلیم در کلانشهری همچون تهران بیش از پیش جهت دستیابی به توسعه‌ی پایدار الزامی به نظر می‌رسد.
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نوع مطالعه: پژوهشي | موضوع مقاله: تخصصي
دریافت: ۱۳۹۶/۹/۳۰ | پذیرش: ۱۳۹۶/۹/۳۰ | انتشار: ۱۳۹۶/۹/۳۰

فهرست منابع
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50. André, C.; C. Ottle, A. Royer and F. Maignan. 2015. Land surface temperature retrieval over circumpolar Arctic using SSM/I–SSMIS and MODIS data. Remote Sensing of Environment, 162, 1-10.
51. Bhang, K.; S. Park. 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.
52. Bobrinskaya, M. 2012. “Remote Sensing for Analysis of Rela- Tionships between Land Cover and Land Surface Temperature in Ten Megacities.” (December).
53. Chander, G.; B. Markham and D. Helder. 2009. Summary of current radiometric, Remote sensing of environmental, 113(5): 893-903.
54. Collatz, G.; L. Bounoua, S. Los, D. Randall, I. Fung and P. Sellers. 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.
55. Gartland, L. 2008. HEAT ISLANDS UNDERSTANDING AND MITIGATING HEAT IN URBAN AREAS. Earthscan in the UK and USA in: Typeset by MapSet Ltd, Gateshead,UK.
56. Guillevic, P.; J. Privette, B. Coudert, M. Palecki, J. Demarty, C. Ottle and J. Augustine. 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.
57. Huang, C.; S. Goward, J. Masek, N. Thomas, Z. Zhu and J. Vogelmann. 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens. Environ. 114, 183–198.
58. José, A.; J. Jimenez and L. Paolini. 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90, 434 – 440.
59. Kerr, Y.; J. Lagouarde, F. Nerry and C. Ottle. 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.
60. Kotroni, J.; S. Petrova, R. Mitzeva, J. Latham and E. Peneva. 2009. Analyses of summer lightning activity and precipitation in the Central and Eastern Mediterranean. Atmospheric Research, 91, 453-458.
61. Li, H. 2016. Pavement Materials for Heat Island Mitigation: Design and Management Strategies, Oxford, UK: Elsevier.
62. Markham, B.; J. Storey, D. Williams and J. Irons. 2004. Landsat sensor performance: History and current status. IEEE Trans. Geosci. Remote Sens. 42, 2691–2694.
63. Meng, Q.; D. Spector, S. Colome and B. Turpin. 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.
64. Moran, M.; R. Scott, T. Keefer, W. Emmerich, M. Hernandez and G. Nearing. 2009. Partitioning evapotranspiration in semiarid grassland and shrubland ecosystems using time series of soil surface temperature. Agricultural and Forest Meteorology, 149, 59–72.
65. Niu, C.; A. Musa and Y. Liu. 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.
66. Nuruzzaman, M. 2015. “Urban Heat Island : Causes , Effects and Mitigation Measures.” 3(2): 67–73.
67. Owen, T.; T. Carlson and R. Gillies. 1998. Remotely sensed surface parameters governing urban climate change, Internal Journal of Remote Sensing, 19, 1663-1681.
68. Pitman, A.; F. Avila, G. Abramowitz, Y. Wang, S. Phipps and N. Noblet. 2011. Importance of background climate in determining impact of land-cover change on regional climate, Nature Climate Change, 1, 472–475, 2011.
69. Rajeshwari,A.; N. Mani. 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.
70. Rezaei Rad, Hadi.; M. Rafieian. 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, Tehran.
71. Roy, D.; M. Wulder, T. Loveland and C. Woodcock. 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154–172.
72. Santamouris, M.;.D. Kolokotsa. 2016. “URBAN CLIMATE MITIGATION”, First published 2016 by Routledge, New York.
73. Shukla, J.; Y. Mintz. 1982. The influence of land-surface-evapotranspiration on the earth’s climate. Science, 247, 1322–1325.
74. Skelhorn, C. 2013. “A Fine Scale Assessment of Urban Greenspace Impacts on Microclimate and Building Energy in Manchester.”
75. Sobrino, J.A.; V. Caselles and C. Coll. 1993. Caselles, V.; Coll, C. Theoretical split-window algorithms for determining the actual surface temperature. Il Nuovo Cimento, 16, 219–236.
76. Srivanit, M.; H. Kazunori. 2012. Thermal Infrared Remote Sensing for Urban Climate and Environmental Studies: An Application for the City of Bangkok, Thailand, JARS, 9(1).
77. Sun, J.; D. Salvucci, D. Entekhabi and L. Farhadi. 2011. Parameter estimation of coupled water and energy balance models based on stationari constraints of surface state, Water Resour. Res., 47,W02515.
78. Svensson, M.; I. Eliasson. 2002. Diurnal air temperatures in built up areas in relation to urban planning, Landsc. Urban Plan., vol. 61, no. 1, pp. 37–54.
79. Tan, J.; Y. Zheng, X. Tang, C. Guo, L. Li, G. Song, X. Zhen, D. Yuan, A. Kalkstein and F. Li. 2010. The urban heat island and its impact on heat waves and human health in Shanghai. Int. J. Biometeorol. 54, 75–84.
80. Tran, N.; B. Powell, H. Marks, R. West and A. Kvasnak. 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.
81. 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.
82. Weng, Q.; P. Fu and F. Gao. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sens. Environ. 145, 55–67.
83. Yang, X.; L. Zhao, M. Bruse and Q. Meng. 2013. Evaluation of a microclimate model for predicting the thermal behavior of different ground surfaces, Build. Environ., vol. 60, pp. 93–104.
84. Yuan, F.; M. Bauer. 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.
85. Yue, W.; J. Xu, W. Tan and L. Xu. 2007. The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM + data, International Journal of Remote Sensing, Vol. 28, No. 15, pp: 3205-3226.
86. Zareie, S.; H. Khosravi and A. Nasiri. 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.
87. Zhou, Y.; G. Ren. 2011.Change in extreme temperature event frequency over mainland China, 1961–2008, Clim. Res., 50, 125–139.
88. http://asterweb.jpl.nasa.gov

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