تحلیل فضایی مخاطرات محیطی

تحلیل فضایی مخاطرات محیطی

سنجش اثرات سبزینگی گیاهی در تحولات فضایی شدت جزیره حرارتی سطح کلانشهر تهران با استفاده از تصاویر ماهواره‌ای LANDSAT8 و ASTER

نویسندگان
مجتبی رفیعیان
هادی رضایی راد
چکیده
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.
کلیدواژه‌ها
جزیره‌ حرارتی
حرارت سطوح شهری
تعادل انرژی سطح
پوشش گیاهی نرمال شده
کلانشهر تهران

عنوان مقاله English

Measuring the Impact of Vegetation Greenness on Spatial Changes of Heat Island Intensity in Tehran Metropolitan by Using ASTER and Landsat8 Satellite Images

نویسندگان English

mojtaba rafiean
hadi rezai rad
چکیده English

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. The urban thermal environment is influenced by the physical characteristics of the land surface and by human socioeconomic activities. The thermal environment can be considered to be the most important indicator for representing the urban environment. Vegetation is another important component of the urban ecosystem that has been the subject of much basic and applied research. Urban vegetation influences the physical environment of cities through selective absorption and reflection of incident radiation and regulation of latent and sensible heat exchange Satellite-borne instruments can provide quantitative physical data at high spatial or temporal resolutions. Visible and near-infrared remote sensing systems have been used extensively to classify phenomena such as city growth, land use /cover changes, vegetation index and population statistics. Finally, we propose a model applying non-parametric regression to estimate future urban climate patterns using predicted Normalized Difference Vegetation Index and Heat Island Intensity.

I conducted all spatial analysis in the UTM Zone 39 Northern Hemisphere projection. The fundamental procedure I used for evaluating change in land surface temperature was to relative temperature for both images, so that the values are temperature difference between the coldest and hottest areas in Tehran metropolitan. subtracting these images from each other results in relative temperature change from 2003 to 2015. Landsat satellite data were used to extract land use/land cover information and their changes for the abovementioned cities. Land surface temperature was retrieved from Landsat thermal images. The relationship between land surface temperature and landuse /land-cover classes, as well as the normalized vegetation index (NDVI) was analyzed.

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. NDVI negatively affected LST and Urban Heat Island in vegetation areas in 2003 and 2015 in Tehran metropolitan. This analysis provides an effective tool in evaluating the environmental influences of zoning in urban ecosystems with remote sensing and geographical information systems. This method exhibits a promising performance in UHI forecast. The predicted LST confirms that urban growth has severely influenced UHI pattern through expanding the hot area. Our study confirmed that LST prediction performance is strongly depended on the resolution.

The results reveal that the urban LST is affected mainly by the land surface characteristics and has a close relation to the abundance of vegetation greenness. The spatial distance from the UHI centre is another important factor influencing the LST in some areas. The methodology presented in this paper can be broadly applied in other metropolitans which exhibit a similar dynamic growth. Our findings can represent a useful tool for policy makers and the community awareness of environmental assessment by providing a scientific basis for sustainable urban planning and management. This provides an effective tool in evaluating the vegetation greenness of different zoning in urban ecosystems with remote sensing and geographical information systems. From the perspective of land use planning and urban management, it is recommend that planners and policy makers should pay serious attention to future land use policies that maintain a relevant proportion of public space, green areas, and land surface physical characteristics.

کلیدواژه‌ها English

Urban Heat Island
land surface temperature
Surface Energy Balance
Normalized Difference Vegetation Index
Tehran metropolitan
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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.
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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.
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http://asterweb.jpl.nasa.gov
Abrams, M.; H. Simon. 2005. ASTER User Handbook, Version2, Jet Propulsion Laboratory.
Anbazhagan, S.; C. Paramasivam. 2016. Statistical Correlation between Land Surface Temperature (LST) and Vegetation Index (NDVI) using Multi-Temporal Landsat TM Data, International Journal of Advanced Earth Science and Engineering, vol. 5(1): pp.333-346.
Anderson, M.; J. Norman, W. Kustas, R. Houborg, P. Starks and N. Agam. 2008. A thermal- based remote sensing technigue for routine mapping of land- surface carbon, water and energy fluxes from field to regional scales. Remote Sensing of Environment, 112(12): 4227-4241.
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.
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.
Bobrinskaya, M. 2012. “Remote Sensing for Analysis of Rela- Tionships between Land Cover and Land Surface Temperature in Ten Megacities.” (December).
Chander, G.; B. Markham and D. Helder. 2009. Summary of current radiometric, Remote sensing of environmental, 113(5): 893-903.
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.
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.
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.
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.
José, A.; J. Jimenez and L. Paolini. 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90, 434 – 440.
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.
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.
Li, H. 2016. Pavement Materials for Heat Island Mitigation: Design and Management Strategies, Oxford, UK: Elsevier.
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.
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.
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.
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.
Nuruzzaman, M. 2015. “Urban Heat Island : Causes , Effects and Mitigation Measures.” 3(2): 67–73.
Owen, T.; T. Carlson and R. Gillies. 1998. Remotely sensed surface parameters governing urban climate change, Internal Journal of Remote Sensing, 19, 1663-1681.
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.
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.
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.
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.
Santamouris, M.;.D. Kolokotsa. 2016. “URBAN CLIMATE MITIGATION”, First published 2016 by Routledge, New York.
Shukla, J.; Y. Mintz. 1982. The influence of land-surface-evapotranspiration on the earth’s climate. Science, 247, 1322–1325.
Skelhorn, C. 2013. “A Fine Scale Assessment of Urban Greenspace Impacts on Microclimate and Building Energy in Manchester.”
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.
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).
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.
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.
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.
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.
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.; 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.
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.
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.
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.
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.
Zhou, Y.; G. Ren. 2011.Change in extreme temperature event frequency over mainland China, 1961–2008, Clim. Res., 50, 125–139.
http://asterweb.jpl.nasa.gov