|تعداد مشاهده مقاله||104,919,891|
|تعداد دریافت فایل اصل مقاله||81,996,717|
تاثیر الگوهای فضایی ساختار سبز شهری برتغییر دمای جزایر حرارتی مورد مطالعه: شهر تهران
|مقاله 1، دوره 46، شماره 2، شهریور 1399، صفحه 239-245 اصل مقاله (952.84 K)|
|نوع مقاله: مقاله پژوهشی|
|شناسه دیجیتال (DOI): 10.22059/jes.2021.308065.1008059|
|بهناز امین زاده گوهرریزی 1؛ سهیل قشلاق پور2|
|1استادیار پردیس هنرهای زیبا دانشگاه تهران|
|2دانشکده شهرسازی- دانشگاه تهران|
|هدف از این پژوهش تدقیق رابطه بین فضای سبز و دمای سطح زمین بعنوان عامل مهم در ایجاد جزابر حرارتی در شهرهاست. موضوعی که علیرغم توجه محققین خصوصا در دو دهه اخیر نتایج ضد و نقیضی را نشان داده است. شهر تهران که به عنوان نمونه موردی انتخاب شده است در دهههای اخیر با تغییرات مشخصی از طریق توسعه بخشهای ساخته شده و در نتیجه تغییر الگوی فضاهای باز و سبز طبیعی و نیز تغییرات میانگین دما روبرو بوده است. روش کار با استفاده از طبقه بندی LCZ، سنجش متریک های منتخب سیمای سرزمین و تحلیل ارتباطات از طریق همبستگی پیرسون و پیرسون جزئی است. نتایج نشانگر آن است که مناطق با پوشش درختی در هر دو حالت تراکم زیاد/ کم تاثیر کاهندهای بر دما دارند. میانگین اندازه لکه های سبز متراکم درختی همراه با گیاهان با ارتفاع کم، عامل مهمی درکاهش دماست، در مقابل آن سطح مناطق سبز و میزان تراکم حاشیهای فضاهای سبز شامل علفزارها با تراکم پایین و درختچه های پراکنده با کفپوش خاک تاثیر فزاینده ای بر دمای سطح زمین دارند. این نتایج امکان تاثیر بر میزان کاهش دمای جزایر حرارتی شهر را از طریق برنامه ریزی الگوهای فضایی مناسب مناطق سبز فراهم می کند.|
|الگوهای فضایی؛ جزایر حرارتی؛ ساختار فضای سبز شهری؛ شهر تهران|
|عنوان مقاله [English]|
|The Effects of Spatial Patterns of Urban Green Structure on the Thermal Changes of Urban Heat Islands: The Case Study of Tehran|
|Behnaz Aminzadeh Goharrizi1؛ Sohail Gheshlaghpour2|
|1Faculty of Urban Planning, College of Fine Arts, University of Tehran, Tehran, Iran|
|2Faculty of Urban Planning, College of Fine Arts, University of Tehran, Tehran, Iran|
Urbanization especially in big cities of developing and developed countries has major impacts on climate change by producing greenhouse gas and increasing average temperature, and thus creating urban heat islands(UHI). The unplanned urban development is one of the main factor, which is responsible for making such circumstances. Lack of enough attention to preserving natural and green infrastructure is one of the factors causes city warming and urban heat islands challenges that are important issues in urban environmental planning nowadays. Urban heat island consists of air temperature and surface temperature. Studies show that land cover planning and management can control surface temperature. The relationship between increasing the green spaces as an important element of the green infrastructure and decreasing surface temperature is already has been studied. Regarding the literature has been reviewed in this paper, the purpose of this study is to investigate and clarify the detailed relationship between the characteristics of spatial patterns of urban green spaces and their influences on surface temperature. Spatial composition and spatial configuration are two main elements of spatial patterns of urban green areas. Classification of green land cover based on Local Climate Zone (LCZ) help to discover the detailed relationship between each patterns’ components and the classified green spaces. The case under study is the city of Tehran, which has witnessed certain changes in relation to the development of built-up areas (both in form of planned and unplanned developments), reduction of green spaces and their spatial patterns, as well as rising average temperature.
Materials & Methods
In the process of investigating the relationship between urban spatial patterns of greenspaces in city of Tehran and land surface temperature, different methods and techniques are applied. The greenspace classification map of the city of Tehran was produced with the help of Landsat 8 satellite (2019) and LCZ method of land use classification, which divides green areas into 4 classes as follows:
A; heavily wooded landscape of deciduous and/or evergreen trees. Land cover mostly pervious(low plants). Zone function is natural forest, tree cultivation, or urban park.
B; Lightly wooded landscape of deciduous and/or evergreen trees. Land cover mostly pervious(low plants). Zone function is natural forest, tree cultivation, or urban park.
C; Open arrangement of bushes, shrubs, and short, woody trees. Land cover mostly pervious (bare soil or sand). Zone function is natural scrubland or agriculture.
D; Featureless landscape of grass or herbaceous plants/crops. Few or no trees. Zone function is natural grassland, agriculture, or urban park.
Kappa coefficient and overall accuracy of this map was 0.8706, 88.172 percent, which confirms its accuracy. The next step was selecting landscape metrics. Based on the aim of the study and the reviewed literature spatial composition and spatial configuration are selected as two main elements of spatial patterns of urban green areas. The relationship between land cover patterns and surface temperature is analyzed and discussed by using Pearson and Pearson Partial correlation method.
Discussion of Results
The result of Pearson correlation analysis showed that there is a significant and negative correlation between spatial composition of A, B and D land cover classes with surface temperature. The highest negative correlation belongs to class B (scattered trees) and the lowest belongs to class A (dense trees). In contrast to these negative correlations, the correlation coefficient of class C with surface temperature is positive and significant.
The result of Pearson correlation analysis regarding spatial configuration showed that the average size of each green space class has a continuous and significant negative relationship with the surface temperature, though, the size of these correlations varies in different classes. The correlation also showed that besides size and significance, the direction of green marginal density of each class also differs. It should be noted that the surface area of green space classes (as a composition metric) has a great impact on the results so that the correct and clear correlation of configuration metrics with temperature could not be distinguished. This issue was resolved by using Partial Pearson correlation coefficient and controlling the effect of Class Area metric. As a result, the relationship between configuration metrics and LST changed significantly. Before controlling the Class Area metric, almost all metrics were correlated with LST, however, the new detailed findings showed that only the Mean Size of Patches in A and D classes and Edge Density in B and C classes had a significant relationship with surface temperature.
The study shows that spatial composition of green spaces in Tehran in relation to the Class area of classes A, B and D had a negative and inverse relationship with surface temperature. Class B, located in the east and west of Tehran, has the highest negative correlation. Class A in the east and center of the city with the lowest surface area and its scattered distribution pattern in comparison to other classes has the least negative correlation with surface temperature (95% confidence level). Class D, located mostly in the south and west of the city, has a negative relationship between Class area and temperature at the 99% confidence level. The correlation of spatial composition of class C in the northern half of the city is not like the other three classes and indicates a positive and significant relationship with surface temperature due to the presence of shrubs and grasslands with low density, scattered shrubs, and soil.
Regarding the partial Pearson correlation of spatial configuration metrics, the Mean Patch size of Class A at 99% confidence level shows a negative and significant relationship with temperature, but due to its subdivision and uneven distribution of green space in this class, the effect of this class in the reduction of temperature is not significant. The Mean Patch size of Class D has a significant negative relationship with surface temperature at 95% confidence level, although its cooling effect is not considerable.
Both Edge Densities of classes B and C at 95% confidence level had a significant positive correlation with surface temperature, but as trees did not exist in a dominant and dense manner to cause shading and temperature adjustment in these type of greenspace classes, a positive correlation between the Edge Density of them and surface temperature is occurred.
This paper has demonstrated the relationship between urban heat islands and spatial patterns of green spaces in Tehran city. The literature based study showed the scope of the problem explaining that urban green spaces contribute to mitigate climate change impacts through decreasing the surface temperature. The spatial form and pattern of urban green spaces have different effect on surface temperature as indicated in several studies. Importantly, planners and designers, need more detailed studies to take into account the relation between effects of spatial composition and configuration of different classification of plants and their effects on urban surface temperature. In this research, greenspaces patterns was studied using Local Climate Zone(LCZ) method and correlation of spatial pattern (composition and configuration) of each of LCZ green classes with the surface temperature were provided. The results of the analysis of the spatial composition of these classes showed that tree canopy greenspaces in both cases of high / low density and low plants has a reducing effect on temperature, but low-density grasslands and scattered shrubs with soil cover, has a positive relationship with temperature. More detailed results on the spatial configuration shows that only the mean patch size in dense tree areas and low plants has a significant negative correlation with temperature. But Edge density of scattered trees and open arrangement of bushes had a significant positive relationship with temperature. Thus urban green space planning and management, through determining the type, composition, and configuration of existing patterns and their improvements based on their effect on the reduction of surface temperature will help to decrease urban heat island impacts.
|spatial pattern, urban green structure, urban heat Islands, Tehran city|
معرب، ی؛ امیری، م، (1397). بررسی، ارزیابی و تدوین تابآوری کاربری اراضی شهری بر پایه رویکرد توسعه پایدار، محیط شناسی، 44(1)، صص 169-149.
مهندسین مشاور همکار، (1384). طرح ساماندهی اراضی عباس آباد( بخش مطالعات محیط زیست).
یزدان پناه، م؛ یاوری، ا؛ زبردست، ل؛ آل محمد، س، (1394). ارزیابی زیرساختهای سبز شهری به منظور اصلاح تدریجی آنها در سیمای سرزمین تهران، محیط شناسی، 41(3)، صص 625-613.
Al-Dabbous, A. N., & Kumar, P. (2014). The inﬂuence of roadside vegetation barriers on airborne nanoparticles and pedestrians exposure under varying wind condition. Atmospheric Environment, 90, 113–124.
Buyantuyev, A., & Wu, J. (2010). Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecology, 25(1), 17–33.
Cao, X., Onishi, A., Chen, J., & Imura, H. (2010). Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landscape and Urban Planning, 96(4), 224–23.
Chen, A., Yao, X. A., Sun, R., & Chen, L. (2014). Effect of urban green patterns on surface urban cool islands and its seasonal variations. Urban Foresty and UrbanGreening, 13(4), 646–654.
Connors, J. P., Galletti, C. S., & Chow, W. T. L. (2012). Landscape configuration and urban heat island effects: Assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landscape Ecology, 28, 271–283.
Dugord, P. A., Lauf, S., Schuster, C., & Kleinschmit, B. (2014). Land use patterns, temperature distribution, and potential heat stress risk – the case study Berlin, Germany. Computers,Environment and Urban Systems, 48, 86–98.
Das, M., & Das, A. (2020). Assessing the relationship between local climatic zones (LCZs) and land surface temperature (LST)–A case study of Sriniketan-Santiniketan Planning Area (SSPA), West Bengal, India. Urban Climate, 32, 100591.
Guo, G., Zhifeng, W., & Chen, Y. (2019).Complex mechanisms linking land surface temperature to greenspace spatial patterns: Evidence from four southeastern Chinese cities. Science of the total Environment, 674, 77-87.
Hondula, D. M., Georgescu, & M., Balling, R. C. (2014). Challenges associated with projecting urbanization-induced heat-related mortality. Science of the total Environment, 490, 538–544.
Kong, F. H., Yin, H. W., Wang, C. Z., Cavan, G., & James, P. (2014). A satellite image-based analysis of factors contributing to the green-space cool island intensity on a city scale. Urban Foresty and Urban Greening, 13, 846–853.
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.
Liu, W., Ji, C., Zhong, J., Jiang, X., & Zheng, Z. (2007). Temporal characteristics of the Beijing urban heat island. Theoretical and Applied Climatology, 87(1), 213–221.
Li, J., Song, C., Cao, L., Zhu, F., Meng, X., & Wu, J. (2011). Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sensing of Environment, 115(12), 3249–3263.
Li, X. M., Zhou, W. Q., Ouyang, Z. Y., Xu, W. H., & Zheng, H. (2012). Spatial pattern of green space affects land surface temperature: Evidence from the heavily urbanized Beijing metropolitan area, China. Landscape Ecology, 27, 887–898.
Li, X., Zhou, W., & Ouyang, Z. (2013). Relationship between land surface temperature and spatial pattern of greenspace: what are the effects of spatial resolution?. Landscape and Urban Planing, 114(8), 1–8.
Lin, Z., & Xu, H. (2016). A study of Urban heat island intensity based on local climate zones: a case study in Fuzhou, China. Paper presented at the 2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Guangzhou, doi: 10.1109/EORSA.2016.7552807.
McGarigal, K., Cushman, S. A., Neel, M. C., & Ene, E. (2002). FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. https://www.umass.edu/landeco/research/fragstats/fragstats.html.
McIntyre, N. E., Rango, J., Fagan, W. F., & Faeth, S. H. (2001). Ground arthropod community structure in a heterogeneous urban environment. Landscape and Urban Planing, 52(4), 257–274.
Myint, S. W., Wentz, E. A., Brazel, A. J., & Quattrochi, D. A. (2013). The impact of distinct anthropogenic and vegetation features on urban warming. Landscape Ecology, 28(5), 959–978.
McGarigal, K., Ene, E., & Holmes, C. (2002b). FRAGSTATS (Version 3): FRAGSTATS metrics.Universityof Massachusetts-Produced Program. http://www. Umass .edu /landec /research/fragstats /documents /fragstats documents.html
Maimaitiyiming, M., Ghulam, A., Tiyip, T., Pla, F., Latorre-Carmona, P., Halik, U., Sawut, M., & Caetano, M. (2014). Effects of green space spatial pattern on land surface temperature: implications for sustainable urban planning and climate change adaptation. ISPRSJournal of Photogrammetry and Remote Sensing, 89, 59–66.
Monteiro, M. V., Doick, K. J., Handley, P., & Peace, A. (2016). The impact of greenspace size on the extent of local nocturnal air temperature cooling in London. Urban Foresty and UrbanGreening, 16, 160–169.
Naumann, D., McKenna, T., Kaphengst, M., & Pieterse M. (2011). Rayment Design, implementation and cost elements of Green Infrastructure projects. Final report to the European Commission, DG Environment, Contract no. 070307/2010/577182/ETU/F.1.
Pearlmutter, D., Calfapietra, C., Samson, R., O'Brien, L., Krajter Ostoić, S., Sanesi, G., & Alonso del Amo, R. ( 2017). The Urban Forest: Cultivating Green Infrastructure for People and the Environment. Springer. https://www.springer.com/gp/book/9783319502793.
Peng, J., Wang, Y. L., Zhang, Y., Wu, J. S., Li, W. F., & Li, Y. (2010). Evaluating the effectiveness of
Santamouris, M., Cartalis, C., Synnefa, A., & Kolokotsa, D. (2015). On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings—a review. Energy and Buildings, 98, 119–124.
Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletio of American Meteorological society, 93(12), 1879–1900.
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(4), 335–344.
Yokohari, M., Brown, R., Kato, Y., & Moriyama, H. (1997). Effects of paddy fields on summertime air and surface temperatures in urban fringe areas of Tokyo, Japan. Landscape andUrban Planing, 38(1–2), 1–11
Zhou, W. Q., Huang, G. L., & Cadenasso, M. L. (2011). Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape andUrban Planing, 102, 54–63
Zhou, W., Wang, J., & Cadenasso, M. L. (2017). Effects of the spatial configuration of trees on urban heat mitigation: a comparative study. Remote Sensing of Environment, 195, 1–12.
Zhang, X. Y., Zhong, T. Y., Feng, X. Z., & Wang, K. (2009). Estimation of the relationship between vegetation patches and urban land surface temperature with remote slensing. International Journal of Remote Sensing, 30(8), 2105
تعداد مشاهده مقاله: 512
تعداد دریافت فایل اصل مقاله: 517