Journal of South Architecture

Analysis of Urban Thermal Environment Improved by Blue-Green Spaces on the Landsat Data: A Case Study on Tianjin

CHENTian, TANNing

Abstract


China’s urbanization is characterized by rapid speed, large scale and high energy consumption, which causes a series of ecological problems. Among these problems, urban thermal environment is particularly prominent, and the most intuitive reflection is the heat island effect. It is of great significance to optimize the urban layout to give full play to the cooling effect of blue-green spaces for improving urban thermal environment. Compared with other methods, it is more extensive and feasible. However, the current research on urban thermal environment mostly focuses on revealing the driving effects of different land use, and lacks of in-depth discussions on blue-green spaces. Tianjin is located in the northeast of the North China Plain, and has a typical warm temperate sub-humid monsoon climate. This paper takes the six districts of Tianjin City as the research area, covering Heping District, Nankai District, Hongqiao District, Hebei District, Hexi District and Hedong District, with a total area of about 181.18 square kilometers. The typical climate and urbanization characteristics of Tianjin can provide reference for cities with the same characteristics. Firstly, this paper had adopted the Landsat changing scenario of the city gained during the periods of 2004, 2011 and 2017 to formulate the land surface temperature by Mono-window Algorithm, and divided the results into seven temperature zones by standard deviation classification method. In regard to blue-green spaces, this paper had identified Landsat satellite data by Maximum Likelihood Classifier, and classified land use into three categories: blue spaces, green spaces, and non-blue and green spaces. And, next, this paper had studied the correlation between the landscape pattern of blue-green spaces and urban thermal environment by Landscape Pattern Analysis, and used Moving-window Analysis to compare the relationship between thermal environment and blue-green spaces at different spatial scales. And so the research results prove that, the high-temperature regions of Tianjin has been expanding along with the expansion and agglomeration of the urban areas and across the river space restrictions, while the low-temperature regions of the city are mainly concentrated in the blue-green spaces, during the periods of 2004, 2011 and 2017. Furthermore, in the landscape pattern level, there is a significant negative correlation between patch perimeter index of blue-green spaces and land surface temperature, and a significant positive correlation between patch perimeter area ratio index of blue-green spaces and land surface temperature. In the multi-scale spatial pattern, the correlation between the Percent of Landscape of blue spaces patches and land surface temperature is the highest in the 1500 meters × 1500 meters sample, and the Percent of Landscape of green spaces patches and land surface temperature is the highest in the 300 meters × 300 meters sample. In addition, the improvement efficiency of thermal environment in blue spaces is higher. In the end, the optimization suggestions of adjusting the layout of blue-green spaces to effectively improve the urban thermal environment problem are proposed. By optimizing the blue-green space landscape patterns at the optimal scale, increasing the blue spaces in the northern high temperature range, widening and transforming the existing waterways, constructing riverside greenways and other strategies to effectively deal with the thermal environmental problems, improving the ecology and livability of the city. It is hoped to provide new ideas and references for the construction of ecological and livable city under the background of rapid urbanization. In the research process, due to the limitation of data sources, calculation methods and other factors, the relevant conclusion is still limited at the spatial scale. In the future research, the thermal environment and the pattern of blue-green spatial landscape at the fine scale will be studied.

Keywords


blue-green spaces; urban thermal environment; land surface temperature; landscape pattern

Full Text:

PDF

References


SHEN Zhongjian, ZENG Jian. Analysis of Spatiotemporal Patterns and Evolution of Regional Thermal Islands in Fujian Delta Urban Agglomeration During Decade of 1996 2017[J]. Journal of Safety and Environment, 2020, 20(4): 1567-1578.

STEWART I D, OKE T R. Local Climate Zones for Urban Temperature Studies[J]. Bulletin of the American Meteorological Society, 2012, 93(12): 1879-1900.

CHEN Ailian, SUN Ranhao, CHEN Li-ding. Studies on Urban Heat Island From a Landscape Pattern View: a Review[J]. Acta Ecologica Sinica, 2012, 32(14): 4553-4565.

RIZWAN A M,DENNIS Y C L,LIU C. A Review on the Generation, Determination and Mitigation of Urban Heat Island[J]. Journal of Environmental Sciences, 2008(1): 120-128.

LIU Shihan, CAO Yingui, JIA Yanhui, et al. Research Progress of Urban Heat Island Effect[J]. Anhui Agricultural Science Bulletin, 2019, 25(23): 117-121.

YIN Shi, WERNER L, XIAO Yiqiang. Summer Thermal Environment of Traditional Shophouse Neighborhood in Hot and Humid Climate Zone[J]. South Architecture, 2019, 25(23): 117-121.

RAO P K. Remote Sensing of Urban “Heat Islands”from an Environmental Satellite[J]. Bulletin of the American Meteorological Society, 1972(53): 647-648.

EMMANUEL R,KRUGER E. Urban Heat Island and Its Impact on Climate Change Resilience in a Shrinking City: the Case of Glasgow, UK[J]. Building and Environment, 2012(53) : 137-149.

CHEN Kai, TANG Yan. Research Progress of Local Climate Zones and Its Applications in Urban Planning[J]. South Architecture,2017(2): 21-28.

SINGH P, KIKON N, VERMA P. Impact of Land Use Change and Urbanization on Urban Heat Island in Lucknow City, Central India. A Remote Sensing Based Estimate[J]. Sustainable Cities and Society, 2017(32) : 100-114.

DING Haiyong, SHI Hengchang. Detection and Changing Analysis of the Urban Heat Islands Based on the Landsat Data—by Taking Nanjing City as a Case Study Sample[J]. Journal of Safety and Environment, 2018, 18(5): 2033-2044.

QIN Menglin, SONG Wenbo, SONG Yuanzhen, et al. Study on Spatial Features and Evolutionary Trend of Heat Islands in Beibu Gulf Urban Agglomeration[J]. Journal of Safety and Environment, 2020, 20(4): 1557-1566.

DENG Yujiao, DU Yaodong, WAND Jiechun, et al. Spatiotemporal Characteristics and Driving Factors of Urban Heat Islands in Guangdong-Hong Kong-Marco Greater Bay Area[J]. Chinese Journal of Ecology, 2020, 39(8): 2671-2677.

CHEN Chen, CAI Zhe, YAN Wei, et al. Study of Temporal and Spatial Variation of Urban Heat Island Based on Landsat TM in Central City and Binhai New Area of Tianjin[J]. Journal of natural Resources, 2010, 25(10): 1727-1737.

YUE Hui, LIU Ying. Comparison and Analysis of Land Surface Temperature Retrieval Algorithms Based on Landsat 8 TIRS[J]. Science Technology and Engineering, 2018, 18(20): 200-205.

DING Feng, XU Hanqiu. Comparison of Three Algorithms for Retrieving Land Surface Temperature from Landsat TM Thermal Infrared Band[J]. Journal of Fujian Normal University (Natural Science Edition), 2008, 99(1): 91-96.

SORINO J, JIMENEZ-MUNOZ J, GUILLEM S, et al. Land Surface Emissivity Retrieval from Different VNIR and TIR sensors [J]. Ieee Transactions on Geoscience and Remote Sensin, 2008, 46(2): 316-327.

LI Xiaoyong, KUANG Wenhui. The Effects of Urban Land Cov-er Composition and Structure on Land Surface Temperature in Beijing,Tianjin ,and Shijiazhuang[J]. Chinese Journal of Ecology, 2019, 38(10): 3057-3065.

WU Jianguo. Landscape Ecology Pattern, Process, Scale and Hierarchy[M] . 2nd ed. Beijing: Higher Education Press, 2007: 107.

LU Huimin, LI Fei, ZHANG Meiliang, et al. Effects of Landscape Pattern on Annual Variation of Thermal Environment in Hangzhou[J]. Remote Sensing Technology and Application, 2018, 33(3):398-407.

CHEN Ailian, SUN Ranhao, CHEN Liding. Applicability of Traditional Landscape Metrics in Evaluating Urban Heat Island Effect[J]. Chinese Journal of Applied Ecology, 2012, 23 (8):

-2086.

ESTOQUE R C, MURAYAMA Y, MYINT S W. Effects of Landscape Composition and Pattern on Land Surface Temperature: An Urban Heat Island Study in the Megacities of Southeast Asia[J]. Science of The Total Environment, 2017(577) : 349-359.

XU Shuang, LI Feixue, ZHANG Lubei, et al. Spatiotemporal Changes of Thermal Environment Landscape Pattern in Changsha[J]. Acta Ecologica Sinica, 2015, 35(11): 3743-3754.

ZOU Jing, ZENG Hui. Relationships Between Urban Landscape Pattern and Land Surface Temperature: A Case Study of Shenzhen[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2017, 53(3): 436-444.

SHEN Zhongjian, ZENG Jian, REN Lanhong. The Spatiotemporal Coupling Relationship of Landscape Pattern and Thermal Environment in Xiamen, 2002—2017[J]. Chinese Landscape Architecture, 2021, 37(3): 100-105.




DOI: https://doi.org/10.33142/jsa.v1i2.12572

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Tian CHEN, Ning TAN

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.