Abstract
A city is a mixed ecosystem of nature, economy, and society and is simultaneously transforming natural areas and adapting to nature. Urbanization causes the population to expand rapidly, leading to rapid expansions of scale. Consequently, the proportions of impermeable surfaces (ISs) and greenspaces (GSs) change drastically, which has a considerable influence on the urban thermal environment. The aim of this study was to research the effects of spatio-temporal landscape patterns on land surface temperature (LST) and between GS and IS in the city of Xi’an using the urban-rural gradient, the moving split-window algorithm (MSA), multiple grid resolutions, and landscape metrics based on three-phase Landsat data. The results showed that there was a significantly positively correlated with IS density and significantly negatively correlated with the GS density from the urban center to rural areas. Over the past 25 years, the main urban area of Xi’an has expanded by nearly 6.2 times its initial size. The correlation between IS density and LST increased with increasing grid size, and the correlation between GS density and LST increased with decreasing grid size. Thus, LST is highly sensitive to the ISs and GSs at particular grid sizes. The correlation coefficients of the ISs and GSs with LST increased with decreasing grid size during 1992–2016. Hence, the LST was less sensitive to IS and the GS densities in conjunction with larger grid sizes. The class area (CA) and the landscape shape index (LSI) of the ISs were significantly positively correlated with the LST, whereas the CA and largest patch index (LPI) of the GSs were negatively correlated with the LST. The LST of the ISs in 1992, 2006, and 2016 were 1.6, 1.8, and 3.9 °C higher, respectively, than those of the GSs, indicating that GSs are important to lowering urban LSTs. Therefore, the government and urban planning departments should consider optimizing the spatial patterns of ISs and GSs to fully exploit the cooling effect of optimally configured GSs, which would be conducive to the sustainable development of the urban ecological environment.
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References
Ahmed, B., Kamruzzaman, M., Zhu, X., Rahman, M., & Choi, K. (2013). Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote Sensing, 5(11), 5969–5998. https://doi.org/10.3390/rs5115969.
Brown, D. R. N., Jorgenson, M. T., Kielland, K., Verbyla, D. L., Prakash, A., & Koch, J. C. (2016). Landscape effects of wildfire on permafrost distribution in interior Alaska derived from remote sensing. Remote Sensing, 8(8). https://doi.org/10.3390/rs8080654.
Cao, H., Liu, J., Fu, C., Zhang, W., Wang, G., Yang, G., & Luo, L. (2017). Urban expansion and its impact on the land use pattern in Xishuangbanna since the reform and opening up of China. Remote Sensing, 9(2), 1–21. https://doi.org/10.3390/rs9020137.
Connors, J. P., Galletti, C. S., & Chow, W. T. L. (2013). Landscape configuration and urban heat island effects: Assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landscape Ecology, 28(2), 271–283. https://doi.org/10.1007/s10980-012-9833-1.
De Faria, L., José, A., Lucena, D., Corrêa, O., Filho, R., Ricardo, J., & França, D. A. (2018). The urban heat island in Rio de Janeiro , Brazil , in the last 30 years using remote sensing data. Int J Appl Earth Obs Geoinformation, 64(September 2017), 104–116. https://doi.org/10.1016/j.jag.2017.08.012.
Estoque, R. C., & Murayama, Y. (2017). Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015). ISPRS Journal of Photogrammetry and Remote Sensing, 133, 18–29. https://doi.org/10.1016/j.isprsjprs.2017.09.008.
Estoque, R. C., Murayama, Y., & Myint, S. W. (2017). Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of southeast Asia. Science of the Total Environment, 577, 349–359. https://doi.org/10.1016/j.scitotenv.2016.10.195.
Fan, C., Myint, S. W., & Zheng, B. (2015). Measuring the spatial arrangement of urban vegetation and its impacts on seasonal surface temperatures. Progress in Physical Geography, 39(2), 199–219. https://doi.org/10.1177/0309133314567583.
Gao, M., Shen, H., Han, X., Li, H., & Zhang, L. (2018). Multiple timescale analysis of the urban heat island effect based on the community land model: a case study of the city of Xi’an, China. Environmental Monitoring and Assessment, 190(1). https://doi.org/10.1007/s10661-017-6320-9.
Henits, L., Mucsi, L., & Liska, C. M. (2017). Monitoring the changes in impervious surface ratio and urban heat island intensity between 1987 and 2011 in Szeged, Hungary. Environmental Monitoring and Assessment, 189(2). https://doi.org/10.1007/s10661-017-5779-8.
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. https://doi.org/10.1016/j.rse.2011.07.008.
Li, X., Zhou, W., Ouyang, Z., Xu, W., & Zheng, H. (2012). Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landscape Ecology, 27(6), 887–898. https://doi.org/10.1007/s10980-012-9731-6.
Li, B., Ren, Z., Li, R., & Wang, Y. (2014). Urban heat island intensity quantitative inversion and city land surface temperatures in Xi’an. Resources Science, 36, 2631–2636 (in Chinese).
Ma, Q., Wu, J., & He, C. (2016). A hierarchical analysis of the relationship between urban impervious surfaces and land surface temperatures: spatial scale dependence, temporal variations, and bioclimatic modulation. Landscape Ecology, 31(5), 1139–1153. https://doi.org/10.1007/s10980-016-0356-z.
Morabito, M., Crisci, A., Messeri, A., Orlandini, S., Raschi, A., Maracchi, G., & Munafò, M. (2016). The impact of built-up surfaces on land surface temperatures in Italian urban areas. Science of the Total Environment, 551–552, 317–326. https://doi.org/10.1016/j.scitotenv.2016.02.029.
Oke, T. R. (1973). City size and the urban heat island. Atmospheric Environment, 7, 769–779.
Qin, Z., Karnieli, A., & Berliner, P. (2010). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22, 3719–3746. https://doi.org/10.1080/01431160010006971.
Rouse, J. W. J., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with erts. Nasa Special Publication, 351, 309.
Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), 370–384. https://doi.org/10.1016/S0034-4257(03)00079-8.
Wang, C., Myint, S. W., Wang, Z., & Song, J. (2016). Spatio-temporal modeling of the urban heat island in the Phoenix metropolitan area: land use change implications. Remote Sensing, 8(3). https://doi.org/10.3390/rs8030185.
Webster, R. (1973). Automatic soil-boundary location from transect data. Journal of the International Association for Mathematical Geology, 5, 27–37.
Webster, R., & Wong, I. F. T. (1969). A numerical procedure for testing soil boundaries interpreted from air photographs. Photogrammetria, 24, 59–72.
Whittaker, R. H. (1960). Vegetation of the Siskiyou Mountains. Oregon and California. Ecol Mongogr, 30, 279–338.
Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179.
Xu, S. (2009). An approach to analyzing the intensity of the daytime surface urban heat island effect at a local scale. Environmental Monitoring and Assessment, 151(1–4), 289–300. https://doi.org/10.1007/s10661-008-0270-1.
Zhang, Y., Han, L., & Xie, L. (2012). Study of heat island effect in Xi’an based on moving split-window analysis. Science of Surveying and Mapping, 37, 148–150 (in Chinese).
Zhang, Y., Odeh, I. O. A., & Ramadan, E. (2013). Assessment of land surface temperature in relation to landscape metrics and fractional vegetation cover in an urban/peri-urban region using Landsat data. International Journal of Remote Sensing, 34(1), 168–189. https://doi.org/10.1080/01431161.2012.712227.
Zhou, D., Zhang, L., Hao, L., Sun, G., Liu, Y., & Zhu, C. (2016). Spatiotemporal trends of urban heat island effect along the urban development intensity gradient in China. Science of the Total Environment, 544(219), 617–626. https://doi.org/10.1016/j.scitotenv.2015.11.168.
Acknowledgments
The authors would like to thank Professor Wei Wang for critically reviewing the manuscript.
Funding
This work was supported by a grant from the National Key R&D Program of China (2017YFC0803700), the National 863 Plan (Grant number: 2013AA01A608), and the National Science and Technology Major Special Water Special Project (Grant number: 2013ZX07503001-06).
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Li, B., Wang, W., Bai, L. et al. Effects of spatio-temporal landscape patterns on land surface temperature: a case study of Xi’an city, China. Environ Monit Assess 190, 419 (2018). https://doi.org/10.1007/s10661-018-6787-z
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DOI: https://doi.org/10.1007/s10661-018-6787-z