Analysis of the spatiotemporally varying effects of urban spatial patterns on land surface temperatures
Abstract
Urban heat islands (UHIs) are a worldwide phenomenon that have many ecological and social consequences. It has become increasingly important to examine the relationships between land surface temperatures (LSTs) and all related factors. This study analyses Landsat data, spatial metrics, and a geographically weighted regression (GWR) model for a case study of Hangzhou, China, to explore the correlation between LST and urban spatial patterns. The LST data were retrieved from Landsat images. Spatial metrics were used to quantify the urban spatial patterns. The effects of the urban spatial patterns on LSTs were further investigated using Pearson correlation analysis and a GWR model, both at three spatial scales. The results show that the LST patterns have changed significantly, which can be explained by the concurrent changes in urban spatial patterns. The correlation coefficients between the spatial metrics and LSTs decrease as the spatial scale increases. The GWR model performs better than an ordinary least squares analysis in exploring the relationship of LSTs and urban spatial patterns, which is indicated by the higher adjusted R2 values, lower corrected Akaike information criterion and reduced spatial autocorrelations. The GWR model results indicate that the effects of urban spatial patterns on LSTs are spatiotemporally variable. Moreover, their effects vary spatially with the use of different spatial scales. The findings of this study can aid in sustainable urban planning and the mitigation the UHI effect.
Keyword : land surface temperature, urban spatial pattern, geographically weighted regression, spatiotemporally heterogeneity, scale effect
How to Cite
Li, C., Zhao, J., Thinh, N. X., Yang, W., & Li, Z. (2018). Analysis of the spatiotemporally varying effects of urban spatial patterns on land surface temperatures. Journal of Environmental Engineering and Landscape Management, 26(3), 216-231. https://doi.org/10.3846/jeelm.2018.5378
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Li, C.; Thinh, N. X. 2013. Investigation and comparison of landcover change patterns in Xuzhou city, China, and Dortmund city region, Germany, using multitemporal Landsat images, Journal of Applied Remote Sensing 7(1): 073458. https://doi.org/10.1117/1.JRS.7.073458
Li, C.; Thinh, N. X.; Zhao, J. 2014. Spatiotemporally varying relationships between urban growth patterns and driving factors in Xuzhou city, China, Photogrammetrie Fernerkundung Geoinformation 6: 535–548. https://doi.org/10.1127/pfg/2014/0246
Li, F.; Jackson, T. J.; Kustas, W. P.; Schmugge, T. J.; French, A. N.; Cosh, M. H.; Bindlish, R. 2004. Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX, Remote Sensing of Environment 92: 521–534. https://doi.org/10.1016/j.rse.2004.02.018
Li, J.; Song, C.; Cao, L.; Zhu, F.; Meng, X. 2011. Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China, Remote Sensing of Environment 115: 3249–3263. https://doi.org/10.1016/j.rse.2011.07.008
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 Planning 114: 1–8. https://doi.org/10.1016/j.landurbplan.2013.02.005
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
Liang, B.; Weng, Q. 2008. Multi-scale analysis of census-based land surface temperature variations and determinants in Indianapolis, United States, Journal of Urban Planning D-ASCE 134(30): 129–139. https://doi.org/10.1061/(ASCE)0733-9488(2008)134:3(129)
Lu, D.; Weng, Q. H. 2004. Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ Imagery, Photogrammetric Engineering & Remote Sensing 70(9): 1053–1062. https://doi.org/10.14358/PERS.70.9.1053
Luck, M.; Wu, J. 2002. A gradient analysis of the landscape pattern of urbanization in the Phoenix metropolitan area of USA, Landscape Ecology 17(4): 327–339. https://doi.org/10.1023/A:1020512723753
Maimaitiyiming, M.; Ghulam, A.; Tiyip, T.; Pla, F.; Latorre-Carmona, P.; Halik, Ü. 2014. Effects of green space spatial pattern on land surface temperature: implications for sustainable urban planning and climate change adaptation, ISPRS Journal of Photogrammetry and Remote Sensing 89: 59–66. https://doi.org/10.1016/j.isprsjprs.2013.12.010
Mallick, J.; Rhaman, A.; Singh, C. K. 2013. Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city, Delhi-India, Advances in Space Research 52(4): 639–655. https://doi.org/10.1016/j.asr.2013.04.025
Marceau, D. G. 1999. The scale issue in social and natural sciences, Canadian Journal of Remote Sensing 25(4): 347–356. https://doi.org/10.1080/07038992.1999.10874734
Markham, B. L.; Barker, J. L. 1985. Spectral characterization of the LANDSAT thematic mapper sensors, International Journal of Remote Sensing 6(5): 697–716. https://doi.org/10.1080/01431168508948492
McGarigal, K.; Cushman, S. A.; Ene, E. 2012. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps [online]. Computer software program produced by the authors at the University of Massachusetts, Amherst [cited 8 April 2016]. Available from Internet: http://www.umass.edu/landeco/research/fragstats/fragstats.html
Kikon, N.; Singh, P.; Singh, S. K.; Vyas, A. 2016. Assessment of urban heat islands (UHI) of Noida City, India using multitemporal satellite data, Sustainable Cities and Society 22: 19–28. https://doi.org/10.1016/j.scs.2016.01.005
Kumar, D.; Shekhar, S. 2015. Statistical analysis of land surface temperature-vegetation indexes relationship through thermal remote sensing, Ecotoxicology and Environmental Safety 121: 39–44. https://doi.org/10.1016/j.ecoenv.2015.07.004
Oke, T. R. 1982. The energetic basis of the urban heat island, Quarterly Journal of the Royal Meteorological Society 108(455): 1–24. https://doi.org/10.1002/qj.49710845502
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