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Analysis of the spatiotemporally varying effects of urban spatial patterns on land surface temperatures

    Cheng Li Affiliation
    ; Jie Zhao Affiliation
    ; Nguyen Xuan Thinh Affiliation
    ; Wenfu Yang Affiliation
    ; Zhen Li Affiliation

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
Published in Issue
Oct 9, 2018
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References

Akbari, H.; Pomerantz, M.; Taha, H. 2001. Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas, Solar Energy 70: 295–310. https://doi.org/10.1016/S0038-092X(00)00089-X

Arnold, C. L.; Gibbons, C. J. 1996. Impervious surface coverage: the emergence of a key environmental indicator, Journal of the American Planning Association 62(2): 243–258. https://doi.org/10.1080/01944369608975688

Artis D. A.; Carnahan, W. H. 1982. Survey of emissivity variability in thermography of urban areas, Remote Sensing of Environment 12(4): 313–329. https://doi.org/10.1016/0034-4257(82)90043-8

Batisani, N.; Yarnal, B. 2009. Urban expansion in Centre country, Pennsylvania: spatial dynamics and landscape transformations, Applied Geography 29(2): 235–249. https://doi.org/10.1016/j.apgeog.2008.08.007

Bokaie M.; Zarkesh, M. K.; Arasteh, P. D.; Hosseini, A. 2016. Assessment of Urban Heat Island based on the relationship between land surface temperature and Land Use/ Land Cover in Tehran, Sustainable Cities and Society 23: 94–104. https://doi.org/10.1016/j.scs.2016.03.009

Brunsdon, C.; Fotheringham, A. S.; Charlton, M. 1996. Geographically weighted regression: a method for exploring nonstationarity, Geographical Analysis 28(4): 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x

Buyantuyev, V.; 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. https://doi.org/10.1007/s10980-009-9402-4

Carlson, T. N.; Ripley, D. A. 1997. On the relation between NDVI fractional vegetation cover, and leaf area index, Remote Sensing of Environment 62: 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1

Chander, G.; Markham, B. L. 2003. Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges, IEEE Transactions on Geoscience and Remote Sensing 41(11): 2674–2677. https://doi.org/10.1109/TGRS.2003.818464

Chander, G.; Markham, B. L.; Helder, D. L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sensing of Environment 113(5): 893–903. https://doi.org/10.1016/j.rse.2009.01.007

Chavez, P. S. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data, Remote Sensing of Environment 24(3): 459–479. https://doi.org/10.1016/0034-4257(88)90019-3

Connors, J. P.; Galletti, C. S.; Chow, W. T. L. 2013. Landscape configuration and urban heat island effects: assessing the re lationship 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

Dewan, A. M.; Yamaguchi, Y. 2009. Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization, Applied Geography 29(3): 390–401. https://doi.org/10.1016/j.apgeog.2008.12.005

Fang, C.; Wang, S.; Li, G. 2015. Changing urban forms and carbon dioxide emissions in China: a case study of 30 provincial capital cities, Applied Energy 158: 519–531. https://doi.org/10.1016/j.apenergy.2015.08.095

Forman, R. T. T. 1995. Land mosaics: the ecology of landscapes and regions. Cambridge: Cambridge University Press.

Fotheringham, A. S.; Charlton, M.; Brunsdon, C. 1996. The geography of parameter space: an investigation of spatial nonstationarity, International Journal of Geographical Information System 10(5): 605–627. https://doi.org/10.1080/026937996137909

Gao, J.; Li, S. 2011. Detecting spatially non-stationary and scaledependent relationships between urban landscape fragmentation and related factors using Geographically Weighted Regression, Applied Geography 31(1): 292–302. https://doi.org/10.1016/j.apgeog.2010.06.003

Guo, G.; Zhou, X.; Wu, Z.; Xiao, R.; Chen, Y. 2016. Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou, China, Environmental Modelling & Software 84: 427–439. https://doi.org/10.1016/j.envsoft.2016.06.021

Gustafson, E. J. 1998. Quantifying landscape spatial pattern: what is the state of the art?, Ecosystems 1(2): 143–156. https://doi.org/10.1007/s100219900011

Hamada, S.; Ohta, T. 2010. Seasonal variations in the cooling effect of urban green areas on surrounding urban areas, Urban Forestry & Urban Greening 9: 15–24. https://doi.org/10.1016/j.ufug.2009.10.002

Herold, M.; Couclelis, H.; Clarke, K. C. 2005. The role of spatial metrics in the analysis and modeling of urban land use change, Computer, Environment and Urban Systems 29(4): 369–399. https://doi.org/10.1016/j.compenvurbsys.2003.12.001

Huang, G.; Cadenasso, M. L. 2016. People, landscape, and urban heat island: dynamics among neighborhood social conditions, land cover and surface temperatures, Landscape Ecology 31(10): 2507–2515. https://doi.org/10.1007/s10980-016-0437-z

Jantz, C. A.; Goetz, S. J.; Shelley, M. K. 2004. Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltmore-Washington metropolitan area, Environment and Planning B: Planning and Design 31(2): 251–271. https://doi.org/10.1068/b2983

Jimenez-Munoz, J. C.; Sobrino, J. A. 2003. A generalized single channel method for retrieving land surface temperature from remote sensing data, Journal of Geophysical Research 108(D22): 2015–2023. https://doi.org/10.1029/2003JD003480

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

Patino, J. E.; Duque, J. C. 2013. A review of regional science applications of satellite remote sensing in urban settings, Computers, Environment and Urban Systems 37: 1–17. https://doi.org/10.1016/j.compenvurbsys.2012.06.003

Pham, H. M.; Yamaguchi, Y.; Bui, T. Q. 2011. A case study on the relation between city planning and urban growth using remote sensing and spatial metrics, Landscape and Urban Planning 100(3): 223–230. https://doi.org/10.1016/j.landurbplan.2010.12.009

Qin, Z.; Karnieli, A.; Berliner, P. 2001. 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(18): 3719–3746. https://doi.org/10.1080/01431160010006971

Ridd, M. K. 1995. Exploring a V-I-S (Vegetation-Impervious Surface-Soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities, International Journal of Remote Sensing 16(2): 2165–2185. https://doi.org/10.1080/01431169508954549

Schwarz, N. 2010. Urban form revisited-Selecting indicators for characterising European cities, Landscape and Urban Planning 96(1): 29–47. https://doi.org/10.1016/j.landurbplan.2010.01.007

Setiawan, H.; Mathieu, R.; Thompson-Fawcett, M. 2006. Assessing the applicability of the V-I-S model to map urban land use in the developing world: case study of Yogyakarta, Indonesia, Computers, Environment and Urban System 30(4): 503–522. https://doi.org/10.1016/j.compenvurbsys.2005.04.003

Sobrino, J. A.; Jménez-Muñoz, J. C.; El-Kharraz, J.; Gómez, M.; Romaguera, M.; Sòria, M. 2004. Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site, International Journal of Remote Sensing 24: 215–230. https://doi.org/10.1080/0143116031000115210

Song, J.; Du, S.; Feng, X.; Guo, L. 2014. The relationships between landscape compositions and land surface temperature: quantifying their resolution sensitivity with spatial regression models, Landscape and Urban Planning 123: 145–157. https://doi.org/10.1016/j.landurbplan.2013.11.014

Streutker, D. R. 2002. A remote sensing study of the urban heat island of Houston, Texas, International Journal of Remote Sensing 23(13): 2595–2608. https://doi.org/10.1080/01431160110115023

Su, S.; Xiao, R.; Zhang, Y. 2011. Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression, Applied Geography 32(2): 360–375. https://doi.org/10.1016/j.apgeog.2011.06.005

Tu, J. 2011. Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression, Applied Geography 31(1): 376–392. https://doi.org/10.1016/j.apgeog.2010.08.001

Voogt, J. A.; Oke, T. R. 1998. Effects of urban surface geometry on remotely-sensed surface temperature, International Journal of Remote Sensing 19: 895–920. https://doi.org/10.1080/014311698215784

Voogt, J. A.; Oke, T. R. 2003. Thermal remote sensing of urban climates, Remote Sensing of Environment 86: 370–384. https://doi.org/10.1016/S0034-4257(03)00079-8

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. https://doi.org/10.1016/j.isprsjprs.2009.03.007

Weng, Y. C. 2007. Spatiotemporal changes of landscape pattern in response to urbanization, Landscape and Urban Planning 81(4): 341–353. https://doi.org/10.1016/j.landurbplan.2007.01.009

White, M. A.; Nemani, R. R.; Thornton, P. E.; Running, S. W. 2002. Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest, Ecosystems 5: 260–277. https://doi.org/10.1007/s10021-001-0070-8

Wu, C. D.; Lung, S. C. C.; Jan, J. F. 2013. Development of a 3-D urbanization index using digital terrain models for surface urban heat island effects, ISPRS Journal of Photogrammetry & Remote Sensing 81(7): 1–11. https://doi.org/10.1016/j.isprsjprs.2013.03.009

Wu, J.; Shen, W.; Sun, W.; Tueller, P. T. 2002. Empirical patterns of the effects of changing scale on landscape metrics, Landscape Ecology 17: 761–782. https://doi.org/10.1023/A:1022995922992

Yuan, F.; Bauer, M. E. 2007. Comparison of impervious surface area and normalized difference vegetation index as indica tors of surface urban heat island effects in Landsat imagery, Remote Sensing of Environment 106(3): 375–386. https://doi.org/10.1016/j.rse.2006.09.003

Zhang, H.; Qi, Z.; Ye, X.; Cai, Y.; Ma, W.; Chen, M. 2013. Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China, Applied Geography 44: 121–133. https://doi.org/10.1016/j.apgeog.2013.07.021

Zhou, W.; Huang, G.; Cadenasso, M. L. 2011. Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes, Landscape and Urban Planning 102(1): 54–63. https://doi.org/10.1016/j.landurbplan.2011.03.009

Zhou, W.; Qian, Y.; Li, X.; Han, L. 2014. Relationships between land cover and the surface urban heat island: Seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures, Landscape Ecology 29(1): 153–167. https://doi.org/10.1007/s10980-013-9950-5

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. https://doi.org/10.1016/j.rse.2017.03.043