Understanding the effects of the impervious surfaces pattern on land surface temperature in an urban area

Frontiers of Earth Science

Volumn 9, Issue 2, 2015, Pages 276-285

  Nie, Qin ^a,b   Xu, Jianhua ^a,b  

^a XIAMEN UNIVERSITY OF TECHNOLOGY   (China)

^b EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

land surface temperature; Shanghai city; spatial pattern; urban impervious surfaces

Indexed keywords

IMAGE ANALYSIS; IMAGE CLASSIFICATION; LAND SURFACE; REMOTE SENSING; SPATIOTEMPORAL ANALYSIS; TEMPERATURE EFFECT; URBAN AREA;

CHINA; SHANGHAI;

EID: 84946166727     PISSN: 20950195     EISSN: 20950209     Source Type: Journal    
DOI: 10.1007/s11707-014-0459-2     Document Type: Article

References (29)

1. Artis D A, Carnahan W H (1982). Survey of emissivity variability in thermography of urban areas. Remote Sens Environ, 12(4): 313–329
2. Ding J C, Zhang Z K, Xi H, Zhou H M (2002). A study of the high temperature distribution and the heat island effect in the summer of the Shanghai area. Chin J Atmos Sci, 26(3): 412–420 (in Chinese)
3. Gong A D, Jiang Z X, Li J, Chen Y H, Hu H L (2005). Urban land surface temperature retrieval based on landsat TM remote sensing images in Beijing. Remote Sensing Information, (3): 18–20
4. Hope A, Engstrom R, Stow D (2005). Relationship between AVHRR surface temperature and NDVI in Arctic tundra ecosystems. Int J Remote Sens, 26(8): 1771–1776
5. Lu D, Weng Q (2006). Use of impervious surface in urban land-use classification. Remote Sens Environ, 102(1–2): 146–160
6. 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. Available at the following web site: http://www.umass.edu/landeco/research/fragstats/fragstats.html
7. Meiyappan P, Jain A K (2012). Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Front Earth Sci, 6(2): 122–139
8. Meng X L (2010). Multi-scale Relationships Between Impervious Surface, Vegetation, Water and Urban HeatIsland. East China Normal University
9. Ridd M K (1995). Exploring a V-I-S (vegetation-impervious surfacesoil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities. Int J Remote Sens, 16(12): 2165–2185
10. Slonecker E T, Jennings D B, Garofalo D (2001). Remote sensing of impervious surfaces: a review. Remote Sens Rev, 20(3): 227–255
11. Small C (2001). Estimation of urban vegetation abundance by spectral mixture analysis. Int J Remote Sens, 22(7): 1305–1334
12. Van De Gfiend A, Owe M (1993). On the relationship between thermal emissivity and the normalized difference vegetation index for nature surfaces. In ternational Journal of Remote Sensing, 14(6): 1119–1131
13. Weng Q (2012). Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens Environ, 117: 34–49
14. Weng Q, Liu H, Lu D (2007). Assessing the effects of land use and land cover patterns on thermal conditions using land scape metrics in city of Indianapolis, United States. Urban Ecosyst, 10(2): 203–219
15. Weng Q, Lu D, Schubring J (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens Environ, 89(4): 467–483
16. Wong N H, Yu C (2005). Study of green areas and urban heat island in a tropical city. Habitat Int, 29(3): 547–558
17. Xian G, Crane M, Su J (2007). An analysis of urban development and its environmental impact on the Tampa Bay watershed. J Environ Manage, 85(4): 965–976
18. Xiao R B, Ouyang Z Y, Zheng H, Li E F, Schienke E W Wang X K (2007). Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. J Environ Sci (China), 19(2): 250–256
19. Xu J H, Ai N S, Chen Y, Mei A X, Liao H J (2003). Quantitative analysis and fractal modeling on the mosaic structure of landscape in the central area of Shanghai metropolis. Chin Geogr Sci, 13(3): 199–206
20. Xu J H, Lu Y, Ai N S, Yue W Z (2001). A study on landscape mosaic structure in urban-rural area in Northwest of China with RS and GIS. Chin Geogr Sci, 11(4): 366–376
21. Yang X, Liu Z (2005). Use of satellite-derived landscape imperviousness index to characterize urban spatial growth. Computers, Environment and Urban Systems, 29(5): 524–540.
22. Yue W (2005). Study on Urban Landscape Pattern and Its Thermal Environment Eeffect Based on Remote Sensing Image. East China Normal University
23. Yuan F, Bauer M E (2005). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3): 375–386.
24. Yue W (2009). Improvement of urban impervious surface estimation in Shanghai using Landsat7 ETM+ data. Chin Geogr Sci, 19(3): 283–290
25. Yue W, Liu Y, Fan P, Ye X, Wu C (2012). Assessing spatial pattern of urban thermal environment in Shanghai, China. Stochastic Environ Res Risk Assess, 26(7): 899–911
26. Zhang Y, Odeh I OA, Han C (2009a). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11(4): 256–264
27. Zhang X, Zhong T, Feng X, Wang K (2009b). Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing. Int J Remote Sens, 30(8): 2105–2118
28. Zhou S Z, Zhang C (1982). On the Shanghai urban heat island effect. Acta Geographica Sinica, 37(4): 372–381 (in Chinese)
29. 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. Landsc Urban Plan, 102(1):54–63