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Environmental Monitoring and Assessment

, Volume 144, Issue 1–3, pp 199–219 | Cite as

Seasonal variations in the relationship between landscape pattern and land surface temperature in Indianapolis, USA

  • Hua Liu
  • Qihao WengEmail author
Article

Abstract

This paper intended to examine the seasonal variations in the relationship between landscape pattern and land surface temperature based on a case study of Indianapolis, United States. The integration of remote sensing, GIS, and landscape ecology methods was used in this study. Four Terra’s ASTER images were used to derive the landscape patterns and land surface temperatures (LST) in four seasons in the study area. The spatial and ecological characteristics of landscape patterns and LSTs were examined by the use of landscape metrics. The impact of each land use and land cover type on LST was analyzed based on the measurements of landscape metrics. The results show that the landscape and LST patterns in the winter were unique. The rest of three seasons apparently had more agreeable landscape and LST patterns. The spatial configuration of each LST zone conformed to that of each land use and land cover type with more than 50% of overlap in area for all seasons. This paper may provide useful information for urban planers and environmental managers for assessing and monitoring urban thermal environments as result of urbanization.

Keywords

Landscape patterns Land surface temperatures Landscape metrics Seasonal variation Urban areas 

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References

  1. Aguiar, R., Oliveira, M., & Goncalves, H. (2002). Climate change impacts on the thermal performance of Portuguese buildings. Results of the SIAM study. Building Service Engineers Research and Technology, 23(4), 223–231.CrossRefGoogle Scholar
  2. Alberti, M. (2005). The effects of urban patterns on ecosystem function. International Regional Science Review, 28(2), 168–192.CrossRefGoogle Scholar
  3. ASTER online products description. (2005). Retrieved from http://asterweb.jpl.nasa.gov/content/03_data/01_Data_Products/SurfaceTemperature.pdf.
  4. Bain, D. J., & Brush, G. S. (2004). Placing the pieces: Reconstructing the original property mosaic in a warrant and patent watershed. Landscape Ecology, 19(8), 843–856.CrossRefGoogle Scholar
  5. Bender, O., Boehmer, H. H., Jens, D., & Schumacher, K. P. (2005). Analysis of land-use change in a sector of Upper Franconia (Bavaria, Germany) since 1850 using land register records. Landscape Ecology, 20(2), 149–163.CrossRefGoogle Scholar
  6. Boyd, D. S., Foody, G. M., Curran, P. J., Lucas, R. M., & Honzak, M. (1996). An assessment of radiance in Landsat TM middle and thermal infrared wavebands for the detection of tropical forest regeneration. International Journal of Remote Sensing, 17(2), 249–261.CrossRefGoogle Scholar
  7. Conway, T. G. (1997). Improved remote heat sensing. Mechanical Engineering, 119, 88–89.Google Scholar
  8. Forman, R. T. T., & Godron, M. (1986). Landscape ecology. New York: Wiley.Google Scholar
  9. Franklin, J. F., & Forman, R. T. T. (1987). Creating landscape patterns by forest cutting: ecological consequences and principles. Landscape Ecology, 1, 5–18.CrossRefGoogle Scholar
  10. Frohn, R. C. (1998). Remote sensing for landscape ecology. Boca Raton, FL: Lewis.Google Scholar
  11. Goetz, S. Z. (1997). Multisensor analysis of NDVI, surface temperature and biophysicalvariables at a mixed grassland site. International Journal of Remote Sensing, 18, 71–94.CrossRefGoogle Scholar
  12. Gustafson, E. J. (1998). Quantifying landscape spatial pattern: What is the state of the art? Ecosystems, 1, 143–156.CrossRefGoogle Scholar
  13. Jacob, F., Olioso, A., Gu, X., Su, Z., & Seguin, B. (2002). Mapping surface fluxes using visible, near infrared, thermal infrared remote sensing data with a spatialized surface energy balance model. Agronomie: Agriculture and Environment, 22, 669–680.Google Scholar
  14. Kasischke, E. S., Smith, K. B., Bourgeau-Chavez, L. L., Romanowicz, E. A., Brunzell, S., & Richardson, C. J. (2003). Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery. Remote Sensing of Environment, 88, 423–441.CrossRefGoogle Scholar
  15. Kato, S., & Yamaguchi, Y. (2006). Analysis of urban heat-island effect using ASTER and ETM+data: separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux. Remote Sensing of Environment, 99, 44–54.CrossRefGoogle Scholar
  16. Krummel, J. R., Gardner, R. H., Sugihara, G., O’Neill, R. V., & Coleman, P. R. (1987). Landscape patterns in a distrurbed environment. Oikos, 48, 321–324.CrossRefGoogle Scholar
  17. Li, H., & Wu, J. (2004). Use and misuse of landscape indices. Landscape Ecology, 19(4), 389–399.CrossRefGoogle Scholar
  18. Lu, D., & Weng, Q. (2004). Spectral mixture analysis of the urban landscape in indianapolis with landsat ETM+imagery. Photogrammetric Engineering & Remote Sensing, 70(9), 1053–1062.Google Scholar
  19. Luvall, J. C., & Holbo, H. R. (1991). Thermal remote sensing methods in landscape ecology. In quantitative methods in landscape ecology. New York: Springer.Google Scholar
  20. Magee, N., Curtis, J., & Wendler, G. (1999). The urban heat island effect at Fairbanks, Alaska. Theoretical and Applied Climatology, 64, 39–47.CrossRefGoogle Scholar
  21. Mandelbrot, B. B. (1983). The fractal geometry of nature. San Francisco, CA: Freeman.Google Scholar
  22. McGarigal, K., Marks, B. J. (1995). FRAGSTATS: Spatial pattern analysis program for quantifying landscape structure. General Technical Report PNW-GTR-351. Portland, OR: USDA Forest Service. Pacific Northwest Research Station.Google Scholar
  23. 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. Retrieved from www.umass.Edu/landeco/research/fragstats/fragstats.
  24. McVicar, T. R., & Jupp, D. L. B. (1998). The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review. Agriculture Systems, 57, 399–468.CrossRefGoogle Scholar
  25. Myeong, S., Nowak, D. J., & Duggin, M. J. (2006). A temporal analysis of urban forest carbon storage using remote sensing. Remote Sensing of Environment, 101, 277–282.CrossRefGoogle Scholar
  26. O’Neill, R. V., Krummel, J. R., Gardner, R. H., Sugihara, G., Jackson, B., DeAngelis, D. L., Milne, B. T., Turner, M. G., Zygmunt, B., Christensen, S., Dale, V. H., & Graham, R. L. (1988). Indices of landscape pattern. Landscape Ecology, 1, 153–162.CrossRefGoogle Scholar
  27. Pan, D., Domon, G., Marceau, D., & Bouchard, A. (2001). Spatial pattern of coniferous and deciduous forest patches in an Eastern North America agricultural landscape: the influence of land use and physical attributes. Landscape Ecology, 16(2), 99–110.CrossRefGoogle Scholar
  28. Peterjohn, W. T., & Correll, D. L. (1984). Nutrient dynamics in an agricultural watershed: observations on the role of a riparian forest. Ecology, 65, 1466–1475.CrossRefGoogle Scholar
  29. Pickett, S. T. A., Kolasa, J., & Jones, C. G. (1994). Ecological understanding. San Diego, Carlifornia, USA: Academic.Google Scholar
  30. Quattrochi, D. A., & Luvall, J. C. (1999). Thermal infrared remote sensing for analysis of landscape ecological process: methods and applications: methods and applications. Landscape Ecology, 14, 577–598.CrossRefGoogle Scholar
  31. Quattrochi, D. A., & Ridd, M. K. (1998). Analysis of vegetation within a semi-arid urban environment using high spatial resolution airborne thermal infrared remote sensing data. Atmosphere Environment, 32(1), 19–33.CrossRefGoogle Scholar
  32. Riitters, K. H., O’Neill, R. V., Hunsaker, C. T., Wickham, J. D., Yankee, D. H., & Timmins, S. P. (1995). A factor analysis of landscape pattern and structure metrics. Landscape Ecology, 10(1), 23–39.CrossRefGoogle Scholar
  33. Smith, R. M. (1986). Comparing traditional methods for selecting class intervals on choropleth maps. Professional Geographer, 38(1), 62–67.CrossRefGoogle Scholar
  34. Streutker, D. (2002). A remote-sensing study of the urban heat island of Houston, Texas. International Journal of Remote Sensing, 23, 2595–2608.CrossRefGoogle Scholar
  35. Streutker, D. (2003). Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing of Environment, 85, 282–289.CrossRefGoogle Scholar
  36. Turner, M. G. (1990). Spatial and temporal analysis of landscape patterns. Landscape Ecology, 4(1), 21–30.CrossRefGoogle Scholar
  37. Voogt, J. A., & Oke, T. R. (1997). Complete urban surface temperatures. Journal of Applied Meteorology, 36, 1117–1132.CrossRefGoogle Scholar
  38. Voogt, J. A., & Oke, T. R. (1998). Effects of urban surface geometry on remotely-sensed surface temperature. International Journal of Remote Sensing, 19(5), 895–920.CrossRefGoogle Scholar
  39. Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86, 370–384.CrossRefGoogle Scholar
  40. Wan, Z., & Dozier, J. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(2), 892–905.Google Scholar
  41. Wan, Z., Wang, P., & Li, X. (2004a). Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. International Journal of Remote Sensing, 25(1), 61–72.CrossRefGoogle Scholar
  42. Wan, Z., Zhang, Y., Zhang, Q., & Li, Z.-L. (2004b). Quality assessment and validation of the MODIS global land surface temperature. International Journal of Remote Sensing, 25(1), 261–274.CrossRefGoogle Scholar
  43. Wang, Y., Zhang, X., Liu, H., Ruthie, H. K. (1999). Landscape characterization of metropolitan Chicago region by Landsat TM. The Proceeding of ASPRS annual conference, 238–247.Google Scholar
  44. Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89, 467–483.CrossRefGoogle Scholar
  45. Wu, J., Dennis, E. J., Matt, L., & Paul, T. T. (2000). Multiscale analysis of landscape heterogeneity: scale variance and pattern metrics. Geographic Information Sciences, 6(1), 6–19.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Center for Urban and Environmental Change, Department of Geography, Geology, and AnthropologyIndiana State UniversityTerre HauteUSA

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