A quantitative approach for analyzing the relationship between urban heat islands and land cover

Remote Sensing

Volumn 4, Issue 11, 2012, Pages 3596-3618

  Ogashawara, Igor ^a   da Silva Brum, Vanessa da Silva Brum ^a  

^a INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

Author keywords

Brightness temperature; Correlation matrix; Heat island; Land cover; NDBI; NDVI; NDWI

Indexed keywords

BRIGHTNESS TEMPERATURES; CORRELATION MATRIX; HEAT ISLAND; LAND COVER; NDBI; NDVI; NDWI;

ATMOSPHERIC HUMIDITY; ATMOSPHERIC TEMPERATURE; ECONOMIC AND SOCIAL EFFECTS; VEGETATION;

LANDFORMS;

EID: 84870776155     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs4113544     Document Type: Article

References (45)

1. Lombardo, M.A. Ilha de Calor Nas Metrópoles: O Exemplo de São Paulo (in Portuguese), 1st ed.; Hucitec: São Paulo, Brazil, 1985; p. 244.
2. Oke, T.R. Boundary Layer Climates, 2nd ed.; Methuen: London, UK, 1987; p. 435.
3. Tan, J.; Zheng, Y.; Tang, X.; Guo, C.; Li, L.; Song, G.; Zhen, X.; Yuan, D.; Kalkstei, A.J.; Li, F.; et al. The urban heat island and its impact on heat waves and human health in Shanghai. Int J. Biometeorol. 2010, 54, 75-84.
4. Tarifa, J.R. Análise comparativa da temperatura e umidade na área urbana e rural de São José dos ampos (in Portuguese). Boletim de Geografia Teorética 1977, 4, 59-80.
5. Ashworth, J.R. The influence of smoke and hot gases from factory chimneys on rainfall. Q. J. Roy. Meteorol. Soc. 1929, 55, 341-350.
6. Chandler, T.J. The Climate of London, 1st ed.; Hutchinson & Co.: London, UK, 1965.
7. Voogt, J.A. Urban Heat Island: Hotter Cities; ActionBioscience: North Port, FL, USA, 2004. Available online: http://www.actionbioscience.org/environment/voogt.html (accessed on 10 October 2012).
8. Fabrizi, R.; Bonafoni, S.; Biondi, R. Satellite and ground-based sensors for the urban heat island analysis in the City of Rome. Remote Sens. 2010, 2, 1400-1415.
9. Voogt, J.A.; Oke, T.R. Thermal remote sensing of urban climates. Remote Sens. Environ. 2003, 86, 370-384.
10. Bechtel, B.; Zaksek, K.; Hoshyaripour, G. Downscaling land surface temperature in an urban area: A case study for Hamburg, Germany. Remote Sens. 2012, 4, 3184-3200.
11. Rao, P.K. Remote sensing of urban "heat islands" from an environmental satellite. Bull. Am. Meteorol. Soc. 1972, 53, 647-648.
12. Baptista, G.M.M. Estudo multitemporal do fenômeno de ilhas de calor no Distrito Federal (in Portuguese). Revista Meio Ambiente 2002, 02, 03-17.
13. Xiong, Y.; Huang, S.; Chen, F.; Ye, H.; Wang, C.; Zhu, C. The impacts of rapid urbanization on the thermal environment: A remote sensing study of Guangzhou, South China. Remote Sens. 2012, 4, 2033-2056.
14. Rinner, C.; Hussain, M. Toronto's urban heat island-Exploring the relationship between land use and surface temperature. Remote Sens. 2011, 3, 1251-1265.
15. Costa, P.E.O. Legislação Urbanística e Crescimento Urbano em São José dos Campos (in Portuguese). Master Thesis, Universidade de São Paulo, São Paulo, Brasil, 2007.
16. Câmara, G.; Souza, R.C.M.; Freitas, U.M.; Garrido, J.C.P. Spring: Integrating remote sensing and GIS with object-oriented data modelling. Comput. Graph. 1996, 15, 13-22.
17. Rees, W.G. Physical Principles of Remote Sensing, 2nd ed.; Cambrigde University Press: Cambrigde, UK, 2001; p. 343.
18. Cristóbal, J.; Jiménez-Muñoz, J.C.; Sobrino, J.A.; Ninyerola, M.; Pons, X. Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. J. Geophys. Res. 2009, 114, 1-16.
19. Jiménez-Muñoz, J.C.; Sobrino, J.A. A generalized single-channel method for retrieving land surface temperature from remote sensing data. J. Geophys. Res. 2003, 108, 1-9.
20. Chen, Y.; Wang, J.; Li, X. A study on urban thermal field in summer based on satellite remote sensing. Remote Sens. Land Resour. 2002, 4, 55-59.
21. Sobrino, J.A.; Jiménez-Muñoz, J.C.; Paolini, L. Land surface temperature retrieval from Landsat TM 5. Remote Sens. Environ. 2004, 90, 434-440.
22. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS. In Proceedings of the Third ERTS Symposium NASA SP-351, Washignton, DC, USA, 10-14 December 1973; Volume 1, pp. 309-317.
23. Gao, B.C. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257-266.
24. Zha, Y.; Gao, J.; Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 2005, 24, 583-594.
25. Chander, G.; Markham, B.L.; Helder, D.L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors. Remote Sens. Environ. 2009, 113, 893-903.
26. FLAASH Module. Atmospheric Correction Module: QUAC and FLAASH User's Guide; Version 4.7; ITT Visual Information Solutions: Boulder, CO, USA, 2009; p. 44.
27. Ponzoni, F.J.; Shimabukuro, Y.E. Sensoriamento Remoto no Estudo da Vegetação, 1st ed.; Parênteses: São José dos Campos, Brazil, 2009.
28. Chen, X.L.; Zhao, H.M.; Li, P.X.; Yin, Z.Y. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 2006, 104, 133-146.
29. Bosch, D.D.; Cosh, M.H. SMEX03 Landsat Thematic Mapper NDVI and NDWI: Georgia; National Snow and Ice Data Center: Boulder, CO, USA, 2008. Available online: http://nsidc.org/data/docs/daac/nsidc0330_smex03_srs_ndvi_ndwi_ga.gd.html (accessed on 10 October 2012).
30. Jackson, T.J.; Cosh, M.H. SMEX03 Landsat Thematic Mapper NDVI and NDWI: Oklahoma; National Snow and Ice Data Center: Boulder, CO, USA, 2007. Available online: http://nsidc.org/data/docs/daac/nsidc0330_smex03_srs_ndvi_ndwi_ga.gd.html (accessed on 10 October 2012).
31. DeAlwis, D.A.; Easton, Z.M.; Dahlke, H.E.; Philpot, W.D.; Steebhuis, T.S. Unsupervised classification of saturated areas using a time series of remotely sensed images. Hydrol. Earth Syst. Sci. Discuss. 2007, 4, 1663-1696.
32. Liu, L.; Zhang, Y. Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sens. 2011, 3, 1535-1552.
33. Kamusoko, C.; Aniya, M. Hybrid classification of Landsat data and GIS for land use/cover change analysis of the Bindura district, Zimbabwe. Int. J. Remote Sens. 2009, 30, 97-115.
34. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2012. Available online: http://www.R-project.org (accessed on 08 September 2012).
35. Pearson, K. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos. Mag. Lett. 1990, 50, 157-175.
36. Manno, I. Introduction to the Monte Carlo Method; Akadémia Kiado: Budapest, Hungary, 1999.
37. IBGE. 2010 Population Census. Available on: http://www.ibge.gov.br/english/estatistica/populacao/censo2010/default.shtm (accessed on 5 September 2012).
38. Silva, M.A. Contribuição ao Estudo do Processo de Industrialização no Município de São José dos Campos-SP (in Portuguese). M.Sc. Thesis, Universidade do Vale do Paraíba, São José dos Campos, Brazil, 2002.
39. Maia, L.P.; Raventos, J.S.; Oliveira, P.M.P.; Meireles, A.J.A. Alterações climáticas na região de Fortaleza causadas por fatores naturais e antrópicos (in Portuguese). Revista de Geologia 1996, 9, 111-121.
40. Bellinaso, H.; Demattê, J.A.M.; Romeiro, S.A. Soil spectral library and its use in soil classification (in Portuguese). Revista Brasileira de Ciência do Solo 2010, 34, 861-870.
41. Espig, S.A.; Reis, I.A.; Araújo, E.P.; Formaggio, A.R. Relação Entre o Fator de Reflectância e o Teor de óxido de Ferro em Latossolos Brasileiros (in Portuguese). In Proceedings of Simpósio Brasileiro de Sensoriamento Remoto, Goiânia, GO, Brasil, 16-21 April 2005; pp. 371-379.
42. Nicacias, M.M. Evaluating the Effect of Moisture Stress on Tomato Using Non-Destructive Remote Sensing Techniques. Master Thesis, University of Limpopo, Mankweng, South Africa, 2009.
43. Moura, Y.M.; Galvão, L.S.; dos Santos, J.R.; Roberts, D.A.; Breunig, F.M. Use of MISR/Terra data to study intra- and inter-annual EVI variations in the dry season of tropical forest. Remote Sens. Environ. 2012, 127, 260-270.
44. Price, J.C. Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Trans. Geosci. Remote Sens. 1990, 28, 940-948.
45. Reuter, D.; Richardson, C.; Irons, J.; Allen, R.; Anderson, M.; Budinoff, J.; Casto, G.; Coltharp, C.; Finneran, P.; Forsbacka, B.; et al. The Thermal Infrared Sensor on the Landsat Data Continuity Mission. In Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25-30 July 2010; pp. 754-757.