RCIN and OZwRCIN projects

Object

Title: Remote sensing of urban microclimate with special reference to Urban Heat Island using Landsat thermal data

Creator:

Singh, Ram Babu ; Grover, Aakriti

Date issued/created:

2014

Resource type:

Text

Subtitle:

Geographia Polonica Vol. 87 No. 4 (2014)

Publisher:

IGiPZ PAN

Place of publishing:

Warszawa

Description:

24 cm

Type of object:

Journal/Article

Abstract:

Remote sensing studies have shown that urban areas have unique environmental, climatic, land use/cover characteristics as a result of intense anthropogenic activities. Consequently, urban areas have developed distinct microclimate and elevated temperatures. Thermal remote sensing data has been widely used to study these characteristics. In this study, an attempt has been made to review the studies involving Landsat remote sensing dataset for investigating land surface temperature. Landsat is oldest finer resolution thermal dataset, which has been effectively used in mapping and analysis of land surface temperature, urban heat island and urban microclimate. Since 1978, it has been providing thermal data through Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Thermal Infrared Sensor (TIRS) sensors.

References:

1. AMIRI R., WENG Q., ALIMOHAMMADI A., ALAVIPANAH S.K., 2009. Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, vol. 113, no. 12, pp. 2606-2617.
http://dx.doi.org/10.1016/j.rse.2009.07.021 -
2. Bagan H., Yamagata Y., 2012. Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40 years. Remote Sensing of Environment, vol. 127, pp. 210-222.
http://dx.doi.org/10.1016/j.rse.2012.09.011 -
3. Benali A., Carvalho A.C., Nunes J.P., Carvalhais N., Santos A., 2012, Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment, vol. 124, pp. 108-121.
http://dx.doi.org/10.1016/j.rse.2012.04.024 -
4. CHANDER G., MARKHAM B., 2003. Revised Landsat-5 TM radiometric calibration procedures and post-calibration dynamic ranges. IEEE Transactions on Geosciences and Remote Sensing, vol. 41, no. 11, pp. 2674-2677.
5. Chander G., Markham B., Helder D., 2009. Summary of current radiometric calibration coeffcients for Landsat MSS, TM, ETM+ and EO-1 ALI Sensors. Remote Sensing of Environment, vol. 113, no. 5, pp. 893-903.
http://dx.doi.org/10.1016/j.rse.2009.01.007 -
6. CHEN X.L., ZHAO H.M., LI P.X., YIN Z.Y., 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, vol. 104, no. 2, pp. 133-146.
http://dx.doi.org/10.1016/j.rse.2005.11.016 -
7. Ding H., Shi W., 2013. Land-use/land-cover change and its influence on surface temperature: A case study in Beijing City. International Journal of Remote Sensing, vol. 34, no. 15, pp. 5503-5517.
http://dx.doi.org/10.1080/01431161.2013.792966 -
8. Howard L., 1833. The climate of London, vol. I-III, London.
9. Hung T., Uchihama D., Ochi S., Yasuoka Y., 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation, vol. 8, no. 1, pp. 34-48.
http://dx.doi.org/10.1016/j.jag.2005.05.003 -
10. Jiang J., Tian G., 2010. Analysis of the impact of Land use/Land cover change on Land Surface Temperature with Remote Sensing. Procedia Environmental Sciences, vol. 2, pp. 571-575.
http://dx.doi.org/10.1016/j.proenv.2010.10.062 -
11. Jiménez-Muñoz J.C., Cristóbal J., Sobrino J.A., Sòria G., Ninyerola M., Pons X., 2009. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-Infrared data. IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 1, part 2, pp. 339-349.
12. Jusuf S.K., Wong N.H., Hagen E., Anggoro R., Hong Y., 2007. The influence of land use on the urban heat island in Singapore. Habitat International, vol. 31, no. 2, pp. 232-242.
http://dx.doi.org/10.1016/j.habitatint.2007.02.006 -
13. KAWASHIMA S., 1994. Relation between vegetation, surface temperature, and surface composition Tokyo region during winter. Remote Sensing of Environment, vol. 50, no. 1, pp. 52-60.
14. Kłysik K., Fortuniak K., 1999. Temporal and spatial characteristics of the urban heat island of Łódź, Poland. Atmospheric Environment, vol. 33, pp. 3885-3895.
http://dx.doi.org/10.1016/S1352-2310(99)00131-4 -
15. Li J., Song C., Cao L., Zhu F., Meng X., Wu J., 2011. Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sensing of Environment, vol. 115, no. 12, pp. 3249-3263.
http://dx.doi.org/10.1016/j.rse.2011.07.008 -
16. LI Y.Y., ZHANG H., KAINZ W., 2012. Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis, China: Using time-series of Landsat TM/ETM+ data. International Journal of Applied Earth Observation and Geoinformation, vol. 19, pp. 127-138.
http://dx.doi.org/10.1016/j.jag.2012.05.001 -
17. Liu L., Zhang Y., 2011. Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing, vol. 3, no. 7, pp. 1535-1552.
http://dx.doi.org/10.3390/rs3071535 -
18. Lo C.P., Quattrochi D.A., 2003. Land-use and land-cover change, urban heat island phenomenon, and health implications: A remote sensing approach. Photogrammetric Engineering & Remote Sensing, vol. 69, no. 9, pp. 1053-1063.
http://dx.doi.org/10.14358/PERS.69.9.1053 -
19. Mallick J., Kant Y., Bharath B.D., 2008. Estimation of land surface temperature over Delhi using Landsat-7 ETM. Journal of Indian Geophysical Union, vol. 12, no. 3, pp. 131-140.
20. Mallick J., Singh C.K., Shashtri C.S., Rahman A., Mukherjee S., 2012. Land surface emissivity retrieval based nn moisture index from Landsat TM satellite data over heterogeneous surfaces of Delhi city. International Journal of Applied Earth Observation and Geoinformation, vol. 19, pp. 348-358.
http://dx.doi.org/10.1016/j.jag.2012.06.002 -
21. Markam B.L., Barker J.L., 1986. Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures. EOSAT Landsat Technical Notes, vol. 1, pp. 3-8.
22. NASA, 2003. Landsat 7 Science Data Users Handbook, http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf [11 January 2014].
23. Nichol J., 2005. Remote sensing of urban heat islands by day and night. Photogrammetric Engineering & Remote Sensing, vol. 71, no. 5, pp. 613-621.
http://dx.doi.org/10.14358/PERS.71.5.613 -
24. OKE T.R., 1982. The energetic basis of the urban heat island. Quarterly Journal of The Royal Meteorological Society, vol. 108, no. 455, pp. 1-24.
http://dx.doi.org/10.1256/smsqj.45501 -
http://dx.doi.org/10.1002/qj.49710845502 -
25. Owen T.W., Carlson T.N., Gillies R.R., 1998. An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. International Journal of Remote Sensing, vol. 19, no. 9, pp. 1663-1681.
http://dx.doi.org/10.1080/014311698215171 -
26. Pandey P., Kumar D., Prakash A., Kumar K., Jain V.K., 2009. A study of the summertime urban heat island over Delhi. International Journal of Sustainability Science and Studies, vol. 1, no. 1, pp. 27-34.
27. Pichierri M., Bonafoni S., Biondi R., 2012. Satellite air temperature estimation for monitoring the canopy layer heat island of Milan. Remote Sensing of Environment, vol. 127, pp. 130-138.
http://dx.doi.org/10.1016/j.rse.2012.08.025 -
28. PONGRÁCZ R., BARTHOLY J., DEZSO Z., 2010. Application of remotely sensed thermal information to urban climatology of Central European cities. Physics and Chemistry of the Earth, vol. 35, no. 1-2, pp. 95-99.
29. Qin Z., Karnieli A., Berliner P., 2001. A monowindow algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel–Egypt border region. International Journal of Remote Sensing, vol. 22, no. 18, pp. 3719-3746.
http://dx.doi.org/10.1080/01431160010006971 -
30. RAHMAN A., NETZBAND M., SINGH A., MALLICK J., 2009. An assessment of urban environmental issues using remote sensing and GIS techniques: an integrated approach. A case study: Delhi, India [in:] A. de Sherbiniin, A. Rahman, A. Barbieri, J.C. Fotso, Y. Zhu (eds.), Urban population-environment dynamics in the developing world: Case studies and lessons learned, Paris: Committee for International Cooperation in National Research in Demography (CICRED), pp. 181-211.
31. Raykar P.S., 2005. Defining relationship between urban heat islands and urban morphology: a case study Ahmedabad. CEPT University, School of Planning, pp. 96 [typescript].
32. SCHOTT J.R., HOOK S.J., BARSI J.A., MARKHAM B.L., MILLER J., PADULA F.P., RAQUENO N.G., 2012. Thermal infrared radiometric calibration of the entire Landsat 4, 5, and 7 archive (1982-2010). Remote Sensing of Environment, vol. 122, pp. 41-49.
http://dx.doi.org/10.1016/j.rse.2011.07.022 -
33. Sharma R., Joshi P.K., 2012. Monitoring urban landscape dynamics over Delhi (India) using remote sensing (1998-2011) inputs. Journal of the Indian Society of Remote Sensing, vol. 41, no. 3, pp. 641-650.
http://dx.doi.org/10.1007/s12524-012-0248-x -
34. Singh R.B., Grover A., Zhan J., 2014. Inter-seasonal variations of surface temperature in the urbanized environment of Delhi using Landsat thermal data. Energies, vol. 7, no. 3, pp. 1811-1828.
http://dx.doi.org/10.3390/en7031811 -
35. Sobrino J. A., Jiménez-Muñoz J.C., Paolini L., 2004. Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment, vol. 90, no. 4, pp. 434-440.
http://dx.doi.org/10.1016/j.rse.2004.02.003 -
36. Southworth J., 2004. An assessment of Landsat TM band 6 thermal data for analyzing land cover in tropical dry forest regions. International Journal of Remote Sensing, vol. 25, no. 4, pp. 689-706.
http://dx.doi.org/10.1080/0143116031000139917 -
37. Szymanowski M., Kryza M., 2011. Application of remotely sensed data for spatial approximation of urban heat island in the city of Wrocław, Poland. 2011 Joint Urban Remote Sensing Event (JURSE), Munich, Germany: 11-13 April 2011, pp. 353-356.
38. UN-Habitat, 2011. Cities and climate change: global report on human settlements. London, Washington, DC: Earthscan, United Nations Human Settlements Programme, UN Habitat.
39. Valsson S., Bharat A., 2009. Urban heat island: Cause for microclimate variations. Architecture– Time Space & People, April 2009, pp. 20-25.
40. Voogt J.A., Oke T.R., 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, vol. 86, no. 3, pp. 370-384.
http://dx.doi.org/10.1016/S0034-4257(03)00079-8 -
41. Walawender J.P., Szymanowski M., Hajto M.J., Bokwa A., 2014. Land surface temperature patterns in the urban agglomeration of Krakow (Poland) derived from Landsat-7/ETM+ data. Pure and Applied Geophysics, vol. 171, no. 6, pp. 913-940.
http://dx.doi.org/10.1007/s00024-013-0685-7 -
42. Weng Q., Lu D., Schubring D., 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, vol. 89, no. 4, pp. 467-483.
http://dx.doi.org/10.1016/j.rse.2003.11.005 -
43. Weng Q., Yang S., 2004. Managing the adverse thermal effects of urban development in a densely populated Chinese city. Journal of Environmental Management, vol. 70, no. 2, pp. 145-156.
http://dx.doi.org/10.1016/j.jenvman.2003.11.006 -
44. Xiao H., Weng Q., 2007. The impact of land use and land cover changes on land surface temperature in a karst area of China. Journal of Environmental Management, vol. 85, no. 1, pp. 245-257.
http://dx.doi.org/10.1016/j.jenvman.2006.07.016 -
45. Xiong Y., Huang S., Chen F., Ye H., Wang C., Zhu C., 2012. The impacts of rapid urbanization on the thermal environment: A remote sensing study of Guangzhou, South China. Remote Sensing, vol. 4, no. 7, pp. 2033-2056.
http://dx.doi.org/10.3390/rs4072033 -
46. Xipo A.N., Edward N.G., Chao R., 2007. Urban heat island in Hong Kong. A Position Paper, Hong Kong: Department of Architecture, Chinese University of Hong Kong.
47. Yuan F., Bauer M.E., 2007. 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, vol. 106, no. 3, pp. 375-386.
http://dx.doi.org/10.1016/j.rse.2006.09.003 -
48. YUE W., XU J., TAN W., XU L., 2007. The relationship between land surface temperature and NDVI with remote sensing: Application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, vol. 28, no. 15, pp. 3205-3226.
http://dx.doi.org/10.1080/01431160500306906 -
49. Zhang J., Wang Y., 2008. Study of the relationships between the spatial extent of surface urban heat islands and urban characteristic factors based on Landsat ETM+ Data. Sensors, vol. 8, no. 11, pp. 7453-7468.
http://dx.doi.org/10.3390/s8117453 -
50. Zhang X.X., Wu P.F., Chen B., 2010. Relationship between vegetation greenness and urban heat island effect in Beijing city of China. Procedia Environmental Sciences, vol. 2, pp. 1438-1450.
http://dx.doi.org/10.1016/j.proenv.2010.10.157 -
51. Zhang Y., Yiyun C., Qing D., Jiang P., 2012. Study on urban heat island effect based on Normalized Difference Vegetated Index: A case study of Wuhan city. Procedia Environmental Sciences, vol. 13, pp. 574-581.
http://dx.doi.org/10.1016/j.proenv.2012.01.048 -
52. Zoran M., 2011. Satellite observation of urban heat island effect. Proceedings of the Global Conference on Global Warming, Lisbon, Portugal: 11-14 July 2011, pp. 1-7.

Relation:

Geographia Polonica

Volume:

87

Issue:

4

Start page:

555

End page:

568

Detailed Resource Type:

Article

Format:

File size 3,5 MB ; application/pdf

Resource Identifier:

oai:rcin.org.pl:50354 ; 0016-7282 ; 10.7163/GPol.2014.38

Source:

CBGiOS. IGiPZ PAN, call nos.: Cz.2085, Cz.2173, Cz.2406 ; click here to follow the link

Language:

eng

Rights:

Creative Commons Attribution BY-ND 3.0 PL license

Terms of use:

Copyright-protected material. [CC BY-ND 3.0 PL] May be used within the scope specified in Creative Commons Attribution BY-ND 3.0 PL license, full text available at: ; -

Digitizing institution:

Institute of Geography and Spatial Organization of the Polish Academy of Sciences

Original in:

Central Library of Geography and Environmental Protection. Institute of Geography and Spatial Organization PAS

Projects co-financed by:

European Union. European Regional Development Fund ; Programme Innovative Economy, 2010-2014, Priority Axis 2. R&D infrastructure

Access:

Open

×

Citation

Citation style:

This page uses 'cookies'. More information