Evaluation of the intensity of the daytime surface urban heat island: how can remote sensing help?

International Journal of Image and Data Fusion

Volumn 6, Issue 4, 2015, Pages 348-365

  Ayanlade, Ayansina ^a   Jegede, O O ^a  

^a OBAFEMI AWOLOWO UNIVERSITY   (Nigeria)

Author keywords

daytime surface urban heat island; remote sensing data

Indexed keywords

AIRPORT GROUND EQUIPMENT; ATMOSPHERIC TEMPERATURE; LANDFORMS; SPACE OPTICS; SURFACE MEASUREMENT; SURFACE PROPERTIES; THERMAL POLLUTION;

LAND SURFACE CHARACTERISTICS; LAND SURFACE TEMPERATURE; METEOROLOGICAL MEASUREMENTS; METEOROLOGICAL STATION; REMOTE SENSING DATA; SPATIAL AND TEMPORAL VARIABILITY; STRONG HETEROGENEITIES; SURFACE URBAN HEAT ISLANDS;

REMOTE SENSING;

EID: 84943455874     PISSN: 19479832     EISSN: 19479824     Source Type: Journal    
DOI: 10.1080/19479832.2014.985618     Document Type: Article

References (47)

1. I.O.Adelekan,, 2005. Urban climate research in Nigeria. Country report. Newslett. International Association of Urban Climate, 13, 8–10.
2. O.M.Akinbode,, A.O.Eludoyin,, and O.A.Fashae,, 2008. Temperature and relative humidity distributions in a medium-size administrative town in southwest Nigeria. Journal Environment, 87 (1), 95–105.
3. C.E.Akumu,, et al., 2010. Monitoring coastal wetland communities in north-eastern NSW using ASTER and Landsat satellite data. Wetlands Ecology Manage, 18, 357–365. doi:10.1007/s11273-010-9176-0
4. M.C.Anderson,, et al., 2008. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sensing of Environment, 112, 4227–4241. doi:10.1016/j.rse.2008.07.009
5. M.Atitar, and J.A.Sobrino,, 2009. A split-window algorithm for estimating LST from Meteosat 9 data: test and comparison with in situ data and MODIS LSTs. IEEE Geoscience and Remote Sensing Letters, 6, 122–126. doi:10.1109/LGRS.2008.2006410
6. Building Nigeria’s Response to Climate Change (BNRCC), 2012. Towards a Lagos state climate change adaptation strategy [online]. Available from: http://nigeriaclimatechange.org/docs/lasAug2012.pdf [Accessed 14December 2013].
7. Y.Charabi, and A.Bakhit,, 2011. Assessment of the canopy urban heat island of a coastal arid tropical city: the case of Muscat, Oman. Atmospheric Research, 101, 215–227. doi:10.1016/j.atmosres.2011.02.010
8. J.Chen,, et al., 2011. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment, 115, 1053–1064. doi:10.1016/j.rse.2010.12.010
9. L.D.Chen,, B.J.Fu,, and W.W.Zhao,, 2006a. Source–sink landscape and its ecological significance. Acta Ecologica Sinica, 26 (5), 1444–1449.
10. X.L.Chen,, et al., 2006b. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104, 133–146. doi:10.1016/j.rse.2005.11.016
11. P.Dash,, et al., 2002. Land surface temperature and emissivity estimation from passive sensor data: theory and practice – current trends. International Journal of Remote Sensing, 23 (13), 2563–2594. doi:10.1080/01431160110115041
12. C.Deng, and C.Wu,, 2013. Examining the impacts of urban biophysical compositions on surface urban heat island: a spectral unmixing and thermal mixing approach. Remote Sensing of Environment, 131, 262–274. doi:10.1016/j.rse.2012.12.020
13. B.Dousset, and F.Gourmelon,, 2003. Satellite multi-sensor data analysis of urban surface temperatures and landcover. ISPRS Journal of Photogrammetry and Remote Sensing, 58 (1–2), 43–54. doi:10.1016/S0924-2716(03)00016-9
14. O.M.Eludoyin,, et al., 2013. Air temperature, relative humidity, climate regionalization and thermal comfort of Nigeria. International Journal of Climatology [online]. Available from: http://onlinelibrary.wiley.com/doi/10.1002/joc.3817/abstract [Accessed 10December 2013].
15. J.Gao, and Y.Liu,, 2010. Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection. International Journal of Applied Earth Observation and Geoinformation, 12, 9–16. doi:10.1016/j.jag.2009.08.003
16. X.Hu, and Q.Weng,, 2009. Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113 (10), 2089–2102. doi:10.1016/j.rse.2009.05.014
17. X.Hu, and Q.Weng,, 2011. Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method. Geocarto International, 26 (1), 3–20. doi:10.1080/10106049.2010.535616
18. J.C.Jiménez-Muñoz, and J.A.Sobrino,, 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research, 108, 4688–4695. doi:10.1029/2003JD003480
19. B.Kifle,, 2003. Urban heat island and its feature in Addis Ababa (a case study). In: Fifth International Conference on Urban Climate [online]. Lodz. Available from: http://nargeo.geo.uni.lodz.pl/~icuc5/text/P_6_11.pdf [Accessed 14August 2011].
20. M.Lazzarini,, P.R.Marpu,, and H.Ghedir,, 2013. Temperature-land cover interactions: the inversion of urban heat island phenomenon in desert city areas. Remote Sensing of Environment, 130, 136–152. doi:10.1016/j.rse.2012.11.007
21. C.F.Li, and J.Y.Yin,, 2013. A study on urban thermal field of Shanghai using multi-source remote sensing data. Journal Indian Social Remote Sensing, 41 (4), 1009–1019. doi:10.1007/s12524-013-0268-1
22. J.Li,, et al., 2011. Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sensing of Environment, 115, 3249–3263. doi:10.1016/j.rse.2011.07.008
23. J.J.Li,, et al., 2009. Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecological Complexity, 6, 413–420. doi:10.1016/j.ecocom.2009.02.002
24. Z.L.Li,, et al., 2013. Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131 (15), 14–37. doi:10.1016/j.rse.2012.12.008
25. T.M.Lillesand, and R.W.Kiefer,, 2000. Remote sensing and image interpretation. 4th ed. New York, NY: John Wiley and Sons, Inc., 470–605.
26. D.Lu, and Q.Weng,, 2004. Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogrammetric Engineering and Remote Sensing, 70, 1053–1062. doi:10.14358/PERS.70.9.1053
27. S.K.Maxwell,, G.L.Schmidt,, and J.C.Storey,, 2007. A multi-scale segmentation approach to filling gaps in Landsat ETM+ SLC-off images. International Journal of Remote Sensing, 28, 5339–5356. doi:10.1080/01431160601034902
28. A.O.Onojeghuo, and G.A.Blackburn,, 2011. Forest transition in an ecologically important region: patterns and causes for landscape dynamics in the Niger Delta. Ecological Indicators, 11, 1437–1446.
29. M.J.Pringle,, M.Schmidt,, and J.S.Muir,, 2009. Geostatistical interpolation of SLC-off Landsat ETM+ images. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 654–664. doi:10.1016/j.isprsjprs.2009.06.001
30. Z.Qin,, A.Karnieli,, and P.Berliner,, 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, 3719–3746. doi:10.1080/01431160010006971
31. H.Radhi,, F.Fikry,, and S.Sharple,, 2013. Impacts of urbanisation on the thermal behaviour of new built up environments: a scoping study of the urban heat island in Bahrain. Landscape and Urban Planning, 113, 47–61. doi:10.1016/j.landurbplan.2013.01.013
32. S.A.Saleh,, 2011. Impact of urban expansion on surface temperature in Baghdad, Iraq using remote sensing and GIS techniques. Canadian Journal on Environmental, Construction and Civil Engineering, 2 (8), 193–202.
33. W.L.Smith,, 1972. Satellite techniques for observing the temperature structure of the atmosphere. Bulletin of the American Meteorological Society, 53, 1074–1082. doi:10.1175/1520-0477(1972)053<1074:STFOTT>2.0.CO;2
34. M.Stathopoulou,, C.Cartalis,, and M.Petrakis,, 2007. Integrating corine land cover data and Landsat TM for surface emissivity definition: an application for the urban area of Athens, Greece. International Journal of Remote Sensing, 28, 3291–3304. doi:10.1080/01431160600993421
35. D.R.Streutker,, 2003. Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing of Environment, 85, 282–289. doi:10.1016/S0034-4257(03)00007-5
36. H.Tran,, et al., 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation, 8, 34–48. doi:10.1016/j.jag.2005.05.003
37. USGS. (2004). Phase 2 gap-fill algorithm: SLC-off gap-filled products gap-fill algorithm methodology [online]. Available from: http://landsat.usgs.gov/documents/L7SLCGapFilledMethod.pdf [Accessed 2June 2012].
38. E.Vardoulakis,, et al., 2013. The urban heat island effect in a small Mediterranean city of high summer temperatures and cooling energy demands. Solar Energy, 94, 128–144. doi:10.1016/j.solener.2013.04.016
39. J.A.Voogt, and T.R.Oke,, 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86, 370–384. doi:10.1016/S0034-4257(03)00079-8
40. N.Wang,, et al., 2011. Temperature and emissivity retrievals from hyperspectral thermal infrared data using linear spectral emissivity constraint. IEEE Transactions on Geoscience and Remote Sensing, 49, 1291–1303. doi:10.1109/TGRS.2010.2062527
41. Q.X.Wang,, Z.F.Wang,, and H.Guo,, 2006. The study of urban Island in Beijing city. Climatic and Environmental Research, 11 (5), 627–636.
42. Q.Weng,, 2012. Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends. Remote Sensing of Environment, 117 (15), 34–49. doi:10.1016/j.rse.2011.02.030
43. G.Xian, and M.Crane,, 2006. An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sensing of Environment, 104, 147–156. doi:10.1016/j.rse.2005.09.023
44. S.Xu,, 2009. An approach to analyzing the intensity of the daytime surface urban heat island effect at a local scale. Environmental Monitoring and Assessment, 151, 289–300. doi:10.1007/s10661-008-0270-1
45. H.Zhang,, et al., 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. doi:10.1016/j.apgeog.2013.07.021
46. Y.Y.Zhou, and Y.Q.Wang,, 2008. Extraction of impervious, surface areas from high spatial resolution imagery by multiple agent segmentation and classification. Photogrammetric Engineering and Remote Sensing, 74 (7), 857–868. doi:10.14358/PERS.74.7.857
47. X.Zhu,, D.Liu,, and J.Chen,, 2012. A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment, 124, 49–60. doi:10.1016/j.rse.2012.04.019