Effects of rapid urbanisation on the urban thermal environment between 1990 and 2011 in Dhaka Megacity, Bangladesh

Lewis Trotter; Ashraf Dewan; Todd Robinson; Lewis Trotter; Ashraf Dewan; Todd Robinson

doi:10.3934/environsci.2017.1.145

AIMS Environmental Science

2017, Volume 4, Issue 1: 145-167. doi: 10.3934/environsci.2017.1.145

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Research article Special Issues

Effects of rapid urbanisation on the urban thermal environment between 1990 and 2011 in Dhaka Megacity, Bangladesh

Department of Spatial Sciences, Curtin University, Kent Street Bentley, Building 207, Perth Western Australia 6845, Australia

Received: 24 August 2016 Accepted: 03 February 2017 Published: 14 February 2017

This study investigates the influence of land-use/land-cover (LULC) change on land surface temperature (LST) in Dhaka Megacity, Bangladesh during a period of rapid urbanisation. LST was derived from Landsat 5 TM scenes captured in 1990, 2000 and 2011 and compared to contemporaneous LULC maps. We compared index-based and linear spectral mixture analysis (LSMA) techniques for modelling LST. LSMA derived biophysical parameters corresponded more strongly to LST than those produced using index-based parameters. Results indicated that vegetation and water surfaces had relatively stable LST but it increased by around 2 °C when these surfaces were converted to built-up areas with extensive impervious surfaces. Knowledge of the expected change in LST when one land-cover is converted to another can inform land planners of the potential impact of future changes and urges the development of better management strategies.
- LULC,
- LST,
- urban thermal environment,
- emissivity,
- surface urban heat islands
Citation: Lewis Trotter, Ashraf Dewan, Todd Robinson. Effects of rapid urbanisation on the urban thermal environment between 1990 and 2011 in Dhaka Megacity, Bangladesh[J]. AIMS Environmental Science, 2017, 4(1): 145-167. doi: 10.3934/environsci.2017.1.145

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Abstract

This study investigates the influence of land-use/land-cover (LULC) change on land surface temperature (LST) in Dhaka Megacity, Bangladesh during a period of rapid urbanisation. LST was derived from Landsat 5 TM scenes captured in 1990, 2000 and 2011 and compared to contemporaneous LULC maps. We compared index-based and linear spectral mixture analysis (LSMA) techniques for modelling LST. LSMA derived biophysical parameters corresponded more strongly to LST than those produced using index-based parameters. Results indicated that vegetation and water surfaces had relatively stable LST but it increased by around 2 °C when these surfaces were converted to built-up areas with extensive impervious surfaces. Knowledge of the expected change in LST when one land-cover is converted to another can inform land planners of the potential impact of future changes and urges the development of better management strategies.

References

[1]	Grimm NB, Faeth SH, Golubiewski NE, et al. (2008) Global change and the ecology of cities. Science 319: 756-760. doi: 10.1126/science.1150195
[2]	United Nations (UN), World urbanization prospects: the 2011 revision. United Nations, 2012. Available from: http://www.un.org/en/desa/population/publications/pdf/urbanization/WUP2011_Report.pdf
[3]	United Nations Population Fund (UNFPA), The state of world population: 2011. United Nations Population Fund, 2011. Available from: http://www.unfpa.org/sites/default/files/pub-pdf/EN-SWOP2011-FINAL.pdf.
[4]	Black D, Henderson V (1999) A theory of urban growth. JPE 107: 252-284. doi: 10.1086/250060
[5]	Dewan AM, Yamaguchi Y (2009) Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Appl Geogr 29: 390-401. doi: 10.1016/j.apgeog.2008.12.005
[6]	Rana MMP (2011) Urbanization and sustainability: challenges and strategies for sustainable urban development in Bangladesh. Environment, Development and Sustainability 13: 237-256. doi: 10.1007/s10668-010-9258-4
[7]	Hasan S, Mulamoottil G (1994) Environmental problems of Dhaka City: a study of mismanagement. Cities 11: 195-200. doi: 10.1016/0264-2751(94)90059-0
[8]	Azad A, Kitada T (1998) Characteristics of the air pollution in the city of Dhaka, Bangladesh in winter. Atmospheric Environment 32: 1991-2005. doi: 10.1016/S1352-2310(97)00508-6
[9]	Dewan AM, Corner RJ (2013) Introduction to Dhaka Megacity, In: Dewan A. M & Corner R. J, Dhaka megacity: geospatial perspectives on urbanisation, environment and health, 2 Eds., New York: Springer Science & Business Media, 1-48.
[10]	Foley JA, Defries R, Asner GP, et al. (2005) Global consequences of land use. Science 309: 570-574. doi: 10.1126/science.1111772
[11]	Voogt JA, Oke TR (1998) Effects of urban surface geometry on remotely-sensed surface temperature. Int J Remote Sens 19: 895-920. doi: 10.1080/014311698215784
[12]	Yuan F, Bauer ME (2007) Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ 106: 375-386. doi: 10.1016/j.rse.2006.09.003
[13]	Kovats S, Akhtar H (2008) Climate, climate change and human health in Asian cities. Environ Urban 20: 165-175. doi: 10.1177/0956247808089154
[14]	Patz JA, Olson SH (2006) Climate change and health: global to local influences on disease risk. Ann Trop Med Parasit 100: 535-549. doi: 10.1179/136485906X97426
[15]	Shahid S (2009) Probable impacts of climate change on public health in Bangladesh. Asia Pac J Public Health 124: 432-444.
[16]	Hashizume M, Dewan AM, Sunahara T, et al. (2012) Hydroclimatological variability and dengue transmission in Dhaka, Bangladesh: a time-series study. BMC Infect Dis 12: 98. doi: 10.1186/1471-2334-12-98
[17]	Dewan AM, Corner R, Hashizume M, et al. (2014) Typhoid fever and its association with environmental factors in the Dhaka metropolitan area of Bangladesh: a spatial and time-series approach. PLoS Negl Trop Dis 7: 1998.
[18]	Byomkesh T, Nakagoshi N, Dewan AM (2011) Urbanization and green space dynamics in greater Dhaka, Bangladesh. Landsc Ecol Eng 8: 45-58.
[19]	Fortuniak K, Kłysik K, Wibig J (2005) Urban–rural contrasts of meteorological parameters in Łódź. Theor Appl Climatol 84: 91-101.
[20]	Wong NH, Yu C (2005) Study of green areas and urban heat island in a tropical city. Habitat Int 29: 547-558. doi: 10.1016/j.habitatint.2004.04.008
[21]	Saaroni H, Ben-Dor E, Bitan A, et al. (2000) Spatial distribution and microscale characteristics of the urban heat island in Tel-Aviv, Israel. Landscape Urban Plan 48: 1-18. doi: 10.1016/S0169-2046(99)00075-4
[22]	Yow DM (2007) Urban heat islands: observations, impacts and adaptation. Geography Compass 1: 1227-1251. doi: 10.1111/j.1749-8198.2007.00063.x
[23]	Weng Q (2009) Thermal infrared remote sensing for urban climate and environmental studies: methods, applications and trends. ISPRS J Photogramm 64: 335-344. doi: 10.1016/j.isprsjprs.2009.03.007
[24]	Zhou X, Wang YC (2011) Dynamics of land surface temperature in response to land-use/cover change. Geogr Res 49: 23-36. doi: 10.1111/j.1745-5871.2010.00686.x
[25]	Carnahan WH, Larson RC (1990) An analysis of an urban heat sink. Remote Sens Environ 33: 65-71. doi: 10.1016/0034-4257(90)90056-R
[26]	Streutker DR (2002) A remote sensing study of the urban heat island of Houston, Texas. Int J Remote Sens 23: 2595-2608. doi: 10.1080/01431160110115023
[27]	Walawender JP, Szymanowski M, Hajto MJ, et al. (2013) Land surface temperature patterns in the urban agglomeration of Krakow (Poland) derived from landsat-7/etm+ data. Pure Appl Geophys 4: 23-54.
[28]	Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86: 370-384. doi: 10.1016/S0034-4257(03)00079-8
[29]	Carlson TN, Gillies RR, Perry EM (1994) A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sens Rev 9: 161-173. doi: 10.1080/02757259409532220
[30]	Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24: 583-594. doi: 10.1080/01431160304987
[31]	Weng Q (2008) Medium spatial resolution satellite imagery for estimating and mapping urban impervious surfaces using LSMA and ANN. IEEE T Geosci Remote 46: 2397-2406. doi: 10.1109/TGRS.2008.917601
[32]	Ma Y, Kuang Y, Huang N (2010) Coupling urbanization analyses for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM+ imagery. Int J Appl Earth Obs 12: 110-118. doi: 10.1016/j.jag.2009.12.002
[33]	Adams J (1995) Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon. Remote Sens Environ 52: 137-154. doi: 10.1016/0034-4257(94)00098-8
[34]	Brown M, Lewis HG, Gunn SR (2000) Linear spectral mixture models and support vector machines for remote sensing. IEEE T Geosci Remote 38: 2346-2360. doi: 10.1109/36.868891
[35]	Rashid H (1978) Geography of Dhaka, In: Rashid, H, Geography of Bangladesh, 2 Eds., Dhaka: University Press, 78-94.
[36]	Tareq SM, Maruo M, Ohta K (2013) Characteristics and role of groundwater dissolved organic matter on arsenic mobilization and poisoning in Bangladesh. Phys Chem Earth 1: 77-84.
[37]	Water Resources Planning Organization (WARPO), Datum and map projections for GIS and GPS applications in Bangladesh. Water Resources Planning Organization, 1996. Available from: http://www.cegisbd.com/pdf/tn10DatumMapProjections.pdf.
[38]	Chander G, Markham B (2003) Revised landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE T Geosci Remote 41: 2674-2677. doi: 10.1109/TGRS.2003.818464
[39]	Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors. Remote Sens Environ 113: 893-903. doi: 10.1016/j.rse.2009.01.007
[40]	Chavez P (1996) Image-based atmospheric corrections: revisited and improved. Photogramm eng rem s 62: 1025-1035.
[41]	IDRISI Selva-GIS and Image Processing Software (Version 17), (2012). Worcester, Massachusetts: Clark Laboratories.
[42]	Anderson R, Hardy EE, Roach JT, et al. (1976) A land use and land cover classification system for use with remote sensor data, In: United States Geological Survey (USGS), Professional Papers Vol. 964, 1 Eds., Sioux Falls: USGS Professional Printing, 41-78.
[43]	Mas J (1999) Monitoring land-cover changes: a comparison of change detection techniques. Int J Remote Sens 20: 139-152. doi: 10.1080/014311699213659
[44]	Streatfield P, Karar Z (2008) Population challenges for Bangladesh in the coming decades. JHPS 26: 261-272.
[45]	Qin Z, Karnieli A, Berliner P (2001) A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int J Remote Sens 22: 3719-3746. doi: 10.1080/01431160010006971
[46]	Jiménez-Muñoz JC, Sobrino JA (2003) A generalised single channel method for retrieving land surface temperature from remote sensing data. J Geophys Res 108: 4688. doi: 10.1029/2003JD003480
[47]	Chen XL, Zhao HM, Li P, et al. (2006) Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens Environ 104: 133-146. doi: 10.1016/j.rse.2005.11.016
[48]	Lo CP, Quattrochi DA (2003) Land-use and land-cover change, urban heat island phenomenon, and health implications. Photogramm Eng Rem S 69: 1053-1063. doi: 10.14358/PERS.69.9.1053
[49]	Tran H, Uchihama D, Ochi S, et al. (2006) Assessment with satellite data of the urban heat island effects in Asian mega cities. Int J Appl Earth Obs 8: 147-156.
[50]	Artis DA, Carnahan WH (1982) Survey of emissivity variability in thermography of urban areas. Remote Sens Environ 12: 313-329. doi: 10.1016/0034-4257(82)90043-8
[51]	Sobrino JA, Jiménez-Muñoz JC, Sòria G, et al. (2008) Land surface emissivity retrieval from different VNIR and TIR sensors. Int J Remote Sens 46: 316-327. doi: 10.1109/TGRS.2007.904834
[52]	Xiao R, Ouyang Z, Zheng H, et al. (2007) Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. J Environ Sci 19: 250-256. doi: 10.1016/S1001-0742(07)60041-2
[53]	Mackey CW, Lee X, Smith RB (2012) Remotely sensing the cooling effects of city scale efforts to reduce urban heat island. Build Environ 49: 348-358. doi: 10.1016/j.buildenv.2011.08.004
[54]	Nichol JE (1994) Approach to a GIS-Based monitoring survey of microclimate of Singapore's housing estates. Cities 60: 43-56.
[55]	Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from Landsat TM 5. Remote Sens Environ 90: 434-440. doi: 10.1016/j.rse.2004.02.003
[56]	Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover and leaf area index. Remote Sens Environ 62: 241-252. doi: 10.1016/S0034-4257(97)00104-1
[57]	Sobrino JA, Caselles V, Becker F (1990) Significance of the remotely sensed thermal infrared measurements obtained over a citrus orchard. ISPRS J Photogramm 44: 343-354. doi: 10.1016/0924-2716(90)90077-O
[58]	Walawender JP, Hajto MJ, Iwaniuk P (2012) A new ArcGIS toolset for automated mapping of land surface temperature with the use of landsat satellite data. IEEE International Geoscience and Remote Sensing Symposium 2012: 4371-4374.
[59]	Carlson TN, Arthur ST (2000) The impact of land use–land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global Planet Change 25: 49-65. doi: 10.1016/S0921-8181(00)00021-7
[60]	Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89: 467-483. doi: 10.1016/j.rse.2003.11.005
[61]	Zhang X, Zhong T, Wang K, et al. (2009) Scaling of impervious surface area and vegetation as indicators to urban land surface temperature using satellite data. Int J Remote Sens 30: 841-859. doi: 10.1080/01431160802395219
[62]	Huete A, Didan K, Miura T, et al. (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83: 195-213. doi: 10.1016/S0034-4257(02)00096-2
[63]	Sun F, Sun W, Chen J, et al. (2012). Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. Int J Remote Sens 33: 6854-6875. doi: 10.1080/01431161.2012.692829
[64]	ArcGIS-GIS Software (Version 10.1), (2012). Redlands, California: Environmental Systems Research Institute (ESRI).
[65]	Yang X, Liu Z (2005) Use of satellite-derived landscape imperviousness index to characterize urban spatial growth. Computers, Environment and Urban Systems 29: 524-540. doi: 10.1016/j.compenvurbsys.2005.01.005
[66]	ENVI-GIS software (Version 4.8), (2012). Boulder, Colorado: Harris Geospatial Solutions.
[67]	Wu C (2004) Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sens Environ 93: 480-492. doi: 10.1016/j.rse.2004.08.003
[68]	Green AA, Berman M, Switzer P, et al. (1988) A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE T Geosci Remote 26: 65-74. doi: 10.1109/36.3001
[69]	Boardman J (1993) Automating spectral unmixing of AVIRIS data using convex geometry concepts. 4th Annual JPL Airborne Geoscience Conference and Worskhop 2: 2-5.
[70]	Boardman J, Kruse F, Green R (1995) Mapping target signatures via partial unmixing of AVIRIS data. 5th Annual JPL Airborne Geoscience Conference and Worskhop 3: 3-6.
[71]	Tukey JW (1953) The problem of multiple comparisons. In: Unpublished Manuscript, Trenton: Princeton University, 1-38.
[72]	Liu H, Weng Q (2008) Seasonal variations in the relationship between landscape pattern and land surface temperature in Indianapolis, USA. Environ Monit Assess 144: 199-219. doi: 10.1007/s10661-007-9979-5
[73]	National Aeronautics and Space Administration, Landsat 7 science data users handbook. National Aeronautics and Space Administration, 2000. Available from: https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/08/Landsat7_Handbook.pdf.
[74]	SPSS- Statistics Software (Version 22), (2012). Armonk, New York: IBM Corporation.
[75]	Cohen J, Cohen P, West S, et al. (2013) Data-Analytic Strategies Using Multiple Regression/Correlation, In: Cohen J, Cohen P, Applied multiple regression/correlation analysis for the behavioural sciences, 3 Eds., London: Lawrence Erlbaum Associates, 151-192.
[76]	Ahmed B, Kamruzzaman M, Zhu X, et al. (2013) Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote Sens 5: 5969-5998. doi: 10.3390/rs5115969
[77]	Raja DR (2012) Spatial analysis of land surface temperature in Dhaka metropolitan area. J Bangladesh Institute of Planners 5: 151-167.
[78]	Xiao H, Weng Q (2007) The impact of land use and land cover changes on land surface temperature in a karst area of China. J Environ Manage 85: 245-257. doi: 10.1016/j.jenvman.2006.07.016
[79]	Xu H, Dongfeng L, Tang F (2013) The impact of impervious surface development on land surface temperature in a subtropical city: Xiamen, China. Int J Climatol 33: 1873-1883. doi: 10.1002/joc.3554
[80]	Li 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. Int J Appl Earth Obs 19: 127-138. doi: 10.1016/j.jag.2012.05.001
[81]	Tan KC, San-Lim H, Matjafri MZ, et al. (2010) Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environ Earth Sci 60: 1509-1521. doi: 10.1007/s12665-009-0286-z
[82]	Li J, Wang J, Ma J, et al. (2009) Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecol Complex 6: 413-420. doi: 10.1016/j.ecocom.2009.02.002
[83]	Li J, Song C, Cao L, et al. (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens Environ 115: 3249-3263. doi: 10.1016/j.rse.2011.07.008
[84]	Roth M, Oke TR, Emery WJ (1989) Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology. Int J Remote Sens 10: 1699-1720. doi: 10.1080/01431168908904002
[85]	Owen TW, Carlson TN, Gillies RR (1998) An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. Int J Remote Sens 19: 1663-1681. doi: 10.1080/014311698215171
[86]	Cao L, Li P, Zhang L, et al. (2002) Remote sensing image-based analysis of the relationship between urban heat island and vegetation fraction. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1: 1379-1383.
[87]	Giri C, Pengra B, Zhu Z, et al. (2007) Monitoring mangrove forest dynamics of the sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar Coast Shelf S 73: 91-100. doi: 10.1016/j.ecss.2006.12.019
[88]	Bangladesh Department of Environment Climate Change Cell (2006) Bangladesh climate change impacts and vulnerability: a synthesis. Dhaka, Department of Environment Publishing.
[89]	Goward SN (1981) Thermal behavior of urban landscapes and the urban heat island. Phys Geogr 2: 19-33.
[90]	Grimmond CSB (2005) Progress in measuring and observing the urban atmosphere. Theor Appl Climatol 84: 3-22.
[91]	Oke TR (1982) The energetic basis of the urban heat island. J Roy Meteor Soc 108: 1-24.
[92]	Xu H, Ding F, Wen X (2009) Urban expansion and heat island dynamics in the Quanzhou region, China. IEEE J-STAEORS 2: 74-79.
[93]	Weng Q (2001) A remote sensing GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. Int J Remote Sens 22: 1999-2014.
[94]	Dewan A, Corner R (2014). Impact of land use and land cover changes on urban land surface temperature, In: Dewan AM, Corner RJ, Dhaka megacity: geospatial perspectives on urbanisation, environment and health, 2 Eds., New York: Springer Science & Business Media, 219-238.
[95]	Raja DR, Neema MN (2013) Impact of urban development and vegetation on land surface temperature of Dhaka city. Comp Sci Appl 7973: 351-367.
[96]	Weng Q, Hu X, Lu D (2008). Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison. Int J Remote Sens 29: 3209-3232. doi: 10.1080/01431160701469024
[97]	Song C, Woodcock CE, Seto KC (2001) Classification and change detection using landsat TM data. Remote Sens Environ 75: 230-244. doi: 10.1016/S0034-4257(00)00169-3
[98]	Weng Q, Lu D (2008) Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison. Int J Remote Sens 29: 3209-3232. doi: 10.1080/01431160701469024
[99]	Xu H (2008) A new remote sensing index for fastly extracting impervious surface information. GIS 8: 12-28.

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