Abstract
We have estimated the amplitude and impact of urbanization on Chittagong metropolitan area (CMA), and investigated the correlation between land cover features and land surface temperature (LST). We have also located the urban heat island (UHI) and calculated the UHI intensity. Support vector machine (SVM) for land cover classification and mono-window algorithm (MWA) for LST estimation were applied to Landsat images of years 1990, 2000, 2010 and 2020. Validation of classification and temperature was also done by various statistical parameters giving credible results. The findings reveal that in Chittagong, between 1990 and 2020, the urban area has raised by almost 12%, whereas vegetative areas have decreased by about 14%. With the rise of the urban area, the temperature has also shifted from 19.8 °C to 23.7 °C. Moreover, the UHI intensity has also raised from 1 °C to 2.3 °C, which is the indication of an unplanned and unsustainable urban development. Evidently, the whole city can turn into a UHI if proper steps are not taken immediately.
Highlights
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LST has increased almost 4 °C between 1990 and 2020
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The UHI intensity decreases with the increasing distance from the urban center
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References
Ahmed B, Kamruzzaman MD, Zhu X, Rahman M, Choi K (2013) Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote Sens 5:5969–5998. https://doi.org/10.3390/rs5115969
Ajaj QM, Pradhan B, Noori AM, Jebur MN (2017) Spatial monitoring of desertification extent in western Iraq using Landsat images and GIS. L Degrad Dev 28:2418–2431
Akter T, Gazi MY, Mia MB (2021) Assessment of land cover dynamics, land surface temperature, and heat island growth in northwestern Bangladesh using satellite imagery. Environ Process 8:661–690. https://doi.org/10.1007/s40710-020-00491-y
Al Kafy A, Rahman MS, Faisal A-A, Hasan MM, Islam M (2020) Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sens Appl Soc Environ 18:100314. https://doi.org/10.1016/j.rsase.2020.100314
Al Kafy A, Abdullah-Al-Faisal, Rahman MS, Islam M, Al Rakib A, Islam MA, Khan MHH, Sikdar MS, Sarker MHS, Mawa J, Sattar GS (2021) Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh. Sustain Cities Soc 64:102542. https://doi.org/10.1016/j.scs.2020.102542
Arifwidodo SD, Tanaka T (2015) The characteristics of urban heat island in Bangkok, Thailand. Procedia - Soc Behav Sci 195:423–428. https://doi.org/10.1016/j.sbspro.2015.06.484
Baker F, Smith C (2019) A GIS and object based image analysis approach to mapping the greenspace composition of domestic gardens in Leicester, UK. Landsc Urban Plan 183:133–146
BBS (2011) Bangladesh population and housing census 2011, volume 3: Urban area report. Dhaka, Bangladesh
Berg A, Findell K, Lintner B, Giannini A, Seneviratne SI, Van Den Hurk B, Lorenz R, Pitman A, Hagemann S, Meier A (2016) Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat Clim Chang 6:869–874. https://doi.org/10.1038/s41561-018-0114-8
Bian J, Ren H, Liu P, Zhang Y (2018) Sustainable urbanization performance evaluation based on “origin” and “modernization” perspectives: a case study of Chongqing, China. Int J Environ Res Public Health 15:1714. https://doi.org/10.3390/ijerph15081714
Busato F, Lazzarin RM, Noro M (2014) Three years of study of the urban Heat Island in Padua: Experimental results. Sustain Cities Soc 10:251–258. https://doi.org/10.1016/j.scs.2013.05.001
Chaudhuri G, Mishra NB (2016) Spatio-temporal dynamics of land cover and land surface temperature in Ganges-Brahmaputra delta: a comparative analysis between India and Bangladesh. Appl Geogr 68:68–83. https://doi.org/10.1016/j.apgeog.2016.01.002
Chun B, Guldmann J-M (2014) Spatial statistical analysis and simulation of the urban heat island in high-density central cities. Landsc Urban Plan 125:76–88. https://doi.org/10.1016/j.landurbplan.2014.01.016
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46. https://doi.org/10.1177/001316446002000104
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press
Cristóbal J, Jiménez-Muñoz JC, Prakash A, Mattar C, Skoković D, Sobrino JA (2018) An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sens 10:431. https://doi.org/10.3390/rs10030431
Dai Z, Guldmann J-M, Hu Y (2018) Spatial regression models of park and land-use impacts on the urban heat island in Central Beijing. Sci Total Environ 626:1136–1147. https://doi.org/10.1016/j.scitotenv.2018.01.165
Darmanto NS, Varquez ACG, Kawano N, Kanda M (2019) Future urban climate projection in a tropical megacity based on global climate change and local urbanization scenarios. Urban Clim 29:100482. https://doi.org/10.1016/j.uclim.2019.100482
Deilami K, Kamruzzaman M, Liu Y (2018) Urban heat island effect: a systematic review of spatio-temporal factors, data, methods, and mitigation measures. Int J Appl Earth Obs Geoinf 67:30–42. https://doi.org/10.1016/j.jag.2017.12.009
Dewan AM, Corner RJ (2012) The impact of land use and land cover changes on land surface temperature in a rapidly urbanizing megacity. In: 2012 IEEE international geoscience and remote sensing symposium. IEEE, Munich, Germany, pp 6337–6339
Dinda S, Das Chatterjee N, Ghosh S (2021) An integrated simulation approach to the assessment of urban growth pattern and loss in urban green space in Kolkata, India: a GIS-based analysis. Ecol Indic 121:107178. https://doi.org/10.1016/j.ecolind.2020.107178
Emadodin I, Taravat A, Rajaei M (2016) Effects of urban sprawl on local climate: a case study, north Central Iran. Urban Clim 17:230–247. https://doi.org/10.1016/j.uclim.2016.08.008
Emmanuel R, Krüger E (2012) Urban heat island and its impact on climate change resilience in a shrinking city: the case of Glasgow, UK. Build Environ 53:137–149. https://doi.org/10.1016/j.buildenv.2012.01.020
Feizizadeh B, Roodposhti MS, Blaschke T, Aryal J (2017) Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci 10:1–13
Foody GM (1992) On the compensation for chance agreement in image classification accuracy assessment. Photogramm Eng Remote Sensing 58:1459–1460
Gazi MY, Rahman MZ, Uddin MM, Rahman FMA (2020) Spatio-temporal dynamic land cover changes and their impacts on the urban thermal environment in the Chittagong metropolitan area, Bangladesh. GeoJournal 86:2119–2134. https://doi.org/10.1007/s10708-020-10178-4
Ghosh S, Chatterjee N Das, Dinda S (2019) Relation between urban biophysical composition and dynamics of land surface temperature in the Kolkata metropolitan area: a GIS and statistical based analysis for sustainable planning. Model Earth Syst Environ 5:307–329. https://doi.org/10.1007/s40808-018-0535-9
Gohain KJ, Mohammad P, Goswami A (2021) Assessing the impact of land use land cover changes on land surface temperature over Pune city, India. Quat Int 575:259–269. https://doi.org/10.1016/j.quaint.2020.04.052
Guha S, Govil H, Diwan P (2019) Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index. J Appl Remote Sens 13:24518. https://doi.org/10.1117/1.JRS.13.024518
Gwenzi W, Nyamadzawo G (2014) Hydrological impacts of urbanization and urban roof water harvesting in water-limited catchments: a review. Environ Process 1:573–593. https://doi.org/10.1007/s40710-014-0037-3
Halder B, Bandyopadhyay J, Banik P (2021) Monitoring the effect of urban development on urban heat island based on remote sensing and geo-spatial approach in Kolkata and adjacent areas, India. Sustain Cities Soc 74:103186. https://doi.org/10.1016/j.scs.2021.103186
Hasan SS, Sarmin NS, Miah MG (2020) Assessment of scenario-based land use changes in the Chittagong Hill tracts of Bangladesh. Environ Dev 34:100463. https://doi.org/10.1016/j.envdev.2019.100463
Hatab AA, Cavinato MER, Lindemer A, Lagerkvist C-J (2019) Urban sprawl, food security and agricultural systems in developing countries: a systematic review of the literature. Cities 94:129–142. https://doi.org/10.1016/j.cities.2019.06.001
Howard L (1818) The climate of London: deduced from meteorological observations, made at different places in the neighbourhood of the metropolis. W. Phillips, George Yard, Lombard Street, sold also by J. and A. Arch
Hung K-LJ, Ascher JS, Holway DA (2017) Urbanization-induced habitat fragmentation erodes multiple components of temporal diversity in a Southern California native bee assemblage. PLoS One 12:e0184136. https://doi.org/10.1371/journal.pone.0184136
Hurskainen P, Adhikari H, Siljander M, Pellikka PKE, Hemp A (2019) Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes. Remote Sens Environ 233:111354. https://doi.org/10.1016/j.rse.2019.111354
Imran HM, Hossain A, Islam AKMS, Rahman A, Bhuiyan MAE, Paul S, Alam A (2021) Impact of land cover changes on land surface temperature and human thermal comfort in Dhaka City of Bangladesh. Earth Syst Environ 5:667–693. https://doi.org/10.1007/s41748-021-00243-4
Jiménez-Muñoz JC, Sobrino JA (2003) A generalized single-channel method for retrieving land surface temperature from remote sensing data. J Geophys Res Atmos 108(D22)
Kaczynski AT, Jake-Schoffman DE, Peters NA, Dunn CG, Wilcox S, Forthofer M (2018) Development and testing of the church environment audit tool. Am J Health Behav 42:17–26. https://doi.org/10.5993/AJHB.42.3.2
Karakuş CB (2019) The impact of land use/land cover (LULC) changes on land surface temperature in Sivas City center and its surroundings and assessment of urban heat island. Asia-Pacific J Atmos Sci 55:669–684. https://doi.org/10.1007/s13143-019-00109-w
Kashem MA, Rahman MM (2019) CO2 emissions and development indicators: a causality analysis for Bangladesh. Environ Process 6:433–455. https://doi.org/10.1007/s40710-019-00365-y
Kolokotroni M, Giridharan R (2008) Urban heat island intensity in London: an investigation of the impact of physical characteristics on changes in outdoor air temperature during summer. Sol Energy 82:986–998. https://doi.org/10.1016/j.solener.2008.05.004
Kotharkar R, Ramesh A, Bagade A (2018) Urban heat island studies in South Asia: a critical review. Urban Clim 24:1011–1026. https://doi.org/10.1016/j.uclim.2017.12.006
Kuang W, Liu Y, Dou Y, Chi W, Chen G, Gao C, Yang T, Liu J, Zhang R (2015) What are hot and what are not in an urban landscape: quantifying and explaining the land surface temperature pattern in Beijing, China. Landsc Ecol 30:357–373. https://doi.org/10.1007/s10980-014-0128-6
Lee S-H, Baik J-J (2011) Evaluation of the vegetated urban canopy model (VUCM) and its impacts on urban boundary layer simulation. Asia-Pacific J Atmos Sci 47:151–165. https://doi.org/10.1007/s13143-011-0005-z
Li X, Li W, Middel A, Harlan SL, Brazel AJ, Turner Ii BL (2016) Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: combined effects of land composition and configuration and cadastral–demographic–economic factors. Remote Sens Environ 174:233–243. https://doi.org/10.1016/j.rse.2015.12.022
Li X, Wang X, Yang A, Rong M (2019) Partial discharge source localization in GIS based on image edge detection and support vector machine. IEEE Trans Power Deliv 34:1795–1802. https://doi.org/10.1109/TPWRD.2019.2925034
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 Geoinf 19:127–138. https://doi.org/10.1016/j.jag.2012.05.001
Liang W, Yang M (2019) Urbanization, economic growth and environmental pollution: evidence from China. Sustain Comput Informatics Syst 21:1–9. https://doi.org/10.1016/j.suscom.2018.11.007
Lokoshchenko MA (2014) Urban ‘heat island’ in Moscow. Urban Clim 10:550–562. https://doi.org/10.1016/j.uclim.2014.01.008
Ma Z, Redmond RL (1995) Tau coefficients for accuracy assessment of classification of remote sensing data. Photogramm Eng Remote Sensing 61:435–439
Mberu B, Béguy D, Ezeh AC (2017) Internal migration, urbanization and slums in sub-Saharan Africa. In: Africa’s population: in search of a demographic dividend. Springer International Publishing, Cham, pp 315–332
McMillin LM (1975) Estimation of sea surface temperatures from two infrared window measurements with different absorption. J Geophys Res 80:5113–5117. https://doi.org/10.1029/JC080i036p05113
Mia M, Fujimitsu Y, Nishijima J (2018) Monitoring of thermal activity at the Hatchobaru–Otake geothermal area in Japan using multi-source satellite images—with comparisons of methods, and solar and seasonal effects. Remote Sens 10:1430. https://doi.org/10.3390/rs10091430
Mollalo A, Mao L, Rashidi P, Glass GE (2019) A GIS-based artificial neural network model for spatial distribution of tuberculosis across the continental United States. Int J Environ Res Public Health 16:157
Monserud RA, Leemans R (1992) Comparing global vegetation maps with the kappa statistic. Ecol Model 62:275–293. https://doi.org/10.1016/0304-3800(92)90003-W
Morshed SR, Fattah MA, Haque MN, Morshed SY (2021) Future ecosystem service value modeling with land cover dynamics by using machine learning based artificial neural network model for Jashore city, Bangladesh. Phys Chem Earth, Parts A/B/C 103021. https://doi.org/10.1016/j.pce.2021.103021
Muhammad S, Long X, Salman M, Dauda L (2020) Effect of urbanization and international trade on CO2 emissions across 65 belt and road initiative countries. Energy 196:117102. https://doi.org/10.1016/j.energy.2020.117102
Mushore TD, Mutanga O, Odindi J, Dube T (2017) Linking major shifts in land surface temperatures to long term land use and land cover changes: a case of Harare, Zimbabwe. Urban Clim 20:120–134. https://doi.org/10.1016/j.uclim.2017.04.005
Naikoo MW, Rihan M, Ishtiaque M, Shahfahad (2020) Analyses of land use land cover (LULC) change and built-up expansion in the suburb of a metropolitan city: Spatio-temporal analysis of Delhi NCR using landsat datasets. J Urban Manag 9:347–359. https://doi.org/10.1016/j.jum.2020.05.004
Parker DE (2010) Urban heat island effects on estimates of observed climate change. WIREs Clim Chang 1:123–133. https://doi.org/10.1002/wcc.21
Pata UK (2018) The effect of urbanization and industrialization on carbon emissions in Turkey: evidence from ARDL bounds testing procedure. Environ Sci Pollut Res 25:7740–7747. https://doi.org/10.1007/s11356-017-1088-6
Peng J, Xie P, Liu Y, Ma J (2016) Urban thermal environment dynamics and associated landscape pattern factors: a case study in the Beijing metropolitan region. Remote Sens Environ 173:145–155. https://doi.org/10.1016/j.rse.2015.11.027
Price JC (1984) Land surface temperature measurements from the split window channels of the NOAA 7 advanced very high resolution radiometer. J Geophys Res Atmos 89:7231–7237. https://doi.org/10.1029/JD089iD05p07231
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. https://doi.org/10.1080/01431160010006971
Ranagalage M, Wang R, Gunarathna M, Dissanayake D, Murayama Y, Simwanda M (2019) Spatial forecasting of the landscape in rapidly urbanizing hill stations of South Asia: a case study of Nuwara Eliya, Sri Lanka (1996–2037). Remote Sens 11:1743. https://doi.org/10.3390/rs11151743
Rani M, Kumar P, Pandey PC, Srivastava PK, Chaudhary BS, Tomar V, Mandal VP (2018) Multi-temporal NDVI and surface temperature analysis for urban Heat Island inbuilt surrounding of sub-humid region: a case study of two geographical regions. Remote Sens Appl Soc Environ 10:163–172. https://doi.org/10.1016/j.rsase.2018.03.007
Rasul G (2009) Ecosystem services and agricultural land-use practices: a case study of the Chittagong Hill tracts of Bangladesh. Sustain Sci Pract Policy 5:15–27. https://doi.org/10.1080/15487733.2009.11908032
Richard K, Abdel-Rahman EM, Subramanian S, Nyasani JO, Thiel M, Jozani H, Borgemeister C, Landmann T (2017) Maize cropping systems mapping using rapideye observations in agro-ecological landscapes in Kenya. Sensors 17:2537. https://doi.org/10.3390/s17112537
Roy B, Bari E, Nipa NJ, Ani SA (2021) Comparison of temporal changes in urban settlements and land surface temperature in Rangpur and Gazipur Sadar, Bangladesh after the establishment of city corporation. Remote Sens Appl Soc Environ 23:100587. https://doi.org/10.1016/j.rsase.2021.100587
Sadiq Khan M, Ullah S, Sun T, Rehman A, Chen L (2020) Land-use/land-cover changes and its contribution to urban heat island: a case study of Islamabad, Pakistan. Sustainability 12:3861. https://doi.org/10.3390/su12093861
Shi Y, Zhang Y (2018) Remote sensing retrieval of urban land surface temperature in hot-humid region. Urban Clim 24:299–310. https://doi.org/10.1016/j.uclim.2017.01.001
Sinha S, Sharma LK, Nathawat MS (2015) Improved land-use/land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. Egypt J Remote Sens Sp Sci 18:217–233. https://doi.org/10.1016/j.ejrs.2015.09.005
Soydan O (2020) Effects of landscape composition and patterns on land surface temperature: urban heat island case study for Nigde, Turkey. Urban Clim 34:100688. https://doi.org/10.1016/j.uclim.2020.100688
Streutker D (2003) Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sens Environ 85:282–289. https://doi.org/10.1016/S0034-4257(03)00007-5
Tepanosyan G, Muradyan V, Hovsepyan A, Pinigin G, Medvedev A, Asmaryan S (2021) Studying spatial-temporal changes and relationship of land cover and surface urban Heat Island derived through remote sensing in Yerevan, Armenia. Build Environ 187:107390. https://doi.org/10.1016/j.buildenv.2020.107390
Thapa R, Murayama Y (2009) Examining spatiotemporal urbanization patterns in Kathmandu Valley, Nepal: remote sensing and spatial metrics approaches. Remote Sens 1:534–556. https://doi.org/10.3390/rs1030534
United Nations (2018) World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420). New York: United Nations
Vanhellemont Q (2020) Automated water surface temperature retrieval from Landsat 8/TIRS. Remote Sens Environ 237:111518. https://doi.org/10.1016/j.buildenv.2020.107390
Vapnik V (1998) Statistical learning theory, New York, 1998. NY Wiley 1:2
Voogt JA (2004) Urban heat island: hotter cities. American Institute of Biological Sciences. Reston, VA
Wang F, Qin Z, Song C, Tu L, Karnieli A, Zhao S (2015) An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sens 7:4268–4289. https://doi.org/10.3390/rs70404268
Xian G, Crane M (2006) An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sens Environ 104:147–156. https://doi.org/10.1016/j.rse.2005.09.023
Xue Z, Hou G, Zhang Z, Lyu X, Jiang M, Zou Y, Shen X, Wang J, Liu X (2019) Quantifying the cooling-effects of urban and peri-urban wetlands using remote sensing data: case study of cities of Northeast China. Landsc Urban Plan 182:92–100. https://doi.org/10.1016/j.landurbplan.2018.10.015
Zhou SZ, Zhang C (1985) Introduction to urban climatology. East China Norm Univ Press Shanghai (In Chinese)
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Shahriar Abdullah: Conceptualization, Investigation, Supervision, Methodology, Writing - review & editing. Dhrubo Barua: Investigation, Resources, Software, Visualization, Original draft.
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Abdullah, S., Barua, D. Modeling Land Surface Temperature with a Mono-Window Algorithm to Estimate Urban Heat Island Intensity in an Expanding Urban Area. Environ. Process. 9, 14 (2022). https://doi.org/10.1007/s40710-021-00554-8
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DOI: https://doi.org/10.1007/s40710-021-00554-8