Abstract
Urbanization has a significant impact on microclimate, which eventually contributes to local and regional climate change. Unplanned urbanization is widespread in develo** countries like Bangladesh. Chittagong, the second largest city, is experiencing rapid urban expansion. Since urban growth introduces a number of environmental issues, including changes in land surface temperature (LST), it is important to investigate the association between urbanization pattern and LST in Chittagong. In this work, we have analyzed the influence of land use and land cover (LULC) of Chittagong Metropolitan Area (CMA) on LST using multi-date Landsat data of 1990, 2005 and 2020. We have used an artificial neural network (ANN) algorithm for LULC classification and an image-based method to compute LST from Landsat data. The results revealed that built-up areas, waterbodies and agricultural lands have increased by 4.57%, 1.04% and 0.94%, respectively, whereas vegetation has decreased by 0.34% and bare lands by 0.87% between 1990 and 2020. As expected, built-up area experienced maximum temperatures followed by bare lands. Waterbodies, on the other hand, exhibited minimum temperature in all years considered, followed by vegetation. Correlations between biophysical variables, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI) and Bare Soil Index (BSI), and LST indicated that NDVI and MNDWI were in a strong negative relationship, whereas NDBI and BSI have showed positive correlation with LST. Lastly, LST is predicted based on the relationship between LST and biophysical variables with an ANN algorithm, which demonstrated that the temperature may reach to a critical state by 2050, if the present trend of urban growth continues.
Similar content being viewed by others
Availability of Data and Materials
Not applicable.
Code Availability
Not applicable.
References
Adnan MSG, Dewan A, Zannat KE, Md Abdullah AY (2019) The use of watershed geomorphic data in flash flood susceptibility zoning: a case study of the Karnaphuli and Sangu river basins of Bangladesh. Nat Hazards 99(1):425–448. https://doi.org/10.1007/s11069-019-03749-3
Al Kafy A, Abdullah-Al-Faisal, Al Rakib A, Akter KS, Rahaman ZA, Jahir DMA, Subramanyam G, Michel OO, Bhatt A (2021) The operational role of remote sensing in assessing and predicting land use/land cover and seasonal land surface temperature using machine learning algorithms in Rajshahi, Bangladesh. Appl Geomatics. https://doi.org/10.1007/s12518-021-00390-3
Astuti IS, Sahoo K, Milewski A, Mishra DR (2019) Impact of land use land cover (LULC) change on surface runoff in an increasingly urbanized tropical watershed. Water Resour Manage 33(12):4087–4103. https://doi.org/10.1007/s11269-019-02320-w
BBS (2011) Bangladesh population and housing census 2011, vol 3. Urban Area Report. Dhaka
Behera MD, Tripathi P, Das P, Srivastava SK, Roy PS, Joshi C, Behera PR, Deka J, Kumar P, Khan ML (2018) Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985. J Environ Manage 206:1192–1203. https://doi.org/10.1016/j.jenvman.2017.10.015
Cai M, Ren C, Xu Y, Lau KK-L, Wang R (2018) Investigating the relationship between local climate zone and land surface temperature using an improved WUDAPT methodology—a case study of Yangtze River Delta, China. Urban Clim 24:485–502. https://doi.org/10.1016/j.uclim.2017.05.010
Carlson TN, Traci Arthur S (2000) The impact of land use—land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Glob Planet Change 25(1–2):49–65. https://doi.org/10.1016/S0921-8181(00)00021-7
Chakraborty T, Lee X (2019) A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability. Int J Appl Earth Obs Geoinf 74:269–280. https://doi.org/10.1016/j.jag.2018.09.015
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
Cohenx J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46
Corner RJ, Dewan AM, Chakma S (2014) Monitoring and prediction of land-use and land-cover (LULC) change. In: Dhaka megacity. Springer, pp 75–97
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(3):431. https://doi.org/10.3390/rs10030431
Das S, Angadi DP (2020) Land use-land cover (LULC) transformation and its relation with land surface temperature changes: a case study of Barrackpore Subdivision, West Bengal, India. Remote Sens Appl Soc Environ 19:100322. https://doi.org/10.1016/j.rsase.2020.100322
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, pp 6337–6339
Dewan AM, Corner RJ (2014a) Dhaka megacity: Geospatial perspectives on urbanisation, environment and health. Dhaka Megacity Geospatial Perspect Urban Environ Heal. https://doi.org/10.1007/978-94-007-6735-5
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(3):390–401
Dewan A, Kiselev G, Botje D (2021) Diurnal and seasonal trends and associated determinants of surface urban heat islands in large Bangladesh cities. Appl Geogr 135:102533. https://doi.org/10.1016/j.apgeog.2021.102533
Dewan A, Kiselev G, Botje D, Mahmud GI, Bhuian MdH, Hassan QK (2021) Surface urban heat island intensity in five major cities of Bangladesh: Patterns drivers and trends. Sustain Cities Soc. 71: https://doi.org/10.1016/j.scs.2021.102926
El-Zeiny AM, Effat HA (2017) Environmental monitoring of spatiotemporal change in land use/land cover and its impact on land surface temperature in El-Fayoum governorate, Egypt. Remote Sens Appl Soc Environ 8:266–277. https://doi.org/10.1016/j.rsase.2017.10.003
Foody GM (1992) On the compensation for chance agreement in image classification accuracy assessment. Photogramm Eng Remote Sensing 58(10):1459–1460
Gazi MY, Rahman MZ, Uddin MM, Rahman FMA (2021) Spatio-temporal dynamic land cover changes and their impacts on the urban thermal environment in the Chittagong metropolitan area, Bangladesh. GeoJournal 86(5):2119–2134. https://doi.org/10.1007/s10708-020-10178-4
Ghosh S, Das CN, 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(1):307–329. https://doi.org/10.1007/s40808-018-0535-9
Gopal S, Woodcock C (1996) Remote sensing of forest change using artificial neural networks. IEEE Trans Geosci Remote Sens 34(2):398–404. https://doi.org/10.1109/36.485117
Hatab AA, Cavinato MER, Lindemer A, Lagerkvist C-J (2019) Urban sprawl, food security and agricultural systems in develo** countries: a systematic review of the literature. Cities 94:129–142. https://doi.org/10.1016/j.cities.2019.06.001
He J, Zhao W, Li A, Wen F, Yu D (2019) The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas. Int J Remote Sens 40(5–6):1808–1827. https://doi.org/10.1080/01431161.2018.1466082
Hua AK (2017) Land use land cover changes in detection of water quality: a study based on remote sensing and multivariate statistics. J Environ Public Health. https://doi.org/10.1155/2017/7515130
Islam MA, Murshed S, Kabir SMM, Farazi AH, Gazi MY, Jahan I, Akhter SH (2017) Utilization of open source spatial data for landslide susceptibility map** at Chittagong district of Bangladesh—an appraisal for disaster risk reduction and mitigation approach. Int J Geosci 08(04):577–598. https://doi.org/10.4236/ijg.2017.84031
Jiménez-Muñoz JC, Sobrino JA (2003) A generalized single-channel method for retrieving land surface temperature from remote sensing data. J Geophys Res Atmos. https://doi.org/10.1029/2003JD003480
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(4):669–684. https://doi.org/10.1007/s13143-019-00109-w
Kayet N, Pathak K, Chakrabarty A, Sahoo S (2016) Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Model Earth Syst Environ 2(3):1–10. https://doi.org/10.1007/s40808-016-0159-x
Landsat 7 Data Users Handbook (2019) Landsat Project Science Ofce at NASA’s Goddard Space Flight Center (GSFC) in Greenbelt, Maryland vol 2, Issue November. https://www.usgs.gov/landresources/nli/landsat/landsat-7-data-users-handbook
Landsat 8 Data Users Handbook (2019) Landsat Project Science Ofce at NASA’s Goddard Space Flight Center (GSFC) in Greenbelt, Maryland, vol 8, Issue November. https://www.usgs.gov/landresources/nli/landsat/landsat-8-data-users-handbook
Mas JF, Flores JJ (2008) The application of artificial neural networks to the analysis of remotely sensed data. Int J Remote Sens 29(3):617–663. https://doi.org/10.1080/01431160701352154
Mberu B, Béguy D, Ezeh AC (2017) Internal Migration, Urbanization and Slums in Sub-Saharan Africa. Africa’s Population: In Search of a Demographic Dividend. Springer International Publishing, Cham, pp 315–332
Mishra VN, Rai PK (2016) A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arab J Geosci 9(4):249. https://doi.org/10.1007/s12517-015-2138-3
Nurwanda A, Honjo T (2020) The prediction of city expansion and land surface temperature in Bogor City, Indonesia. Sustain Cities Soc 52:101772. https://doi.org/10.1016/j.scs.2019.101772
Panday PK (2020) Urbanization and Urban Poverty in Bangladesh. In: The Face of Urbanization and Urban Poverty in Bangladesh. Springer, pp 43–55
Pawe CK, Saikia A (2018) Unplanned urban growth: land use/land cover change in the Guwahati Metropolitan Area. India Geogr Tidsskr J Geogr 118(1):88–100. https://doi.org/10.1080/00167223.2017.1405357
Peng J, Ma J, Liu Q, Liu Y, Li Y, Yue Y (2018) Spatial-temporal change of land surface temperature across 285 cities in China: an urban-rural contrast perspective. Sci Total Environ 635:487–497. https://doi.org/10.1016/j.scitotenv.2018.04.105
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(18):3719–3746. https://doi.org/10.1080/01431160010006971
Rahman M, Ningsheng C, Mahmud GI, Islam MM, Pourghasemi HR, Ahmad H, Habumugisha JM, Washakh RMA, Alam M, Liu E, Han Z, Ni H, Shufeng T, Dewan A (2021) Flooding and its relationship with land cover change, population growth, and road density. Geosci Front 12(6):101224. https://doi.org/10.1016/j.gsf.2021.101224
Raja DR, Hredoy MSN, Islam MK, Islam KMA, Adnan MSG (2021) Spatial distribution of heatwave vulnerability in a coastal city of Bangladesh. Environ Challenges 4(March):100122. https://doi.org/10.1016/j.envc.2021.100122
Rikimaru A, Roy PS, Miyatake S (2002) Tropical forest cover density map**. Trop Ecol 43(1):39–47
Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec Publ 351(1974):309
Roy S, Pandit S, Eva EA, Bagmar MSH, Papia M, Banik L, Dube T, Rahman F, Razi MA (2020) Examining the nexus between land surface temperature and urban growth in Chattogram Metropolitan Area of Bangladesh using long term Landsat series data. Urban Clim 32(November 2019):100593. https://doi.org/10.1016/j.uclim.2020.100593
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
Sannigrahi S, Bhatt S, Rahmat S, Uniyal B, Banerjee S, Chakraborti S, Jha S, Lahiri S, Santra K, Bhatt A (2018) Analyzing the role of biophysical compositions in minimizing urban land surface temperature and urban heating. Urban Clim 24:803–819. https://doi.org/10.1016/j.uclim.2017.10.002
Schalkoff RJ (1997) Artificial neural networks. McGraw-Hill Higher Education
Sharma R, Nehren U, Rahman SA, Meyer M, Rimal B, Aria Seta G, Baral H (2018) Modeling land use and land cover changes and their effects on biodiversity in Central Kalimantan, Indonesia. Land 7(2):57. https://doi.org/10.3390/land7020057
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
Silva E, Clarke K (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Comput Environ Urban Syst 26(6):525–552. https://doi.org/10.1016/S0198-9715(01)00014-X
Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90(4):434–440. https://doi.org/10.1016/j.rse.2004.02.003
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
Srivastava PK, Han D, Rico-Ramirez MA, Bray M, Islam T (2012) Selection of classification techniques for land use/land cover change investigation. Adv Sp Res 50(9):1250–1265. https://doi.org/10.1016/j.asr.2012.06.032
Story M, Congalton RG (1986) Accuracy assessment: a user’s perspective. Photogramm Eng Remote Sensing 52(3):397–399
Tariq A, Riaz I, Ahmad Z, Yang B, Amin M, Kausar R, Andleeb S, Farooqi MA, Rafiq M (2020) Land surface temperature relation with normalized satellite indices for the estimation of spatio-temporal trends in temperature among various land use land cover classes of an arid Potohar region using Landsat data. Environ Earth Sci 79(1):1–15. https://doi.org/10.1007/s12665-019-8766-2
Trotter L, Dewan A, Robinson T (2017) Effects of rapid urbanisation on the urban thermal environment between 1990 and 2011 in Dhaka Megacity, Bangladesh. AIMS Environ Sci 4(1):145–167. https://doi.org/10.3934/environsci.2017.1.145
Ullah S, Tahir AA, Akbar TA, Hassan QK, Dewan A, Khan AJ, Khan M (2019) Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the lower Himalayan Region. Sustain 11(19):5492. https://doi.org/10.3390/su11195492
USGS (2016) Landsat 8 Data Users Handbook. Greenbelt, Maryland
Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384. https://doi.org/10.1016/S0034-4257(03)00079-8
Wang C, Li Y, Myint SW, Zhao Q, Wentz EA (2019) Impacts of spatial clustering of urban land cover on land surface temperature across Köppen climate zones in the contiguous United States. Landsc Urban Plan 192:103668. https://doi.org/10.1016/j.landurbplan.2019.103668
Wurm M, Taubenböck H (2018) Detecting social groups from space–Assessment of remote sensing-based mapped morphological slums using income data. Remote Sens Lett 9(1):41–50. https://doi.org/10.1080/2150704X.2017.1384586
Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27(14):3025–3033. https://doi.org/10.1080/01431160600589179
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
Yohannes H, Soromessa T, Argaw M, Dewan A (2021) Impact of landscape pattern changes on hydrological ecosystem services in the Beressa watershed of the Blue Nile Basin in Ethiopia. Sci Total Environ 793:148559. https://doi.org/10.1016/j.scitotenv.2021.148559
Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically map** urban areas from TM imagery. Int J Remote Sens 24(3):583–594. https://doi.org/10.1080/01431160304987
Zhou X, Wang Y (2011) Dynamics of land surface temperature in response to land-use/cover change. Geogr Res 49(1):23–36. https://doi.org/10.1111/j.1745-5871.2010.00686.x
Funding
No funding is received for this work.
Author information
Authors and Affiliations
Contributions
SA: conceptualization, investigation, supervision, methodology, writing—review and editing. DB: investigation, resources, software, visualization, original draft. SMAA: conceptualization, writing—review and editing. YWR: conceptualization, writing—review and editing.
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that there is no conflict of interest.
Ethics Approval
This work has not been published in whole or in part elsewhere, not currently being considered for publication in another, and all authors have been personally and actively involved in this work.
Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Rights and permissions
About this article
Cite this article
Abdullah, S., Barua, D., Abdullah, S.M.A. et al. Investigating the Impact of Land Use/Land Cover Change on Present and Future Land Surface Temperature (LST) of Chittagong, Bangladesh. Earth Syst Environ 6, 221–235 (2022). https://doi.org/10.1007/s41748-021-00291-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41748-021-00291-w