Abstract
Urban expansion and land surface temperature (LST) fluctuations have been negatively influenced by rapid urbanisation, which degrades the urban thermal environment. This study has endeavoured to incorporate future urban growth modelling and assess the impact of urbanization on LST over Indore City, India, using temporal Landsat satellite data for the years 2010, 2015 and 2020. The present study has utilized the Cellular Automata (CA) model to project future urban growth for the year 2025. The result shows that the built-up land has increased by 28.7% during 2010–2015 and 24.9% during 2015–2020, while a future projection of 4.7% is evident in 2025. The mean LST over the city signifies a decreasing trend during the winter and summer seasons from 2010 to 2020. Moreover, an occurrence of higher LST in the surrounding rural area is observed, witnessing cool heat island phenomenon. The urban thermal field variation index (UTFVI) map shows non-UTFVI in the urban area, which shows an increasing trend and strongest UTFVI in rural regions with decreasing trend in summer season during 2010–2020. Whereas in the winter season, the observed result suggests the opposite behaviour of UTFVI.
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Data availability
The data used in this study were obtained from the USGS earth explorer website (https://earthexplorer.usgs.gov/). The process data can be obtained from the corresponding author on request.
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Not applicable.
References
Ai-Ageili M, Mouhoub M, Piwowar J (2013) Integrating remote sensing, GIS and dynamic models: cellular automata approach for the simulation of urban growth for the city of Montreal. Can Conf Electr Comput Eng. https://doi.org/10.1109/CCECE.2013.6567844
Andersson C, Lindgren K, Rasmussen S, White R (2002) Urban growth simulation from “first principles.” Phys Rev E-Stat Phys, Plasmas, Fluids, Related Interdiscip Topics 66(2). https://doi.org/10.1103/PhysRevE.66.026204
Asokan A, Anitha J, Ciobanu M, Gabor A, Naaji A, Hemanth DJ (2020) Image processing techniques for analysis of satellite images for historical maps classification-an overview. Appl Sci (Switzerland) 10(12). https://doi.org/10.3390/app10124207
Bai Y, Deng X, Jiang S, Zhang Q, Wang Z (2018) Exploring the relationship between urbanization and urban eco-efficiency: evidence from prefecture-level cities in China. J Clean Prod 195:1487–1496. https://doi.org/10.1016/j.jclepro.2017.11.115
Becker FG (2015) Accuracy assessment of supervised classification of submersed macrophytes using GIS and error matrices: the case of Lake Sevan, Armenia, pp 1–21.https://www.academia.edu/5928921/Accuracy_Assessment_of_Supervised_Classification_of_Submersed_Macrophytes_Using_GIS_and_Error_Matrices_The_Case_of_Lake_Sevan_Armenia
Cheng L, Guan D, Zhou L, Zhao Z, Zhou J (2019) Urban cooling island effect of main river on a landscape scale in Chongqing, China. Sustain Cities Soc 47(December 2018):101501. https://doi.org/10.1016/j.scs.2019.101501
Estoque RC, Murayama Y (2017) Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015). ISPRS J Photogramm Remote Sens 133:18–29. https://doi.org/10.1016/j.isprsjprs.2017.09.008
Estoque RC, Murayama Y, Myint SW (2017) Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Sci Total Environ 577:349–359. https://doi.org/10.1016/j.scitotenv.2016.10.195
Falah N, Karimi A, Harandi AT (2020) Urban growth modeling using cellular automata model and AHP (case study: Qazvin city). Model Earth Syst Environ 6(1):235–248. https://doi.org/10.1007/s40808-019-00674-z
Forest Survey of India (2019) Madhya pradesh 11.15.1. Madhya Pradesh 11.15:141–151. https://fsi.nic.in/isfr19/vol2/isfr-2019-vol-ii-madhya-pradesh.pdf
Frohlich ED, Ré RN, Navar LG (1999) United Nations Human Settlements Programme (UN-Habitat), 2019. In Hypertension 33(1). https://doi.org/10.1161/01.hyp.33.1.1
Gao C, Zhao E, Li C, Qian Y, Ma L, Tang L, Jiang X, Huo H (2015) Study of aerosol influence on nighttime land surface temperature retrieval based on two methods. Adv Meteorol 2015:18–23. https://doi.org/10.1155/2015/496458
Gašparović M (2020) Chapter 3 - urban growth pattern detection and analysis (P. Verma, P. Singh, R. Singh, & A. S. B. T.-U. E. Raghubanshi (eds.); 35–48. Elsevier. https://doi.org/10.1016/B978-0-12-820730-7.00003-3
Gogoi PP, Vinoj V, Swain D, Roberts G, Dash J, Tripathy S (2019) Land use and land cover change effect on surface temperature over Eastern India. Sci Rep 9(1):1–10. https://doi.org/10.1038/s41598-019-45213-z
Gupta P, Gadhavi H, Jayaraman A (2003) Aerosol optical depth variation observed using sun-photometer over Indore. Indian J Radio Space Phys 32(4):229–237
Gupta K, Hall RP (2017) The Indian perspective of smart cities. 2017 Smart Cities Symp Prague, SCSP 2017 - IEEE Proc. https://doi.org/10.1109/SCSP.2017.7973837
Gupta A, Swain S, Kumari M (2014) Urban growth trend analysis of Indore city (2005 – 2014 ) Through index based models abstract: about the author: 15th Esri India User Conference 2014 Urban, pp 1–7. https://www.esri.in/~/media/esri-india/files/pdfs/events/uc2014/proceedings/papers/UCP0030.pdf
Guttikunda SK, Jawahar P (2014) Atmospheric emissions and pollution from the coal-fired thermal power plants in India. Atmos Environ 92:449–460. https://doi.org/10.1016/j.atmosenv.2014.04.057
Halder B, Bandyopadhyay J (2021) Evaluating the impact of climate change on urban environment using geospatial technologies in the planning area of Bilaspur. India. Environ Chall 5(May):100286. https://doi.org/10.1016/j.envc.2021.100286
Han X, Cao T (2022) Urbanization level, industrial structure adjustment and spatial effect of urban haze pollution: evidence from China’s Yangtze River Delta urban agglomeration. Atmos Pollut Res 13(6):101427. https://doi.org/10.1016/j.apr.2022.101427
Hussain A, Bhalla P, Palria S (2014) Remote sensing based analysis of the role of land use/land cover on surface temperature and temporal changes in temperature; a case study of Ajmer District, Rajasthan. Int Arch Photogramm Remote Sens Spatial Inf Sci XL-8:1447–1454. https://doi.org/10.5194/isprsarchives-XL-8-1447-2014
Indore Municipal Corporation (2018) Indore Smart City Abd master plan (Final), pp 1–274. https://www.smartcityindore.org/proposed-master-plan/. Accessed 19 Feb 2022
Jain S, Sannigrahi S, Sen S, Bhatt S, Chakraborti S, Rahmat S (2019) Urban heat island intensity and its mitigation strategies in the fast-growing urban area. J Urban Manag 9(1):54–66. https://doi.org/10.1016/j.jum.2019.09.004
Jalan S. Sharma K (2014) Spatio-temporal assessment of land use/ land cover dynamics and urban heat Island of Jaipur City using satellite data. Int Arch Photogramm Remote Sens Spatial Inf Sci XL-8:767–772. https://doi.org/10.5194/isprsarchives-XL-8-767-2014
Kar R, Obi Reddy GP, Kumar N, Singh SK (2018) Monitoring spatio-temporal dynamics of urban and peri-urban landscape using remote sensing and GIS – a case study from Central India. Egypt J Remote Sens Space Sci 21(3):401–411. https://doi.org/10.1016/j.ejrs.2017.12.006
Karimi A, Mohammad P, Gachkar S, Gachkar D, García-Martínez A, Moreno-Rangel D, Brown RD (2021) Surface urban heat island assessment of a cold desert city: a case study over the Isfahan Metropolitan Area of Iran. Atmosphere 12(10):1368. https://doi.org/10.3390/atmos12101368
Kaur RR, Luthra A (2018) Population growth, urbanization and electricity - challenges and initiatives in the state of Punjab, India. Energ Strat Rev 21:50–61. https://doi.org/10.1016/j.esr.2018.04.005
Kawadia G, Tiwari E (2017) Understanding climate change in Indore District: an empirical investigation of trends and shifts. Amity Journal of Economics 2(1):64–78. https://amity.edu/UserFiles/admaa/02cc0Paper%205.pdf
Khan F, Das B, Mohammad P (2022) Urban growth modeling and prediction of land use land cover change over Nagpur City, India using cellular automata approach. In Geospatial Technol Landsc Environ Manag 261–282. Springer, Singapore. https://doi.org/10.1007/978-981-16-7373-3_13
Khandelwal S, Goyal R, Kaul N, Mathew A (2018) Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. Egypt J Remote Sens Space Sci 21(1):87–94. https://doi.org/10.1016/j.ejrs.2017.01.005
Kuddus MA, Tynan E, McBryde E (2020) Urbanization: a problem for the rich and the poor? Public Health Rev 41(1):1–4. https://doi.org/10.1186/s40985-019-0116-0
Kumar A (2020) Spatio-temporal variations in satellite based aerosol optical depths & aerosol index over Indian subcontinent: impact of urbanization and climate change. Urban Clim 32(January):100598. https://doi.org/10.1016/j.uclim.2020.100598
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 Sens Environ 115(12):3249–3263. https://doi.org/10.1016/j.rse.2011.07.008
Liang Z, Wang W, Wang Y, Ma L, Liang C, Li P, Yang C, Wei F, Li S, Zhang L (2021) Urbanization, ambient air pollution, and prevalence of chronic kidney disease: a nationwide cross-sectional study. Environ Int 156:106752. https://doi.org/10.1016/j.envint.2021.106752
Lim YK, Cai M, Kalnay E, Zhou L (2005) Observational evidence of sensitivity of surface climate changes to land types and urbanization. Geophys Res Lett 32(22):1–4. https://doi.org/10.1029/2005GL024267
Liu L, Zhang Y (2011) Urban heat island analysis using the landsat TM data and ASTER Data: a case study in Hong Kong. Remote Sens 3(7):1535–1552. https://doi.org/10.3390/rs3071535
Liu Y, Fang F, Li Y (2014) Key issues of land use in China and implications for policy making. Land Use Policy 40(July 2019):6–12. https://doi.org/10.1016/j.landusepol.2013.03.013
Maheshwarkar P, Sunder Raman R (2021) Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model. Sci Rep 11(1):1–13. https://doi.org/10.1038/s41598-020-79229-7
Maithani S (2010) Cellular automata based model of urban spatial growth. J Indian Soc Remote Sens 38(4):604–610. https://doi.org/10.1007/s12524-010-0053-3
Mohammad P, Goswami A (2019) Temperature and precipitation trend over 139 major Indian cities: an assessment over a century. Model Earth Syst Environ 5(4):1481–1493. https://doi.org/10.1007/s40808-019-00642-7
Mohammad P, Goswami A, Bonafoni S (2019) The impact of the land cover dynamics on surface urban heat island variations in semi-arid cities: a case study in Ahmedabad City, India, using multi-sensor/source data. Sensors (Switzerland) 19(17). https://doi.org/10.3390/s19173701
Mohammad P, Goswami (2020) Surface urban heat island variation over major Indian cities across different climatic zone. EGU General Assembly Conference Abstracts, p 6444. https://meetingorganizer.copernicus.org/EGU2020/EGU2020-6444.html
Mohammad P, Goswami A (2021a) Quantifying diurnal and seasonal variation of surface urban heat island intensity and its associated determinants across different climatic zones over Indian cities. Gisci Remote Sens 58(7):955–981. https://doi.org/10.1080/15481603.2021.1940739
Mohammad P, Goswami A (2021b) Spatial variation of surface urban heat island magnitude along the urban-rural gradient of four rapidly growing Indian cities. Geocarto Int 0(0):1–21. https://doi.org/10.1080/10106049.2021.1886338
Mohamed A, Worku H (2020) Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Clim 31(October 2019). https://doi.org/10.1016/j.uclim.2019.100545
Mohammad P, Goswami A, Chauhan S, Nayak S (2022) Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India. Urban Clim 42(March 2022):101116. https://doi.org/10.1016/j.uclim.2022.101116
Nayak S, Mandal M (2019) Impact of land use and land cover changes on temperature trends over India. Land Use Policy 89(September):104238. https://doi.org/10.1016/j.landusepol.2019.104238
Novin MS, Ebrahimipour A (2019) Spatio-temporal modelling of land use changes by means of CA–Markov model. Model Earth Syst Environ 5(4):1253–1263. https://doi.org/10.1007/s40808-019-00633-8
Nugroho F, Al-Sanjary OI (2018) A review of simulation urban growth model. Int J Eng Technol (UAE) 7(4):17–23. https://doi.org/10.14419/ijet.v7i4.11.20681
Orfanidis S, Panayotidis P, Stamatis N (2003) An insight to the ecological evaluation index (EEI). Ecol Ind 3(1):27–33. https://doi.org/10.1016/S1470-160X(03)00008-6
Rasul A, Balzter H, Smith C (2016) Diurnal and seasonal variation of surface urban cool and heat islands in the semi-arid city of Erbil. Iraq Clim 4(3):42. https://doi.org/10.3390/cli4030042
Shahfahad, Mourya M, Kumari B, Tayyab M, Paarcha A, Asif, Rahman A (2021) Indices based assessment of built-up density and urban expansion of fast growing Surat city using multi-temporal Landsat data sets. GeoJournal 86(4):1607–1623.https://doi.org/10.1007/s10708-020-10148-w
Shukla A, Jain K (2019) Critical analysis of rural-urban transitions and transformations in Lucknow city, India. Remote Sens Appl: Soc Environ 13(December 2018):445–456. https://doi.org/10.1016/j.rsase.2019.01.001
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
Sobrino JA, Irakulis I (2020) A methodology for comparing the surface urban heat Island in selected urban agglomerations around the world from Sentinel-3 SLSTR data. Remote Sens 12(12). https://doi.org/10.3390/RS12122052
Song SK, Choi YN, Choi Y, Flynn J, Sadeghi B (2021) Characteristics of aerosol chemical components and their impacts on direct radiative forcing at urban and suburban locations in Southeast Texas. Atmos Environ 246(June 2020):118151. https://doi.org/10.1016/j.atmosenv.2020.118151
Subhasini S, Thirumaran K SVAARA (2016) A comparative analysis of land surface retrieval methods using landsat 7 and 8 data to study urban heat island effect in Madurai. https://www.academia.edu/31007716/A_Comparative_Analysis_of_Land_Surface_Retrieval_Methods_Using_Landsat_7_and_8_Data_to_Study_Urban_Heat_Island_Effect_in_Madurai
Tian G, Ma B, Xu X, Liu X, Xu L, Liu X, Xiao L, Kong L (2016) Simulation of urban expansion and encroachment using cellular automata and multi-agent system model—a case study of Tianjin metropolitan region, China. Ecol Ind 70:439–450. https://doi.org/10.1016/j.ecolind.2016.06.021
Tiwari A, Mishra PK (2019) A study of urban-landscape characteristics of Bhopal City (India) in a geo-spatial environment. Springer International Publishing, In Urban Book Series (Issue December). https://doi.org/10.1007/978-3-319-94932-1_15
Tran DX, Pla F, Latorre-Carmona P, Myint SW, Caetano M, Kieu HV (2017) Characterizing the relationship between land use land cover change and land surface temperature. ISPRS J Photogramm Remote Sens 124:119–132. https://doi.org/10.1016/j.isprsjprs.2017.01.001
United Nations (2018) World urbanization prospects. In Demographic Research. https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/files/documents/2020/Jan/un_2018_worldcities_databooklet.pdf
United Nations, Department of Economic and Social Affairs PD (2019) World population prospects 2019. In Department of economic and social affairs. World population prospects 2019, Issue 141. http://www.ncbi.nlm.nih.gov/pubmed/12283219. Accessed 03 Jan 2022
USGS (2019) Landsat 8 data users handbook. Nasa 8:97. https://landsat.usgs.gov/documents/Landsat8DataUsersHandbook.pdf. Accessed 23 Mar 2022
Vizzari M, Sigura M (2015) Landscape sequences along the urban–rural–natural gradient: a novel geospatial approach for identification and analysis. Landsc Urban Plan 140:42–55. https://doi.org/10.1016/j.landurbplan.2015.04.001
Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89(4):467–483. https://doi.org/10.1016/j.rse.2003.11.005
Zhang Y, Sun L (2019) Spatial-temporal impacts of urban land use land cover on land surface temperature: case studies of two Canadian urban areas. Int J Appl Earth Obs Geoinformation 75(October 2018):171–181. https://doi.org/10.1016/j.jag.2018.10.005
Zhao C, Wang B (2022) How does new-type urbanization affect air pollution? Empirical evidence based on spatial spillover effect and spatial Durbin model. Environ Int 165(1037):107304. https://doi.org/10.1016/j.envint.2022.107304
Acknowledgements
The authors would like to acknowledge the Department of Earth Sciences, Indian Institute of Technology Roorkee and Institute of Environment Education and Research, Bharati Vidyapeeth University, Pune, India, for providing the necessary infrastructure facilities to carry out the research work. The author would also acknowledge NASA USGS for providing the Landsat data freely available to public, which makes the foundation of the research.
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KJG: conceptualization, software, investigation, analysis, validation, visualization, writing — original draft. AG: conceptualization, software, final figure preparation, writing — review and editing, supervision. PM: conceptualization, software, investigation, analysis, validation, visualization, evaluation, writing — original draft, writing — review and editing. SK: conceptualization, supervision, writing — review and editing.
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Gohain, K.J., Goswami, A., Mohammad, P. et al. Modelling relationship between land use land cover changes, land surface temperature and urban heat island in Indore city of central India. Theor Appl Climatol 151, 1981–2000 (2023). https://doi.org/10.1007/s00704-023-04371-x
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DOI: https://doi.org/10.1007/s00704-023-04371-x