Skip to main content

Advertisement

SpringerLink
Log in

A study on the urban growth and dynamics over 16 major cities of India

  • Published:
Journal of Earth System Science Aims and scope Submit manuscript

Abstract

Urban agglomerations across the world have witnessed haphazard and unprecedented growth in the recent past, giving rise to urban sprawling. This study analyses the spatio-temporal growth dynamics of 16 major Indian cities (population above one million) using remote sensing approaches. Land Use and Land Cover (LULC) thematic datasets are considered for the years 2005, 2010, 2015, and 2021. The variability of the five LULC classes, viz., urban built-up, vegetation, water body, agriculture, and barren land, implied that urban expansion mostly took place at the cost of barren lands. The urbanised landscape mainly portrayed dispersive outward growth since the beginning of the 21st century, with significant compaction (infill urban growth) near the urban core in recent years. The results derived through Shannon’s Entropy, spatial metrics, and urban density gradient analysis (in eight directions) indicated the same. Population density variation with respect to the horizontal urban growth and dynamics in each considered direction, further supported the concept of overcrowded city centres and sprawled outskirts. Besides population density, other factors that could be associated with urbanisation include the local environment, meteorology, and some geophysical characteristics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15

Similar content being viewed by others

References

  • Aithal B H and Ramachandra T V 2016 Visualisation of urban growth pattern in Chennai using geoinformatics and spatial metrics; J. Ind. Soc. Remote Sens. 44(4) 617–633.

    Article  Google Scholar 

  • Angel S, Sheppard S, Civco D L, Buckley R, Chabaeva A, Gitlin L, Kraley A, Parent J and Perlin M 2005 The dynamics of global urban expansion; World Bank, Transport and Urban Development Department, Washington, DC, 205p.

    Google Scholar 

  • Arnfield A J 2003 Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island; Int. J. Climatol. 23(1) 1–26.

    Article  Google Scholar 

  • Barnes K B, Morgan III J M, Roberge M C and Lowe S 2001 Sprawl development: Its patterns, consequences, and measurement; Towson University, Towson, pp. 1–24.

  • Bhatta B 2009 Analysis of urban growth pattern using remote sensing and GIS: A case study of Kolkata, India; Int. J. Remote Sens. 30(18) 4733–4746.

    Article  Google Scholar 

  • Bose S 2012 Historic buildings at Chitpur in Kolkata: Problems and prospects through urban conservation and planning; disP-The Planning Review 48(1) 68–82.

  • Buechner M 1989 Are small-scale landscape features important factors for field studies of small mammal dispersal sinks?; Landscape Ecology 2(3) 191–199.

    Article  Google Scholar 

  • Cengiz S, Görmüş S and Oğuz D 2022 Analysis of the urban growth pattern through spatial metrics, Ankara City; Land Use Policy 112 105812.

    Article  Google Scholar 

  • Census 2011 Census of India; http://censusindia.gov.in/.

  • Chettry V and Surawar M 2020 Urban sprawl assessment in Raipur and Bhubaneswar urban agglomerations from 1991 to 2018 using geoinformatics; Arab. J. Geosci. 13(14) 1–17.

    Article  Google Scholar 

  • Chopra R 2016 Environmental degradation in India: Causes and consequences; Int. J. Appl. Environ. Sci. 11(6) 1593–1601.

    Google Scholar 

  • Davis K 1965 The urbanisation of the human population; Sci. Am. 213(3) 40–53.

    Article  Google Scholar 

  • Doxsey-Whitfield E, MacManus K, Adamo S B, Pistolesi L, Squires J, Borkovska O and Baptista S R 2015 Taking advantage of the improved availability of census data: A first look at the gridded population of the world, version 4; Pap. Appl. Geogr. 1(3) 226–234.

    Article  Google Scholar 

  • Ghosh S, Kumar D and Kumari R 2022 Assessing spatiotemporal variations in land surface temperature and SUHI intensity with a cloud-based computational system over five major cities of India; Sustain. Cities Soc. 85 104060.

    Article  Google Scholar 

  • Gupta N, Mathew A and Khandelwal S 2019 Analysis of cooling effect of water bodies on land surface temperature in nearby region: A case study of Ahmedabad and Chandigarh cities in India; Egypt. J. Remote Sens. Space Sci. 22(1) 81–93.

    Google Scholar 

  • Habibi S and Asadi N 2011 Causes, results and methods of controlling urban sprawl; Proc. Eng. 21 133–141.

    Article  Google Scholar 

  • Hammer M S, van Donkelaar A, Li C, Lyapustin A, Sayer A M, Hsu N C, Levy R C, Garay M J, Kalashnikova O V, Kahn R A, Brauer M, Apte J S, Henze D K, Zhang L, Zhang Q, Ford B, Pierce J R and Martin R V 2020 Global estimates and long-term trends of fine particulate matter concentrations (1998–2018); Environ. Sci. Technol. 54(13) 7879–7890.

    Article  CAS  Google Scholar 

  • Handy S, Cao X and Mokhtarian P 2005 Correlation or causality between the built environment and travel behavior? Evidence from Northern California; Transp. Res. D Transp. Environ. 10(6) 427–444.

    Article  Google Scholar 

  • Hatab A A, Ravula P, Nedumaran S and Lagerkvist C J 2021 Perceptions of the impacts of urban sprawl among urban and peri-urban dwellers of Hyderabad, India: A latent class clustering analysis; Environ. Dev. Sustain. 24 12,787–12,812.

    Article  Google Scholar 

  • He J, Wang S, Liu Y, Ma H and Liu Q 2017 Examining the relationship between urbanisation and the eco-environment using a coupling analysis: Case study of Shanghai, China; Ecol. Indic. 77 185–193.

    Article  Google Scholar 

  • Iyer N K, Kulkarni S and Raghavaswamy V 2007 Economy, population and urban sprawl a comparative study of urban agglomerations of Bangalore and Hyderabad, India using remote sensing and GIS techniques; In: PRIPODE workshop on urban population, development and environment dynamics in developing countries, pp. 1–37.

  • Jat M K, Garg P K and Khare D 2008 Modelling of urban growth using spatial analysis techniques: A case study of Ajmer city (India); Int. J. Remote Sens. 29(2) 543–567.

    Article  Google Scholar 

  • Jauregui E 1997 Heat island development in Mexico City; Atmos. Environ. 31(22) 3821–3831.

    Article  CAS  Google Scholar 

  • Kantakumar L N, Kumar S and Schneider K 2016 Spatiotemporal urban expansion in Pune metropolis, India using remote sensing; Habitat Int. 51 11–22.

  • Karra K, Kontgis C, Statman-Weil Z, Mazzariello J C, Mathis M and Brumby S P 2021 Global land use/land cover with Sentinel 2 and deep learning; In: 2021 IEEE international geoscience and remote sensing symposium IGARSS, pp. 4704–4707.

  • Kavitha A, Somashekar R K and Nagaraja B C 2015 Urban expansion and loss of Agriculture land – A case of Bengaluru city; Int. J. Geomat. Geosci. 5(3) 492.

    Google Scholar 

  • Khan F, Das B and Mohammad P 2022 Urban growth modeling and prediction of land use land cover change over Nagpur City, India using cellular automata approach; Geospatial Technology for Landscape and Environmental Management: Sustainable Assessment and Planning, pp. 261–282.

  • Kłysik K and Fortuniak K 1999 Temporal and spatial characteristics of the urban heat island of Łódź, Poland; Atmos. Environ. 33(24–25) 3885–3895.

    Article  Google Scholar 

  • Li X X, Koh T Y, Entekhabi D, Roth M, Panda J and Norford L K 2013 A multi-resolution ensemble study of a tropical urban environment and its interactions with the background regional atmosphere; J. Geophs. Res. Atmos. 118(17) 9804–9818.

    Article  Google Scholar 

  • Lin C H 2005 Seismicity increase after the construction of the world's tallest building: An active blind fault beneath the Taipei 101; Geophys. Res. Lett. 32(22).

  • Lyapustin A and Wang Y 2022 MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1 km SIN Grid V061 [Data set]; NASA EOSDIS Land Processes Distributed Active Archive Center.

  • McGarigal K 2014 FRAGSTATS help; Documentation for FRAGSTATS v4, https://ibis.geog.ubc.ca/courses/geob479/labs/fragstats.help.4.pdf.

  • McGarigal K and Marks B J 1995 Spatial pattern analysis program for quantifying landscape structure; Gen. Tech. Rep. PNW-GTR-351 US Department of Agriculture, Forest Service, Pacific Northwest Research Station, 122p.

  • McGarigal K, Cushman S A, Neel M C and Ene E 2002 FRAGSTATS v3: spatial pattern analysis program for categorical maps; Computer Software Program Produced by the Authors at the University of Massachusetts, Amherst., http://www.umass.edu/landeco/research/fragstats/fragstats.html.

  • Mitra C, Shepherd J M and Jordan T 2012 On the relationship between the pre-monsoonal rainfall climatology and urban land cover dynamics in Kolkata city, India; Int. J. Climatol. 32(9) 1443–1454.

    Article  Google Scholar 

  • Mohanty S, Panda J and Rath S S 2021 Geospatial technology in urban sprawl assessment: A review; In: Methods and applications of geospatial technology in sustainable urbanism, pp. 1–33. https://doi.org/10.4018/978-1-7998-2249-3.ch001.

  • MOHUA 2019 Ministry of Housing and Urban Affairs, Government of India's Municipal Performance Index, 2019: Assessment Framework, New Delhi, Ministry of Housing and Urban Affairs, https://amplifi.mohua.gov.in/mpi-landing.

  • Mouratidis K 2019 Compact city, urban sprawl, and subjective well-being; Cities 92 261–272.

    Article  Google Scholar 

  • Mukherjee K and Das P 2018 Modelling the relationship between urban growth modes and the thermal environment – A case study of the Barasat municipality, West Bengal; J. Geogr. Environ. Earth Sci. Int. 17(2) 1–19.

    Google Scholar 

  • Munshi T, Zuidgeest M, Brussel M and van Maarseveen M 2014 Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad, India; Cities 39 68–86.

    Article  Google Scholar 

  • Oke T R 1974 Review of urban climatology 1968–1973; WMO Tech. Note 134 1–132.

  • O’Neill R V, Krummel J R, Gardner R E A, Sugihara G, Jackson B, DeAngelis D L, Milne B T, Turner M G, Zygmunt B, Christensen S W, Dale V H and Graham R L 1988 Indices of landscape pattern; Landsc. Ecol. 1(3) 153–162.

    Article  Google Scholar 

  • Panda J and Rath S S 2022 Observed and simulated characteristics of 2015 Chennai heavy rain event: Impact of land-use change, SST, and high-resolution global analyses; Pure Appl. Geophys. 179(9) 3391–3409.

    Article  Google Scholar 

  • Panda J, Mukherjee A and Rath S S 2022 Urban Heat Island and mitigation strategies: International studies and Indian perspectives; Chapter 2; In: Urban heat islands reexamined (Satyaprakash and Anne Edn), ISBN: 979-8-88697-215-3, https://doi.org/10.52305/RKFG7202.

  • Parsons T 2021 The weight of cities: Urbanisation effects on Earth's subsurface; AGU Advances 2(1) e2020AV000277.

  • Pataki D E, Xu T, Luo Y Q and Ehleringer J R 2007 Inferring biogenic and anthropogenic carbon dioxide sources across an urban to rural gradient; Oecologia 152 307–322.

    Article  CAS  Google Scholar 

  • Prabu P and Dar M A 2018 Land-use/cover change in Coimbatore urban area (Tamil Nadu, India) – a remote sensing and GIS-based study; Environ. Monit. Assess. 190(8) 1–14.

    Article  Google Scholar 

  • Punia M and Singh L 2012 Entropy approach for assessment of urban growth: A case study of Jaipur, India; J. Ind. Soc. Remote Sens. 40(2) 231–244.

    Article  Google Scholar 

  • Ramachandra T V and Kumar U 2010 Greater Bangalore: Emerging urban heat island; GIS Development 14(1) 86–104.

    Google Scholar 

  • Ramachandra T V and Aithal B H 2013 Urbanisation and sprawl in the Tier II City: Metrics, dynamics and modelling using spatio-temporal data; Int. J. Remote Sens. Appl. 3(2) 65–74.

    Google Scholar 

  • Ramachandra T V, Aithal B H and Sanna D D 2012 Insights to urban dynamics through landscape spatial pattern analysis; Int. J. Appl. Earth Obs. Geoinf. 18 329–343.

    Google Scholar 

  • Ramachandra T V, Aithal B H and Sowmyashree M V 2014 Urban structure in Kolkata: Metrics and modelling through geo-informatics; Appl. Geomat. 6(4) 229–244.

    Article  Google Scholar 

  • Rath S S and Panda J 2020 Urban induced land-use change impact during pre-monsoon thunderstorms over Bhubaneswar–Cuttack urban complex; Urban Climate 32 100628.

    Article  Google Scholar 

  • Rath S S, Mohanty S and Panda J 2022a Analysing the fragmentation of urban footprints in eastern and southern Indian cities and driving factors; J. Ind. Soc. Remote Sens. 50(8) 1499–1517.

    Article  Google Scholar 

  • Rath S S, Panda J and Sarkar A 2022b Distinct urban land cover response to meteorology in WRF simulated pre-monsoon thunderstorms over the tropical city of Kolkata; Meteorol. Atmos. Phys. 134(4) 76.

    Article  Google Scholar 

  • Richardson H W, Bae C H C and Baxamusa M 2002 Compact cities in developing countries: Assessment and implications; In: Compact Cities, pp. 37–48.

  • Roy B and Kasemi N 2021 Monitoring urban growth dynamics using remote sensing and GIS techniques of Raiganj Urban Agglomeration, India; Egypt. J. Remote Sens. Space Sci. 24(2) 221–230.

    Google Scholar 

  • Roy P S, Meiyappan P, Joshi P K, Kale M P, Srivastav V K, Srivastava S K, Behera M D, Roy A, Sharma Y, Ramachandran R M, Bhavani P, Jain A K and Krishnamurthy Y V N 2016 Decadal land use and land cover classifications across India, 1985, 1995, 2005; ORNL DAAC.

  • Saini S S and Kaushik S P 2011 Land use changes in Haryana sub-region of Chandigarh periphery controlled area: A spatio-temporal study; Institute of Town Planners, India Journal 8(4) 96–106.

    Google Scholar 

  • Sarkar A, Amal K K, Sarkar T, Panda J and Paul D 2021 Variability in air-pollutants, aerosols, and associated meteorology over peninsular India and neighboring ocean regions during COVID-19 lockdown to unlock phases; Atmos. Pollut. Res. 12(12) 101231.

    Article  CAS  Google Scholar 

  • Sarkar A, Panda J, Kant S and Mukherjee A 2022 Influence of smoke aerosols on low-level clouds over the Indian region during winter; Atmos. Res. 278 106358.

    Article  Google Scholar 

  • Schneider A and Woodcock C E 2008 Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information; Urban Studies 45(3) 659–692.

    Article  Google Scholar 

  • Seto K C and Shepherd J M 2009 Global urban land-use trends and climate impacts; Curr. Opin. Environ. Sustain. 1(1) 89–95.

    Article  Google Scholar 

  • Shafizadeh-Moghadam H and Helbich M 2015 Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai; Int. J. Appl. Earth Obs. Geoinf. 35 187–198.

    Google Scholar 

  • Shaw A and Satish M K 2007 Metropolitan restructuring in post-liberalised India: Separating the global and the local; Cities 24(2) 148–163.

    Article  Google Scholar 

  • Shekhar S 2004 Urban sprawl assessment entropy approach; GIS Development, Noida.

  • Shem W and Shepherd M 2009 On the impact of urbanisation on summertime thunderstorms in Atlanta: Two numerical model case studies; Atmos. Res. 92(2) 172–189.

    Article  Google Scholar 

  • Siddiqui A, Kushwaha G, Nikam B, Srivastav S K, Shelar A and Kumar P 2021 Analysing the day/night seasonal and annual changes and trends in land surface temperature and surface urban heat island intensity (SUHII) for Indian cities; Sustain. Cities Soc. 75 103374.

    Article  Google Scholar 

  • Singh R and Kalota D 2019 Urban sprawl and its impact on generation of urban heat island: A case study of Ludhiana city; J. Ind. Soc. Remote Sens. 47(9) 1567–1576.

    Article  Google Scholar 

  • Somayajula V K A, Ghai D, Kumar S, Tripathi S L, Verma C, Safirescu C O and Mihaltan T C 2022 Classification and validation of spatio-temporal changes in land use/land cover and land surface temperature of multitemporal images; Sustainability 14(23) 15677.

    Article  Google Scholar 

  • Sreekanth V, Mahesh B and Niranjan K 2018 Gradients in PM2.5 over India: Five city study; Urban Climate 25 99–108.

  • Sridharan N 2011 Spatial inequality and the politics of urban expansion; Environ. Urban. Asia 2(2) 187–204.

    Article  Google Scholar 

  • Subba Rao N and Prathap Reddy R 2004 Geoenvironmental appraisal in a developing urban area; Environ. Geol. 47(1) 20–29.

    Article  Google Scholar 

  • Sudhira H S, Ramachandra T V and Jagadish K S 2004 Urban sprawl: Metrics, dynamics and modelling using GIS; Int. J. Appl. Earth Obs. Geoinf. 5(1) 29–39.

    Google Scholar 

  • Tiwari D K, Hari M, Kundu B, Jha B, Tyagi B and Malik K 2023 Delhi urbanisation footprint and its effect on the earth’s subsurface state-of-stress through decadal seismicity modulation; Sci. Rep. 13(1) 11750.

    Article  CAS  Google Scholar 

  • Tripathy P and Kumar A 2019 Monitoring and modelling spatio-temporal urban growth of Delhi using cellular automata and geoinformatics; Cities 90 52–63.

    Article  Google Scholar 

  • Turner M G and Ruscher C L 1988 Changes in the spatial patterns of land use in Georgia; Landsc. Ecol. 1(4) 241–251.

    Article  Google Scholar 

  • Tyagi B, Choudhury G, Vissa N K, Singh J and Tesche M 2021 Changing air pollution scenario during COVID-19: Redefining the hotspot regions over India; Environ. Pollut. 271 116354.

    Article  CAS  Google Scholar 

  • Verma S, Chatterjee A and Mandal N R 2017 Analysing urban sprawl and shifting of urban growth centre of Bengaluru city, India using Shannon’s entropy method; J. Sett. Spat. Planning 8(2) 89–98.

    Google Scholar 

  • Wan Z, Hook S and Hulley G 2021 MODIS/Terra land surface temperature/emissivity daily L3 Global 1km SIN Grid V061 [Data set]; NASA EOSDIS Land Processes Distributed Active Archive Center.

  • Williams B and Shiels P 2000 Acceleration into sprawl: Causes and potential policy responses; Quarterly Economic Commentary, June 2000, pp. 37–73.

  • Yeh A G O and Li X 2001 Measurement and monitoring of urban sprawl in a rapidly growing region using entropy; Photogramm. Eng. Remote Sens. 67(1) 83–90.

    Google Scholar 

Download references

Acknowledgements

The authors thank the previous research scholar, Dr Sudhansu Sekhar Rath (currently at the University of Illinois, Urbana Champaign, USA), Ms Srutisudha Mohanty (previous project staff), and Sreyasi Biswas (previous Masters student at the Department of Earth and Atmospheric Sciences, National Institute of Technology Rourkela) for their technical support during this study. Our sincere thanks are extended to Dr Bhaskar Kundu (Assistant Professor at the Department of Earth and Atmospheric Sciences, National Institute of Technology Rourkela) for his help in addressing one of the reviewer comments. The authors would like to acknowledge the considered data sources from ISRO (https://bhuvan-app1.nrsc.gov.in/thematic/thematic/index.php), ESRI (https://www.arcgis.com/apps/instant/media/index.html?appid=fc92d38533d440078f17678ebc20e8e2), and SEDAC (https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11). The fellowship support from the INSPIRE forum (IF190926) of the Ministry of Science and Technology and project funding (MoES/16/09/2018-RDEAS-THUMP-2) from the Ministry of Earth Sciences, Government of India are sincerely acknowledged.

Author information

Authors and Affiliations

  1. Department of Earth and Atmospheric Sciences, National Institute of Technology, Rourkela, 769 008, India

    Asmita Mukherjee & Jagabandhu Panda

Authors
  1. Asmita Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jagabandhu Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Asmita Mukherjee (AM) produced figures, contributed to methodology, wrote the first draft, and performed analysis of the results obtained. Jagabandhu Panda (JP) hypothesised the work, contributed to writing and editing the manuscript, besides helping in the analysis of the results. AM and JP both contributed to the revision of the manuscript, while JP supervised the overall works and provided infrastructure for its execution.

Corresponding author

Correspondence to Jagabandhu Panda.

Additional information

Communicated by Saumitra Mukherjee

Supplementary materials pertaining to this article are available on the Journal of Earth System Science website (http://www.ias.ac.in/Journals/Journal_of_Earth_System_Science).

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 9017 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mukherjee, A., Panda, J. A study on the urban growth and dynamics over 16 major cities of India. J Earth Syst Sci 133, 66 (2024). https://doi.org/10.1007/s12040-024-02280-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12040-024-02280-9

Keywords

Search

Navigation