Abstract
Analyzing long term urban growth trends can provide valuable insights into a city’s future growth. This study employs LANDSAT satellite images from 1990, 2000, 2010 and 2019 to perform a spatiotemporal assessment and predict Ahmedabad’s urban growth. Land Use Land Change (LULC) maps developed using the Maximum Likelihood classifier produce four principal classes: Built-up, Vegetation, Water body, and “Others”. In between 1990–2019, the total built-up area expanded by 130%, 132 km2 in 1990 to 305 km2 in 2019. Rapid population growth is the chief contributor towards urban growth as the city added 3.9 km2 of additional built-up area to accommodate every 100,000 new residents. Further, a Multi-Layer Perceptron — Markov Chain model (MLP-MC) predicts Ahmedabad’s urban expansion by 2030. Compared to 2019, the MLP-MC model predicts a 25% and 19% increase in Ahmed-abad’s total urban area and population by 2030. Unaltered, these trends shall generate many socio-economic and environmental problems. Thus, future urban development policies must balance further development and environmental damage.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Abd EL-kawy O R et al., 2019. Temporal detection and prediction of agricultural land consumption by urbanization using remote sensing. Egyptian Journal of Remote Sensing and Space Science, 22(3): 237–246. doi: https://doi.org/10.1016/j.ejrs.2019.05.001.
Ahmad F, Goparaju L, Qayum A, 2017. LULC analysis of urban spaces using Markov chain predictive model at Ranchi in India. Spatial Information Research, 25(3): 351–359. doi: https://doi.org/10.1007/s41324-017-0102-x.
Ahmed B et al., 2013. Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote Sensing, 5(11): 5969–5998. doi: https://doi.org/10.3390/rs5115969.
Ahmed B, Ahmed R, 2012. Modeling urban land cover growth dynamics using multioral satellite images: A case study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information, 1(1): 3–31. doi: https://doi.org/10.3390/ijgi1010003.
Alqurashi A F, Kumar L, 2014. Land use and land cover change detection in the Saudi Arabian desert cities of Makkah and Al-Taif using satellite data. Advances in Remote Sensing, 3(3): 106–119. doi: https://doi.org/10.4236/ars.2014.33009.
Araya Y H, Cabral P, 2010. Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2(6): 1549–1563. doi: https://doi.org/10.3390/rs2061549.
Arulbalaji P, 2019. Analysis of land use land cover changes using geospatial techniques in Salem district, Tamil Nadu, South India. SN Applied Sciences, 1(5). doi: https://doi.org/10.1007/s42452-019-0485-5.
Atkinson P M, Tatnall A R L, 1997. Introduction neural networks in remote sensing. International Journal of Remote Sensing, 18(4): 699–709. doi: https://doi.org/10.1080/014311697218700.
Baby S, 2015. Monitoring the coastal land use land cover changes (LULCC) of Kuwait from spaceborne LANDSAT sensors. Indian Journal of Geo-Marine Sciences (IJMS), 44(6): 927–932.
Belal A A, Moghanm F S, 2011. Detecting urban growth using remote sensing and GIS techniques in Al Gharbiya governorate, Egypt. Egyptian Journal of Remote Sensing and Space Science, 14(2): 73–79. doi: https://doi.org/10.1016/j.ejrs.2011.09.001.
Bhatt M, 2003. Case studies for the Global Report on Human Settlements: Ahmedabad, India: 1–23. Available at: https://www.ucl.ac.uk/dpu-projects/Global_Report/pdfs/Ahmedabad_bw.pdf.
Bhugeloo A et al., 2019. Tracking indigenous forest cover within an urban matrix through land use analysis: The case of a rapidly developing African city. Remote Sensing Applications: Society and Environment, 13(December 2018): 328–336. doi: https://doi.org/10.1016/j.rsase.2018.12.003.
Borbora J, Das A K, 2014. Summertime Urban Heat Island study for Guwahati City, India. Sustainable Cities and Society, 11: 61–66. doi: https://doi.org/10.1016/j.scs.2013.12.001.
Chang-Martínez L A et al., 2015. Modeling historical land cover and land use: A review from contemporary modeling. ISPRS International Journal of Geo-Information, 4(4): 1791–1812. doi: https://doi.org/10.3390/ijgi4041791.
Congalton R G, Green K, 2019. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. The CRC Press.
Dewan A M, Yamaguchi Y, 2009. Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3): 390–401. doi: https://doi.org/10.1016/j.apgeog.2008.12.005.
DNA, 2010. Cheers Ahmedabad City is racing ahead. Available at: https://www.dnaindia.com/india/report-cheers-ahmedabad-city-is-racing-ahead-1453361 (Accessed: May 18, 2021).
Eastman J, 2009. IDRISI Taiga: Guide to GIS and Image Processing Volume: Manual version 16.02. (August): 325.
GeoKnowledge, 2020. Image Processing for ERDAS | Learning Materials. Available at: http://learningzone.rspsoc.org.uk/index.php/Learning-Materials/Image-Processing-for-ERDAS/6.1.-Introduction (Accessed: 20 May 2021).
Gohain K J, Mohammad P, Goswami A, 2021. Assessing the impact of land use land cover changes on land surface temperature over Pune city, India. Quaternary International, 575, 259–269. doi: https://doi.org/10.1016/j.quaint.2020.04.052.
Gupta M et al., 2021. Transmission dynamics of the COVID-19 epidemic in India and modeling optimal lock-down exit strategies. International Journal of Infectious Diseases, 103: 579–589. doi: https://doi.org/10.1016/j.ijid.2020.11.206.
Haklay M, Weber P, 2008. OpenStreet map: User-generated street maps. IEEE Pervasive Computing, 7(4): 12–18. doi: https://doi.org/10.1109/MPRV.2008.80.
Han H, Yang C, Song J, 2015. Scenario simulation and the prediction of land use and land cover change in Beijing, China. Sustainability (Switzerland), 7(4): 4260–4279. doi: https://doi.org/10.3390/su7044260.
Hassan Z et al., 2016. Dynamics of land use and land cover change (LULCC) using geospatial techniques: A case study of Islamabad Pakistan. SpringerPlus, 5(1). doi: https://doi.org/10.1186/s40064-016-2414-z.
Hegazy I R, Kaloop M R, 2015. Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1): 117–124. doi: https://doi.org/10.1016/j.ijsbe.2015.02.005.
Herold M et al., 2006. Evolving standards in land cover characterization. Journal of Land Use Science, 1(2–4): 157–168. doi: https://doi.org/10.1080/17474230601079316.
Islam M A, Dinar Y, 2021. Evaluation and spatial analysis of road accidents in Bangladesh: An emerging and alarming issue. Transportation in Developing Economies, 7(1): 1–14. doi: https://doi.org/10.1007/s40890-021-00118-3.
Kaliraj S et al., 2017. Coastal landuse and land cover change and transformations of Kanyakumari coast, India using remote sensing and GIS. Egyptian Journal of Remote Sensing and Space Science, 20(2): 169–185. doi: https://doi.org/10.1016/j.ejrs.2017.04.003.
Kookana R S et al., 2020. Urbanisation and emerging economies: Issues and potential solutions for water and food security. Science of the Total Environment, 732: 139057. doi: https://doi.org/10.1016/j.scitotenv.2020.139057.
Kuddus M A, Tynan E, McBryde E, 2020. Urbanization: A problem for the rich and the poor? Public Health Reviews, 41(1): 1–4. doi: https://doi.org/10.1186/s40985-019-0116-0.
Losiri C et al., 2016. Modeling urban expansion in Bangkok Metropolitan region using demographic-economic data through cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain models. Sustainability (Switzerland), 8(7). doi: https://doi.org/10.3390/su8070686.
Mahadevia D, Desai R, Vyas S, 2014. City Profile: Ahmedabad Darshini. Centre for Urban Equity — Working Paper Series, 74. Available at: https://cept.ac.in/UserFiles/File/CUE/WorkingPapers/RevisedNew/26CUEWP26_CityProfileAhmedabad.pdf.
Mansour S, Al-Belushi M, Al-Awadhi T, 2020. Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy, 91(June 2019): 104414. doi: https://doi.org/10.1016/j.landusepol.2019.104414.
Meshesha T W, Tripathi S K, Khare D, 2016. Analyses of land use and land cover change dynamics using GIS and remote sensing during 1984 and 2015 in the Beressa Watershed Northern Central Highland of Ethiopia. Modeling Earth Systems and Environment, 2(4). doi: https://doi.org/10.1007/s40808-016-0233-4.
Mishra V N, Rai P K, 2016. A remote sensing aided multi-layer perceptron: Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences, 9(4). doi: https://doi.org/10.1007/s12517-015-2138-3.
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 Climate, 31(October 2019): 100545. doi: https://doi.org/10.1016/j.uclim.2019.100545.
MohanRajan S N, Loganathan A, Manoharan P, 2020. Survey on Land use land cover (LULC) change analysis in remote sensing and GIS environment: Techniques and challenges. Environmental Science and Pollution Research, 27(24): 29900–29926. doi: https://doi.org/10.1007/s11356-020-09091-7.
Nurwanda A, Honjo T, 2020. The prediction of city expansion and land surface temperature in Bogor City, Indonesia. Sustainable Cities and Society, 52(December 2018): 101772. doi: https://doi.org/10.1016/j.scs.2019.101772.
Nwaogu C, Benc A, Pechanec V, 2017. Prediction models for landscape development in GIS. In: Proceedings of GIS Ostrava, 289–304. doi: https://doi.org/10.1007/978-3-319-61297-3.289304.
Pal S, Ziaul S, 2017. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egyptian Journal of Remote Sensing and Space Science, 20(1): 125–145. doi: https://doi.org/10.1016/j.ejrs.2016.11.003.
Power A L et al., 2018. Monitoring impacts of urbanisation and industrialisation on air quality in the Anthropocene using urban pond sediments. Frontiers in Earth Science, 6: 131. doi: https://doi.org/10.3389/feart.2018.00131.
Rahman M T, 2016. Detection of land use land cover changes and urban sprawl in Al-Khobar, Saudi Arabia: An analysis of multi-temporal remote sensing data. ISPRS International Journal of Geo-Information, 5(2). doi: https://doi.org/10.3390/ijgi5020015.
Rahman M T, Aldosary A S, Mortoja M G, 2017. Modeling future land cover changes and their effects on the land surface temperatures in the Saudi Arabian eastern coastal city of Dammam. Land, 6(2). doi: https://doi.org/10.3390/land6020036.
Rawat J S, Biswas V, Kumar M, 2013. Changes in land use/cover using geospatial techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India. Egyptian Journal of Remote Sensing and Space Science, 16(1): 111–117. doi: https://doi.org/10.1016/j.ejrs.2013.04.002.
Saravanan V S et al., 2016. Urbanization and human health in urban India: Institutional analysis of water-borne diseases in Ahmedabad. Health Policy and Planning, 31(8): 1089–1099. doi: https://doi.org/10.1093/heapol/czw039.
Seitzinger S P et al., 2015. International Geosphere-Biosphere Programme and Earth system science: Three decades of co-evolution. Anthropocene, 12(2015): 3–16. doi: https://doi.org/10.1016/j.ancene.2016.01.001.
Shafizadeh Moghadam H, Helbich M, 2013. Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography, 40: 140–149. doi:https://doi.org/10.1016/j.apgeog.2013.01.009.
Al shawabkeh R et al., 2019. The role of land use change in developing city spatial models in Jordan: The case of the Irbid master plan (1970–2017). Alexandria Engineering Journal, 58(3). doi: https://doi.org/10.1016/j.aej.2019.08.001.
Shi K et al., 2016. Urban expansion and agricultural land loss in China: A multiscale perspective. Sustainability (Switzerland), 8(8): 1–16. doi: https://doi.org/10.3390/su8080790.
Shukla A, Jain K, 2019. Modeling urban growth trajectories and spatiotemporal pattern: A case study of Lucknow City, India. Journal of the Indian Society of Remote Sensing, 47(1): 139–152. doi: https://doi.org/10.1007/s12524-018-0880-1.
Silva L P E et al., 2020. Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil. Global Ecology and Conservation, 21: e00811. doi: https://doi.org/10.1016/j.gecco.2019.e00811.
Sultana S, Satyanarayana A N V, 2020. Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000–2018 over a sub-tropical Indian city. Sustainable Cities and Society, 52(September 2019): 101846. doi: https://doi.org/10.1016/j.scs.2019.101846.
Suribabu, C. R., Bhaskar, J. and Neelakantan, T. R. (2012. Land use/cover change detection of Tiruchirapalli City, India, using integrated remote sensing and GIS tools. Journal of the Indian Society of Remote Sensing, 40(4): 699–708. doi: https://doi.org/10.1007/s12524-011-0196-x.
Tahir M, Imam E. Hussain T, 2013. Evaluation of land use land cover changes in Mekelle City, Ethiopia using Remote Sensing and GIS. Computational Ecology and Software, 3(1): 9–16.
Tarawally M et al., 2019. Land use land cover change evaluation using land change modeller: A comparative analysis between two main cities in Sierra Leone. Remote Sensing Applications: Society and Environment, 16(February): 100262. doi: https://doi.org/10.1016/j.rsase.2019.100262.
Tripathi D K, Kumar M, 2012. Remote sensing based analysis of land use/land cover dynamics in Takula Block, Almora District (Uttarakhand). Journal of Human Ecology, 38(3): 207–212. doi: https://doi.org/10.1080/09709274.2012.11906489.
Usman M et al., 2015. Land use land cover classification and its change detection using multi-temporal MODIS NDVI data. Journal of Geographical Sciences, 25(12): 1479–1506. doi: https://doi.org/10.1007/s11442-015-1247-y.
Vermeulen L C et al., 2015. Modelling the impact of sanitation, population growth and urbanization on human emissions of Cryptosporidium to surface waters: A case study for Bangladesh and India. Environmental Research Letters, 10(9). doi: https://doi.org/10.1088/1748-9326/10/9/094017.
Wakode H B et al., 2014. Analysis of urban growth using Landsat TM/ETM data and GIS: A case study of Hyderabad, India. Arabian Journal of Geosciences, 7(1): 109–121. doi: https://doi.org/10.1007/s12517-013-0843-3.
Welsh P, 2004. Urban future. Highways, 74(2): 47–48. doi: https://doi.org/10.4324/9781315652597-13.
Wikipedia, 2021a. Ahmedabad — Wikipedia. Available at: https://en.wikipedia.org/wiki/Ahmedabad (Accessed: May 18, 2021).
Wikipedia, 2021b. Geography of Ahmedabad. Available at: https://en.wikipedia.org/wiki/Geography_of_Ahmedabad (Accessed: 21 May 2021).
Wikipedia, 2021c. Landsat program. Available at: https://en.wikipedia.org/wiki/Landsat_program (Accessed: 21 May 2021).
Wikipedia, 2021d. List of largest cities. Available at: https://en.wikipedia.org/wiki/List_of_largest_cities#List (Accessed: 22 May 2021).
World Population Review, 2021. Ahmedabad Population 2021 (Demographics, Maps, Graphs). Available at: https://worldpopulationreview.com/en/world-cities/ahmedabad-population (Accessed: 18 May 2021).
Zhu H M, You W H, Zeng Z fa, 2012. Urbanization and CO2 emissions: A semi-parametric panel data analysis. Economics Letters, 117(3): 848–850. doi: https://doi.org/10.1016/j.econlet.2012.09.001.
Acknowledgements
The authors would like to acknowledge the funding received from the Department of Science and Technology, Government of India (DST/TMD/UKBEE/2017/17). Projects: Zero Peak Energy Demand for India (ZED-I) and Engineering and Physics Research Council EPSRC (EP/R008612/1).
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation
Zero Peak Energy Demand for India (ZED-I) and Engineering and Physics Research Council EPSRC, No.EP/R008612/1
Author
Shobhit Chaturvedi (1991–), PhD Candidate, specialized in regional sustainable development and urban remote sensing. E-mail: shobhitchaturvedi101@gmail.com
Rights and permissions
About this article
Cite this article
Chaturvedi, S., Shukla, K., Rajasekar, E. et al. A spatio-temporal assessment and prediction of Ahmedabad’s urban growth between 1990–2030. J. Geogr. Sci. 32, 1791–1812 (2022). https://doi.org/10.1007/s11442-022-2023-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11442-022-2023-4