[
Alsharif, A. A. A., Pradhan, B., Mansor, S., & Shafri, H. Z. M. (2015). Urban expansion assessment by using remotely sensed data and the relative Shannon entropy model in GIS: A case study of Tripoli, Libya. Theoretical and Empirical Researches in Urban Management, 10(1), 55–71.
]Search in Google Scholar
[
Angel, S., Sheppard, S. C., & Civco, D. L. (2005). The Dynamics of Global Urban Expansion. The World Bank, September, 205. http://www.citiesalliance.org/sites/citiesalliance.org/files/CA_Docs/resources/upgrading/urban-expansion/worldbankreportsept2005.pdf
]Search in Google Scholar
[
Anguluri, R., & Narayanan, P. (2017). Role of green space in urban planning: Outlook towards smart cities. Urban Forestry and Urban Greening, 25, 58–65. https://doi.org/10.1016/j.ufug.2017.04.007
]Search in Google Scholar
[
Arsanjani, J. J., Helbich, M., Kainz, W., & Boloorani, A. D. (2013). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21(1), 265–275. https://doi.org/10.1016/j.jag.2011.12.014
]Search in Google Scholar
[
Barnes, K., Morgan, J., Roberge, M., & Lowe, S. (2001). Sprawl Development: Its Patterns, Consequences, and Measurement. Annals of Physics, 54, 24. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:No+Title#0
]Search in Google Scholar
[
Bharath, H. A., Chandan, M. C., Vinay, S., & Ramachandra, T. V. (2018). Modelling urban dynamics in rapidly urbanizing Indian cities. Egyptian Journal of Remote Sensing and Space Science, 21(3), 201–210. https://doi.org/10.1016/j.ejrs.2017.08.002
]Search in Google Scholar
[
Bhatta, B. (2009). Modelling of urban growth boundary using geoinformatics. International Journal of Digital Earth, 2(4), 359–381. https://doi.org/10.1080/17538940902971383
]Search in Google Scholar
[
Bhatta, B., Saraswati, S., & Bandyopadhyay, D. (2010). Urban sprawl measurement from remote sensing data. Applied Geography, 30(4), 731–740. https://doi.org/10.1016/j.apgeog.2010.02.002
]Search in Google Scholar
[
Bourne, L. S. (2001). Myths, Realities and Hidden Agendas. Plan Canada, 41(4), 26–28.
]Search in Google Scholar
[
Brueckner, J. K. (2000). Urban sprawl: Diagnosis and remedies. International Regional Science Review, 23(2), 160–171. https://doi.org/10.1177/016001700761012710
]Search in Google Scholar
[
Brueckner, J. K., & Fansler, D. A. (2006). The Economics of Urban Sprawl: Theory and Evidence on the Spatial Sizes of Cities. The Review of Economics and Statistics, 65(3), 479. https://doi.org/10.2307/1924193
]Search in Google Scholar
[
Christiansen, P., & Loftsgarden, T. (2011). Drivers behind Urban Sprawl in Europe (Issue May). https://www.toi.no/getfile.php/Publikasjoner/TØIrapporter/2011/1136-2011/1136- 2011-el.pdf
]Search in Google Scholar
[
Dahal, K. R., Benner, S., & Lindquist, E. (2018). Analyzing Spatiotemporal Patterns of Urbanization in Treasure Valley, Idaho, USA. Applied Spatial Analysis and Policy, 11(2), 205–226. https://doi.org/10.1007/s12061-016-9215-1
]Search in Google Scholar
[
Dixon, B., & Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: One or the other, or both? International Journal of Remote Sensing, 29(4), 1185–1206. https://doi.org/10.1080/01431160701294661
]Search in Google Scholar
[
Ewing, R., Pendall, R., & Chen, D. (2007). Measuring Sprawl and Its Transportation Impacts. Transportation Research Record: Journal of the Transportation Research Board, 1831(1), 175–183. https://doi.org/10.3141/1831-20
]Search in Google Scholar
[
Gavrilidis, A. A., Niță, M. R., Onose, D. A., Badiu, D. L., & Năstase, I. I. (2019). Methodological framework for urban sprawl control through sustainable planning of urban green infrastructure. Ecological Indicators, 96(October), 67–78. https://doi.org/10.1016/j.ecolind.2017.10.054
]Search in Google Scholar
[
Gowri, V. S., Ramachandran, S., Ramesh, R., Pramiladevi, I. R. R., & Krishnaveni, K. (2008). Application of GIS in the study of mass transport of pollutants by Adyar and Cooum Rivers in Chennai, Tamilnadu. Environmental Monitoring and Assessment, 138(1–3), 41–49. https://doi.org/10.1007/s10661-007-9789-917562203
]Search in Google Scholar
[
Grand, J., DeLuca, W. V., McGarigal, K., Compton, B. W., Plunkett, E. B., & Willey, L. L. (2018). Modeling non-stationary urban growth: The SPRAWL model and the ecological impacts of development. Landscape and Urban Planning, 177(October 2017), 178–190. https://doi.org/10.1016/j.landurbplan.2018.04.018
]Search in Google Scholar
[
Grigorescu, I., Kucsicsa, G., Popovici, E. A., Mitrică, B., Mocanu, I., & Dumitraşcu, M. (2021). Modelling land use/cover change to assess future urban sprawl in Romania. Geocarto International, 36(7), 721–739. https://doi.org/10.1080/10106049.2019.1624981
]Search in Google Scholar
[
Habibi, S., & Asadi, N. (2011). Causes, results and methods of controlling urban sprawl. Procedia Engineering, 21, 133–141. https://doi.org/10.1016/j.proeng.2011.11.1996
]Search in Google Scholar
[
Hayden, D. (2004). A field guide to sprawl. WW Norton & Company.
]Search in Google Scholar
[
He, Q., Zeng, C., Xie, P., Tan, S., & Wu, J. (2019). Comparison of urban growth patterns and changes between three urban agglomerations in China and three metropolises in the USA from 1995 to 2015. Sustainable Cities and Society, 50(April), 101649. https://doi.org/10.1016/j.scs.2019.101649
]Search in Google Scholar
[
Hong, G., Zhang, A., Zhou, F., & Brisco, B. (2014). Integration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area. International Journal of Applied Earth Observation and Geoinformation, 28(1), 12–19. https://doi.org/10.1016/j.jag.2013.10.003
]Search in Google Scholar
[
Jain, S., Kohli, D., & Rao, R. M. (2011). Spatial Metrics to Analyze the Impact of Regional Factors on Pattern of Urbanization in Gurgaon, India. 39(June), 203–212. https://doi.org/10.1007/s12524-011-0088-0
]Search in Google Scholar
[
Jat, M. K., Garg, P. K., & Khare, D. (2008). Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation, 10(1), 26–43. https://doi.org/10.1016/j.jag.2007.04.002
]Search in Google Scholar
[
Jayaprakash, M., Senthil Kumar, R., Giridharan, L., Sujitha, S. B., Sarkar, S. K., & Jonathan, M. P. (2015). Bioaccumulation of metals in fish species from water and sediments in macrotidal Ennore creek, Chennai, SE coast of India: A metropolitan city effect. Ecotoxicology and Environmental Safety, 120, 243–255. https://doi.org/10.1016/j.ecoenv.2015.05.04226092556
]Search in Google Scholar
[
Kantakumar, L. N., Kumar, S., & Schneider, K. (2015). Spatiotemporal urban expansion in
]Search in Google Scholar
[
Pune metropolis, India using remote sensing. Habitat International, 51, 11–22. https://doi.org/10.1016/j.habitatint.2015.10.007
]Search in Google Scholar
[
Kantakumar, L. N., Kumar, S., & Schneider, K. (2016). Spatiotemporal urban expansion in Pune metropolis, India using remote sensing. Habitat International, 51, 11–22. https://doi.org/10.1016/j.habitatint.2015.10.007
]Search in Google Scholar
[
Ke, X., Qi, L., & Zeng, C. (2016). A partitioned and asynchronous cellular automata model for urban growth simulation. International Journal of Geographical Information Science, 30(4), 637–659. https://doi.org/10.1080/13658816.2015.1084510
]Search in Google Scholar
[
Lal, K., Kumar, D., & Kumar, A. (2017). Spatio-temporal landscape modeling of urban growth patterns in Dhanbad Urban Agglomeration, India using geoinformatics techniques. Egyptian Journal of Remote Sensing and Space Science, 20(1), 91–102. https://doi.org/10.1016/j.ejrs.2017.01.003
]Search in Google Scholar
[
Liu, X., Li, X., Chen, Y., Tan, Z., Li, S., & Ai, B. (2010). A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landscape Ecology, 25(5), 671–682. https://doi.org/10.1007/s10980-010-9454-5
]Search in Google Scholar
[
Long, Y., Han, H., Lai, S. K., & Mao, Q. (2013). Urban growth boundaries of the Beijing Metropolitan Area: Comparison of simulation and artwork. Cities, 31, 337–348. https://doi.org/10.1016/j.cities.2012.10.013
]Search in Google Scholar
[
Mahtta, R., Fragkias, M., Güneralp, B., Mahendra, A., Reba, M., Wentz, E. A., & Seto, K. C. (2022). Urban land expansion: the role of population and economic growth for 300+ cities. In npj Urban Sustainability (Vol. 2, Issue 1). https://doi.org/10.1038/s42949-022-00048-y
]Search in Google Scholar
[
Maithani, S. (2010). Cellular Automata Based Model of Urban Spatial Growth. Journal of the Indian Society of Remote Sensing, 38(4), 604–610. https://doi.org/10.1007/s12524-010-0053-3
]Search in Google Scholar
[
Martellozzo, F., & Clarke, K. C. (2011). Measuring urban sprawl, coalescence, and dispersal: A case study of Pordenone, Italy. Environment and Planning B: Planning and Design, 38(6), 1085–1104. https://doi.org/10.1068/b36090
]Search in Google Scholar
[
Mohammadi, J., Zarabi, A., & Mobaraki, O. (2012). Urban Sprawl Pattern and Effective Factors on Them : the Case of Urmia City, Iran. Geography, IV, 77–89.
]Search in Google Scholar
[
Mondal, B., Das, D. N., & Dolui, G. (2015). Modeling spatial variation of explanatory factors of urban expansion of Kolkata: a geographically weighted regression approach. Modeling Earth Systems and Environment, 1(4), 1–13. https://doi.org/10.1007/s40808-015-0026-1
]Search in Google Scholar
[
Naikoo, M. W., 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. Journal of Urban Management, 9(3), 347–359. https://doi.org/10.1016/j.jum.2020.05.004
]Search in Google Scholar
[
Ozturk, D. (2015). Urban growth simulation of Atakum (Samsun, Turkey) using cellular automata-Markov chain and Multi-layer Perceptron-Markov chain models. Remote Sensing, 7(5), 5918–5950. https://doi.org/10.3390/rs70505918
]Search in Google Scholar
[
Padmanaban, R., Bhowmik, A. K., Cabral, P., Zamyatin, A., Almegdadi, O., & Wang, S. (2017). Modelling urban sprawl using remotely sensed data: A case study of Chennai city, Tamilnadu. Entropy, 19(4). https://doi.org/10.3390/e19040163
]Search in Google Scholar
[
Pandey, P. C., Koutsias, N., Petropoulos, G. P., Srivastava, P. K., & Ben Dor, E. (2021). Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art. Geocarto International, 36(9), 957–988. https://doi.org/10.1080/10106049.2019.1629647
]Search in Google Scholar
[
Parvinnezhad, D., Delavar, M. R., Claramunt, C., & Pijanowski, B. C. (2021). A modified spatial entropy for urban sprawl assessment. Geocarto International, 36(16), 1804–1819. https://doi.org/10.1080/10106049.2019.1678676
]Search in Google Scholar
[
Pijanowski, B.., Tayyebi, A., Delavar, M. R., & Yazdanpanah, M. J. (2009). Urban expansion simulation using geospatial information system and artificial neural networks. International Journal of Environmental Research, 3(4), 495–502.
]Search in Google Scholar
[
Ramachandra, T. V., Aithal, B. H., & Sowmyashree, M. V. (2014). Urban structure in Kolkata: metrics and modelling through geo-informatics. Applied Geomatics, 6(4), 229–244. https://doi.org/10.1007/s12518-014-0135-y
]Search in Google Scholar
[
Ramachandra, T. V., Bharath, A. H., & Sowmyashree, M. V. (2015). Monitoring urbanization and its implications in a mega city from space: Spatiotemporal patterns and its indicators. Journal of Environmental Management, 148, 67–81. https://doi.org/10.1016/j.jenvman.2014.02.01524768450
]Search in Google Scholar
[
Ramachandra, T. V, Bharath, H. A., & Sowmyashree, M. V. (2014). Urban footprint of Mumbai - the commercial capital of India. Journal of Urban and Regional Analysis, 6(1), 71–94. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904006635&partnerID=40&md5=55006c6860115bd88f73dbda359dfb78
]Search in Google Scholar
[
Raman, N., & Sathiya Narayanan, D. (2008). Impact of solid waste effect on ground water and soil quality nearer to pallavaram solid waste landfill site in Chennai. Rasayan Journal of Chemistry, 1(4), 828–836.
]Search in Google Scholar
[
Rastogi, K., & Jain, G. V. (2018). Urban Sprawl Analysis Using Shannon’S Entropy and Fractal Analysis: a Case Study on Tiruchirappalli City, India. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–5(November), 761–766. https://doi.org/10.5194/isprs-archives-xlii-5-761-2018
]Search in Google Scholar
[
Sahana, M., Hong, H., & Sajjad, H. (2018). Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India. Science of the Total Environment, 628–629, 1557–1566. https://doi.org/10.1016/j.scitotenv.2018.02.17030045573
]Search in Google Scholar
[
Schneider, A., & 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. https://doi.org/10.1177/0042098007087340
]Search in Google Scholar
[
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. https://doi.org/10.1016/j.apgeog.2013.01.009
]Search in Google Scholar
[
Sridhar, M. B., & Sathyanathan, R. (2022). Spatiotemporal Patterns of Urbanization in Chennai City, Tamil Nadu, India Using Remote Sensing Data. Lecture Notes in Civil Engineering, 191, 23–34. https://doi.org/10.1007/978-981-16-5839-6_3
]Search in Google Scholar
[
Stefanski, J., Kuemmerle, T., Chaskovskyy, O., Griffiths, P., Havryluk, V., Knorn, J., Korol, N., Sieber, A., & Waske, B. (2014). Mapping land management regimes in western Ukraine using optical and SAR data. Remote Sensing, 6(6), 5279–5305. https://doi.org/10.3390/rs6065279
]Search in Google Scholar
[
Sudhira, H. S., Ramachandra, T. V., & Jagadish, K. S. (2004). Urban sprawl: Metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29–39. https://doi.org/10.1016/j.jag.2003.08.002
]Search in Google Scholar
[
Tang, J., Wang, L., & Yao, Z. (2006). Analyzing urban sprawl spatial fragmentation using multi-temporal satellite images. GIScience and Remote Sensing, 43(3), 218–232. https://doi.org/10.2747/1548-1603.43.3.218
]Search in Google Scholar
[
Taubenböck, H., Wegmann, M., Roth, A., Mehl, H., & Dech, S. (2009). Urbanization in India - Spatiotemporal analysis using remote sensing data. Computers, Environment and Urban Systems, 33(3), 179–188. https://doi.org/10.1016/j.compenvurbsys.2008.09.003
]Search in Google Scholar
[
Tayyebi, A., Pijanowski, B. C., & Tayyebi, A. H. (2011). An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran. Landscape and Urban Planning, 100(1–2), 35–44. https://doi.org/10.1016/j.landurbplan.2010.10.007
]Search in Google Scholar
[
Triantakonstantis, D., & Stathakis, D. (2015). Examining urban sprawl in Europe using spatial metrics. Geocarto International, 30(10), 1092–1112. https://doi.org/10.1080/10106049.2015.1027289
]Search in Google Scholar
[
Weng, Q. (2001). A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing, 22(10), 1999–2014. https://doi.org/10.1080/713860788
]Search in Google Scholar
[
Xu, C., Liu, M., Zhang, C., An, S., Yu, W., & Chen, J. M. (2007). The spatiotemporal dynamics of rapid urban growth in the Nanjing metropolitan region of China. Landscape Ecology, 22(6), 925–937. https://doi.org/10.1007/s10980-007-9079-5
]Search in Google Scholar
[
Yeh, A. G. O., & Li, X. (2001). Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogrammetric Engineering and Remote Sensing, 67(1), 83–90.
]Search in Google Scholar
[
Yue, W., Liu, Y., & Fan, P. (2013). Measuring urban sprawl and its drivers in large Chinese cities: The case of Hangzhou. Land Use Policy, 31, 358–370. https://doi.org/10.1016/j.landusepol.2012.07.018
]Search in Google Scholar
[
Zhao, C., Li, Y., & Weng, M. (2021). A fractal approach to urban boundary delineation based on raster land use maps: A case of shanghai, china. Land, 10(9). https://doi.org/10.3390/land10090941
]Search in Google Scholar