Abstract
Rapid urbanization in combination with poor planning processes and/or policies give rise to dispersed and fragmented patches. Small pockets of urban sprawl forming fringes, secondary cores, or satellite towns in the peri-urban space hinder city sustainability. The current study comprehends the magnitude of fragmentation in terms of urban spatial dynamics over the cities of eastern and southern India. A total of eight cities considered, those are existing metropolitans or possess the strong potential to become one shortly. Satellite-derived land use and land cover (LULC) information is used to study the urban expansion by adopting the LULC change assessment, entropy, and spatial metrics approach for detailed understanding of urban dynamics. A novel approach using spatial difference of night-time light (NTL) is carried out to nullify the NTL image saturation and amplify the minute changes to make new urban built-up patches identifiable. The results show a heterogeneous growth pattern and sprawling for different cities with overall expansion, infill, and outlying type of growth. Entropy analysis reveals the aggregation and dispersive nature of the selected cities. An increase in night-time anthropogenic activity noticed within the newly developed urban patches rather than densely populated areas. This analysis also discovered the existing gaps between the scientific and technological advancements and relevant policies while materializing the urban development plans for Indian urban agglomerations.
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Aithal, B. H., & Ramachandra, T. V. (2016). Visualization of urban growth pattern in Chennai using geoinformatics and spatial metrics. Journal of the Indian Society of Remote Sensing, 44(4), 617–633.
Amaral, S., Monteiro, A. M., Câmara, G., & Quintanilha, J. A. (2006). DMSP/OLS night-time light imagery for urban population estimates in the Brazilian Amazon. International Journal of Remote Sensing, 27(05), 855–870.
Angel, S., Parent, J., & Civco, D. L. (2012). The fragmentation of urban landscapes: Global evidence of a key attribute of the spatial structure of cities, 1990–2000. Environment and Urbanization, 24(1), 249–283.
Angel, S., Parent, J., Civco, D. (2007). Urban sprawl metrics: An analysis of global urban expansion using GIS. In Proceedings of ASPRS 2007 Annual Conference, Tampa, Florida May (Vol. 7, No. 11). Citeseer.
Bertinelli, L., & Black, D. (2004). Urbanization and growth. Journal of Urban Economics, 56(1), 80–96.
Bhatta, B. (2009). Analysis of urban growth pattern using remote sensing and GIS: A case study of Kolkata, India. International Journal of Remote Sensing, 30(18), 4733–4746.
Bhatta, B., Saraswati, S., & Bandyopadhyay, D. (2010). Urban sprawl measurement from remote sensing data. Applied Geography, 30(4), 731–740.
Bloom, D. E., Canning, D., & Fink, G. (2008). Urbanization and the wealth of nations. Science, 319(5864), 772–775.
Bosch, M., Jaligot, R., & Chenal, J. (2020). Spatiotemporal patterns of urbanization in three Swiss urban agglomerations: Insights from landscape metrics, growth modes and fractal analysis. Landscape Ecology, 35(4), 879–891.
Buechner, M. (1989). Are small-scale landscape features important factors for field studies of small mammal dispersal sinks? Landscape Ecology, 2(3), 191–199.
Castrence, M., Nong, D., Tran, C., Young, L., & Fox, J. (2014). Mapping urban transitions using multi-temporal Landsat and DMSP-OLS night-time lights imagery of the Red River Delta in Vietnam. Land, 3(1), 148–166.
Chen, M., Zhang, H., Liu, W., & Zhang, W. (2014). The global pattern of urbanization and economic growth: Evidence from the last three decades. PloS one, 9(8), e103799.
Das, S., & Angadi, D. P. (2020). Assessment of urban sprawl using landscape metrics and Shannon’s entropy model approach in town level of Barrackpore sub-divisional region, India. Modeling Earth Systems and Environment, 1–25.
Dasgupta, A., Kumar, U., Ramachandra, T. V. (2009). Urban landscape analysis through spatial metrics. In Proceedings of International Conference on Infrastructure, Sustainable Transportation and Urban Planning, (CISTUP@ CiSTUP), Indian Institute of Science, Bangalore, India (pp. 18–20).
Deka, J., Tripathi, O. P., & Khan, M. L. (2012). Urban growth trend analysis using Shannon Entropy approach-A case study in North-East India. International Journal of Geomatics and Geosciences, 2(4), 1062.
Gallo, K. P., Elvidge, C. D., Yang, L., & Reed, B. C. (2004). Trends in night-time city lights and vegetation indices associated with urbanization within the conterminous USA. International Journal of Remote Sensing, 25(10), 2003–2007.
Gnaneshwar, V. (1995). Urban policies in India—Paradoxes and predicaments. Habitat International, 19(3), 293–316.
Hai, P. M., Yamaguchi, Y. (2008). Characterizing the urban growth of Hanoi, Nagoya, and Shanghai city using remote sensing and spatial metrics. In IGARSS 2008–2008 IEEE International Geoscience and Remote Sensing Symposium (Vol. 5, pp. V-9). IEEE.
Hasnine, M. (2020). An analysis of urban sprawl and prediction of future urban town in urban area of developing nation: Case study in India. Journal of the Indian Society of Remote Sensing, 48(6), 909–920.
Hu, X., Qian, Y., Pickett, S. T., & Zhou, W. (2020). Urban mapping needs up-to-date approaches to provide diverse perspectives of current urbanization: A novel attempt to map urban areas with nighttime light data. Landscape and Urban Planning, 195, 103709.
Jaeger, J. A. (2000). Landscape division, splitting index, and effective mesh size: New measures of landscape fragmentation. Landscape Ecology, 15(2), 115–130.
Jain, M., Dimri, A. P., & Niyogi, D. (2016). Urban sprawl patterns and processes in Delhi from 1977 to 2014 based on remote sensing and spatial metrics approaches. Earth Interactions, 20(14), 1–29.
Jain, G. V., & Sharma, S. A. (2019). Spatio-temporal analysis of urban growth in selected small, medium and large Indian cities. Geocarto International, 34(8), 887–908.
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.
Jha, R., Singh, V. P., & Vatsa, V. (2008). Analysis of urban development of Haridwar, India, using entropy approach. KSCE Journal of Civil Engineering, 12(4), 281–288.
Ji, W., Ma, J., Twibell, R. W., & Underhill, K. (2006). Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics. Computers, Environment and Urban Systems, 30(6), 861–879.
Kantakumar, L. N., Kumar, S., & Schneider, K. (2016). Spatiotemporal urban expansion in Pune metropolis, India using remote sensing. Habitat International, 51, 11–22.
Kiran, G. S., & Joshi, U. B. (2013). Estimation of variables explaining urbanization concomitant with land-use change: A spatial approach. International Journal of Remote Sensing, 34(3), 824–847.
Kong, F., Yin, H., & Nakagoshi, N. (2007). Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: A case study in Jinan City, China. Landscape and Urban Planning, 79(3–4), 240–252.
Kumar, A., Pandey, A. C., Hoda, N., & Jeyaseelan, A. T. (2011). Evaluation of urban sprawl pattern in the tribal-dominated cities of Jharkhand state. India. International Journal of Remote Sensing, 32(22), 7651–7675.
Lata, K. M., Rao, C. S., Prasad, V. K., Badarianth, K. V. S., & Rahgavasamy, V. (2001). Measuring urban sprawl: A case study of Hyderabad. GIS Development, 5(12), 26–29.
Levin, N., & Zhang, Q. (2017). A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas. Remote Sensing of Environment, 190, 366–382.
Li, X., & Zhou, Y. (2017). Urban mapping using DMSP/OLS stable night-time light: A review. International Journal of Remote Sensing, 38(21), 6030–6046.
Li, X., Zhang, L., & Liang, C. (2010). A GIS-based buffer gradient analysis on spatiotemporal dynamics of urban expansion in Shanghai and its major satellite cities. Procedia Environmental Sciences, 2, 1139–1156.
Liang, W., & Yang, M. (2019). Urbanization, economic growth and environmental pollution: Evidence from China. Sustainable Computing: Informatics and Systems, 21, 1–9.
Ma, T., Zhou, C., Pei, T., Haynie, S., & Fan, J. (2014). Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities. Remote Sensing Letters, 5(2), 165–174.
Ma, T., Zhou, Y., Zhou, C., Haynie, S., Pei, T., & Xu, T. (2015). Night-time light derived estimation of spatio-temporal characteristics of urbanization dynamics using DMSP/OLS satellite data. Remote Sensing of Environment, 158, 453–464.
McGarigal, K. (2015). FRAGSTATS help. University of Massachusetts.
McGarigal, K., Cushman, S. A., Neel, M. C., Ene, E. (2002). FRAGSTATS: spatial pattern analysis program for categorical maps.
Moghadam, H. S., & 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.
Oloke, O. C., Fayomi, O. S. I., Oluwatayo, A., Adagunodo, T. A., Akinwumi, I. I., & Amusan, L. M. (2021). The nexus of climate change, urban infrastructure and sustainable development in developing countries. In IOP Conference Series: Earth and Environmental Science (Vol. 665, No. 1, p. 012051). IOP Publishing.
Operti, F. G., Oliveira, E. A., Carmona, H. A., Machado, J. C., & Andrade, J. S., Jr. (2018). The light pollution as a surrogate for urban population of the US cities. Physica A: Statistical Mechanics and its Applications, 492, 1088–1096.
Pandey, B., Joshi, P. K., & Seto, K. C. (2013). Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data. International Journal of Applied Earth Observation and Geoinformation, 23, 49–61.
Pham, H. M., Yamaguchi, Y., & Bui, T. Q. (2011). A case study on the relation between city planning and urban growth using remote sensing and spatial metrics. Landscape and Urban Planning, 100(3), 223–230.
Pozzi, F., Small, C., & Yetman, G. (2003). Modeling the distribution of human population with nighttime satellite imagery and gridded population of the world. Earth Observation Magazine, 12(4), 24–30.
Punia, M., & Singh, L. (2012). Entropy approach for assessment of urban growth: A case study of Jaipur, India. Journal of the Indian Society of Remote Sensing, 40(2), 231–244.
Rahman, A., Kumar, Y., Fazal, S., & Bhaskaran, S. (2011). Urbanization and quality of urban environment using remote sensing and GIS techniques in East Delhi-India. J. Geographic Information System, 3(1), 62–84.
Ramachandra, T. V., Aithal, B. H., & Sanna, D. D. (2012). Insights to urban dynamics through landscape spatial pattern analysis. International Journal of Applied Earth Observation and Geoinformation, 18, 329–343.
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.
Roy, P. S., Meiyappan, P., Joshi, P. K., Kale, M. P., Srivastav, V. K., Srivasatava, S. K., ... & Bhavani, P. (2016). Decadal land use and land cover classifications across India, 1985, 1995, 2005. ORNL DAAC.
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, 1557–1566.
Shekhar, S. (2004). Urban sprawl assessment entropy approach. GIS Development.
Shi, K., Huang, C., Yu, B., Yin, B., Huang, Y., & Wu, J. (2014). Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas. Remote Sensing Letters, 5(4), 358–366.
Small, C., Sousa, D., Yetman, G., Elvidge, C., & MacManus, K. (2018). Decades of urban growth and development on the Asian megadeltas. Global and Planetary Change, 165, 62–89.
Sudhira, H. S., Ramachandra, T. V., Raj, K. S., & Jagadish, K. S. (2003). Urban growth analysis using spatial and temporal data. Journal of the Indian Society of Remote Sensing, 31(4), 299–311.
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.
Sumari, N. S., Cobbinah, P. B., Ujoh, F., & Xu, G. (2020). On the absurdity of rapid urbanization: Spatio-temporal analysis of land-use changes in Morogoro. Tanzania. Cities, 107, 102876.
Sun, Y., Zhao, S., & Qu, W. (2015). Quantifying spatiotemporal patterns of urban expansion in three capital cities in Northeast China over the past three decades using satellite data sets. Environmental Earth Sciences, 73(11), 7221–7235.
Swain, D., Roberts, G. J., Dash, J., Lekshmi, K., Vinoj, V., & Tripathy, S. (2017). Impact of rapid urbanization on the city of Bhubaneswar, India. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 87(4), 845–853.
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.
Terfa, B. K., Chen, N., Zhang, X., & Niyogi, D. (2020). Urbanization in small cities and their significant implications on landscape structures: The case in Ethiopia. Sustainability, 12(3), 1235.
Thomas, R. W. (1981). "Information statistics in geography." Norwich: Geo Abstracts.
Toosi, N. B., Fakheran, S., Soffianian, A. (2012). Analysis of landscape pattern changes in Isfahan city during the last two decades. In International Conference on Applied Life Sciences. IntechOpen.
Xiao, P., Wang, X., Feng, X., Zhang, X., & Yang, Y. (2014). Detecting China’s urban expansion over the past three decades using nighttime light data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4095–4106.
Xie, Y., & Weng, Q. (2016). Updating urban extents with nighttime light imagery by using an object-based thresholding method. Remote Sensing of Environment, 187, 1–13.
Xu, P., Lin, M., & Jin, P. (2021). Spatio-temporal dynamics of urbanization in China Using DMSP/OLS nighttime light data from 1992–2013. Chinese Geographical Science, 31(1), 70–80.
Zachary, D., & Dobson, S. (2021). Urban development and complexity: Shannon entropy as a measure of diversity. Planning Practice & Research, 36(2), 157–173.
Zambrano, L., Aronson, M. F., & Fernandez, T. (2019). The consequences of landscape fragmentation on socio-ecological patterns in a rapidly developing urban area: A case study of the National Autonomous University of Mexico. Frontiers in Environmental Science, 7, 152.
Zhou, X., Feng, X. B., Dai, W., Li, P., Ju, C. Y., Bao, Z. D., & Han, Y. L. (2017). NPP-VIIRS DNB-based reallocating subpopulations to mercury in Urumqi city cluster, central Asia. In IOP Conference Series: Earth and Environmental Science (Vol. 57, No. 1, p. 012021). IOP Publishing.
Acknowledgements
The Science and Engineering Research Board (SERB) and Ministry of Earth Sciences (MoES), Government of India, are sincerely acknowledged for partially funding this research through the projects with file nos. EMR/2015/001358 and MoES/16/09/2018-RDEAS-THUMP-2 respectively. The NRSC of ISRO (http://bhuvan.nrsc.gov.in/bhuvan_links.php) and National Aeronautics and Space Administration (NASA) acknowledged for providing LULC thematic datasets. USGS is sincerely acknowledged for providing SRTM data (https://earthexplorer.usgs.gov/). The gridded population data (http://sedac.ciesin.columbia.edu/data/collection/gpw-v4) provided by the Socioeconomic Data and Applications Centre (SEDAC), NASA. The Earth Observation Group of NOAA’s National Centres for Environmental Information (NCEI) is acknowledged for providing NTL images (https://ngdc.noaa.gov/eog/download.html).
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Rath, S.S., Mohanty, S. & Panda, J. Analyzing the Fragmentation of Urban Footprints in Eastern and Southern Indian Cities and Driving Factors. J Indian Soc Remote Sens 50, 1499–1517 (2022). https://doi.org/10.1007/s12524-022-01546-3
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DOI: https://doi.org/10.1007/s12524-022-01546-3