Abstract
Prayagraj City is one the biggest cities in the State of Uttar Pradesh which has been selected as Smart City by Ministry of Housing and Urban Affairs, Government of India, in 2015. This city has rapidly evolved in the last three decades due to the urbanization process. This study has used multispectral imageries of Landsat Thematic Mapper and Operational Land Imager/Thermal Infrared Sensor of four different time points at the interval of ~ 10 years, i.e., 1988, 1997, 2008, and 2018. These images have been preprocessed and then classified through supervised classification using Maximum Likelihood Classifier in ERDAS IMAGINE version 2014. The overall accuracy achieved is more than 88% in all the four time points. The periodical change results have revealed that all land use/land cover (LU/LC) classes has altered in all periods, i.e., 1988–1997, 1997–2008, 2008–2018, and 1988–2018 with remarkable trends, patterns, and magnitudes. The growth of Built-up land is a major concern. The results of the change matrix (1988–2018) have indicated that the most expanded class was built-up land which has evolved into 64.52% at the cost of Agriculture land by 24.41%, Forest land by 10.56%, and Barren land by 6.01%. The above results of LU/LC dynamics can help in making planning and policy development for restoring and enhancing the carrying capacity of land and sustainability of the environment in a city landscape.
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References
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. https://doi.org/10.1007/s12524-015-0482-0
Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data. In A revision of the land use classification system as presented in U.S. Geological Survey Circular 671. Washington. https://doi.org/10.3133/pp964
Anees, M. M., Sajjad, S., & Joshi, P. K. (2019). Characterizing urban area dynamics in historic city of Kurukshetra, India, using remote sensing and spatial metric tools. Geocarto International, 34(14), 1584–1607. https://doi.org/10.1080/10106049.2018.1499819
Avand, M., Moradi, H., & Lasboyee, M. R. (2020). Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2020.125663
Castellanos-mora, L., & Agudelo-Hz, W. (2020). Spatial scenarios of land use/cover change for the management and conservation of paramos and Andean Forest in Boyacá, Colombia. In Environmental Scieence Proceedinas. https://doi.org/10.3390/IECF2020-08023.
Chakraborti, S., Banerjee, A., Sannigrahi, S., Pramanik, S., Maiti, A., & Jha, S. (2019). Assessing the dynamic relationship among land use pattern and land surface temperature: A spatial regression approach. Asian Geographer. https://doi.org/10.1080/10225706.2019.1623054
Chakraraborty, S. D., Kant, Y., & Mitra, D. (2015). Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data. Journal of Environmental Management, 148, 143–152. https://doi.org/10.1016/j.jenvman.2013.11.034
Chaturvedi, R. (2014). Application of remote sensing and gis in land use / land covers mapping in Allahabad District. International Journal of Advanced Information in Engineering Technology, 4(4), 1–9
Chaudhuri, A. S., Singh, P., & Rai, S. C. (2018). Modelling LULC change dynamics and its impact on environment and water security: geospatial technology based assessment. Ecology, Environment and Conservation, 24, 300–306
Deka, J., Tripathi, O. P., Khan, M. L., & Srivastava, V. K. (2019). Study on land-use and land-cover change dynamics in Eastern Arunanchal Pradesh. India Using Remote Sensing and GIS. Tropical Ecology. https://doi.org/10.1007/s42965-019-00022-3
Dissanayake, D., Morimoto, T., Ranagalage, M., & Murayama, Y. (2019). Land-use/land-cover changes and their impact on surface urban heat Islands: Case study of Kandy City Sri Lanka. Climate, 7(8), 99. https://doi.org/10.3390/cli7080099
Dubovyk, O. (2017). The role of Remote Sensing in land degradation assessments: opportunities and challenges. European Journal of Remote Sensing, 50(1), 601–613. https://doi.org/10.1080/22797254.2017.1378926
Fen, G., Wei, L., Shaobo, S., Zhao, J., Weibin, Z., & Jianwu, Y. (2019). Analysis of the desertification dynamics of sandy lands in Northern China over the period 2000–2017. Geocarto International. https://doi.org/10.1080/10106049.2019.1678677
Fichera, C. R., Modica, G., & Pollino, M. (2012). Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. European Journal of Remote Sensing, 45(1), 1–18. https://doi.org/10.5721/EuJRS20124501
Fonji, S. F., & Taff, G. N. (2014). Using satellite data to monitor land-use land-cover change in North-eastern Latvia. Springerplus, 3(61), 1–15
Garrella, V., Kantak, A., Banerjee, M., Pardhasardhi, M. G., Reddy, R., Patel, T., et al. (2015). City development plan for Allahabad, 2041 (Final City Development Plan-Draft Report), Supported under the ministry of urban development and world bank. Available on: http://allahabadmc.gov.in/documentslist/City_Development_Plan_Allahabad-2041.pdf.
Ghosh, M. K., Kumar, L., & Roy, C. (2016). Mapping long-term changes in mangrove species composition and distribution in the Sundarbans. Forests. https://doi.org/10.3390/f7120305
Govind, N. R., & Ramesh, H. (2019). The impact of spatiotemporal patterns of land use land cover and land surface temperature on an urban cool island: a case study of Bengaluru. Environmental Monitoring and Assessment, 191(5), 1–20. https://doi.org/10.1007/s10661-019-7440-1
Guha, S., Govil, H., & Mukherjee, S. (2017). Dynamic analysis and ecological evaluation of urban heat islands in Raipur city. India. Journal of Applied Remote Sensing, 11(3), 1–24. https://doi.org/10.1117/1.JRS.11.036020
Gumma, M. K., Mohammad, I., Nedumaran, S., Whitbread, A., & Lagerkvist, C. J. (2017). Urban sprawl and adverse impacts on agricultural land: A case study on Hyderabad. India. Remote Sensing, 9(11), 1–16. https://doi.org/10.3390/rs9111136
Haque, M. I., & Basak, R. (2017). Land cover change detection using GIS and remote sensing techniques: A spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh. Egyptian Journal of Remote Sensing and Space Science, 20(2), 251–263. https://doi.org/10.1016/j.ejrs.2016.12.003
IMD (2010). Allahabad climatological table (Period: 1981–2010). Indian Meteorological Department, Government of India. http://www.imd.gov.in/section/climate/extreme/allahabad2.htm. Accessed 22 October 2019.
IPCC (2019). Climate Change and Land. An IPCC Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Summary for Policymakers. Intergovernmental Panel on Climate Change. Retrived from https://doi.org/10.4337/9781784710644
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
Kogo, B. K., Kumar, L., & Koech, R. (2019). Analysis of spatio-temporal dynamics of land use and cover changes in Western Kenya. Geocarto International. https://doi.org/10.1080/10106049.2019.1608594
Kumar, V., & Agrawal, S. (2019). Agricultural land use change analysis using remote sensing and GIS: a case study of Allahabad, India. In ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial. Information Sciences, XLII-3/W6, 397–402. https://doi.org/10.5194/isprs-archives-XLII-3-W6-397-2019.
Langat, P. K., Kumar, L., Koech, R., & Ghosh, M. K. (2019). Monitoring of land use/land -cover dynamics using remote sensing: A case of Tana River Basin. Kenya. Geocarto International. https://doi.org/10.1080/10106049.2019.1655798
Liu, H., & Zhang, Y. (2019). Selection of landsat8 image classification bands based on MLC–RFE. Journal of the Indian Society of Remote Sensing, 47(3), 439–446. https://doi.org/10.1007/s12524-018-0932-6
Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2407. https://doi.org/10.1080/0143116031000139863
Mallupattu, P. K., Reddy, J., & Reddy, S. (2013). Analysis of land use/land cover changes using remote sensing data and GIS at an Urban Area, Tirupati, India. The Scientific World Journal. https://doi.org/10.1155/2013/268623.
Mas, J. F. (1999). Monitoring land-cover changes: A comparison of change detection techniques. International Journal of Remote Sensing, 20(1), 139–152. https://doi.org/10.1080/014311699213659
Mengistu, D. A., & Salami, A. T. (2007). Application of remote sensing and GIS inland use/land cover mapping and change detection in a part of south western Nigeria. African Journal of Environmental Science and Technology, 1(5), 99–109
MoHUA. (2015). Smart citie: Ministry of housing and urban affairs reports, government of India. New Delhi, India. Retrieved from http://www.mhupa.gov.in/Default.aspx?ReturnUrl=%2F.
Mondal, A., Kundu, S., Chandniha, S. K., Shukla, R., & Mishra, P. K. (2012). Comparison of support vector machine and maximum likelihood classification technique using satellite imagery. International Journal of Remote Sensing and GIS, 1(2), 116–123. http://rpublishing.org/Journal/IJRSG/Vol1Issue2/rsg1206.pdf.
Nanda, M. K. (2018). Climatic classification. In D. K. Khan (Ed.), Environmental science (pp. 1–16).
Pal, S., & Talukdar, S. (2020). Assessing the role of hydrological modifications on land use/land cover dynamics in Punarbhaba river basin of Indo-Bangladesh. Environment, Development and Sustainability, 22(1), 363–382. https://doi.org/10.1007/s10668-018-0205-0
Pal, S., & Ziaul, S. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Sciences, 20(1), 125–145. https://doi.org/10.1016/j.ejrs.2016.11.003
Patel, S. K., Verma, P., & Sinsh, G. S. (2019). Agricultural growth and land use land cover change in peri-urban India. Environmental Monitoring and Assessment, 191(600), 1–17
PNN (2019a). Prayag Kumbh. Prayagraj Nagar Nigam, Government of Uttar Pradesh. Retrieved from http://allahabadmc.gov.in/kumbh_mela.html. Accessed 22 October 2019.
PNN (2019b). Maps Section, Prayagraj Nagar Nigam, Government of Uttar Pradesh. Retrieved from http://allahabadmc.gov.in/maps-pdf/ALLA_WARD_MAP.pdf. Available at http://allahabadmc.gov.in/index.html#allmaps. Accessed 22 October 2019.
Polasky, S., Nelson, E., Pennington, D., & Johnson, K. A. (2011). The impact of land-use change on ecosystem services, biodiversity and returns to landowners: A case study in the state of Minnesota. Environmental and Resource Economics, 48(2), 219–242. https://doi.org/10.1007/s10640-010-9407-0
Ramachandra, T. V., Aithal, B. H., & Sowmyashree, M. V. (2014). Monitoring Spatial Patterns of Urban Dynamics in Ahmedabad City, Textile Hub of India. Spatium, 85–91. http://wgbis.ces.iisc.ernet.in/energy/water/paper/Monitoring-Spatial-Patterns/Ramachandra%20et%20al.%20SPATIUM.pdf.
Ranagalage, M., Wang, R., Gunarathna, M. H. J. P., Dissanayake, D., Murayama, Y., & Simwanda, M. (2019). Spatial forecasting of the landscape in rapidly urbanizing hill stations of South Asia : A case study of Nuwara Eliya, Sri Lanka (1996–2037). Remote Sensing, 11(15), 1–31. https://doi.org/10.3390/rs11151743
Rimal, B., Sharma, R., Kunwar, R., Keshtkar, H., Stork, N. E., Rijal, S., et al. (2019). Effects of land use and land cover change on ecosystem services in the Koshi River Basin. Eastern Nepal. Ecosystem Services, 38(March), 100963. https://doi.org/10.1016/j.ecoser.2019.100963
Rousta, I., Sarif, M. O., Gupta, R. D., Olafsson, H., Ranagalage, M., Murayama, Y., et al. (2018). Spatiotemporal analysis of land use/land cover and its effects on surface urban heat Island using landsat data: A case study of metropolitan City Tehran (1988–2018). Sustainability, 10(12), 4433. https://doi.org/10.3390/su10124433
Samant, H. P., & Subramanyan, V. (1998). Landuse/land cover change in Mumbai-Navi Mumbai cities and its effects on the drainage basins and channels- a study using GIS. Journal of the Indian Society of Remote Sensing, 26(1 & 2), 1–6. https://doi.org/10.1007/BF03007333
Sarif, M. O., & Gupta, R. D. (2020). Change assessment of spatio-temporal dynamics of land use/land cover using remote sensing and GIS: A case study of Lucknow city (1993–2019). 39th INCA International Congress, Survey of India, Hathibarkala, Dehradun-248001. In Indian Cartographer (Vol. 39).
Sarif, M. O., Jeganathan, C., & Mondal, S. (2017). MODIS-VCF based forest change analysis in the State of Jharkhand. Proceedings of the National Academy of Sciences India Section A - Physical Sciences, 87(4), 751–767. https://doi.org/10.1007/s40010-017-0446-6.
SESEI. (2018). Report on smart City Mission-India. Seconded European Standardization Expert in India.
Sharma, R., Chakraborty, A., & Joshi, P. K. (2015). Geospatial quantification and analysis of environmental changes in urbanizing city of Kolkata (India). Environmental Monitoring and Assessment, 187(1), 1–12. https://doi.org/10.1007/s10661-014-4206-7
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, 139–152. https://doi.org/10.1007/s12524-018-0880-1
Shukla, P. R., Skea, J., Buendia, E. C., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D. C., et al. (2019). Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Retrieved from https://www.ipcc.ch/site/assets/uploads/20. IPCC (AR6). https://doi.org/10.4337/9781784710644.
Singh, A., Singh, S., Kumar, P., & Khanduri, K. (2013). Land use and land cover change detection : A comparative approach using post classification change matrix and discriminate function change detection methodology of Allahabad City. International Journal of Current Engineering and Technology, 33(1), 142–148
Skidmore, A. K., Bijker, W., Schmidt, K., & Kumar, L. (1997). Use of remote sensing and GIS for sustainable land management. ITC Journal, 3(4), 302–315
Srivastava, S. K., & Gupta, R. D. (2003). Monitoring of changes in land use/land cover using multi-sensor satellite data. In 6th International Conference on GIS/GPS/RS: Map India 2003. New Delhi.
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(101846), 1–14. https://doi.org/10.1016/j.scs.2019.101846
Sultana, S., & Satyanarayana, A. N. V. (2018). Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: impact of urbanization. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2018.1466072
Tamba, V., & Kabba, S. (2011). Analysis of land use and land cover changes, and their ecological implications in Wuhan. China, 3(1), 104–118. https://doi.org/10.5539/jgg.v3n1p104
Tripathy, P., & Kumar, A. (2019). Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics. Cities, 90, 52–63. https://doi.org/10.1016/j.cities.2019.01.021
UN. (2018). The World ’s Cities in 2018. Department of economic and social affairs, population division (2018). The World’s Cities in 2018—Data Booklet (ST/ESA/ SER.A/417). UN, 1–34. Retrieved from https://digitallibrary.un.org/record/3799524?ln=en.
Xu, G., Dong, T., Cobbinah, P. B., Jiao, L., Sumari, N. S., Chai, B., & Liu, Y. (2019). Urban expansion and form changes across African cities with a global outlook: Spatiotemporal analysis of urban land densities. Journal of Cleaner Production, 224, 802–810.
Acknowledgements
The authors are highly thankful to USGS for freely available Landsat datasets for the study area, Prayagraj City. Md. Omar Sarif is highly grateful to University Grants Commission for providing financial assistantship through Maulana Azad National Fellowship (MANF) scheme (2017–2018) to pursue his Doctoral study (Award Letter No. F1-17.1/2017-18/MANF-2017-18-WES-84175/(SA-III/Website)). The authors are very thankful to blind reviewers for their valuable comments and suggestions in enhancing this manuscript.
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Sarif, M.O., Gupta, R.D. Spatiotemporal mapping of Land Use/Land Cover dynamics using Remote Sensing and GIS approach: a case study of Prayagraj City, India (1988–2018). Environ Dev Sustain 24, 888–920 (2022). https://doi.org/10.1007/s10668-021-01475-0
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DOI: https://doi.org/10.1007/s10668-021-01475-0