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Dynamics of land use and land cover change in peri urban area of Burdwan city, India: a remote sensing and GIS based approach

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Abstract

Peri-urban area around a city is a dynamic zone that undergoes considerable changes over time in terms of its functional land use. Analogous to other Indian cities, these changes are also evident in the peri-urban villages of Burdwan city, which has seen intense land use changes between 1987 and 2017. These changes are gauged by using LANDSAT-5 Thematic Mapper (TM) for 1987; LANDSAT-7 Enhanced Thematic Mapper (ETM +) for 2002 and LANDSAT 8 (OLI) for 2017. This study makes a unique contribution by combining the use of remote sensing and intensity analysis to evaluate changes in land use and land cover. The results showed that both the 1987–2002 and 2002–2017 time periods experience rapid change and the land use transformation has been accelerated over the whole period. The profound changes in the vegetation and water bodies are amplified by rampant reclamation for built up and commercial purposes. Results show that the proportion of vegetation and waterbodies decreased by 72.3% and 56.3%, respectively, whereas built-up area has increased by 182.8%. The research also revealed a built up pattern mostly towards the north-west, north, North-East and south-east direction. The current study attempts to analyse the variables causing land transformation and the many actors and their roles in driving these alterations. Finally, the extensive explanation and spatio-temporal differentiation maps and tables created with geospatial data will undoubtedly aid in understanding the peri-urban growth dynamics process and changing form of land-use land cover and aid the decision-making process of local planners, stakeholders, and academicians.

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Abbreviations

DCHB:

District Census Hand Book

ETM+ :

Enhanced thematic mapper plus

EROS:

Earth resources observation and science

FCC:

False colour composite

GIS:

Geographical Information system

IA:

Intensity Analysis

LULC:

Land use and land cover

MLC:

Maximum likelihood classifier

MSS:

Multi-spectral scanner image

OLI:

Operational land imager

TM:

Thematic mapper

TOA:

Top of atmosphere

RGB:

Red, green and blue colour composite

UI:

Uniform intensity

USGS:

United States Geological Survey

References

  • Aldwaik, S., & Pontius, R. G., Jr. (2012). Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition. Landscape and Urban Planning, 106, 103–114. https://doi.org/10.1080/13658816.2013.787618

    Article  Google Scholar 

  • Alsharif, A. A., & Pradhan, B. (2014). Urban sprawl analysis of Tripoli Metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal Indian Society Remote Sensing, 42(1), 149–163. https://doi.org/10.1007/s12524-013-0299-7

    Article  Google Scholar 

  • Anderson JR (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.

  • Arden, H. (1990). Searching for India: Along the Grand Trunk road. National Geographic, 177(5), 118–138.

    Google Scholar 

  • Arif, M., & Gupta, K. (2018). Map** peri-urbanization in a non-primate city: A case study of Burdwan India. European Acad Research Romania, 5(11), 6065–6081.

    Google Scholar 

  • Arif, M., & Gupta, K. (2020a). Application of graph-based model for the quantification of transport network in peri-urban interface of Burdwan City India. Spatial Information Research, 28(4), 447–457. https://doi.org/10.1007/s41324-019-00305-w

    Article  Google Scholar 

  • Arif, M., & Gupta, K. (2020b). Spatial development planning in peri-urban space of Burdwan City, West Bengal, India: Statutory infrastructure as mediating factors. SN Applied Sciences, 2(11), 1–19. https://doi.org/10.1007/s42452-020-03587-0

    Article  Google Scholar 

  • Arif, M., Rao, D. S., & Gupta, K. (2019). Peri-urban livelihood dynamics: A case study from Eastern India. Forum Geografic. https://doi.org/10.5775/fg.2019.012.i

    Article  Google Scholar 

  • Azadi, H. (2020). Monitoring land governance: Understanding roots and shoots. Land Use Policy, 94, 104530. https://doi.org/10.1016/j.landusepol.2020.104530

    Article  Google Scholar 

  • Banzhaf, E., Grescho, V., & Kindler, A. (2009). Monitoring urban to peri-urban development with integrated remote sensing and GIS information: A Leipzig, Germany case study. International Journal of Remote Sensing, 30(7), 1675–1696.

    Article  Google Scholar 

  • Bhagat, R. B., & Mohanty, S. (2009). Emerging pattern of urbanization and the contribution of migration in urban growth in India. Asian Population Studies, 5(1), 5–20.

    Article  Google Scholar 

  • Butt, A., Shabbir, R., Ahmad, S. S., & Aziz, N. (2015). Land use change map** and analysis using remote sensing and GIS: a case study of simly watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 251–259.

    Article  Google Scholar 

  • Cao, X., Feng, Y., & Wang, J. (2017). Remote sensing monitoring the spatio-temporal changes of aridification in the Mongolian Plateau based on the general Ts-NDVI space, 1981–2012. Journal Earth System Science, 126(4), 1–16. https://doi.org/10.1007/s1204.0-017-0835-x

    Article  Google Scholar 

  • Census of India. (2011). Provisional population totals (No. Paper 2). New Delhi: office of the registrar general and census commissioner. Retrieved from http://censusindia.gov.in.

  • Chakraborty, K., Mukherjee, I., & Ghosh, S. (2011). Historical and geo-environmentalappraisal of changing growth centre, urbanization and behavioural dynamics of the rivers of Barddhaman Town West Bengal. Internaltional Journal Current Research, 3(11), 216–224.

    Google Scholar 

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46. https://doi.org/10.1016/0034-4257(91)90048-B

    Article  Google Scholar 

  • Das, S., & Angadi, D. P. (2020). Land use-land cover (LULC) transformation and its relation with land surface temperature changes: A case study of Barrackpore Subdivision, West Bengal. India. Remote Sensing Application Society Environment., 19, 100322. https://doi.org/10.1016/j.rsase.2020.100322

    Article  Google Scholar 

  • 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., 60, 199. https://doi.org/10.1007/s42965-019-00022-3

    Article  Google Scholar 

  • Di, G. A., & Jansen, L. J. (1998). A new concept for a land cover classification system. The Land, 2(1), 55–65.

    Google Scholar 

  • El-Hattab, M. M., & Bay, A. Q. (2016). Applying post classification change detection technique to monitor an Egyptian coastal zone. The Egyptian Journal of Remote Sensing and Space Science., 19(1), 23–36. https://doi.org/10.1016/j.ejrs.2016.02.002

    Article  Google Scholar 

  • Eman, A. A., & Bharti, W. G. (2021). Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings, 2(1), 8–17. https://doi.org/10.1016/j.gltp.2021.01.002

    Article  Google Scholar 

  • Enaruvbe, G., & Pontius, R. G., Jr. (2015). Influence of classification errors on Intensity Analysis of land changes in southern Nigeria. International Journal of Remote Sensing, 31(1), 244–261.

    Article  Google Scholar 

  • Fan, Y., Zhu, X., He, Z., et al. (2017). Urban Expansion Assessment in Huaihe River Basin, China, from 1998 to 2013 using remote sensing data. Journal of Sensors, 2017, 1–10. https://doi.org/10.1155/2017/9281201

    Article  Google Scholar 

  • Fazal, S. (2014). Peri urban livelihoods: Opportunities and challenges. Concept Publishing Company Pvt.

    Google Scholar 

  • Fichera, C. R., Modica, G., & Pollino, M. (2012). Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. Eurpean Journal Remote Sensing, 45(1), 1–18. https://doi.org/10.5721/EuJRS20124.501

    Article  Google Scholar 

  • Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., & Snyder, P. K. (2005). Global consequences of land use. Science, 309(5734), 570–574. https://doi.org/10.1126/scien.ce.1111772

    Article  Google Scholar 

  • Follmann, A., Kennedy, L., Pfeffer, K., & Wu, F. (2023). Peri-urban transformation in the Global South: A comparative socio-spatial analytics approach. Regional Studies, 57(3), 447–461.

    Article  Google Scholar 

  • García, L., Rodríguez, D., Wijnen, M., & Pakulski, I. (Eds.). (2016). Earth observation for water resources management: current use and future opportunities for the water sector. NJ: World Bank Publications.

    Google Scholar 

  • Hamad, R. (2020). An assessment of artificial neural networks, support vector machines and decision trees for land cover classification using sentinel-2A data. Sciences, 8(6), 459–464.

    Google Scholar 

  • Hassan, M. M. (2017). Monitoring land use/land cover change, urban growth dynamics and landscape pattern analysis in five fastest urbanized cities in Bangladesh. Remote Sensing Applications Society and Environment. https://doi.org/10.1016/j.rsase.2017.07.001

    Article  Google Scholar 

  • Janakarajan, S. (2009). Urbanization and periurbanization: Aggressive competition and unresolved conflicts—the case of Chennai City in India. South Asian Water Studies, 1(1), 51–76.

    Google Scholar 

  • Jansen, L. J., & Di, G. A. (2003). Land-use data collection using the “land cover classification system”: Results from a case study in Kenya. Land Use Policy, 20(2), 131–148. https://doi.org/10.1016/s0264-8377(02)00081-9

    Article  Google Scholar 

  • Jansen, L. J. M., & di Gregorio, A. (2002). Parametric land cover and landuse classification as tools for environmental change detection. Agriculture, Ecosystems & Environment, 91, 89–100.

    Article  Google Scholar 

  • Jensen, J. R. (2007). Remote sensing of the environment: An earth resource perspective (2nd ed.). Pearson Prentice Hall.

    Google Scholar 

  • Kar, R., Reddy, G. P., Kumar, N., & Singh, S. K. (2018). Monitoring spatiotemporal dynamics of urban and peri-urban landscape using remote sensing and GIS—A case study from Central India. Egypt Journal Remote Sensing Space Science, 21(3), 401–411. https://doi.org/10.1016/j.ejrs.2017.12.006

    Article  Google Scholar 

  • Laha, M., & Arambagh, H. (2019). Centripetal forces of urbanization in barddhaman municipality West Bengal. Transactions, 41(1), 33.

    Google Scholar 

  • Langat, P. K., Kumar, L., Koech, R., et al. (2019). Monitoring of land use/land -cover dynamics using remote sensing: A case of tana river basin. Kenya. Geocarto International, 36(13), 1470–1488. https://doi.org/10.1080/10106049.2019.1655798

    Article  Google Scholar 

  • Lea C, Curtis AC (2010) Thematic accuracy assessment procedures: National Park Service vegetation inventory, version 2.0. Natural resource report NPS/2010/NRR-2010/204. Fort Collins: National Park Service, US Department of the Interior.

  • Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. Wiley.

    Google Scholar 

  • López, E., Bocco, G., Mendoza, M., et al. (2001). Predicting land-cover and land-use change in the urban fringe. Landscape and Urban Planning, 55(4), 271–285. https://doi.org/10.1016/s0169-2046(01)00160-8

    Article  Google Scholar 

  • Lu, D., Mausel, P., Brondizio, E., et al. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401.

    Article  Google Scholar 

  • Lu, D., & Weng, Q. (2005). Urban classification using full spectral information of Landsat ETM? imagery in marion country Indiana. Photogramm Eng Remote Sensing, 71(11), 1275–1284.

    Article  Google Scholar 

  • Meyer, W. B., & Turner, B. L. (1992). Human population growth and global land-use/cover change. Annual Review of Ecology Evolution and Systematics, 23(1), 39–61. https://doi.org/10.1146/annurev.es.23.110192.000351

    Article  Google Scholar 

  • Mondal, A., Kundu, S., Chandniha, S. K., et al. (2012). Comparison of support vector machine and maximum likelihood classification technique using satellite imagery. International Journal of Remote Sensing, 1(2), 116–123.

    Google Scholar 

  • Mosammam, H., Nia, J., Khani, H., et al. (2017). Monitoring land use change and measuring urban sprawl based on its spatial forms the case of Qom City. Egypt Journal Remote Sensing Space Science, 20(1), 103–116.

    Google Scholar 

  • Narain, V. (2009). Growing City, Shrinking Hinterland: Land Acquisition, Transition and Conflict in Peri urban Gurgaon, India. Environment and Urbanization, 27, 501–512. https://doi.org/10.1177/0956247809339660

    Article  Google Scholar 

  • Nengroo, Z. A., Shah, A. H., & Bhat, M. S. (2017). Assessment of the impact of land use change on natural resource land of srinagar metropolitan region of kashmir valley. IOSR Journal Humanit Socity Science, 22(07), 54–60. https://doi.org/10.9790/0837-2207015460

    Article  Google Scholar 

  • Nilsson, K., Pauleit, S., Bell, S., Aalbers, C., & Nielsen, T. A. S. (Eds.). (2013). Peri-urban futures: Scenarios and models for land use change in Europe. London: Springer.

    Google Scholar 

  • Nuissl, H., Haase, D., Lanzendorf, M., et al. (2009). Environmental impact assessment of urban land use transitions-A context-sensitive approach. Land Use Policy, 26(2), 414–424. https://doi.org/10.1016/j.landusepol.2008.05.006

    Article  Google Scholar 

  • Office of the Registrar General and Census Commissioner, Census of India (1991) New Delhi: Government of India, Accessed on March 12, 2017

  • Office of Registrar General of India and Census Commissioner, Census of India (2001) New Delhi: Government of India, Accessed on March 21, 2017

  • Office of the Registrar General and Census Commissioner (2011). District statistical handbook, Barddhaman, Part XII A-B. Directorate of census operations, Government of West Bengal. https://censusindia.gov.in/2011census/dchb/1909_PART_B_DCHB_BARDDHAMAN.pdf on 17 December 2017. Accessed 15 February 2021.

  • Patel, S. K., Verma, P., & Singh, G. S. (2019). Agricultural growth and land use land cover change in peri-urban India. Environmental Monitoring and Assessment, 191(9), 1–17.

    Article  Google Scholar 

  • Quan, B., Pontius, R. G., & Song, H. (2020). Intensity analysis to communicate land change during three time intervals in two regions of Quanzhou City. China. Giscience & Remote Sensing, 57(1), 21–36. https://doi.org/10.1080/15481603.2019.1658420

    Article  Google Scholar 

  • Qviström M (2018) Peri-urban landscape studies. The Routledge Companion to Landscape Studies, pp 523–533. doi:https://doi.org/10.4324/9781315195063-42

  • Radhakrishnan, N., Eerni, S. K., & Kumar, S. (2014). Analysis of urban sprawl pattern in Tiruchirappalli city using applications of remote sensing and GIS. Arabian Journal for Science and Engineering, 39(7), 5555–5563. https://doi.org/10.1007/s13369-014-1099-2

    Article  Google Scholar 

  • 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), 36.

    Article  Google Scholar 

  • Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. Egypt Journal Remote Sensing Space Science., 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002

    Article  Google Scholar 

  • Sarin M (2019) Urban planning in the third world: The Chandigarh experience. Routledge.

  • Sarkar, A., & Chouhan, P. (2020). Modeling spatial determinants of urban expansion of Siliguri a metropolitan city of India using logistic regression. Model Earth System Environmental, 6, 2317–2331. https://doi.org/10.1007/s40808-020-00815-9

    Article  Google Scholar 

  • Seif, A., & Mokarram, M. (2012). Change detection of gil playa in the northeast of Fars province. Iran America Journal Science Research, 86, 122–130.

    Google Scholar 

  • Sengupta, S., Mohinuddin, Sk., Arif, M., Sengupta, B., & Zhang, W. (2022). Assessment of agricultural land suitability using GIS and fuzzy analytical hierarchy process approach in Ranchi District India. Geocarto International, 37(26), 13337–13368. https://doi.org/10.1080/10106049.2022.2076925

    Article  Google Scholar 

  • Sewnet, A. (2015). Land use/cover change at infraz watershed North Western Ethiopia. Journal Landscape Ecology, 8(1), 69–83. https://doi.org/10.1515/jleco.l-2015-0005

    Article  Google Scholar 

  • Shaw, R., & Das, A. (2018). Identifying peri-urban growth in small and medium towns using GIS and remote sensing technique: A case study of English Bazar Urban agglomeration, West Bengal, India. The Egyptian Journal of Remote Sensing and Space Science, 21(2), 159–172. https://doi.org/10.1016/j.ejrs.2017.01.002

    Article  Google Scholar 

  • Simon, D. (2008). Urban environments: issues on the peri-urban fringe. Annual Review of Environment and Resources, 33(1), 167–185. https://doi.org/10.1146/annurev.environ.33.021407.093240

    Article  Google Scholar 

  • Sims, D. (1997). Negotiating a sustainable future for or land-Structural and institutional guidelines for land resource management in the 21st Century.

  • Spate, O. H. K., Learmonth, A. T. A., & Learmonth, A. M. (1965). India and Pakistan: A General and Regional Geography. Methuen.

    Google Scholar 

  • Strahler, A. H. (1980). The use of PRIOR PROBABILITIES IN maximum likelihood classification of remotely sensed data. Remote Sensing of Environment, 10(2), 135–163.

    Article  Google Scholar 

  • United Nations (2018) Department of economic and social affairs. https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html. Accessed 26 April 2021

  • Verburg, P. H., van Eck, J. R. R., de Nijs, T. C. M., et al. (2004). Determinants of land-Use change patterns in the Netherlands. Environment and Planning B: Planning and Design, 31(1), 125–150. https://doi.org/10.1068/b307

    Article  Google Scholar 

  • Wolff, S., Mdemu, M. V., & Lakes, T. (2021). Defining the peri-urban: A multidimensional characterization of spatio-temporal land use along an urban-rural gradient in dares salaam Tanzania. Land, 10(2), 177.

    Article  Google Scholar 

  • Yadav, N., & Sharma, C. (2018). Spatial variations of intra-city urban heat island in megacity Delhi. Sustainable Cities and Society, 37, 298–306.

    Article  Google Scholar 

  • Zaehringer, J. G., Eckert, S., & Messerli, P. (2015). Revealing regional deforestation dynamics in North- Eastern Madagascar-insights from multi-temporal land cover change analysis. Land, 4, 454–474.

    Article  Google Scholar 

  • Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370–384. https://doi.org/10.1016/j.isprsjprs.2017.06.013

    Article  Google Scholar 

  • Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171. https://doi.org/10.1016/j.rse.2014.01.011

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge all the agencies like United States Geological Survey, Survey of India for obtaining the maps required for the study. Furthermore, we acknowledge the anonymous reviewer’s contribution for their constructive comments to improve and revise the manuscript.

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This work was supported by University Grants Commission, New Delhi for providing research assistance.

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Arif, M., Sengupta, S., Mohinuddin, S.K. et al. Dynamics of land use and land cover change in peri urban area of Burdwan city, India: a remote sensing and GIS based approach. GeoJournal 88, 4189–4213 (2023). https://doi.org/10.1007/s10708-023-10860-3

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