Spatial Inequality of Urbanisation, Land Transformation and Environmental Quality in Bihar: A Remote Sensing and Socio-Spatial Vulnerability Framework
Main Article Content
Abstract
Bihar is among the least urbanised and most densely populated states of India, yet its urban growth is highly concentrated and its land base is under intense pressure. This paper develops an integrated remote sensing and socio-spatial vulnerability framework to measure how unevenly urbanisation, land transformation, and environmental quality are distributed across the state’s 38 districts. The framework combines a satellite-based land-transformation module (land use/land cover classification, NDVI, NDBI, MNDWI, and land surface temperature) with three district-level composite indices built from Census of India 2011 data: an Urbanisation–Land Pressure Index (ULPI), a household Environmental Quality Index (EQI), and a Social Vulnerability Index (SVI). The indices are weighted by the entropy method and analysed with population-weighted Gini and Theil coefficients, global and local Moran’s I, and a coupling coordination model. Urban households range from 3.8% of all households in Madhubani to 45.6% in Patna, a twelve-fold gap, and access to tap water varies more than twenty-fold. Between 49% and 73% of the Theil inequality in each indicator arises between the nine administrative divisions. ULPI and EQI are strongly correlated (ρ = 0.84, p < 0.001), and both are inversely related to social vulnerability. Local spatial statistics reveal a high-urbanisation, high-environmental-quality corridor along the Ganga around Patna and a low–low cluster in the Kosi–Seemanchal region, where Supaul and Araria form a significant hot spot of combined socio-environmental vulnerability. Fifteen districts, home to 38% of the state’s population, fall in a rural-deprived priority class. Published remote sensing evidence for Patna, where built-up land expanded from 38% to 80% of the city area between 1988 and 2022, shows that where urbanisation is most advanced it is accompanied by rapid vegetation loss and surface warming. The framework offers a transparent basis for spatially targeted investment in sanitation, clean energy, piped water, and green infrastructure.
Article impact statement: An integrated remote sensing and census-based vulnerability framework shows that urbanisation, household environmental quality and social vulnerability in Bihar are sharply and regionally unequal, and identifies fifteen priority districts for spatially targeted investment.
Article Details
Section
How to Cite
References
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
2. Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. https://doi.org/10.2307/2332142
3. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240. https://doi.org/10.2307/143141
4. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
5. Shorrocks, A. F. (1980). The class of additively decomposable inequality measures. Econometrica, 48(3), 613–625. https://doi.org/10.2307/1913126
6. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
7. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
8. Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242–261. https://doi.org/10.1111/1540-6237.8402002
9. Turner, B. L., II, et al. (2003). A framework for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences of the USA, 100(14), 8074–8079. https://doi.org/10.1073/pnas.1231335100
10. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987
11. Cohen, B. (2006). Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technology in Society, 28(1–2), 63–80. https://doi.org/10.1016/j.techsoc.2005.10.005
12. Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268–281. https://doi.org/10.1016/j.gloenvcha.2006.02.006
13. Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
14. Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., & Briggs, J. M. (2008). Global change and the ecology of cities. Science, 319(5864), 756–760. https://doi.org/10.1126/science.1150195
15. Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 335–344. https://doi.org/10.1016/j.isprsjprs.2009.03.007
16. Census of India. (2011). Primary Census Abstract and Houselisting and Housing Census Data, Bihar. Office of the Registrar General & Census Commissioner, India, Ministry of Home Affairs, Government of India, New Delhi. https://censusindia.gov.in
17. Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences of the USA, 109(40), 16083–16088. https://doi.org/10.1073/pnas.1211658109
18. Li, Y., Li, Y., Zhou, Y., Shi, Y., & Zhu, X. (2012). Investigation of a coupling model of coordination between urbanization and the environment. Journal of Environmental Management, 98, 127–133. https://doi.org/10.1016/j.jenvman.2011.12.025
19. Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94. https://doi.org/10.1016/j.rse.2011.10.028
20. Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. https://doi.org/10.1016/j.rse.2014.02.015
21. 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, 249. https://doi.org/10.1007/s12517-015-2138-3
22. Fu, P., & Weng, Q. (2016). A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175, 205–214. https://doi.org/10.1016/j.rse.2015.12.040
23. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
24. Gogoi, P. P., Vinoj, V., Swain, D., Roberts, G., Dash, J., & Tripathy, S. (2019). Land use and land cover change effect on surface temperature over Eastern India. Scientific Reports, 9. https://doi.org/10.1038/s41598-019-45213-z
25. Chettry, V., & Surawar, M. (2021). Assessment of urban sprawl characteristics in Indian cities using remote sensing: Case studies of Patna, Ranchi, and Srinagar. Environment, Development and Sustainability, 23, 11913–11935. https://doi.org/10.1007/s10668-020-01149-3
26. van Donkelaar, A., et al. (2021). Monthly global estimates of fine particulate matter and their uncertainty. Environmental Science & Technology, 55(22), 15287–15300. https://doi.org/10.1021/acs.est.1c05309
27. Akram, W., Amzad, & Khan, D. (2025). Urban expansion and its influence on land surface temperature: A case study of Patna City, India. Journal of Landscape Ecology, 18(1), 1–24. https://doi.org/10.2478/jlecol-2025-0001
28. Kawano, A., et al. (2025). Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality. Science Advances, 11(4), eadq1071. https://doi.org/10.1126/sciadv.adq1071