Spatio-Temporal Assessment of Land Use/Land Cover Change, Land Surface Temperature, and Groundwater Fluctuations Using Remote Sensing and GIS: A Case Study of Ghaziabad District
DOI:
https://doi.org/10.54386/jam.v28i3.3575Keywords:
Land use land cover, Accuracy assessment, Groundwater fluctuation, Urbanization, Geographic information systemAbstract
Urbanization has been increasing at a rapid rate, which has led to transformation in the land use/land cover (LULC) and thus has a significant impact on groundwater resources and land surface temperature (LST) in rapidly developing cities. The present study aims to identify the spatio-temporal dynamics of LULC, LST, and the variability of groundwater in Ghaziabad District, Uttar Pradesh, India, from 2013 to 2023 using Remote Sensing and GIS techniques. Multi-temporal Landsat data were classified using the Maximum Likelihood Supervised Classification algorithm for the LULC maps that included 5 classes: built-up area, agriculture, vegetation, barren land, and water bodies. The accuracy of the classifications was assessed by the Kappa coefficient, and the Ordinary Kriging (OK) method was applied to spatially interpolate the groundwater depth in ArcGIS 10.8. Between 2013 and 2023, substantial increases in built-up areas and loss of vegetation cover were observed in the findings. Over the same time period, groundwater depth continued to decline in a progressive manner, and high LST areas became larger, especially in dense urban settings. The integrated analysis shows that surface thermal conditions and groundwater stress have intensified due to LULC transformation. The results highlight the importance of sustainable urban planning, groundwater recharge initiatives, and green infrastructure to reduce thermal risks and promote environmental sustainability in rapidly urbanizing areas.
References
Aman, A., Randriamanantena, H.P., Podaire, A. and Froutin, R. (1992). Upscale Integration of Normalized Difference Vegetation Index: The Problem of Spatial Heterogeneity. IEEE Transactions on Geoscience and Remote Sensing, 30, 326-338. https://doi.org/10.1109/36.134082.
Anand S, Bhati A, Singh P, Singh A, Mayala V. K, Kumar H. (2024.) Spatio-Temporal Change of Landscape and Its Impact on Agriculture Development in Ghaziabad District. Current Agriculture Research Journal, 12(3). http://dx.doi.org/10.12944/CARJ.12.3.25
Antonakos, A., Lambrakis, N. (2021). Spatial Interpolation for the Distribution of Groundwater Level in an Area of Complex Geology Using Widely Available GIS Tools. Environ. Process, 8, 993–1026. https://doi.org/10.1007/s40710-021-00529-9.
Battista, G. & De Lieto Vollaro, R. (2017). Correlation between air pollution and weather data in urban areas: Assessment of the city of Rome (Italy) as spatially and temporally independent regarding pollutants; Atmospheric Environment, 165, 240-247. https://doi.org/10.1016/j.atmosenv.2017.06.050.
Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3), 241–252.
Central Ground Water Board. (2023). Ground Water Year Book Uttar Pradesh. Ground Water Department, Government of Uttar Pradesh.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20. https://doi.org/10.1177/001316446002000104.
Guha, S., Govil, H., Dey, A., & Gill, N. (2018). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. European Journal of Remote Sensing, 51(1), 667–678. https://doi.org/10.1080/22797254.2018.1474494
Guha, S., & Govil, H. (2025). Evaluating the stability of the relationship between land surface temperature and land use/land cover indices: a case study in Hyderabad city, India. Geology, Ecology, and Landscapes, 9(1), 231–243. https://doi.org/10.1080/24749508.2023.2182083.
Hasan, K., Paul, S., Chy, T.J. , Antipova, A. (2021). Analysis of groundwater table variability and trend using ordinary kriging: the case study of Sylhet, Bangladesh. Appl Water Sci 11, 120. https://doi.org/10.1007/s13201-021-01454-w.
Jadhav, P., Manekar, V.L., Patel, J.N. (2023). Spatiotemporal Land Use Land Cover Change Impacts on Groundwater Table in Surat District, India. In: Timbadiya, P.V., Patel, P.L., Singh, V.P., Mirajkar, A.B. (eds) Geospatial and Soft Computing Techniques (pp 101–112). HYDRO 2021. Lecture Notes in Civil Engineering, vol 339. Springer, Singapore. https://doi.org/10.1007/978-981-99-1901-7_10.
Moharir, K. N., Baliram Pande, C., Gautam, V. K., Dash, S. S., Mishra, A. P., Yadav, K. K., Darwish, H. W., Pramanik, M., & Elsahabi, M. (2025). Estimation of land surface temperature and LULC changes impact on groundwater resources in the semi-arid region of Madhya Pradesh, India. Advances in Space Research, 75(1), 233–247. https://doi.org/10.1016/j.asr.2024.09.025.
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.
Patra, S., Sahoo, S., Mishra, P., Mahapatra, S.C. et al. (2018). Impacts of urbanization on land use/cover changes and its probable implications on local climate and groundwater level. Journal of Urban Management, 7,70-84. https://doi.org/10.1016/j.jum.2018.04.006
Pushkar, D.S., Mishra, A., Arya, D.S., Jaiswal, S., Kumar, A. (2025). Investigating the effects of land use, land cover change, and urban heat island effects in Agra city, India, using a remote sensing approach. Discover Cities, 2, 105. https://doi.org/10.1007/s44327-025-00149-0.
Ramaiah, M., Avtar, R., & Rahman, M. M. (2020). Land Cover Influences on LST in Two Proposed Smart Cities of India: Comparative Analysis Using Spectral Indices. Land, 9(9):292. https://doi.org/10.3390/land9090292.
Rouse, J. W., Hass, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. NASA/GSFC Type III Final Report. Greenbelt, MD: NASA/ GSFC.
Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment, 90(4), 434–440.
Saha, J., Ria, S. S., Sultana, J., Shima, U. A., Seyam, M. M. H., & Rahman, M. M. (2024). Assessing seasonal dynamics of land surface temperature (LST) and land use land cover (LULC) in Bhairab, Kishoreganj, Bangladesh: A geospatial analysis from 2008 to 2023. Case Studies in Chemical and Environmental Engineering, 9, 100560. https://doi.org/10.1016/j.cscee.2023.100560.
Singh, A., Mishra, V., N. (2020). Estimation of changes in land surface temperature using multi-temporal Landsat data of Ghaziabad District, India. Forum geographic, 19(1), 45-59. http://dx.doi.org/10.5775/fg.2020.040.i
Tyagi & Sharma (2021). Seasonal Variability, Index Modeling and Spatiotemporal Profling of Groundwater Usability in Semi Urban Region of Western Uttar Pradesh, India. Environ Earth Sci, 80, 761. https://doi.org/10.1007/s12665-021-10018-9.
Uttar Pradesh Ground Water Department (2022). Pre-monsoon and Post-monsoon Ground Water Level Data (In Mbgl). Ground Water Department, Government of Uttar Pradesh. http://upgwd.gov.in/MediaGallery/WLD08092017.pdf. Accessed 16 Nov 2020
Verma, P., Singh, P., Srivastava, S.K. (2020). Impact of land use change dynamics on sustainability of groundwater resources using earth observation data. Environ Developm Sustain, 22(6), 5185–5198.
Wang, W., Zhang, Z., Duan, L., Wang, Z., Zhao, Y., Zhang, Q., Dai, M., Liu, H., Zheng, X., & Sun, Y. (2018). Response of the groundwater system in the Guanzhong Basin (central China) to climate change and human activities. Hydrogeology journal, 26(5), 1429-1441. https://doi.org/10.1007/s10040-018-1757-7
Zahran, H., Ali, M. Z., Jadoon, K. Z., Yousafzai, H. U. K., Rahman, K. U., & Sheikh, N. A. (2023). Impact of Urbanization on Groundwater and Surface Temperature Changes: A Case Study of Lahore City. Sustainability, 15(8), 6864. https://doi.org/10.3390/su15086864.
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