Spatiotemporal analysis of urban growth using cardinal analysis and Shannon’s entropy for Kurukshetra city, India

The study presents a quantitative spatio-temporal assessment of urban growth, vegetation and population, their inter-relationship, using remote sensing indices and Shannon’s Entropy model with an objective to evaluate the extent, direction, and sustainability of urban expansion in historical city of Kurukshetra, India. Multi-temporal Landsat imagery and GIS-based analyses were employed to derive Cardinal analysis, NDVI, SAVI, SRVI, NDBI, and BUI, enabling a detailed understanding of vegetation dynamics, built-up land transformation and their strong correlation with population during 1990–2020. Results show that the urban area increased from 17.59 km2 in 1990 to 33.33 km2 in 2020, reflecting an 89% expansion over three decades. The rise in Shannon’s Entropy value from 2.815 to 2.917 (normalized Hn = 0.94–0.97, Hn1 = 0.94–0.97, Hn​2 = 0.94–0.97) indicated a marked transition from a compact urban form to a spatially dispersed and polycentric structure. Vegetation indices exhibited a decline in mean NDVI (0.18 to 0.16) and SAVI (0.25 to 0.21), while built-up indices (NDBI and BUI) increased significantly (slopes of + 0.021 and + 0.027, respectively), confirming a strong negative correlation (r ≈ –0.9) between urbanization and vegetation decline.The novelty of this work lies in the integration of entropy modeling with multi-index vegetation and built-up analyses to quantify both the spatial dispersion and ecological consequences of urbanization, an approach rarely applied to medium-sized Indian cities. The findings provide robust empirical evidence that population induced horizontal expansion, leading to ecological degradation and loss of vegetative cover is persistent in Kurukshetra city, India. This research contributes a data-driven, reproducible framework for monitoring urban sprawl and offers valuable insights for sustainable urban planning, land-use regulation, and environmental conservation in rapidly urbanizing regions.