Amid global concerns regarding climate change and urbanization, understanding the interplay between land use/land cover (LULC) changes, the urban heat island (UHI) effect, and land surface temperatures (LST) is paramount. This study provides an in-depth exploration of these relationships in the context of the Kamrup Metropolitan District, Northeast India, over a period of 22 years (2000–2022) and forecasts the potential implications up to 2032. Employing a high-accuracy supervised machine learning algorithm for LULC analysis, significant transformations are revealed, including the considerable growth in urban built-up areas and the corresponding decline in cultivated land. Concurrently, a progressive rise in LST is observed, underlining the escalating UHI effect. This association is further substantiated through correlation studies involving the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI). The study further leverages the cellular automata–artificial neural network (CA-ANN) model to project the potential scenario in 2032, indicating a predicted intensification in LST, especially in regions undergoing rapid urban expansion. The findings underscore the environmental implications of unchecked urban growth, such as rising temperatures and the intensification of UHI effects. Consequently, this research stresses the critical need for sustainable land management and urban planning strategies, as well as proactive measures to mitigate adverse environmental changes. The results serve as a vital resource for policymakers, urban planners, and environmental scientists working towards harmonizing urban growth with environmental sustainability in the face of escalating global climate change.

Urbanization is a major driver of land-use change, with significant implications for sustainable development in rapidly growing cities. In this study, we assess the spatiotemporal patterns of urban expansion in Lucknow, India, from 1991 to 2021, using geospatial techniques and Landsat satellite imagery. The key objective is to analyze urban growth patterns, identify contributing factors, and evaluate the sustainability implications of these trends. Results reveal a nearly fivefold increase in built-up area, from 53.86 km² in 1991 to 261.45 km² in 2021. Edge development accounted for 55.99% of total urban expansion from 1991 to 2001, while ribbon development increased from 18.06% to 21.03% by 2021. Leapfrog development, considered unsustainable, rose from 12.35% in 1991 to 17.35% in 2021. Compared to previous studies, findings highlight the significant influence of transportation infrastructure and industrialization on urban sprawl, particularly in the northeastern and northwestern zones. This study contributes by offering a zone-wise analysis of growth patterns, which provides deeper insights into the dynamics of urban expansion. The findings are crucial for urban planners, as they suggest the need for more stringent zoning regulations and targeted infrastructure investments to manage future growth sustainably while minimising environmental impact.

This research focuses on the urban expansion occurring in the Kamrup Metropolitan District—an area experiencing significant urbanization—with the aim of understanding its patterns and projecting future growth. The research covers the period from 2000 to 2022 and projects growth up to 2052, providing insights for sustainable urban planning. The study utilizes the maximum likelihood method for land use/land cover (LULC) delineation and the Shannon entropy technique for assessing urban sprawl. Additionally, it integrates the cellular automata (CA)-Markov model and the analytical hierarchy process (AHP) for future projections. The results indicate a considerable shift from non-built-up to built-up areas, with the proportion of built-up areas expected to reach 36.2% by 2032 and 40.54% by 2052. These findings emphasize the importance of strategic urban management and sustainable planning. The study recommends adaptive urban planning strategies and highlights the value of integrating the CA Markov model and AHP for policymakers and urban planners. This can contribute to the discourse on sustainable urban development and informed decision-making.