Assessing Land Use/Land Cover Changes and Urban Heat Island Intensification: A Case Study of Kamrup Metropolitan District, Northeast India (2000–2032)

Abstract

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.

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

“…Consequently, LULC change prediction models with 80% or higher accuracy are typically considered highly reliable predictive tools [60]. The Kstandard value in this study was slightly higher than those reported in other recent studies that employed the CA-Markov model for LULC change simulations, such as 0.88 [61] and 0.83 [62]. Similarly, the Kno value, which represents the overall accuracy of the simulated LULC maps, was also higher than the values reported in the aforementioned studies.…”

Projecting Urban Expansion by Analyzing Growth Patterns and Sustainable Planning Strategies—A Case Study of Kamrup Metropolitan, Assam, North-East India

“…According to the 2011 Census of India, the population of the Guwahati Municipal Corporation area surged from 43,615 in 1951 to 962,334 in 2011, driving substantial urban development. This rapid growth has altered land usage, affected local climate, and raised urban health concerns, underscoring the necessity for systematic urban planning and sustainable development strategies to alleviate environmental and public health impacts [31]. In recent times, the Kamrup Metropolitan District has experienced a significant acceleration in urban development, evidenced by an 18.34% increase in its urban population since the 2001 census, according to the 2011 Census of India data [28].…”

“…According to the 2011 Census of India, the population of the Guwahati Municipal Corporation area surged from 43,615 in 1951 to 962,334 in 2011, driving substantial urban development. This rapid growth has altered land usage, affected local climate, and raised urban health concerns, underscoring the necessity for systematic urban planning and sustainable development strategies to alleviate environmental and public health impacts [31].…”

Geospatial Multi-Criteria Evaluation Using AHP–GIS to Delineate Groundwater Potential Zones in Zakho Basin, Kurdistan Region, Iraq

“…Land use/land cover can refer to natural and/or man-made land cover [54]. The Land Use and Land Cover factor contributes to the Groundwater Potential Index by affecting groundwater recharge, contaminant transport, human activity impacts, natural filtration, and more.…”