Enhancing Sustainable Urban Planning Through Sleuth Modeling: a Case Study of Urban Growth in the Asan Watershed, Uttarakhand, India

Abstract Urban growth monitoring and assessment are crucial for sustainable long-term planning and the efficient utilization of natural resources. Unplanned urbanization poses risks such as pollution and environmental disruption, emphasizing the need for proactive management. Changes in Land Use and Land Cover (LULC) with time indicate ongoing urbanization trends. This study focuses on predicting and simulating urban growth in the Asan watershed, Uttarakhand, India until 2040 using the SLEUTH model and assessing the impact on the surrounding areas. The research utilizes primary datasets from 1980 to 2016, including the Digital Elevation Model (DEM) and LULC, to forecast future urban expansion. The SLEUTH (Slope, Land use, Exclusion, Urban, Transportation, and Hill-shade) model, based on Cellular Automata (CA) principles, is employed to simulate urban growth by analysing and projecting LULC changes from 2016 to 2040. Recent methodologies prioritize the detection of LULC changes through multispectral satellite images, emphasizing factors like radiometric efficiency, spatial uniformity, and climatic conditions. The predicted urban growth output revealed that the projected increase of urban area by 2040 will be 67.73 km² from 36.5 km² in 2016 with an increment of 31.23 km². Additionally, by 2040, urban settlement is expected to occupy around 9.5% of the total watershed area, an increase of 4.3% from the urban area observed in 2016. The study aims to guide infrastructure planning and promote sustainable development practices by comprehending urban dynamics, growth patterns, and resource management.