Assessing Urban Sprawl in Millennium City Kaduna: A Multi-Temporal Change Detection Analysis

Abstract

The rapid expansion of urban areas, particularly in developing regions, presents significant challenges for sustainable development and resource management. In the context of the Millennium City of Kaduna State, the dynamics of urban sprawl have become a focal point due to its burgeoning population and economic activities. This paper presents a comprehensive multi-temporal change detection analysis aimed at assessing the extent and patterns of urban sprawl in Kaduna State. Utilizing remotely sensed imagery spanning multiple years, specifically the moderate resolution Landsat 5, 7, 8, and 9 of 1989, 2002, 2011, and 2022 respectively, were combined with Geographical Information Systems (GIS) and advanced machine learning algorithms. The spatial and temporal dynamics of urban expansion were analyzed.

The methodology encompasses the extraction and classification of land cover changes, focusing on key indicators of urban sprawl such as changes in built-up areas, vegetation loss, and infrastructure development. Results from an interactive supervised classification using the Maximum Likelihood Classifier (MLC) in ArcGIS 10.2 and subsequent change detection analysis indicated a significant distortion in the planned areas of the city. The result shows a progressive increase in built-up areas of the Kaduna Millennium City and environs from 1989 to 2022. Built-up areas slightly decreased from 2.4527% in 1989 to 1.6322% in 2002 and showed a slight increase to 1.6322% in 2011. However, the built-up areas surged to 13.612% in 2022, which is attributable to the sudden expansion of development in the government's new layouts, with attendant haphazard development and proliferation of unplanned and illegal settlements, particularly around the buffer zones of the old city. There were also varying changes in other land cover classes including vegetation, water bodies, and open spaces. The findings provide valuable insights into the rate, direction, and drivers of urban sprawl in Millennium City, facilitating informed decision-making for sustainable urban planning, environmental conservation, and socio-economic development initiatives. This research contributes to the broader discourse on urbanization dynamics in rapidly growing cities, emphasizing the importance of employing remote sensing, GIS technologies, and machine learning algorithms for effective urban sprawl assessment and management.