Spatiotemporal Land Use/Land Cover Mapping and Prediction Based on Hybrid Modeling Approach: A Case Study of Kano Metropolis, Nigeria (2020–2050)

Abstract

The change dynamics of land use/land cover (LULC) is a vital factor that significantly modifies the natural environment. Therefore, mapping and predicting spatiotemporal LULC transformation is crucial in effectively managing the built environment toward achieving Sustainable Development Goal 11, which seeks to make cities all-inclusive, sustainable, and reliable. The study aims to examine the change dynamics of LULC in Kano Metropolis, Nigeria from 1991 to 2020 and predict the city’s future land uses over the next 15 and 30 years, i.e., 2035 and 2050. The maximum likelihood algorithm (MLA) of the supervised classification method was utilized to classify the study area’s land uses using Landsat satellite data and various geographic information system (GIS) techniques. A hybrid simulation model comprising cellular automata and Markov chain (CA-Markov) was then employed in validating and modeling the change dynamics of future LULC. The model integrated the spatial continuity of the CA model with the Markov chain’s ability to address the limitations of individual models in simulating long-term land use prediction. The study revealed substantial changes in the historical LULC pattern of Kano metropolis from 1991 to 2020. It indicated a considerable decline in the city’s barren land from approximately 413.47 km2 in 1991 to 240.89 km2 in 2020. Built-up areas showed the most extensive development over the past 29 years, from about 66.16 km2 in 1991 to 218.72 km2 in 2020. This trend of rapid urban growth is expected to continue over the next three decades, with prediction results indicating the city’s built-up areas expanding to approximately 307.90 km2 in 2035 and 364.88 km2 in 2050. The result also suggests that barren lands are anticipated to decline further with the continuous sustenance of various agricultural activities, while vegetation and water bodies will slightly increase between 2020 and 2050. The findings of this study will help decision-makers and city administrators formulate sustainable land use policies for a more inclusive, safe, and resilient city.

Keywords

• land use/land cover
• land use prediction
• hybrid model
• CA-Markov model
• satellite data
• GIS