Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)

Auwalu Faisal Koko,Roknisadeh Hamed,Wu Yue,Ghali Abdullahi Abubakar,Akram Ahmed Noman Alabsi

doi:10.3390/su122410452

Abstract

Monitoring land use/land cover (LULC) change dynamics plays a crucial role in formulating strategies and policies for the effective planning and sustainable development of rapidly growing cities. Therefore, this study sought to integrate the cellular automata and Markov chain model using remotely sensed data and geographical information system (GIS) techniques to monitor, map, and detect the spatio-temporal LULC change in Zaria city, Nigeria. Multi-temporal satellite images of 1990, 2005, and 2020 were pre-processed, geo-referenced, and mapped using the supervised maximum likelihood classification to examine the city’s historical land cover (1990–2020). Subsequently, an integrated cellular automata (CA)–Markov model was utilized to model, validate, and simulate the future LULC scenario using the land change modeler (LCM) of IDRISI-TerrSet software. The change detection results revealed an expansion in built-up areas and vegetation of 65.88% and 28.95%, respectively, resulting in barren land losing 63.06% over the last three decades. The predicted LULC maps of 2035 and 2050 indicate that these patterns of barren land changing into built-up areas and vegetation will continue over the next 30 years due to urban growth, reforestation, and development of agricultural activities. These results establish past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

Highlights

Land use/land cover (LULC) changes are presently becoming issues of great concern regionally and globally due to numerous environmental challenges
The analysis identified the various areas that changed from one land use/land cover (LULC) type to another in a spatial and quantitative approach during the specified period
The study monitored and predicted the spatio-temporal land use/land cover change in Zaria city through an integrated CA–Markov that combined the advantages of cellular automata and the Markov chain model

Summary

Introduction

Land use/land cover (LULC) changes are presently becoming issues of great concern regionally and globally due to numerous environmental challenges. The continuous transformation of land uses and widespread deforestation has continuously led to multiple scales of emerging environmental issues with severe effects on the ecosystem. These changes have directly and indirectly affected most urban centers [3,4,5,6]. It alters climatic conditions, biodiversity, and physical systems, serving as a significant threat to natural resource management [7]. Monitoring spatio-temporal LULC changes provides essential decision-making information required for appropriate planning, managing natural resources, and achieving sustainable development

Objectives

Results

Conclusion