As urbanization accelerates, sustainable approaches are necessary to counter the adverse environmental impacts. The study investigates the multifaceted challenges of Lagos City due to urbanization and evaluates the effectiveness of sustainable urban development in tackling these problems. It employed a mixed-methods approach to provide informed decisions for liveable urban environments. Census data, satellite imagery, interviews, surveys, and focus group discussions provided a comprehensive understanding of Lagos’s urbanization and its effects. Findings reveal pressing problems such as housing shortages, infrastructure strain, traffic congestion, waste management difficulties, and socio-economic disparities in Lagos City. Local authorities and NGOs have implemented sustainable urban development initiatives, including transport upgrades, green space promotion, waste management, and housing solutions. Such case studies have revealed various interventions, such as informal settlement transformation, renewable energy integration, and urban regeneration. However, persistent issues still exist due to the city’s urbanization. Therefore, Integrated urban planning, inclusivity in policy-making, and technological advances are essential for tackling these challenges. The findings contribute to the academic discourse by providing insights into Lagos City’s urbanization and practical implications for sustainable urban development. It also highlights the need for comprehensive strategies to build a prosperous, equitable, and eco-friendly city.

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.

Lagos, Nigeria’s economic hub and one of Africa’s fastest-growing cities, has experienced remarkable urban growth due to rapid urbanization. The city has recently faced persistent urban flooding due to unsustainable growth and inadequate data for proper planning. Therefore, we analyzed urban growth and land cover change in Lagos, Nigeria, using multi-temporal datasets downloaded from the United States Geological Survey (USGS) at an interval of 10 years from 1990–2020. We employed the maximum likelihood classification and post-classification change detection to analyze the city’s urban growth and change dynamics. The study revealed three phases of growth in Lagos and a substantial increase in the city’s built-up area from 496 km² in 1990 to approximately 860 km² in 2000. This area increased further to 1113 km² in 2010, and presently over 1256 km². The result also revealed a substantial decrease in the city’s vegetation, water bodies, and bare soil by approximately 398 km², 246 km², and 115 km², respectively, between 1990 and 2020. These changes contribute to urban flooding, the prominent natural and human-induced disaster in the city. Therefore, this study’s findings provided the historical and scientific data for the effective planning, management, and sustainable development of the city.

Rapid urbanization in cities and urban centers has recently contributed to notable land use/land cover (LULC) changes, affecting both the climate and environment. Therefore, this study seeks to analyze changes in LULC and its spatiotemporal influence on the surface urban heat islands (UHI) in Abuja metropolis, Nigeria. To achieve this, we employed Multi-temporal Landsat data to monitor the study area’s LULC pattern and land surface temperature (LST) over the last 29 years. The study then analyzed the relationship between LULC, LST, and other vital spectral indices comprising NDVI and NDBI using correlation analysis. The results revealed a significant urban expansion with the transformation of 358.3 sq. km of natural surface into built-up areas. It further showed a considerable increase in the mean LST of Abuja metropolis from 30.65 °C in 1990 to 32.69 °C in 2019, with a notable increase of 2.53 °C between 2009 and 2019. The results also indicated an inverse relationship between LST and NDVI and a positive connection between LST and NDBI. This implies that urban expansion and vegetation decrease influences the development of surface UHI through increased LST. Therefore, the study’s findings will significantly help urban-planners and decision-makers implement sustainable land-use strategies and management for the city.

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.

Rapid urban expansion and the alteration of global land use/land cover (LULC) patterns have contributed substantially to the modification of urban climate, due to variations in Land Surface Temperature (LST). In this study, the LULC change dynamics of Kano metropolis, Nigeria, were analysed over the last three decades, i.e., 1990–2020, using multispectral satellite data to understand the impact of urbanization on LST in the study area. The Maximum Likelihood classification method and the Mono-window algorithm were utilised in classifying land uses and retrieving LST data. Spectral indices comprising the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were also computed. A linear regression analysis was employed in order to examine the correlation between land surface temperature and the various spectral indices. The results indicate significant LULC changes and urban expansion of 152.55 sq. km from 1991 to 2020. During the study period, the city’s barren land and water bodies declined by approximately 172.58 sq. km and 26.55 sq. km, respectively, while vegetation increased slightly by 46.58 sq. km. Further analysis showed a negative correlation between NDVI and LST with a Pearson determination coefficient (R²) of 0.6145, 0.5644, 0.5402, and 0.5184 in 1991, 2000, 2010, and 2020 respectively. NDBI correlated positively with LST, having an R² of 0.4132 in 1991, 0.3965 in 2000, 0.3907 in 2010, and 0.3300 in 2020. The findings of this study provide critical climatic data useful to policy- and decision-makers in optimizing land use and mitigating the impact of urban heat through sustainable urban development.