A rapid increase in the world’s population over the last century has triggered the transformation of the earth surface, especially in urban areas, where more than half of the global population live. Ghana is no exception and a high population growth rate, coupled with economic development over the last three decades, has transformed the Greater Accra region into a hotspot for massive urban growth. The urban extent of the region has expanded extensively, mainly at the expense of the vegetative cover in the region. Although urbanization presents several opportunities, the environmental and social problems cannot be underestimated. Therefore, the need to estimate the rate and extent of land use/land cover changes in the region and the main drivers of these changes is imperative. Geographic Information Systems (GIS) and remote sensing techniques provide effective tools in studying and monitoring land-use/land-cover change over space and time. A post classification change detection of multiple Landsat images was conducted to map and analyse the extent and rate of land use/land cover change in the region between 1991 and 2015. Subsequently, the urban extent of the region was forecasted for the year 2025 using the Markov Chain and the Multi-Layer Perceptron neural network, together with drivers representing proximity, biophysical, and socio-economic variables. The results from the research revealed that built-up areas increased by 277% over the 24-year study period. However, forest areas experienced massive reduction, diminishing from 34% in 1991 to 6.5% in 2015. The 2025 projected land use map revealed that the urban extent will massively increase to cover 70% of the study area, as compared to 44% in 2015. The urban extent is also anticipated to spill into the adjoining districts mainly on the western and eastern sides of the region. The success of this research in generating a future land-use map for 2025, together with the other significant findings, demonstrates the usefulness of spatial models as tools for sustainable city planning and environmental management, especially for urban planners in developing countries.

Existing geosimulation land-use change models are predominantly designed to operate at local or regional spatial scales. When these models are applied on data at the global level, they do not consider the effects of spatial distortions caused by the curvature of the Earth’s surface and often lack some refinements related to land suitability analysis. Therefore, the main objective of this study is to integrate multi-criteria evaluation with spherical cellular automata, to develop a novel modelling approach for simulating global urban land-use change. The world region is clustered into sub-regions to improve the suitability analysis. The obtained results reveal differences in urban growth rate and the size of the extent across the four clusters. The 64% of the total global urbanization are occurring in urban region clusters characterized by high gross domestic product and population density, while urban regions in isolated locations have the lowest urban growth rate.

Deforestation as a land-cover change process is linked to several environmental problems including desertification, biodiversity loss, and ultimately climate change. Understanding the land-cover change process and its relation to human–environment interactions is important for supporting spatial decisions and policy making at the global level. However, current geosimulation model applications mainly focus on characterizing urbanization and agriculture expansion. Existing modelling approaches are also unsuitable for simulating land-cover change processes covering large spatial extents. Thus, the objective of this research is to develop and implement a spherical geographic automata model to simulate deforestation at the global level under different scenarios designed to represent diverse future conditions. Simulation results from the deforestation model indicate the global forest size would decrease by 10.5% under the “business-as-usual” scenario through 2100. The global forest extent would also decline by 15.3% under the accelerated deforestation scenario and 3.7% under the sustainable deforestation scenario by the end of the 21st century. The obtained simulation outputs also revealed the rate of deforestation in protected areas to be considerably lower than the overall forest-cover change rate under all scenarios. The proposed model can be utilized by stakeholders to examine forest conservation programs and support sustainable policy making and implementation.