This study analyzed and assessed spatio-temporal dynamics of land-use change (LUC) and urban expansion (UE) within the Greater Accra Metropolitan Area (GAMA) of Ghana. This region serves as a case to illustrate how a major economic hub and political core area is experiencing massive spatial transformations, resulting in uneven geographies of urban land expansion. Quickbird/Worldview-2 images for the years 2008 and 2017 were segmented and classified to produce LUC maps. LUC and UE were analyzed by post-classification change detection and spatial metrics, respectively. The results revealed an intensive decrease in open-space by 83.46 km2, brushland/farmland (194.29 km2) and waterbody/wetland (3.32 km2). Conversely, forestland and urban built-up area increased by 3.45 km2 and 277.62 km2. Urban extent expanded from 411.45 km2 (27%) in 2008 to 689.07 km2 (46%) in 2017 at a rate of 5.9% and an intensity of 2.06% with an expansion coefficient of 1.5%, indicating low-density urban sprawl. The spatial pattern turned out to be an uneven and spatially differentiated outward expansion, which materialized mainly in districts located within the urban peripheries but intensely towards eastern and western directions, being the frontier and the hotspots of urbanization. Overall, the findings bear important implications for regional spatial planning and development.

Intensive land-cover changes (LCC) driven by unplanned urbanisation continue to threaten the sustainability of ecological assets in many cities in Africa. Evaluating the nature and processes of these changes is key to understanding the extent to which ecological instability may be affecting sustainability futures. This study employed integrated remote sensing, GIS, land accounting techniques and utilisation of high-resolution Quickbird and Worldview 2 images to analyse actual (2008–2017) and future (2017–2030) LCC and explored implications for ecological sustainability in the Greater Accra Metropolitan Area, Ghana. After mapping and classifying actual LCC, multi-layer perception (MLP) neural network and Markov chain were employed to predict future LCC for the year 2030. The results indicate that the built-up area increased substantially from 27% in 2008 to 46% in 2017 and is expected to rise to 73% by 2030. In contrast, open-space (10%), forestlands (5%) and grassland/farmlands (49%) decreased progressively (2008–2030). In effect, these land-cover types experienced area turnover ˃100% during the actual and predicted period, indicating high vulnerability of natural land cover to urban growth, ecological degradation and resource depletion. These findings highlight significant implications of LCC for ecological sustainability in the study area. A proactive land-cover/use management plan is necessary to ensure sustainable urban development and ecological land conservation.

Ghana is experiencing high population growth, rapid urbanization and a constantly accelerating growth of urban areas. Yet, the accurate delineation of urban settlements remains a major challenge faced by urbanists. While emergent urban settlements are being characterized by highly-diverse, heterogeneous, and multiplicity of features, the need to rethink how best to classify new urban growth areas, beyond the commonly used population threshold of ≥ 5000 inhabitants, is becoming increasingly inadequate. Thus, this paper proposes a multi-criteria approach, drawing on the concept of ‘hyper-diversity’. Eight key dimensions – urban form, built-up extent, socio-economic functions, land-use dynamics, occupational structure, governance structure and population size – were identified as a guide to the delineation of new urban settlements. Inferring these dimensions requires accurate spatial and statistical data on prevailing the land-use dynamics. Thus, the paper argues that analyzing satellite-based remote sensing and groundtruth-gathered data may provide standardized and timely information on the aforementioned dimensions. Applying this multi-dimensional approach may be useful for Ghana and similar countries where there is a lack of regular mapping of urban areas.