Floods are hazard which poses immense threat to life and property. Identifying flood-prone areas, will enhance flood mitigation and proper land use planning of affected areas. However, lack of resources, the sizable extent of rural settlements, and the evolving complexities of contemporary flood models have hindered flood hazard mapping of the rural areas in Ghana. This study used supervised Random Forest (RF) classification, Landsat 8 OLI, and Landsat 7 ETM + images to produce flood prone, Land Use Land Cover (LULC), and flood hazard maps of the Nasia Watershed in Ghana. The results indicated that about 418.82 km² area of the watershed is flooded every 2–3 years (normal flooding) and about 689.61 km² is flooded every 7–10 years (extreme flooding). The LULC classification produced an overall accuracy of 92.31% and kappa of 0.9. The flood hazard map indicated that land areas within hazard zones of the river include the Nasia community, Flood Recession Agricultural (FRA), rainfed and woodlands. When compared with a Modified Normalized Difference Water Index (MNDWI), the RF supervised classification had an edge over the MNDWI in estimating the flooded areas. The results from this study can be used by local administrators, national flood disaster management and researchers for flood mitigation and land use planning within the watershed.

Urbanisation in Ghana has been associated with significant effects on the land surface temperature which has its impacts on the natural environment. Conventional literature exhibits that land surface temperature associated with urbanisation have concentrated in the developed world leaving the African terrain unattended to. Hence, its knowledge and implications in Africa and for that matter Ghana is deficient. This study is two-pronged a) examines the relationship between land surface temperature coupled with its spatial patterns (b) examines the surface urban heat island and urban-rural gradient in Greater Accra region of Ghana. Landsat images for 1991, 2002, 2012 and 2020 were used where demarcations for spatial integrity were done. The use of machine learning algorithms were adopted in the land use classification. It was observed that Accra would experience high impervious surface mostly due to rural–urban migration purposely to seek for employment. It was concluded that in order to achieve the Sustainable Development Goals (especially goals 3,6,11 and 13) more efforts should be made to improve the conditions in the rural areas in order to curtail the migration drive which does not only put pressure on the social amenities in the urban areas but also exacerbate in the land surface temperature situation.