Remote sensing data are most often used in water bodies' extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

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Urban Flooding is one of world's problems in recent times because of its frequent occurrence which results in loss of lives and properties. The first step in flood management is the development of hazard maps. Flood hazard mapping forms the foundation of the decision-making process by providing information which is essential to the understanding of nature and characteristics of flooding to risk community or city. Flood modelling is a complex problem and therefore a lot of factors should be considered before the final map showing flood prone areas are produced. The degree at which each of these factors contributes to flooding must be weighted by using multi decision process before incorporating them in an integrating environment such as GIS to produce the final prediction map. In this study, this approach was employed by using GIS and Analytical Hierarchy Process (AHP). The layers that were used in the flooding included; Slope, Drainage basin, Rainfall, Soil, Land Use and Digital Terrain Model (DEM). The result obtained was more accurate as compared to the previous works done on Accra flooding. This is because more than one contributing factors were considered and at the same time, weights were assigned to these contributing factors before overlaying them to produce the final map. The previously occurred flood places were all found in the high possibilities flooding zones. The flood prone map indicates that almost the whole area of Accra and Greater Accra Region has a possibility of flooding. However, the riskiest areas are Accra Metropolitan, Ledzokuku Krowor, Ga West and Ga South.

COVID-19 has presented unusual challenges for individuals, governments and societies across the globe. Several non-medical and non-pharmaceutical interventions have demonstrated to be critical in addressing the resultant impacts. One notable tool among these interventions is the application of technology in identifying infected persons or individuals coming into contact with those infected. Policy think-tanks have invested in geospatial technology and information systems to help resolve contact tracing inefficiencies to curtail the fast spread of the disease. This study highlights the extent of the application of geospatial technology in COVID-19 contact tracing in Ghana. Here, it was demonstrated that majority of young adults that form the greater part of Ghana’s population have access to digital devices which serve as primary catalysts in facilitating effective and efficient contact tracing. Case count of the pandemic continues to surge sharply from one month to the other since the first recorded case on March 12, 2020. A huge number of cases were recorded in the southern part of the country, as against cases recorded in the north. Mobility patterns depicted the migration of more people from regions with a high number of case count to regions with lower counts. We recommend a holistic and proactive approach to the use of smart mobile devices and applications in enhancing contact tracing. Privacy and data protection laws must be prioritized and supported by effective legislative and policy frameworks that serve as the legal basis for the management of personal information.