Omar Bongo University (French: Université Omar Bongo) a public university which was founded as the National University of Gabon in 1970. It was renamed in honor of President Omar Bongo in 1978. It is based in Libreville, and was the country's first university. It is Gabon's largest university and around 30,000 students are enrolled there (2020).The university is under the supervision of the Ministry of Higher Education.
This paper examines the impact of trade liberalisation on income inequality across 24 Sub-Saharan African (SSA) countries from 2000 to 2020. Using IV-Tobit and 2SLS models, we consistently find that greater trade openness significantly exacerbates inequality in the region. Critically, we document an inverted U-shaped relationship between trade and inequality-similar to the Laffer Curve-but this mitigating effect is only observed in high-income, less corrupt, and democratic SSA countries. In addition, trade openness demonstrates a dual, contradictory effect on inequality: the disruptive impact on employment significantly outweighs the mitigating effect of the education channel. This disparity underscores that without robust labour market and social protection policies, the negative employment consequences of trade liberalisation will dominate the potential equalising gains from human capital development.
Land movement phenomena, due to their essentially random nature, cause major damage due to the serious material and human consequences that they can cause. In the PK6-PK11 zone of Libreville, these phenomena are not new, especially since their first manifestation dates to 1989. However, the absence of spatialized digital data for better knowledge of the areas exposed to these phenomena remains a limiting factor for optimizing decision-making by authorities. To this end, this study aims to map the areas susceptible to drainage accidents from PK6 to PK11, and to map areas susceptible to ground movements. To do this, the hierarchical multicriteria analysis of Thomas SAATY (1980) was used, considering seven predisposing factors: slope, relief, drainage density, land use, drainage network distance, the nature of the soil and temperatures. The resulting map made it possible to distinguish five susceptibility classes: areas of very high susceptibility, which cover 22.11
Introduction. The Sentinel-1 (equipped with synthetic aperture radar) and Sentinel-2 (equipped with multispectral cameras) satellites are valuable tools for environmental monitoring, particularly for assessing vegetation cover and soil erosion. The data obtained by these systems can be processed using machine learning techniques to generate accurate vegetation classification maps. Aim. To develop and evaluate machine learning models capable of efficiently fusing Sentinel satellite data to produce more accurate and detailed maps of urban vegetation, essential for urban planning, environmental monitoring, and climate change mitigation. The integration of Sentinel-1 and Sentinel-2 satellite data is intended to overcome the limitations associated with using each data type in isolation, particularly in complex urban environments where spectral signatures can be ambiguous and radar provides only structural information. Materials and methods. A classification map of urban vegetation on Kotlin Island (St Petersburg) was generated by integrating data from Sentinel-1 (synthetic aperture radar) and Sentinel-2 (multispectral imagery) using the Random Forest algorithm, Tensorflow and Sklearn Python libraries. Conventional urban vegetation mapping often relies on a single data source, leading to limited accuracy and inability to differentiate subtle vegetation types. The vegetation classes considered in this research were coniferous forest, deciduous forest, wetland vegetation, and coastal meadows. Results. The integrated analysis of radar and multispectral data enabled more accurate identification of erosion-prone zones in non-vegetated areas and more reliable estimation of plant moisture content. Such a fusion approach showed significantly improved classification accuracy and reduced error rate compared to techniques relying on individual indices. Conclusion. The fusion method demonstrated superior performance in classifying vegetation types, which confirms its potential for applications in remote sensing and environmental monitoring. Future research will focus on integrating radar and multispectral data from Sentinel satellites for urban vegetation classification using the Support Vector Machine algorithm.
