Modelling past and future land use and land cover dynamics in the Nakambe River Basin, West Africa

Abstract

Understanding land use / land cover (LULC) dynamic is of great importance to sustainable development in Africa where deforestation is a common problem. This study aimed to assess the historical and future dynamics of LULC in the Nakambé River Basin. Landsat images were used to determine LULC dynamics for the years 1990, 2005 and 2020 using Random Forest classification system in Google Earth Engine while the predicted LULC of 2050 was simulated using the Markov Chain and Multi-Layer-Perceptron neural network in Land Change Modeler. The findings showed significant changes in LULC patterns. From 1990 to 2020, woodland and shrubland decreased by -45% and -68% respectively while water body, cropland and bare land/built-up increased by 233%, 51%, and 75%, correspondingly. From 2020 to 2050, the results revealed that under the Business-as-usual scenario, bare land/built-up and water bodies could continue to increase by 99% and 1% respectively. However, cropland, shrubland, and woodland could decrease by -32.61%, -33.91%, and -46.86%, respectively. Under the afforestation scenario, the contrary of Business-as-usual could occur. While woodland, shrubland, and cropland would increase by 22.24%, 51.57%, and 18.13%, correspondingly, between 2020 and 2050, the area covered by water bodies and bare land/built-up will decrease by -6.16% and -39.04%, respectively. The results of this research give an insight into past and future LULC dynamics in the Nakambé River Basin and suggest the need to strengthen the policies and actions for better land management in the region.

Modeling Extreme Floods Susceptibility Using The Generalized Extreme Value Distribution: Case Study Of Gonse And Wayen, Burkina Faso.

“…The month with most precipitation is August and the mean annual precipitation is around 700 mm. As stated by [25], the soils are dominated by tropical ferruginous leached soils and poorly evolved alluvial soils characterized by low water retention capacities and proned to erosion. Figure 1 delineates the locations of the rainguage stations considered in this study.…”

Inner Niger Delta Inundation Extent (2010–2022) Based on Landsat Imagery and the Google Earth Engine

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Gender-specific Vulnerability on Climate Change and Food Security Status - A catchment approach on agroforestry systems - a multi-country case study

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