Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model

Land use and Land cover change (LULCC) is a major global problem, and projecting change is critical for policy decision-making. Understanding LULCCs at the watershed level is essential for transboundary river basin management. The present study aims to analyse the past and future LULCCs in two significant watersheds of the Senegal River basin (SRB) in West Africa: Bafing and Faleme. This study used Landsat images from 1986, 2006 and 2020 and the Random Forest classification method to analyze past LULCCs in these two watersheds. The results revealed: In Bafing, vegetation, settlement, agricultural areas and water increased, while the bareground decreased significantly between 1986-2020. In Faleme, two periods have different trends. Between 1986-2006, vegetation, settlement, agricultural areas and water increased, while bareground decreased. Between 2006-2020, settlement increased, while vegetation, agricultural areas, water and bareground decreased. To predict LULCCs in 2050 under business-as-usual assumptions, the Multilayer Perceptron and Marcov Chain model (MLP-MC) was used. The MLP-MC shows better results on Bafing than on Faleme but without questioning its application on the two watersheds. Bafing has seen a trend towards more people, more trees, while Faleme has seen a trend towards more people, more deforestation. These results contribute to develop appropriate land management policies and strategies to achieve or to maintain sustainable development in the SRB.

Automated summary

Land use and Land cover change (LULCC) is a major global problem, and understanding LULCCs at the watershed level is important for transboundary river basin management. This study analyzed the past and future LULCCs in two significant watersheds of the Senegal River basin in West Africa using Landsat images and the Random Forest classification method. The results showed changes in vegetation, settlement, agricultural areas, water, and bareground over time in both watersheds, with different trends between the two periods in Faleme. The study also used the Multilayer Perceptron and Marcov Chain model to predict LULCCs in 2050 under business-as-usual assumptions.