Abstract:
The rapid increase in population and urban development are exacerbating the transformation of
natural environments into unnatural forms. While detailed assessment of the environment is
beneficial for efficient ecosystem system management, it can also be time and resourcesconsuming.
This study aimed to map and quantify the spatio-temporal changes in land use and
land cover (LULC) using the Ou´em´e River Basin as a case study. The supervised classification in
Google Earth Engine (GEE) cloud-computing platform was employed to distinguish Landsat images
for 1986, 2000, 2015 and 2023 into forest areas, settlements/bare lands, savanna areas
(woodlands), agricultural lands and water bodies. Analysis of the LULC changes revealed that
savanna areas and woodlands which were predominant in the basin in 1986 have steadily
declined by 24 % in area in 2023. Forest areas have diminished by 4.3 % at an annual rate of 4 %.
Agricultural lands have however grown exponentially by 28 % since 1986, with a more rapid
increase between 2015 and 2023 at an annual rate of 3.7 %, driven by rising food demand due to
population growth within and around the basin. Settlements and bare areas tripled in area,
reflecting a similar trend to Benin’s urban population growth. Accuracy statistics of the LULC
classification showed overall accuracy and kappa statistic values above 90 % and 86 %, respectively,
indicating the admirable performance and reliability of the Simple Composite Landsat
algorithm for image composition, and the Random Forest Classifier for LULC classification
approach applied in this study. The approach also demonstrates the robustness and potential of
LULC mapping in large and complex ecosystems using the GEE cloud-based remote sensing tool,
which is underutilized in the study area. Overall, the LULC trends provide beneficial insights
useful to policy-makers and any other stakeholders involved in sustainable ecosystem management
planning in the basin.
Description:
A Publication submitted to the West African Science Service Centre on Climate Change and Adapted Land Use and the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Climate Change and Land Use
