Abstract:
Woody vegetation is crucial in maintaining ecological balance, supporting biodiversity, and contributing to carbon storage. However, these ecosystems face increasing threats from deforestation, climate change, and human activities. Despite the current challenges, diagnostics and preliminary information for guiding regreening interventions to restore ecosystems are notably lacking. This study employed Species Distribution Models (SDMs) to predict the spatial distribution and suitability of four woody tree covers (Mangroves, Close Woodlands, Open Woodlands, and Plantations). In each woody cover, a hundred occurrence points were used. The study used machine learning approaches such as Random Forest (RF), MaxEnt, and Generalized Linear Models (GLM) to analyse the relationships between woody cover occurrence data and environmental predictors, including climate, soil properties, anthropogenic factors, and natural disturbances. Results indicate that Salinity is the most significant driver affecting all vegetation types, particularly mangroves. Rainfall strongly influences Close Woodlands and Plantations, while fire disturbances shape Open Woodlands. Predicted suitability maps reveal potential habitat suitability, indicating areas of high restoration potential and underscoring the need for targeted conservation and restoration strategies. Comparison between current coverage and the predicted suitability revealed the smallest gap in Mangroves to cover the optimum suitable area (3.47%) while substantial areas still exist for Close woodlands, Open Woodlands and Plantations with 5,49, 6,03 and 6,41, respectively. Findings from this study provide essential insights for sustainable land management, regreening policy initiatives, and woody ecosystem restoration planning in West Africa's woody coastal areas. By integrating Geographic Information System (GIS) and ecological modelling, this research enhances decision-making for biodiversity conservation and climate resilience.
Description:
A Thesis Publication 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
