Unravelling the Complexity of Tropical Peatland Governance in Greater Amanzule, Ghana

Abstract

The Greater Amanzule Peatland (GAP) in Ghana is an important tropical ecosystem and biodiversity hotspot currently facing escalating pressures from anthropogenic land-use changes driven by rapid agricultural expansion, urbanisation, a burgeoning oil and gas industry, and complex governance dynamics. Effectively addressing these challenges necessitates robust monitoring and active stakeholder engagement. This study therefore aims to enhance land cover classification accuracy, analyse land cover change patterns, and explore local aspirations for peatland governance using a multi-method research approach that combines multi-source remote sensing, machine learning techniques, and stakeholder insights. Optical, radar, and topographical remote sensing data were integrated for land cover classification, significantly outperforming five other data combinations with an overall accuracy of 94%. Analysis of land cover changes between 2010 and 2020 revealed a 12% expansion in peatland cover, equivalent to approximately 6,570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses increased, evidenced by an 85% rise in rubber plantations and a 6% reduction in natural forest cover, while sparse vegetation, including smallholder farms, decreased by 35%. Projections for 2030 and 2040 suggest minimal changes based on current trends; however, these projections do not account for potential impacts from climate change, large-scale developments, and demographic shifts, necessitating cautious interpretation. Stakeholder analysis revealed a disconnect between current governance structures and local aspirations, with strong preferences for sustainable resource use, community participation, and integration of traditional knowledge, alongside concerns over coordination and enforcement gaps. These findings highlight areas of stability and vulnerability within the understudied GAP region, offering critical insights into governance challenges and opportunities for tropical peatlands. Additionally, the methodological framework, which combines optical, radar, and topographical data with machine learning, provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands that is applicable to other regions facing similar environmental challenges.

Date of Award	11 Nov 2024
Original language	English
Awarding Institution	Edge Hill University
Supervisor	CHERITH MOSES (Director of Studies) & JOAQUIN ALBERTO CORTES CARRILLO (Supervisor)