Assessing Forest Resource Depletion and Rural Livelihood Sustainability in Ovia Southwest, Nigeria using Remote Sensing, GIS, and Qualitative Socio-Economic Data
Abstract
Forest resources are fundamental to rural livelihoods in sub-Saharan Africa, yet rapid land-use change threatens their sustainability. This study assessed forest cover change, available forest resources, socio-economic drivers of household dependence, and community perceptions of forest contributions to socio-economic development in Ovia Southwest LGA., Edo State, Nigeria. A mixed-methods approach was used to integrate remote sensing and GIS analysis of Landsat imagery (1987–2024), a household survey (n = 390), factor analysis, multiple regression, and chi-square tests. Land use land cover classification revealed that natural environment (forest cover) declined from 76.95% (202,031.28ha) in 1987 to 42.07% (110,449.55ha) in 2024, representing a 50% loss. Disturbed forest increased from 13.48% to 27.80%, while farmland expanded from 6.90% to 26.08%. Factor analysis identified four dominant forest resource categories: timber/charcoal/fibres (35.4% variance), medicinal plants/roots/forest soil (14.8%), fruits/animal fodder (9.9%), and meat/honey (7.0%). Multiple regression showed socio-economic factors significantly influenced household forest dependence (R² = 0.279, F = 29.779, p < 0.001), with education, age, and occupation as the strongest predictors. Chi-square analysis confirmed that communities perceive a significant contribution of forest resources to socio-economic development (χ² = 221.718, df = 32, p < 0.001). The study concludes that agricultural expansion and timber extraction are primary drivers of forest degradation, and livelihood diversification interventions are urgently needed to reduce forest dependence while sustaining rural well-being. And recommended implementation of community-participatory forest conservation programmes; investment in rural education/expanded vocational training to provide alternative income sources; and regular monitoring using remote sensing should continue to track forest cover trends and inform adaptive management.