Geospatial Modelling of Vegetation Dynamics and Carbon Sequestration Capacity in Ekiti State, Nigeria Using MODIS Data and CASA Models.

Oluwafemi David Bejide , Hezekiah Daramola Olaniran

Accurate estimation of terrestrial carbon sequestration capacity is fundamental to national climate mitigation efforts and achieving Sustainable Development Goals (SDGs). This study assessed vegetation dynamics and modelled Net Primary Productivity (NPP) a proxy for carbon sequestration capacity in Ekiti State, Nigeria, over the 11-year period (2014–2024). The study deployed the seasonal and annual Carnegie Ames Standard Approach (CASA) model which is driven by high-resolution Landsat 8 Enhanced Vegetation Index (EVI), derived APAR ERA5 climate data, and Terraclimate moisture data, all resampled to 30m spatial resolution. The outputs were analysed using the Mann-Kendall trend test and validated against the coarser MODIS (MOD17) NPP product. The NPP estimated byT the CASA model and MODIS product were 482.61 ± 38.95gC m-2y- and 474.63 ± 35.89 gCm⁻² yr⁻¹ respectively. Temporally, the findings revealed a critical discrepancy: the MODIS NPP trend showed a statistically significant decline (tau = -0.491, p=0.041) over the decade, while the higher-resolution CASA NPP showed only a weak, insignificant decline (tau = -0.127, p=0.648). Seasonal driver analysis resolved this contradiction, exposing divergent ecosystem controls: the Wet season was found to be light-limited, where NPP showed a strong negative correlation with the Wetness Index (rho = -0.773, p=0.005) suggesting cloud-cover limitation and a positive correlation with (PAR). In other word, the Dry season was purely water-limited, confirmed by a strong positive correlation between NPP and the Wetness Index (rho = 0.800, p=0.003). These results underscore that local carbon sequestration is controlled by seasonally antagonistic climate factors. The pronounced divergence between the MODIS and CASA estimates emphasizes the necessity of using locally calibrated, high-spatial resolution Light Use Efficiency models for reliably monitoring carbon budgets and informing forest conservation strategies in complex ecological transition zones like Ekiti State.