Assessment of Deforestation Drivers Using Spatial Regression Modelling in the Cross River Rainforest Zone

The Cross River rainforest represents Nigeria's most significant contiguous block of tropical forest and a global biodiversity hotspot, yet deforestation continues despite protected area designations and international conservation investments. This study applies spatial regression modelling to assess the relative importance of proximate and underlying drivers of deforestation in the Cross River rainforest zone, contributing analytical rigour to a literature dominated by qualitative assessments. Annual forest loss layers derived from Global Forest Watch Hansen et al. data and independently verified Sentinel-2 classification are compiled for the period 2019 to 2024. Explanatory variables including road proximity, protected area boundary distance, population density, slope, agricultural commodity prices, and governance quality indicators are assembled as spatial data layers at 1-kilometre resolution. Ordinary least squares regression and spatial error model specifications are compared, with the spatial error model selected on the basis of Moran's I residual diagnostics. Results indicate that road proximity, agricultural commodity price spikes particularly for palm oil, and proximity to reserve boundaries are the strongest positive predictors of forest loss intensity, while slope gradient is the most significant negative predictor. The study identifies border zone forest reserves with statistically anomalous deforestation rates controlling for physical factors, warranting investigation of enforcement effectiveness. Findings inform the Cross River State Forestry Commission and the UN-REDD Nigeria programme on priority intervention areas. Keywords: deforestation drivers, spatial regression, Cross River, rainforest, GIS modelling.