Remote sensing and MaxEnt modeling of canopy and non-canopy forest tree species in Taraba State for biodiversity conservation and ecosystem management

Abstract

This study investigates the distribution and habitat suitability of canopy and non-canopy species in Taraba State, Nigeria, using remote sensing indices (NDVI, NDRE) and species distribution modeling (MaxEnt). Forest ecosystems in this region are increasingly threatened by deforestation, climate change, and land-use change, emphasizing the need for robust monitoring tools to guide conservation strategies. NDVI and NDRE data from 2013 to 2025 were analyzed across six forests, including Gashaka-Gumti National Park, to evaluate vegetation health and distribution. Results revealed clear differences in the sensitivity of canopy and non-canopy species to environmental drivers, with precipitation and temperature variability emerging as the dominant factors influencing distribution. MaxEnt modeling further highlighted the significance of rainfall and temperature seasonality in shaping habitat suitability, showing that non-canopy species are particularly vulnerable to moisture stress during the dry season. Several forests—notably Ngel Yaki (mean NDVI = 0.24), Gashaka-Gumti (0.23), and Gembu (0.21)—exhibited declining vegetation health, emphasizing the urgent need for protection and restoration. The MaxEnt model demonstrated strong predictive performance (AUC = 0.985), providing valuable insights for forest conservation, biodiversity management, and climate adaptation in northern Nigeria, where desertification risk is intensifying.