GeoAI-driven Mapping of Land-Cover Change and Vegetation Degradation Linked to Fuelwood Extraction in Kaduna State: Evidence from Kauru LGA
DOI:
https://doi.org/10.47514/kjg.2026.08.01.063Abstract
Unsustainable fuelwood extraction and agricultural expansion are accelerating forest degradation in the Sudan-Sahel savanna of northern Nigeria, yet local-level evidence remains limited. This study employed an integrated Geospatial Artificial Intelligence (GeoAI) framework to examine land use/land cover changes and vegetation health trends across Kauru Local Government Area, Kaduna State, a rural community dependent on the Libere Forest Reserve for biomass energy, from 2005 to 2025. Multi-temporal Sentinel-2 imagery was classified using a Random Forest algorithm to map land cover for 2015, 2020, and 2025, while the Normalized Difference Vegetation Index (NDVI) derived from Landsat 8/9 and Sentinel-2 MSI imagery characterized vegetation health trends. Spatial driver analysis incorporating road networks, river proximity, elevation, slope, and population density identified key factors influencing extraction pressure. Field interviews with community leaders provided qualitative validation of satellite findings. Results show cultivated land expanded by 340.27 km² between 2015 and 2025, a 62.8% increase concentrated in 2020–2025, while dense vegetation declined from 219.42 km² to 169.37 km². Accuracy assessment using 190 stratified random validation points verified against Google Earth Pro achieved an Overall Accuracy of 85.26% and a Kappa coefficient of 0.789. NDVI analysis revealed a maximum value of 0.215 in 2005, increasing to 0.503 in 2025, with persistently low values (0.12–0.18) in northern wards (Makami, Dawaki, Kauru West) indicating severe degradation, while the central Libere Forest Reserve maintained higher values (0.23–0.50). Field interviews uncovered a two-phase degradation process, commercial timber extraction followed by suppression of Isoberlinia doka regeneration through fuelwood harvesting, and identified traditional Fulani grazing corridors, locally known as Labi, as unrecognized conservation zones. These findings offer a replicable GeoAI monitoring framework and evidence base for sustainable forest management and rural energy policy reform in northern Kaduna State.
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Copyright (c) 2026 Aliyu Jaafar Abubakar, Salisu Ibrahim, Yahya Saleh Ibrahim, Kabiru Shehu, Aliyu Buba (Author)
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