NDVI-Based Vegetation Change And Stability Assessment Of Old Oyo Forest Reserve, Nigeria Using Bi-Temporal Sentinel-2 Data, Google Earth Engine And GIS

Abstract

Accurate assessment of vegetation change and ecological stability in protected tropical forests is essential for evidence-based conservation management. This study applied Normalized Difference Vegetation Index (NDVI) derived from bi-temporal Sentinel-2 Surface Reflectance (Level-2A) imagery (2018 and 2025) to assess vegetation change dynamics and stability in Old Oyo Forest Reserve, Oyo State, southwestern Nigeria. Image acquisition, cloud masking using the QA60 bitmask, median compositing, and NDVI computation from Band 4 (Red) and Band 8 (Near-Infrared) at 10-m spatial resolution were executed in Google Earth Engine (GEE). Threshold-based reclassification (NDVI ≥ 0.174) in ArcGIS delineated vegetated and non-vegetated surfaces; post-classification change detection identified four transition classes. Results revealed that vegetation cover occupied 2,711.45 km² (98.52%) in 2018 and 2,711.80 km² (98.53%) in 2025, yielding a net gain of 0.35 km² (0.013%). Afforestation amounted to 0.85 km² (63% of total change area) against 0.50 km² of deforestation (37%). The computed Stability Index (SI = 99.95%) and annual change rate of 0.051 km²/year confirm near-total landscape persistence over the study interval. Despite overall stability, localized deforestation signals anthropogenic pressures such as farming encroachment, illegal logging, recreation and grazing which requires continuous monitoring. The study validates GEE-integrated Sentinel-2 NDVI analysis as a cost-effective, scalable framework for tropical protected area vegetation monitoring.