Detection and Spatio-Temporal Analysis of Oil Palm (Elaeis Guineensis) Plantations Using Multi-Temporal Sentinel-2 Satellite Imagery in Ekiadolor Forest Reserve, Edo State, Nigeria

Author

Dr. Adeboboye James Adeyemi, Okoli Kingsley Ebuka, Bob Lucy Onyekachi, Onwuzegha Collins Osinakachukwu, Andrew Samuel Ogbonaya


Abstract

 Oil palm (Elaeis guineensis) cultivation is an important agricultural activity in tropical regions, but its expansion can alter natural forest landscapes and influence land-use patterns. This study assessed the spatial and temporal changes in oil-palm plantations within Ekiadolor Forest Reserve, Edo State, Nigeria, between 2020 and 2025 using multi-temporal Sentinel-2 satellite imagery and geographic information system (GIS) techniques. Sentinel-2 imagery for 2020 and 2025 was obtained from the Copernicus Data Space Ecosystem. Bands 2 (Blue), 3 (Green), 4 (Red), and 8 (Near-Infrared) were combined to produce multispectral composites, which were subsequently clipped to the study-area extent. The Normalized Difference Vegetation Index (NDVI) was used to support vegetation interpretation and land-cover discrimination. The imagery was classified into four land-cover classes: forest, bare soil, mature oil palm, and young oil palm. Classification accuracy was evaluated using confusion matrices, producer’s accuracy, user’s accuracy, overall accuracy, and the kappa coefficient in ArcGIS Pro. The 2020 classification achieved an overall accuracy of 87.88% and a kappa coefficient of 0.834, while the 2025 classification achieved 90.48% overall accuracy and a kappa coefficient of 0.856. Post-classification comparison revealed that total mapped oil-palm area increased from 5,909.51 ha in 2020 to 9,388.04 ha in 2025, representing a net increase of 3,478.53 ha or 58.86%. Mature oil palm increased substantially from 586.71 ha to 7,058.60 ha, while young oil palm declined from 5,322.80 ha to 2,329.44 ha, indicating substantial plantation maturation during the study period. The transition matrix further indicated that 3,765.35 ha of land classified as forest in 2020 was classified as oil palm in 2025. The findings demonstrate the usefulness of Sentinel-2 imagery and GIS-based post-classification comparison for monitoring plantation expansion and landscape transformation within forest-reserve environments.


Keywords

oil palm, Sentinel-2, remote sensing, land-use change, change detection, NDVI, GIS, Ekiadolor Forest Reserve, Nigeria



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