Assessment of Spatio-Temporal Land Use and Land Cover Dynamics in Ogoniland, Rivers State, Nigeria

Ogoniland, in Nigeria's Niger Delta, has experienced more than six decades of oil exploration, environmental pollution, and population pressure, yet its landscape change remains poorly quantified at the local scale. This study examined the spatio-temporal dynamics of land use and land cover (LULC) in Ogoniland between 1990 and 2025 using multi-temporal Landsat imagery acquired at five-year intervals. Images were classified into six classes (built-up, cropland, grassland, forest, mangrove, and water body) using the Random Forest algorithm, and vegetation condition was assessed with the Normalized Difference Vegetation Index (NDVI) in Google Earth Engine. Overall classification accuracy ranged from 84.2% to 88.6%, with Kappa coefficients of 0.825–0.868.

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Publication Details

Journal
International Journal of Innovative Science and Research Technology (IJISRT)
Published
2026-10-31
DOI
https://doi.org/10.38124/ijisrt/26oct1055
Primary Topic
Remote Sensing and Land Use
Type
article
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article

Assessment of Spatio-Temporal Land Use and Land Cover Dynamics in Ogoniland, Rivers State, Nigeria

Clifford-Osawaru Wisdom Osamudiamen, Dorka Nwidag Daniel, Ntor Charles, Odiji Caleb et al.
International Journal of Innovative Science and Research Technology (IJISRT)
Remote Sensing and Land Use
article

Assessment of Spatio-Temporal Land Use and Land Cover Dynamics in Ogoniland, Rivers State, Nigeria

Clifford-Osawaru Wisdom Osamudiamen, Dorka Nwidag Daniel, Ntor Charles, Odiji Caleb, Ubido Benjamin
article en

Abstract

Ogoniland, in Nigeria's Niger Delta, has experienced more than six decades of oil exploration, environmental pollution, and population pressure, yet its landscape change remains poorly quantified at the local scale. This study examined the spatio-temporal dynamics of land use and land cover (LULC) in Ogoniland between 1990 and 2025 using multi-temporal Landsat imagery acquired at five-year intervals. Images were classified into six classes (built-up, cropland, grassland, forest, mangrove, and water body) using the Random Forest algorithm, and vegetation condition was assessed with the Normalized Difference Vegetation Index (NDVI) in Google Earth Engine. Overall classification accuracy ranged from 84.2% to 88.6%, with Kappa coefficients of 0.825–0.868.

International Journal of Innovative Science and Research Technology (IJISRT)Vol. 11(10)
Openalex Percentile: Top 21%
Remote Sensing and Land Use
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