Land-use transformation and reservoir surface variability in the Aiba Dam catchment, Nigeria between 1982 and 2023

The achievement of Sustainable Development Goal 6 (SDG 6), which seeks to ensure access to safe and sustainable water resources, depends on effective management of reservoirs and their surrounding catchments, such as Aiba Dam in Iwo, Osun State, Nigeria. This study assessed land-use and land-cover (LULC) dynamics within the Aiba Dam catchment between 1982 and 2023 and projected future changes to 2033 using Geographic Information Systems (GIS), remote sensing, and a Cellular Automata-Markov (CA-Markov) model. Multi-temporal Landsat imagery was processed to generate false-colour composites, supervised LULC classifications, and Normalised Difference Water Index (NDWI) maps. Five LULC classes—built-up area, vegetation, forest reserve/farmland, bare land, and water body—were identified and analysed. Results showed that built-up area increased from 6.32 km 2 in 1982 to 17.38 km 2 in 2023, while vegetation declined from 98.09 to 85.36 km 2 . Forest reserve/farmland increased from 83.12 km 2 to 101.73 km 2 , whereas bare land decreased from 25.24 to 8.21 km 2 . The mapped surface-water extent of the reservoir showed temporal variation, increasing from 0.43 km 2 in 1982 to 0.53 km 2 in 2023, with the highest extent observed in 2002 (0.67 km 2 ). These variations represent changes in surface-water conditions derived from remote sensing data and do not necessarily indicate changes in reservoir storage capacity. The CA-Markov model projected continued built-up expansion to 21.93 km 2 by 2033, accompanied by a decline in vegetation cover to 80.78 km 2 , a slight reduction in forest reserve/farmland to 101.05 km 2 , a marginal increase in bare land to 8.92 km 2 , and a slight decrease in mapped surface-water extent to 0.52 km 2 . Model validation demonstrated good predictive performance, with an overall prediction accuracy of 83.7%, a Kappa coefficient of 0.80, quantity disagreement of 1.5%, allocation disagreement of 3.0%, and a Figure of Merit of 4.34%, indicating satisfactory agreement between simulated and observed LULC patterns. The findings highlight ongoing landscape transformations within the Aiba Dam catchment, particularly increasing built-up development, which may influence future catchment conditions. The study underscores the importance of integrated land-use planning, catchment monitoring, and sustainable reservoir management strategies to support long-term water resource sustainability.

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Journal
Scientific Reports
Published
2026-08-26
DOI
https://doi.org/10.1038/s41598-026-67462-5
Primary Topic
Groundwater and Watershed Analysis
Type
article
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article

Land-use transformation and reservoir surface variability in the Aiba Dam catchment, Nigeria between 1982 and 2023

Timothy O. Ogunbode, David O. Sanni, Emmanuel O. Akinwale, Francis O. Akinluyi
Scientific Reports
Groundwater and Watershed Analysis
article

Land-use transformation and reservoir surface variability in the Aiba Dam catchment, Nigeria between 1982 and 2023

Timothy O. Ogunbode, David O. Sanni, Emmanuel O. Akinwale, Francis O. Akinluyi
article en

Abstract

The achievement of Sustainable Development Goal 6 (SDG 6), which seeks to ensure access to safe and sustainable water resources, depends on effective management of reservoirs and their surrounding catchments, such as Aiba Dam in Iwo, Osun State, Nigeria. This study assessed land-use and land-cover (LULC) dynamics within the Aiba Dam catchment between 1982 and 2023 and projected future changes to 2033 using Geographic Information Systems (GIS), remote sensing, and a Cellular Automata-Markov (CA-Markov) model. Multi-temporal Landsat imagery was processed to generate false-colour composites, supervised LULC classifications, and Normalised Difference Water Index (NDWI) maps. Five LULC classes—built-up area, vegetation, forest reserve/farmland, bare land, and water body—were identified and analysed. Results showed that built-up area increased from 6.32 km 2 in 1982 to 17.38 km 2 in 2023, while vegetation declined from 98.09 to 85.36 km 2 . Forest reserve/farmland increased from 83.12 km 2 to 101.73 km 2 , whereas bare land decreased from 25.24 to 8.21 km 2 . The mapped surface-water extent of the reservoir showed temporal variation, increasing from 0.43 km 2 in 1982 to 0.53 km 2 in 2023, with the highest extent observed in 2002 (0.67 km 2 ). These variations represent changes in surface-water conditions derived from remote sensing data and do not necessarily indicate changes in reservoir storage capacity. The CA-Markov model projected continued built-up expansion to 21.93 km 2 by 2033, accompanied by a decline in vegetation cover to 80.78 km 2 , a slight reduction in forest reserve/farmland to 101.05 km 2 , a marginal increase in bare land to 8.92 km 2 , and a slight decrease in mapped surface-water extent to 0.52 km 2 . Model validation demonstrated good predictive performance, with an overall prediction accuracy of 83.7%, a Kappa coefficient of 0.80, quantity disagreement of 1.5%, allocation disagreement of 3.0%, and a Figure of Merit of 4.34%, indicating satisfactory agreement between simulated and observed LULC patterns. The findings highlight ongoing landscape transformations within the Aiba Dam catchment, particularly increasing built-up development, which may influence future catchment conditions. The study underscores the importance of integrated land-use planning, catchment monitoring, and sustainable reservoir management strategies to support long-term water resource sustainability.

Scientific Reports
Kogi State University (NG), Federal University of Technology (NG), Bowen University (NG)
Openalex Percentile: Top 17%
Groundwater and Watershed Analysis
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