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Land Use and Land Cover Change and Its Impact on Carbon Stocks in Kebbi State, Nigeria (2005–2025): Remote Sensing Assessment

Mansur M.A, ., Abdulrahman, A, ., Atiku, M, ., Ambursa, A.S, ., M.A. Augie, Salisu, I.A, ., and H.K. Wele, Corresponding Author, ORCID ID ---

Abstract

Land use and land cover change drives terrestrial carbon fluxes in rapidly developing areas, but Nigeria's Sudan Savanna's spatiotemporal carbon dynamics are poorly researched and spatially characterized. This study examines the impact of landscape change on carbon stock distribution in Kebbi State's 41,500 km2 using remote sensing data (2005-2025) and field-derived carbon densities. Using supervised algorithms, confusion matrices, post-classification comparison, transition matrices, and annual rate computations, Landsat 7/8 and Sentinel-2 images were classified. GIS raster operations and hotspot analysis were used to overlay field- calibrated carbon pools onto land use and land cover rasters to analyze carbon redistribution. Classification accuracy sometimes exceeds scientific standards (87.3–89.1%; Kappa: 0.84–0.86). Farmland grew by 8,330 km2 (20%), whereas dense vegetation decreased by 1,660 km2 (>57% loss). Growth in developed zones tripled (+2,075 km2), while vegetation decreased by 4,120 km2. The transition matrix showed that 40.3% of conversions included sparse vegetation becoming farmland and 22.1% thick vegetation becoming farmless. Annual change rates show farmland growth (+2.63%) and vegetation loss (-5.95% for thick cover). GIS and carbon pool estimations show that land use and land cover change has redistributed 18–24 Mt C, with 65% of losses from woody ecosystems and 45% of gains from tree-retained agricultural areas. These results set standards for national climate reporting, REDD+ readiness, and spatially explicit land- use planning. Strategic agroforestry, vegetation corridor protection, and stability-conscious carbon accounting are needed to balance food security and urban growth with ecological preservation due to the rapid speed of change.

Keywords

Land use changecarbon redistributionremote sensingSudan Savannatransition matrixclimate mitigationspatial analysis

References

Adenle, A. A., Oyebanji, O. O., & Oladipo, O. O. (2021). Agricultural land expansion and food security in West Africa: Drivers, impacts, and policy pathways. Land Use Policy, 108, 105562. https://doi.org/10.1016/j.landusepol.2021.105562 Akinyemi, F. O., & Ifejika Speranza, C. (2024). Land transformation across agroecological zones reveals expanding cropland and settlement at the expense of tree-cover and wetland areas in Nigeria. Geo-spatial Information Science, 27(4), 1–21. https://doi.org/10.1080/10095020.2024.2362759 Angel, S., Parent, J., Civco, D. L., & Blei, A. M. (2011). The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Lincoln Institute of Land Policy. Arowolo, A. O., Deng, X., Olatunji, O. A., & Obayelu, A. E. (2018). Assessing changes in the value of ecosystem services in response to land-use/land-cover dynamics in Nigeria. Science of the Total Environment, 636, 597–609. https://doi.org/10.1016/j.scitotenv.2018.04.268 Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034- 4257(91)90048-B Dimobe, K., Ouédraogo, A., Soma, S., Goetze, D., Porembski, S., & Thiombiano, A. (2018). Predicting the potential impact of climate change on carbon stock in semi-arid West African savannas. Land, 7(4), 124. https://doi.org/10.3390/land7040124 FAO. (2020). Global forest resources assessment 2020: Main report. Food and Agriculture Organization of the United Nations. Federal Ministry of Environment. (2021). Third National Communication of the Federal Republic of Nigeria to the United Nations Framework Convention on Climate Change. Abuja: FMEnv. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201. https://doi.org/10.1016/S0034-4257(01)00338-6 IPCC. (2022). Climate change 2022: Impacts, adaptation and vulnerability. Cambridge University Press. Lambin, E. F., & Meyfroidt, P. (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences, 108(9), 3465– 3472. https://doi.org/10.1073/pnas.1100480108 Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. https://doi.org/10.1016/j.rse.2014.02.015 Sharp, R., Tallis, H., Ricketts, T., Guerry, A., Wood, S., & Chaplin-Kramer, R. (2020). InVEST 3.9.0 user’s guide: Integrated Valuation of Ecosystem Services and Tradeoffs. Stanford University. UN-Habitat. (2022). World cities report 2022: Envisaging the future of cities. United Nations Human Settlements Programme.

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