Effects of Environmental Factors on Vegetation Health across Three States in Nigeria

Authors

  • Favour Bright Iyalla

    Department of Biology and Biotechnology, School of Science Laboratory and Technology, University of Port Harcourt, Choba P.M.B. 5323, Nigeria

  • Miracle Uzoma

    Department of Biology and Biotechnology, School of Science Laboratory and Technology, University of Port Harcourt, Choba P.M.B. 5323, Nigeria

  • Keayiabarido Jude

    Department of Biology and Biotechnology, School of Science Laboratory and Technology, University of Port Harcourt, Choba P.M.B. 5323, Nigeria

  • Sobomate Chuku

    Department of Biology and Biotechnology, School of Science Laboratory and Technology, University of Port Harcourt, Choba P.M.B. 5323, Nigeria

  • Emmanuel Bakpo

    Department of Biology and Biotechnology, School of Science Laboratory and Technology, University of Port Harcourt, Choba P.M.B. 5323, Nigeria

  • Aroloye Numbere

    Department of Biology and Biotechnology, School of Science Laboratory and Technology, University of Port Harcourt, Choba P.M.B. 5323, Nigeria; Department of Animal and Environmental Biology, Faculty of Science, University of Port Harcourt, Choba P.M.B. 5323, Nigeria

DOI:

https://doi.org/10.30564/jbr.v7i3.12523
Received: 21 October 2025 | Revised:30 October 2025 | Accepted: 3 November 2025 | Published Online: 28 November 2025

Abstract

Vegetation health plays a critical role in sustaining ecosystem functions, particularly in regions experiencing climatic and environmental pressures. This study examined the effects of environmental factors on vegetation health across three ecologically diverse states in Nigeria, Kaduna, Benue, and Bayelsa, between 2014 and 2024. Using Normalized Difference Vegetation Index (NDVI) derived from satellite data, alongside soil temperature, soil moisture, and nitrogen dioxide (NO₂) concentrations, the study employed descriptive statistics, trend analysis, and multiple linear regression to assess spatial and temporal vegetation patterns and their determinants. Results revealed clear spatial variations in NDVI, with Bayelsa exhibiting the highest mean NDVI (0.528), followed by Benue (0.443), and Kaduna (0.391), reflecting ecological gradients from rainforest to savanna. Bayelsa maintained stable vegetation over time, Kaduna showed low and highly variable NDVI, while Benue displayed moderate seasonal fluctuations. Soil temperature emerged as the most significant predictor of NDVI in Kaduna (p = 0.026) and nearly significant in Bayelsa (p = 0.056), indicating its strong influence across ecological zones. Soil moisture and NO₂ showed no significant effects in any state, likely due to annual averaging and scale limitations. Regression models explained vegetation variability best in Kaduna (R² = 0.531), moderately in Bayelsa (R² = 0.458), and least in Benue (R² = 0.237). The study concludes that environmental variables, particularly temperature, strongly influence vegetation health in savanna regions, while human land-use practices may dominate in transitional zones. It recommends region-specific management strategies, seasonal monitoring of moisture, improved pollution data resolution, and integration of satellite data into vegetation management frameworks.

Keywords:

Environmental Health; Google Earth; Land Cover; Landsat; Ndvi; Vegetation Indices

References

[1] Adenle, A.A., Eckert, S., Adedeji, O.I., et al., 2020. Human-induced land degradation dominance in the Nigerian Guinea Savannah between 2003–2018. Remote Sensing Applications: Society and Environment. 19, 100360. DOI: https://doi.org/10.1016/j.rsase.2020.100360

[2] Kuta, A.A., Grebby, S., Boyd, D.S., et al., 2025. Remote monitoring of the impact of oil spills on vegetation in the Niger Delta, Nigeria. Applied Sciences. 15(1), 338. DOI: https://doi.org/10.3390/app15010338

[3] Numbere, A.O., 2022. Application of GIS and remote sensing towards forest resource management in mangrove forest of Niger Delta. In: Jhariya, M.K., Meena, R.S., Banerjee, A. (Eds.). Natural resources conservation and advances for sustainability. Elsevier: London, UK. pp. 433–459. DOI: https://doi.org/10.1016/B978-0-12-822976-7.00024-7

[4] Onyia, N.N., Balzter, H., Berrio, J.-C., 2018. Normalized Difference Vegetation Vigour Index: A new remote sensing approach to biodiversity monitoring in oil-polluted regions. Remote Sensing. 10(6), 897. DOI: https://doi.org/10.3390/rs10060897

[5] Numbere, A.O., 2019. Mangrove habitat loss and the need for the establishment of conservation and protected areas in the Niger Delta, Nigeria. In: Musarella, C.M., Ortiz, A.C., Canas, R.Q. (Eds.). Habitats of the World-Biodiversity and Threats. IntechOpen: London, UK. DOI: https://doi.org/10.5772/intechopen.89623

[6] Menegbo, E.M., 2024. Assessment of vegetation health index (VHI) using MODIS data in Rivers State, Nigeria. International Journal of Advanced Geosciences. 12(2), 75–79. DOI: https://doi.org/10.14419/1w1pqg42

[7] Idisi, E.B., Lawal, O., Deekor, T.N., 2024. Temporal trends in vegetation health across ecological zones of South-South Region of Nigeria (2000–2020). International Journal of Advanced Multidisciplinary Research and Studies. 4(6), 453–468. DOI: https://doi.org/10.62225/2583049X.2024.4.6.3460

[8] Abam, K.O., Ideriah, T.J.K., Gobo, A.E., et al., 2025. Normalized Difference Vegetation Index (NDVI) as an indicator of bio-remediation efficiency in crude oil-impacted soils in Ogoni-land: A case study of Eleme local government area, Rivers State, Nigeria. African Journal of Empirical Research. 6(3), 143–155. DOI: https://doi.org/10.51867/ajernet.6.3.11

[9] Ayanlade, A., Jeje, O.D., Nwaezeigwe, J.O., et al., 2021. Rainfall seasonality effects on vegetation greenness in different ecological zones. Environmental Challenges. 4, 100144. DOI: https://doi.org/10.1016/j.envc.2021.100144

[10] Mlenga, D.H., Jordaan, A.J., Mandebvu, B., 2019. Integrating standard precipitation index and normalised difference vegetation index for near-real-time drought monitoring in Eswatini. Jàmbá: Journal of Disaster Risk Studies. 11(1), 1–9.

[11] Abubakar, M.L., Thomas, D., Ahmed, M.S., et al., 2024. Assessment of the relationship between land surface temperature and vegetation using MODIS NDVI and LST timeseries data in Kaduna metropolis, Nigeria. FUDMA Journal of Sciences. 8(2), 137–148.

[12] Ologunde, O.H., Kelani, M.O., Biru, M.K., et al., 2025. Land use and land cover changes: A case study in Nigeria. Land. 14(2), 389.

[13] Ezeh, C.U., Igwe, O., Asare, M.Y., et al., 2024. A review of soil erosion modeling in Nigeria using the Revised Universal Soil Loss Equation model. Agrosystems, Geosciences & Environment. 7(1), e20471. Available from: https://acsess.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/agg2.20471

[14] Chapin, F.S., Matson, P.A., Vitousek, P., 2022. Principles of Terrestrial Ecosystem Ecology, 3rd ed. Springer: New York, NY, USA.

[15] Ogunleye, A., Agele, S., 2024. GIS-based characterization of land use, land cover patterns, and microclimate of agricultural and agroforestry landscapes in a rainforest zone of Nigeria. International Journal of Environment and Climate Change. 14(2), 1002–1023. DOI: https://doi.org/10.9734/ijecc/2024/v14i24013

[16] Bonan, G.B., Doney, S.C., 2018. Climate, ecosystems, and planetary futures: The need for an Earth system perspective. Annual Review of Environment and Resources. 43(1), 29–61. DOI: https://doi.org/10.1126/science.aam8328

[17] Aiguobarueghian, O., Oriakhi, O.O., Ighodaro, E.J., 2025. NDVI-Based Assessment of Land Cover Change and Urban Expansion in Benin City, Nigeria (2014–2024). World Journal of Advanced Research and Reviews. 28(1), 972–981. DOI: https://doi.org/10.30574/wjarr.2025.28.1.3518

[18] Adeaga, O., Lawal, O., Adedeji, O., 2022. Assessment of vegetation cover dynamics in the agro-ecological zones of Nigeria. Bulletin of Geography. Physical Geography Series. 22, 19–32.

[19] Goudie, A.S., 2018. Human Impact on the Natural Environment: Past, Present, and Future, 8th ed. Wiley-Blackwell: Hoboken, NJ, USA.

[20] Jose, A., Deepak, K.S., Rajamani, N., 2024. Innovation in agriculture and the environment: a roadmap to food security in developing nations. In: Singh, P., Ao, B., Deka, N., et al. (Eds). Food Security in a Developing World: Status, Challenges, and Opportunities. Cham: Springer Nature Switzerland. 259–281. DOI: https://doi.org/10.1007/978-3-031-57283-8_15

[21] Lv, J., Song, C., Gao, Y., et al., 2025. Simulation and analysis of the long-term impacts of 1.5 ° C global climate pledges on China’s land systems. Science China Earth Sciences. 68(2), 457–472. DOI: https://doi.org/10.1007/s11430-023-1501-9

Downloads

How to Cite

Iyalla, F. B., Uzoma, M., Jude, K., Chuku, S., Bakpo, E., & Numbere , A. (2025). Effects of Environmental Factors on Vegetation Health across Three States in Nigeria. Journal of Botanical Research, 7(3), 1–14. https://doi.org/10.30564/jbr.v7i3.12523

Issue

Article Type

Article