Application of Vegetation Indices for Detection and Monitoring Oil Spills in Ahoada West Local Government Area of Rivers State, Nigeria

Updata:01-01-1970

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By:Jonathan Lisa Erebi, Egirani E. Davidson

DOI: https://doi.org/10.30564/jgr.v6i3.5817

Abstract:

The study evaluated the environmental effects of an oil spill in Joinkrama 4 and Akimima Ahoada West LGA, Rivers State, Nigeria, using various vegetation indices. Location data for the spill were obtained from the Nigeria Oil Spill Detection and Response Agency, and Landsat imagery was acquired from the United States Geological Survey. Three soil samples were collected from the affected area, and their analysis included measuring total petroleum hydrocarbons (TPH), total hydrocarbons (THC), and polycyclic aromatic hydrocarbons (PAH). The obtained data were processed with ArcGIS software, utilizing different vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Atmospheric Resistant Vegetation Index (ARVI), Soil Adjusted Vegetation Index (SAVI), Green Short Wave Infrared (GSWIR), and Green Near Infrared (GNIR). Statistical analysis was performed using SPSS and Microsoft Excel. The results consistently indicated a negative impact on the environment resulting from the oil spill. A comparison of spectral reflectance values between the oil spill site and the non-oil spill site showed lower values at the oil spill site across all vegetation indices (NDVI 0.0665-0.2622, ARVI –0.0495-0.1268, SAVI 0.0333-0.1311, GSWIR –0.183-0.0517, GNIR –0.0104-–0.1980), indicating damage to vegetation. Additionally, the study examined the correlation between vegetation indices and environmental parameters associated with the oil spill, revealing significant relationships with TPH, THC, and PAH. A t-test with a significance level of p < 0.05 indicated significantly higher vegetation index values at the non-oil spill site compared to the oil spill site, suggesting a potential disparity in vegetation health between the two areas. Hence, this study emphasizes the harmful effect of oil spills on vegetation and highlights the importance of utilizing vegetation indices and spectral reflectance analysis to detect and monitor the impact of oil spills on vegetation.

References:

[1] Okpobiri, O., Harry, A.A., 2022. Monitoring and detecting the impact of oil sabotage on land using multispectral imagery. International Journal of Multidisciplinary Research and Publications (IJMRAP). 4(9), 66-74. [2] Kadafa, A.A., 2012. Oil exploration and spillage in the Niger Delta of Nigeria. Civil and Environmental Research. 2(3), 38-51. [3] Okonkwo, C.N.P., Kumar, L., Taylor, S., 2015. The Niger Delta wetland ecosystem: What threatens it and why should we protect it? African Journal of Environmental Science and Technology. 9(5), 451-463. DOI: https://doi.org/10.5897/AJEST2014.1841 [4] Environmental Assessment of Ogoniland [Internet]. Available from:http://www.zaragoza.es/contenidos/medioambiente/onu/issue06/1130-engsum.pdf [5] Oyem, A., (2001). Christian call for action on Nigerian oil spill. Sage-Oxford’s Christian Environmental Group. [6] Egirani, D.E., Shehata, N., Ugwu, I.M., et al., 2021. Exposure, geochemical, and spatial distribution patterns of an oil spill in parts of the Niger Delta Region of Nigeria. Health and Environment. 2(1), 103-117. DOI: https://doi.org/10.25082/HE.2021.01.005 [7] Egobueze, F.E., Rowland, E.D., Ebizimo, D.S., 2022. Multispectral imagery for detection and monitoring of vegetation affected by oil spills and migration pattern in Niger Delta region, Nigeria. World Journal of Advanced Research and Reviews. 15(1), 447-458. DOI: https://doi.org/10.30574/wjarr.2022.15.1.0682 [8] Van der Werff, H., Van der Meijde, M., Jansma, F., et al., 2008. A spatial-spectral approach for visualization of vegetation stress resulting from pipeline leakage. Sensors. 8(6), 3733-3743. DOI: https://doi.org/10.3390/s8063733 [9] Guyot, G., Baret, F., Jacquemoud, S., 1992. Imaging spectroscopy for vegetation studies. Kluwer Academic Publishers: Dordrecht. pp. 145-165. [10]Rowland, E.D., Okpobiri, O., 2021. Floodplain mapping and risks assessment of the Orashi River using remote sensing and GIS in the Niger Delta Region, Nigeria. Journal of Geographical Research. 4(2), 10-16. DOI: https://doi.org/10.30564/jgr.v4i2.3014 [11] Rayment, R.A., 1965. Aspects of the geology of Nigeria—The stratigraphy of the cretaceous and cenozoic deposits. Ibadan University Press: Ibadan. [12] Reijers, T., 2011. Stratigraphy and sedimentology of the Niger Delta. Geologos. 17(3), 133-162. [13]Short, K.C., Stauble, A.J., 1967. Outline of geology of Niger Delta. AAPG Bulletin. 51(5), 761-779. [14]Adewoyin, O.O., Joshua, E.O., Akinwumi, I.I., et al., 2017. Evaluation of geotechnical parameters using geophysical data. Journal of Engineering and Technological Sciences. 49(1), 95-113. [15]Etu–Efeotor, J.O., 1997. Fundamentals of petroleum geology. Paragraphic: Port Harcourt. [16]Jiang, Z., Huete, A.R., Didan, K., et al., 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment. 112(10), 3833-3845. DOI: https://doi.org/10.1016/j.rse.2008.06.006 [17]Chavez, P.S., 1996. Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering and Remote Sensing. 62(9),1025-1035. [18]Rowland, E.D., Omonefe, F., 2022. Environmental monitory and impact assessment of solid waste dumpsite using multispectral imagery in Yenagoa, Bayelsa state, Nigeria. International Journal of Environmental Science and Technology. 19(2), 1007-1024. DOI: https://doi.org/10.1007/s13762-021-03456-2 [19]Rouse, J.W.Jr., Haas, R.H., Schell, J.A., et al., 1973. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation [Internet]. Available from: https://ntrs.nasa.gov/citations/19750020419 [20]Running, S.W., Justice, C.O., Salomonson, V., et al., 1994. Terrestrial remote sensing science and algorithms planned for EOS/MODIS. International Journal of Remote Sensing. 15(17), 3587-3620. DOI: https://doi.org/10.1080/01431169408954346 [21]Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. 25(3), 295-309. [22]Huete, A.R., Hua, G., Qi, J., et al., 1992. Normalization of multidirectional red and NIR reflectances with the SAVI. Remote Sensing of Environment. 41(2-3), 143-154. DOI: https://doi.org/10.1016/0034-4257(92)90074-T [23]Rondeaux, G., Steven, M., Baret, F., 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment. 55(2), 95-107. DOI: https://doi.org/10.1016/0034-4257(95)00186-7v [24]Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N., 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote sensing of Environment. 58(3), 289-298. DOI: https://doi.org/10.1016/S0034-4257(96)00072-7 [25]Kaufman, Y.J., Tanre, D., 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing. 30(2), 261-270. DOI: https://doi.org/10.1109/36.134076 [26]Sripada, R.P., Heiniger, R.W., White, J.G., et al., 2006. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal. 98(4), 968-977. DOI: https://doi.org/10.2134/agronj2005.0200 [27]Adamu, B., Tansey, K., Ogutu, B., 2015. Using vegetation spectral indices to detect oil pollution in the Niger Delta. Remote Sensing Letters. 6(2), 145-154. DOI: https://doi.org/10.1080/2150704x.2015.1015656 [28]Herrmann, I., Karnieli, A., Bonfil, D.J., et al., 2010. SWIR-based spectral indices for assessing nitrogen content in potato fields. International Journal of Remote Sensing. 31(19), 5127-5143. DOI: https://doi.org/10.1080/01431160903283892 [29]Karnieli, A., Kaufman, Y.J., Remer, L., et al., 2001. AFRI—Aerosol free vegetation index. Remote Sensing of Environment. 77(1), 10-21. DOI: https://doi.org/10.1016/S0034-4257(01)00190-0

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