Mangrove forests are vital to coastal protection, biodiversity support, and climate regulation. In the Niger Delta, these ecosystems are increasingly threatened by oil spill incidents linked to intensive petroleum activities. This study investigates the extent of mangrove degradation between 1986 and 2022 in the lower Niger Delta, specifically the region between the San Bartolomeo and Imo Rivers, using remote sensing and machine learning. Landsat 5 TM (1986) and Landsat 8 OLI (2022) imagery were classified using the Support Vector Machine (SVM) algorithm. Classification accuracy was high, with overall accuracies of 98% (1986) and 99% (2022) and Kappa coefficients of 0.97 and 0.98. Healthy mangrove cover declined from 2804.37 km² (58%) to 2509.18 km² (52%), while degraded mangroves increased from 72.03 km² (1%) to 327.35 km² (7%), reflecting a 354.46% rise. Water bodies expanded by 101.17 km² (5.61%), potentially due to dredging, erosion, and sea-level rise. Built-up areas declined from 131.85 km² to 61.14 km², possibly reflecting socio-environmental displacement. Statistical analyses, including Chi-square (χ² = 1091.33, p < 0.001) and Kendall’s Tau (τ = 1, p < 0.001), showed strong correlations between oil spills and mangrove degradation. From 2012 to 2022, over 21,914 barrels of oil were spilled, with only 38% recovered. Although paired t-tests and ANOVA results indicated no statistically significant changes at broad scales, localized ecological shifts remain severe. These findings highlight the urgent need for integrated environmental policies and restoration efforts to mitigate mangrove loss and enhance sustainability in the Niger Delta.

1.         Spalding M. World Atlas of Mangroves. Washington DC; 2010.

2.         Alongi DM. Mangroves among the world’s most threatened ecosystems. Bioscience. 2015; 65(9): 947-951.

3.         Ohimain E. Mangroves of the Niger Delta: Their Importance, Threats, and Possible Restoration. Wetland Science & Practice. 2016; 33(4): 110-121. doi: 10.1672/ucrt083-266

4.         Giri C, Pengrahm S, Nightingale J, Longley S. Losses and gains in coastal biomass within the Indo-Pacific region from 1990 to 2008. Environmental monitoring and assessment. 2011; 177(1-4): 143-160.

5.         Richards DR, Friess DA. Rates and drivers of mangrove deforestation and conversion in Southeast Asia. In: Tropical Mangrove Ecosystems. Springer, Cham; 2015.

6.         Alongi DM. Impact of Global Change on Nutrient Dynamics in Mangrove Forests. Forests. 2018; 9(10): 596. doi: 10.3390/f9100596

7.         Farnsworth EA, Ellison JC. The global conservation status of mangroves. Ambio. 1997; 26(6): 328-334.

8.         Ohimain EI. Benefits and Threats of Biodiversity Conservation in Stubbs Creek Forest Reserve, Nigeria. In: Biodiversity in Africa: Potentials, Threats and Conservation. Springer; 2022.

9.         Onuh PA, Omenma TJ, Onyishi CJ, et al. Artisanal refining of crude oil in the Niger Delta: A challenge to clean-up and remediation in Ogoniland. Local Economy: The Journal of the Local Economy Policy Unit. 2021; 36(6): 468-486. doi: 10.1177/02690942211071075

10.      Efenakpo OD, Chris DI, Onuchukwu NC, et al. Illegal Crude Oil Refining and its Implications on the Niger Delta’s Ecosystem. In: Proceedings of the Conference of Ecological Society of Nigeria (ECOSON) titled Natural Ecosystem Sustainability in the 21st Century; 2022.

11.      Hansen MC, Potapov PV, Moore R, et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science. 2013; 342(6160): 850-853. doi: 10.1126/science.1244693

12.      Lillesand TM, Kiefer RW. Remote Sensing and Image Interpretation, 7th ed. Wiley, New York; 2015.

13.      Deng N, Tian Y, Zhang C. Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions. CRC Press; 2012.

14.      Pal M, Mather PM. Support Vector Machines for classification in remote sensing. International Journal of Remote Sensing. 2005; 26(5): 1007-1011. doi: 10.1080/01431160512331314083

15.      Mountrakis G, Adam E, Clerke JF. Support Vector Machines in remote sensing: A review. Journal of Applied Remote Sensing. 2011; 4(1): 041001.

16.      Xiong Y, Zhang Z, Chen F. Comparison of artificial neural network and Support Vector Machine methods for urban land use/cover classifications from remote sensing images A Case Study of Guangzhou, South China. In: Proceedings of the 2010 International Conference on Computer Application and System Modeling (ICCASM 2010); 2010.

17.      Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. In: Proceedings of the IOP Conference Series: Earth and Environmental Science; 2000.

18.      Rosmasita, Siregar VP, Agus SB, et al. An object-based classification of mangrove land cover using Support Vector Machine Algorithm. IOP Conference Series: Earth and Environmental Science. 2019; 284(1): 012024. doi: 10.1088/1755-1315/284/1/012024

19.      Camps-Valls G, Gomez-Chova L, Calpe-Maravilla J, et al. Robust support vector method for hyperspectral data classification and knowledge discovery. IEEE Transactions on Geoscience and Remote Sensing. 2004; 42(7): 1530-1542. doi: 10.1109/tgrs.2004.827262

20.      Darmawan S, Sari DK, Wikantika K, et al. Identification before-after Forest Fire and Prediction of Mangrove Forest Based on Markov-Cellular Automata in Part of Sembilang National Park, Banyuasin, South Sumatra, Indonesia. Remote Sensing. 2020; 12(22): 3700. doi: 10.3390/rs12223700

21.      Shao Y, Lunetta RS. Comparison of Support Vector Machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing. 2012; 70: 78-87. doi: 10.1016/j.isprsjprs.2012.04.001

22.      Ozigis MS, Kaduk JD, Jarvis CH. Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: a case site within the Niger Delta region of Nigeria. Environmental Science and Pollution Research. 2018; 26(4): 3621-3635. doi: 10.1007/s11356-018-3824-y

23.      Alongi DM. Carbon sequestration in mangrove forests. Carbon Management. 2012; 3(3): 313-322. doi: 10.4155/cmt.12.20

24.      Ite AE, Ibok UJ, Ite MU, Petters SW. Petroleum Exploration and Production: Past and Present Environmental Issues in the Nigeria’s Niger Delta. American Journal of Environmental Protection. 2013; 1(4): 78-90. doi: 10.12691/env-1-4-2

25.      Ugochukwu CNC, Ertel J. Negative Impacts of Oil Exploration on Biodiversity Management in the Niger De Area of Nigeria. Impact Assessment and Project Appraisal. 2008; 26: 139-147.

26.      Numbere AO. Application of GIS and remote sensing towards forest resource management in mangrove forest of Niger Delta. Natural Resources Conservation and Advances for Sustainability; 2022.

27.      USGS. EarthExplorer. United States Geological Survey. USGS; 2022.

28.      Roy DP, Wulder MA, Loveland TR, et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment. 2014; 145: 154-172. doi: 10.1016/j.rse.2014.02.001

29.      Wulder MA, Loveland TR, Roy DP, et al. Current status of Landsat program, science, and applications. Remote Sensing of Environment. 2019; 225: 127-147. doi: 10.1016/j.rse.2019.02.015

30.      Congalton RG, Green K. Assessing the Accuracy of Remotely Sensed Data. CRC Press; 2019.

31.      Chander G, Markham BL, Helder DL. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment. 2009; 113(5): 893-903. doi: 10.1016/j.rse.2009.01.007

32.      Markham BL, Helder DL. Forty-year calibrated record of earth-reflected radiance from Landsat: A review. Remote Sensing of Environment. 2012; 122: 30-40. doi: 10.1016/j.rse.2011.06.026

33.      Chavez PS. Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering & Remote Sensing. 1996; 62(9): 1025-1036.

34.      Song C, Woodcock CE, Seto KC, et al. Classification and change detection using Landsat TM data. Remote Sensing of Environment. 2001; 75(2): 230-244.

35.      Lillesand T, Kiefer RW, Chipman J. Remote Sensing and Image Interpretation. Wiley; 2015.

36.      Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing. 2007; 28(5): 823-870. doi: 10.1080/01431160600746456

37.      Veraverbeke S, Somers B, Verstraeten WW. A time-integrated MODIS burn severity index based on aerosol absorption. Remote Sensing of Environment. 2014; 148: 70-79.

38.      Singh A. Review Article Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing. 1989; 10(6): 989-1003. doi: 10.1080/01431168908903939

39.      Cohen J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Lawrence Erlbaum Associates; 1988.

40.      Student. The Probable Error of a Mean. Biometrika. 1908; 6(1): 1. doi: 10.2307/2331554

41.      Fisher RA. Statistical Methods for Research Workers. Oliver & Boyd; 1925.

42.      Montgomery DC, Peck EA, Vining GG. Introduction to Linear Regression Analysis, 5th ed. Wiley; 2012.

43.      Kruskal WH, Wallis WA. Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association. 1952; 47(260): 583-621. doi: 10.1080/01621459.1952.10483441

44.      Pearson KX. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. 1900; 50(302): 157-175. doi: 10.1080/14786440009463897

45.      Kendall MG. A new measure of rank correlation. Biometrika. 1938; 30(1-2): 81-93. doi: 10.1093/biomet/30.1-2.81

46.      Chen Y, Todd AS, Murphy MH, et al. Anticipated Water Quality Changes in Response to Climate Change and Potential Consequences for Inland Fishes. Fisheries. 2016; 41(7): 413-416. doi: 10.1080/03632415.2016.1182509

47.      Ohimain EI, Eteh D. Canalization induced topographic, hydrologic, and land use changes in Olero Creek, Benin River owing to petroleum exploration activities. Environmental Monitoring and Assessment. 2021; 193(8). doi: 10.1007/s10661-021-09319-0

48.      Ohimain EI, Bamidele JF, Omisore OO. The Impacts of Micro-Topographic Changes on Mangroves in the Lower Reaches of the Benin River, Niger Delta. Environmental Research Journal. 2010; 4(1): 167-172. doi: 10.3923/erj.2010.167.172

49.      Adoki A. Trends in vegetation cover changes in Bonny area of the Niger Delta. Journal of Applied Sciences and Environmental Management. 2013; 17(1): 89-103