Remote Sensing of Spatial and Temporal Mapping of Flare Impacts in the Niger Delta, Nigeria

Year 2025, Volume: 20 Issue: 1, 98 - 115

Barnabas Morakınyo

Abstract

This research focuses on the mapping of spatial and temporal effects of flare on vegetation cover. The data (11 Landsat 5 TM, 49 Landsat 7 ETM+, 27 Landsat 8 OLI-TIRS, and 15 Landsat 9 OLI-TIRS) dated from 10/10/1984 to 17/12/2023 with < 3 % cloud cover was used to study 11 flaring sites in the Niger Delta. Data processing and analysis were carried out using MATLAB codes. Normalized Difference Vegetation Index (NDVI) for Landsat 5 and 7 bands (1-4) and Landsat 8 and 9 bands (2-5) was determined from the atmospherically corrected multispectral bands. The results show that the temporal in NDVI is specific to each site, and that the effect of the flares on the vegetation cover does not majorly depend on the size of facility. Eleme I (-2.71 × 10-5-2.32 × 10-5) and II (-1.740 × 10-4-2.074 × 10-5) presented significant results for a small portion of the area. Umurolu (-1.679 × 10-5-5.868 × 10-5) and Bonny (-3.089 × 10-5-2.423 × 10-5) show significant results for a wider area which could be because of the number of flare stacks within them 4 and 5 respectively. All small and medium facilities show statistically significant results which could be attributed to the rate and volume of gas burning from them. Therefore, it can be concluded that Landsat data can be used to map the spatial and temporal impacts of flare on vegetation cover in the Niger Delta.

Keywords

Remote Sensing, Environmental Science, Thematic Mapping, Environmental Studies, Land Cover

Thanks

The Author is grateful to the USGS for the provision of Landsat data. Many thanks to Jill Schwarz for MATLAB coding and guidance.

References

• Berlanga-Robles CA, Ruiz-Luna A, (2002). Land use mapping and change detection in the coastal zone of northwest Mexico using remote sensing techniques. J. Coastal Resour.18(3):514-522. https://www.jstor.org/stable/4299098
• Butt B, (2018) Environmental indicators and governance. Current Opinion on Environ. Sus. 32: 84-89. https://doi.org/10.1016/j.cosust.2018.05.006
• Campbell JB, (2007) Introduction to remote sensing, 4thEdn. The Guil ford Press, New York. https://istncrg.wordpress.com/wp-content/uploads/2019/09/introduction_to_remote _sensing_www.gisman.ir_.pdf
• Chander G, Markham K, (2003). Revised Landsat 5 TM Radiometric Calibration Procedures and Post Calibration Ranges. IEEE Trans. of Geosci.& Remote Sens.41(11): 2674-2677. https://doi.org/10.1109/TGRS.2003.818464
• Chang J, Liu Q, Wang S, Huang C, (2022) Vegetation Dynamics and Their Influencing Factors in China from 1998 to 2019. Remote Sensing14, 3390. https://doi.org/10.3390/rs14143390
• Chavez PS, (1996) Image-based Atmospheric Corrections-Revisited and Improved. Photogrammetry Engineering & Remote Sensing62(9): 1025-1036. https://www.researchgate.net/publication/ 236769129_Image-Based_Atmospheric_Corrections_-_Revisited_and_Improved
• ChrysopolitouV, ApostolakisA, AvtzisD, AvtzisN, DiamandisS, KemitzoglouD, PapadimosD, PerlerouC,TsiaoussiV, DafisS, (2013) Studies on forest health and vegetation changes in Greece under the effects of climate changes. Biodiv. Conser., 22:1133-1150, https://link.springer.com/article/10.1007/s10531-013-0451-2.
• Epstein J, Payne K Kramer E, (2002) Techniques for mapping suburban sprawl. Photogrammetry Engineering Remote Sensing 68(9):913-918 EROS (2021) Earth Resources Observation and Science (EROS)
• ESRI. (2024) Map of Nigeria, map of Rivers State and map of 11 gas flaring studied sites.https://www.esri.com/en-us/arcgis/products/arcgis-storymaps/contest/overview
• Gessner U, Reinermann S, Asam S, Kuenzer C, (2023) Vegetation Stress Monitor-Assessment of Drought and Temperature-Related Effects on Vegetation in Germany Analyzing MODIS Time Series over 23 Years. Remote Sensing 15, 5428. https://doi.org/10.3390/rs15225428
• Guha S, (2021) A long-term monthly assessment of land surface temperature and normalized difference vegetation index using Landsat data. urbe. Revista Brasileira de Gestão Urbana, v.13, e20200345. https://doi.org/10.1590/2175-3369.013.e20200345
• Guha S, GovilH, DiwanP, (2020) Monitoring LST-NDVI Relationship Using Premonsoon Landsat Datasets. Advan. in Meteo.2020, Article ID 4539684, 15 https://doi.org/10.1155/2020/4539684
• Hu Y, Raza A, Syed, NR, AcharkiS, Ray RL, Hussain S, Dehghanisanij H, Zubair M, Elbeltagi A, (2023) Land Use/Land Cover Change Detection and NDVI Estimation in Pakistan’s Southern Punjab Province. Sustain., 15, 3572,https://doi.org/10.3390/su15043572
• HuaX, RenaH, TanseyK, ZhengaY, GhentD, LiuX, YanL, (2019) Agricultural drought monitoring using European Space Agency Sentinel 3A land surface temperature and normalized difference vegetation index imageries. Agri.& Forest Meteor.279, 107707. https://www.scilit.com/publications/ab36ace649bb150860f2a1b0058149ec
• HuangS, TangL, HupyP, WangY, ShaoG, (2020) A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. Forestry Res. 32(1): 1-6,https://doi.org/10.1007/s11676-020-01155-1
• HueteA, DidanK, MiuraT, RodriguezEP, X Gao, FerreiraLG, (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83 (1-2): 195-213, https://doi.org/10.1016/S0034-4257(02) 00096-2
• IhlenV, (2019) Landsat 8 (L8) Data Users Handbook, Version 5.0. Department of the Interior, U.S. Geological Survey. https://www.usgs.gov/landsat-missions/landsat-8-data-users-handbook Jensen JR, (2005) Introductory digital image processing: a remote sensing perspective, 3rd edn. Prentice Hall, Upper Saddle River Campbell JB (2007) Introduction to remote sensing, 4thedn. The Guilford Press, New York. https://www.scribd.com/document/667034923/JENSEN-J-R-Introductory-Digital-Image-Processing-a-Remote-Sensing-Perspective
• Jansen LJM, Di-Gregorio A, (2004) Obtaining land-use information from a remotely sensed land cover map: results from a case study in Lebanon. Int. J. App. Earth Obser. & Geoin. 5 (2):141-157. https://doi.org/10.1016/j.jag.2004.02.001
• Jensen JR, Cowen DC, (1999) Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogr. Eng. Remote Sensing 65:611-622. https://www.asprs.org/wp-content/uploads/pers/99journal/may/1999_may_611-622.pdf
• JiangL, LiuY, WuS, YangC, (2021) Analyzing ecological Environmental change and associated driving factors in China based on NDVI time series data. Ecol. Indi.129, 107933. https://doi.org/10.1016/j.ecolind.2021.107933
• Kalisa W, Igbawua T, Henchiri M, Ali S, Zhang S, Bai Y, Zhang J, (2019) Assessment of climate impact on vegetation dynamics over East Africa from 1982 to 2015. Sci. Reports 9:16865,https://doi.org/10.1038/s41598-019-53150-0
• KarnieliA, AgamN, PinkerRT, AndersonM, ImhoffML, GutmanGG, PanovN, GoldbergA, (2010) Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations. J. Climate 23: 618-633. https://doi.org/10.1175/2009JCLI2900.1
• KloosS, YuanY, CastelliM, MenzelA, (2021) Agricultural Drought Detection with MODIS Based Vegetation Health Indices in Southeast Germany. Remote Sens.13, 3907. https://doi.org/10.3390/rs13193907
• Liang S, Fang H, Chen M, (2001) Atmospheric Correction of Landsat ETM+ Land Surface Imagery - Part I: Methods. IEEE Transactions on Geoscience and Remote Sensing 39(11): 2490-2498. https://doi.org/10.1109/36.964986
• LavenderSJ, (2016) Monitoring land cover dynamics at varying spatial scales using high to very high resolution optical imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Informa. Sci., Vol. XLI-B8, 23rdISPRS Congress, 12-19 July, Prague, Czech Republic.https://isprs-archives.copernicus.org/articles/XLI-B8/937/2016/isprs-archives-XLI-B8-937-2016.pdf
• Lu W, Liu Y, Wang J, Xu W, Wu W, Liu Y, Zhao B, Li H, Li P, (2020) Global proliferation of offshore gas flaring areas. J. Maps 16(2): 396-404, DOI: 10.1080/17445647.2020.1762773
• Maaharjan, A. (2018) Land use/land cover of Katrimandu valley by using Remote Sensing and GIS. M.Sc. Dissertation submitted to Central Department of Environmental Sciences, Inst. Sci.& Tech., Tribhuvan Uni., Kirtipur, Kathmandu, Nepal. https://www.academia.edu/37299127/ land_use_land_cover_of_kathmandu_valley_by_using_remote_sensing_and_gis
• MolliconeD, AchardF, EvaH, BelwardAS, FedericiS, Lumicisi A, (2003) Land use change monitoring in the Framework of the UNFCCC and its Kyoto Protocol: report on current capabilities of satellite remote sensing technology. European Communities, Luxembourg. EUR, 20867.https://publications.jrc.ec.europa.eu/repository/handle/JRC26418
• MorakinyoBO, (2025b) Geospatial Information for Land Use Planning and Sustainable Management. FUDMA Journal of Sciences (FJS)9(2): 105-118, DOI: https://doi.org/10.33003/fjs-2025-0902-3146
• MorakinyoBO, (2024a) Time Series Analysis from 1984 to 2023 of Earth Observation Satellites Data for Evaluating Changes in Vegetation Cover and Health at Flaring Sites in the Niger Delta, Nigeria. Academic Platform Journal of Natural Hazards and Disaster Management5(2): 76-100, DOI: 10.52114/apjhad.1557231
• MorakinyoBO, (2024b) Determination of Mean Sea Level from 1980 TO 2018 Using Tidal Observation Data, Bonny Primary Port, Nigeria. Science World Journal19(4): 1176-1184.
• Morakinyo BO, (2024c) Analysis from 1980 TO 2018 of Tidal Observation Data for Assessing the Stability of Tidal Constants for Primary Port. FUDMA Journal of Sciences (FJS)8(6): 503-513, DOI: https://doi.org/10.33003/fjs-2024-0806-3043
• MorakinyoBO, (2023c) Detection of Impacts of Gas Flaring in the Environment: Application of Landsat Earth Observation Data. BAZE University J. Entrepreneurship and Interdisciplinary Studies 2(1): 74-89.http://dx.doi.org/10.61955/XQBJFP
• MorakinyoBO, (2023b) Assessments of the impacts of environmental factors on vegetation cover at gas flaring sites in the Niger Delta, Nigeria. Review of Environ. and Earth Sci. 10(1): 8-18.http://dx.doi.org/10.18488/80.v10i1.3390
• MorakinyoBO, LavenderS, AbbottV, (2023a) Detection of potentially gas flaring related pollution on vegetation cover and its health using remotely sensed data in the Niger Delta, Nigeria. Asian Review of Environ.& Earth Sci.10(1): 1-13. https://doi.org/10.20448/arees.v10i1.4407
• MorakinyoBO, LavenderS, AbbottV,. (2022b) Evaluation of factors influencing changes in land surface temperature at gas flaring sites in the Niger Delta, Nigeria. BAZE University J. Entrepreneurship and Interdisciplinary Studies 1(2): 1-19.http://dx.doi.org/10.61955/RIIKUF
• MorakinyoBO, LavenderS, AbbottV, (2022a) Investigation of potential prevailing wind impact on land surface temperature at gas flaring sites in the Niger Delta, Nigeria.Int. J. Environ. A&ndGeoinfor. 9(1): 179-190,https://doi.org/10.30897/ijegeo.968687
• MorakinyoBO, LavenderS, AbbottV,. (2021) The methodology and results from ground validation of satellite observations at gas Flaring Sites in Nigeria.Int. J. Environ. and Geoinformatics 8(3): 290-300, https://doi.org/10.30897/ijegeo.749664
• MorakinyoBO, LavenderS, AbbottV, (2020b) Retrieval of land surface temperature from Earth observation satellites for gas flaring sites in the Niger Delta, Nigeria.Int. J. Environmental Monitoring and Analysis8(3): 59-74. https://doi.org/10.11648/j.ijema.20200803.13
• MorakinyoBO, LavenderS, AbbottV, (2020a) Assessment of uncertainties in the computation of atmospheric correction parameters for Landsat 5 TM and Landsat 7 ETM+ thermal band from atmospheric correction parameter (ATMCORR calculator) British J. Environ. Sci.8(1): 20-30. https://eajournals.org/bjes/vol-8-issue-1-february-2020/assessment-of-uncertainties-in-the-computation-of-atmospheric-correction-parameters-for-landsat-5-tm-and-landsat-7-etm-thermal-band-from-atmospheric-correction-parameter-atmcorr-calculator/
• MorakinyoBO, LavenderS, SchwarzJ, AbbottV,. (2019) Mapping of land cover and estimation of their emissivity values for gas flaring sites in the Niger Delta. British J. Environ. Sci. 7(2): 31-58. https://www.eajournals.org/wp-content/uploads/Mapping-of-land-cover-and-estimation-of-their-emissivity-values-for-gas-flaring-sites-in-the-Niger-Delta.pdf
• MorakinyoBO, (2015) Flaring and pollution detection in the Niger Delta using remote sensing. PhD Thesis, University of Plymouth, Plymouth, United Kingdom.https://dx.doi.org/10.24382/4528 Markham BL, Barker JL, (1986) Landsat MSS and TM post-calibration dynamic ranges, exoatmosphericreflectances and at-satellite temperature. EOSAT Landsat Technical Notes: 3-8.https://www.scirp.org/reference/referencespapers?referenceid=1578935
• Musa,DG., Oruonye, E. D., Anger, R. T., Ojeh, V. N & Delphine, D. (2024) Effect of Gas Flaring on Human Well-Being and Environ. in Obodo-Ugwa, Ndokwa West, Local Government Area, Delta State, Nigeria. J. Eng. & Environ. Sci. 2(1): 000108. https://medwinpublishers.com/JEESc/effect-of-gas-flaring-on-human-well-being-and-environment-in-obodo-ugwa,-ndokwa-west,-local-government-area,-delta-state,-nigeria.pdf
• NASA. (2002) National Aeronautics and Space Administration. Landsat 7 ETM+ Science Data Users Handbook.http://www.landsathandbook.gsfc.nasa.gov/data_prod/prog_sect11_3.html [Accessed 23rdJan. 2022].
• NwaoguLA, OnyezeGOC, (2020) Environmental Impacts of Gas Flaring on Ebocha-Egbema, Niger Delta, Nigeria. Energy & Environ. Res.8(1): 1-11.https://dergipark.org.tr/tr/pub/apjhad/issue/89412/1557231 Odukoya OA, (2006) Oil and sustainable development in Nigeria: A case study of the Niger Delta. Human Ecology 20(4): 249-258.DOI: 10.31901/24566608.2006/20.04.03
• Onosode GO, (2003) Environmental Issues and challenges of the Niger Delta (Perspectives from the Niger Delta Environmental Survey Process) Lagos, Lilybank Property & Trust Limited. https://books.google.com/books/about/Environmental_Issues_and_Challenges_of_t.html?id=MmQ7AQAAIAAJ
• Pastor-Guzman, J., Atkinson, P., Dash, J & Rioja-Nieto, R. (2015) Spatio-temporal variation in mangrove chlorophyll concentration using Landsat 8. Remote Sensing of Environ. 7(11): 14530-14558.http://dx.doi.org/10.3390/rs71114530
• PettorelliN, VikJO, MysterudA, GaillardJM, TuckerCJ, StensethNC, (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends of Ecol.Evol.20(9): 503-510.https://doi.org/10.1016/J.TREE.2005.05.011
• Polat AB, Akcay O, Kontas F, (2024) Drought monitoring in Burdur Lake, Turkey using multi-sensor remote sensing data sets. Ad. Geodesy &Geoinfor. (formerly Geodesy and Cartography) 73(1), article no. e47, DOI:10.24425/agg.2023.146159
• RoßbergT, SchmittM, (2023) A Globally Applicable Method for NDVI Estimation from Sentinel‑1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset. PFG, 91: 171-188.http://dx.doi.org/10.1007/s41064-023-00238-y
• SanterR, CarrereV, DubuissonP, RogerJC, (1999) Atmospheric correction over land for MERIS. Int. J. Remote Sens.20: 1819-1840. https://earth.esa.int/eogateway/documents/20142/37627/ MERIS%20ATBD%202-15
• Şatır O, Berberoğlu S, (2012) Land Use/Cover Classification Techniques Using Optical Remotely Sensed Data in Landscape Planning, Landscape Planning, Dr. Murat Ozyavuz (Ed.), ISBN: 978-953-51-0654-8, InTech. [Online]. http://www.intechopen.com/books/landscape-planning/land-use-cover-classificationtechniques-using-optical-remotely-sensed-data-in-landscape-plannin [Accessed 15th May 2023]
• TianJ, WangL, LiX, GongH, ShiC, ZhongR, LiuX, (2017) Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. Int. J. Appl. Earth Obser.&Geoinfor.61: 22-31. http://dx.doi.org/10.1016/j.jag.2017.05.002
• Umbugala,UD&MorakinyoBO, (2023) Detection of Oil and Gas Platforms in the Niger Delta, Nigeria: Role of Digital Technology in Facilities Management. BAZE University J. Entrep.&Interdis. Stud.2(2): 11-22. https://boldscholar.com/Home/ArticleLists? volID=21&journalID=12
• Vicente-Serrano, S. M., Camarero, J. J., Olano, J. M., Martín-Hernández, N., Peña-Gallardo, M., Tomás-BurgueraM, GazolA, Azorin-MolinaC, BhuyanU, El-KenawyA, (2016) Diverse relationships between forest growth and the normalized difference vegetation index at a global scale. Remote Sens Environ. 187: 14-29.https://doi.org/10.1016%2Fj.rse.2016.10.001 Wei W, Zhang H, Zhou J, Zhou L, Xie B, Li C, (2021) Drought monitoring in arid and semi-arid region based on multi-satellite datasets in Northwest China. Environ Sci & Poll.Res. 28: 51556-51574.https://doi.org/10.1007/s11356-021-14122-y
• Xiao J, Shen Y, Ge J, Tateishi R, Tang C, Liang Y, Huang Z, (2006) Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Lands. Urban Plan75(1-2):69-80.https://doi.org/10.1016/j.landurbplan.2004.12.005
• Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME, (2005) Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multi-temporal Landsat remote sensing. Remote Sens. Environ.98(2-3):317-328. https://doi.org/10.1016/j.rse.2005.08.006