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Ozigis2018_Article_MappingTerrestrialOilSpillImpa.pdf (7.76 MB)

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.

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posted on 2019-09-10, 13:01 authored by Mohammed S. Ozigis, Jorg D. Kaduk, Claire H. Jarvis
Terrestrial oil pollution is one of the major causes of ecological damage within the Niger Delta region of Nigeria and has caused a considerable loss of mangroves and arable croplands since the discovery of crude oil in 1956. The exact extent of landcover loss due to oil pollution remains uncertain due to the variability in factors such as volume and size of the oil spills, the age of oil, and its effects on the different vegetation types. Here, the feasibility of identifying oil-impacted land in the Niger Delta region of Nigeria with a machine learning random forest classifier using Landsat 8 (OLI spectral bands) and Vegetation Health Indices is explored. Oil spill incident data for the years 2015 and 2016 were obtained from published records of the National Oil Spill Detection and Response Agency and Shell Petroleum Development Corporation. Various health indices and spectral wavelengths from visible, near-infrared, and shortwave infrared bands were fused and classified using the machine learning random forest classifier to distinguish between oil-free and oil spill-impacted landcover. This provided the basis for the identification of the best variables for discriminating oil polluted from unpolluted land. Results showed that better results for discriminating oil-free and oil polluted landcovers were obtained when individual landcover types were classified separately as opposed to when the full study area image including all landcover types was classified at once. Similarly, the results also showed that biomass density plays a significant role in the characterization and classification of oil contaminated and oil-free pixels as tree cover areas showed higher classification accuracy compared to cropland and grassland.

Funding

Mohammed Shuaibu Ozigis was supported via a scholarship from the Petroleum Technology Development Fund (PTDF) and National Space Research and Development Agency (NASRDA), Nigeria. We also like to acknowledge the National Oil Spill Detection and Response Agency (NOSDRA) and Shell Petroleum Development Corporation (SPDC) for making the oil spill incident record.

History

Citation

Environmental Science and Pollution Research, 2019, 26(4), pp 3621–3635

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment/Physical Geography

Version

  • VoR (Version of Record)

Published in

Environmental Science and Pollution Research

Publisher

Springer (part of Springer Nature) for EuCheMS Division of Chemistry and the Environment

eissn

1614-7499

Acceptance date

2018-11-21

Copyright date

2018

Available date

2019-09-10

Publisher version

https://link.springer.com/article/10.1007/s11356-018-3824-y

Language

en

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