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
| Autor: | Jörg Kaduk, Claire Jarvis, Mohammed S. Ozigis |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Satellite Imagery
Variable importance Health Toxicology and Mutagenesis Vegetation health indices Nigeria 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Grassland Machine Learning chemistry.chemical_compound Rivers Environmental Chemistry Petroleum Pollution 0105 earth and related environmental sciences Biomass (ecology) geography geography.geographical_feature_category business.industry Oil spill General Medicine Vegetation Spectral bands Landcover Pollution Random forest chemistry Environmental science Petroleum Artificial intelligence Arable land Mangrove business computer Research Article Environmental Monitoring |
| Zdroj: | Environmental Science and Pollution Research International |
| ISSN: | 1614-7499 0944-1344 |
| DOI: | 10.1007/s11356-018-3824-y |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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