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Title: Detection and mapping of terrestrial oil spill impact using remote sensing data in combination with machine learning methods : a case site within the Niger Delta Region of Nigeria
Author: Ozigis, Mohammed S. J.
ISNI:       0000 0004 9347 0588
Awarding Body: University of Leicester
Current Institution: University of Leicester
Date of Award: 2020
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In this novel research, the use of optical and SAR images for separate and joint investigations were explored, with the aid of machine learning classifiers for detecting hydrocarbon spill impact on cropland, grassland and dense forested vegetation types. Optical image spectral bands across the Visible, Near-Infrared and Shortwave-Infrared spectrum, various vegetation health indices (including NDVI, NDWI, LAI and SAVI) and SAR derived variables (including backscatter, coherence and textural variables) were used to detect and map oil spill sites within cropland, grassland and TCA vegetation types. Results generally showed that the integration of multi-frequency L, C and X band SAR in the wet (summer) season yielded the best overall classification accuracy in discrimination of polluted and oil-free vegetation types. An overall accuracy (OA) of 82.3%, 66.67% and 70.93% were obtained for Cropland, Grassland and Tree Cover Areas (TCA) vegetation types, respectively. The accuracies recorded were significantly better (P > 0.05) than when the spill was classified using optical imagery only and when integrated optical and SAR image variables are classified. These results were further corroborated by the multi-temporal Sentinel – 1 backscatter analysis, which showed that mean backscatter difference between polluted and oil-free cropland and grassland vegetation are significantly different (P > 0.05) in the wet season than in the dry season. Furthermore, the new fuzzy forest method used for multi-frequency (C and X band) SAR and Optical variable reduction was able to achieve a good result in addressing high dimensionality in cropland and grassland vegetation. This research demonstrates that SAR based monitoring of petroleum hydrocarbon impacts on vegetation is feasible and has high potential for oil pipeline monitoring and facility management. The research also presented a new paradigm into terrestrial oil spill detection, which largely can replace the use of optical data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: Terrestrial Oil Spill Impact ; Remote Sensing ; Machine Learning ; Niger Delta ; Nigeria