Investigating vegetation spectral reflectance for detecting hydrocarbon pipeline leaks from multispectral data

Bashir Adamu; Kevin Tansey; Michael J. Bradshaw

doi:10.1117/12.2028907

17 October 2013 Investigating vegetation spectral reflectance for detecting hydrocarbon pipeline leaks from multispectral data

Bashir Adamu, Kevin Tansey, Michael J. Bradshaw

Author Affiliations +

Proceedings Volume 8892, Image and Signal Processing for Remote Sensing XIX; 889216 (2013) https://doi.org/10.1117/12.2028907
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

The aim of this paper is to analyse spectral reflectance data from Landsat TM of vegetation that has been exposed to hydrocarbon contamination from oil spills from pipelines. The study is undertaken in an area of mangrove and swamp vegetation where the detection of an oil spill is traditionally difficult to make. We used a database of oil spill records to help identify candidate sites for spectral analysis. Extracted vegetation spectra were compared between polluted and nonpolluted sites and supervised (neural network) classification was carried out to map hydrocarbon (HC) contaminated sites from the sample areas. Initial results show that polluted sites are characterised by high reflectance in the visible (VIS) 0.4μm - 0.7μm, and a lower reflectance in the near-infrared (NIR) 0.7μm - 1.1μm. This suggests that the vegetation is in a stressed state. Samples taken from pixels surrounding polluted sites show similar spectral reflectance values to that of polluted sites suggesting possible migration of HC to the wider environment. Further work will focus on increasing the sample size and investigating the impact of an oil spill on a wider buffer zone around the spill site.

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Bashir Adamu, Kevin Tansey, and Michael J. Bradshaw "Investigating vegetation spectral reflectance for detecting hydrocarbon pipeline leaks from multispectral data", Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 889216 (17 October 2013); https://doi.org/10.1117/12.2028907

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