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Using vegetation spectral indices to detect oil pollution in the Niger Delta

Using vegetation spectral indices to detect oil pollution in the Niger Delta
Using vegetation spectral indices to detect oil pollution in the Niger Delta
Vegetation health and vigour may be affected by oil leakage or pollution. This effect can alter a plant’s behaviour and may be used as evidence for detecting oil pollution in the environment. Satellite remote sensing has been shown to be an effective tool and approach to detect and monitor vegetation health and status in polluted areas. Previous research has used vegetation indices derived from remotely sensed satellite data to monitor vegetation health. This study investigated the potential for using broadband multispectral vegetation indices to detect impacts of oil pollution on vegetation conditions. Twenty indices were explored and evaluated in this study. The indices use data acquired at the visible, near infrared and shortwave infrared wavelengths. Comparative index values from the 37 oil polluted and non-polluted (control) sites show that 12 Broadband multispectral vegetation indices (BMVIs) indicated significant differences (p-value < 0.05) between pre- and post-spill observations. The 12 BMVI values at the polluted sites before and after the spill are significantly different with the ones obtained on the spill event date. The result at the non-polluted (control) sites shows that 11 of the 20 BMVI values did not indicate significant change and remained statistically invariant before and after the spill date (p-value > 0.05). Therefore, it can be stated that, in this study, oil spills seem to result in biophysical and biochemical alteration of the vegetation, leading to changes in reflectance signature detected by these indices. Five spectral indices (normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), adjusted resistant vegetation index (ARVI2), green near infrared (G/NIR) and green shortwave infrared (G/SWIR)) were found to be consistently sensitive to the effects of oil pollution on vegetation and hence could be used to map and monitor oil pollution in vegetated areas.
2150-704X
145-154
Adamu, Bashir
2cdf36a9-7e18-4ed4-94a9-a21d1fb348f4
Tansey, Kevin
a363e5c7-b369-415d-8c38-dcb871109198
Ogutu, Booker
4e36f1d2-f417-4274-8f9c-4470d4808746
Adamu, Bashir
2cdf36a9-7e18-4ed4-94a9-a21d1fb348f4
Tansey, Kevin
a363e5c7-b369-415d-8c38-dcb871109198
Ogutu, Booker
4e36f1d2-f417-4274-8f9c-4470d4808746

Adamu, Bashir, Tansey, Kevin and Ogutu, Booker (2015) Using vegetation spectral indices to detect oil pollution in the Niger Delta. Remote Sensing Letters, 6 (2), 145-154. (doi:10.1080/2150704X.2015.1015656).

Record type: Article

Abstract

Vegetation health and vigour may be affected by oil leakage or pollution. This effect can alter a plant’s behaviour and may be used as evidence for detecting oil pollution in the environment. Satellite remote sensing has been shown to be an effective tool and approach to detect and monitor vegetation health and status in polluted areas. Previous research has used vegetation indices derived from remotely sensed satellite data to monitor vegetation health. This study investigated the potential for using broadband multispectral vegetation indices to detect impacts of oil pollution on vegetation conditions. Twenty indices were explored and evaluated in this study. The indices use data acquired at the visible, near infrared and shortwave infrared wavelengths. Comparative index values from the 37 oil polluted and non-polluted (control) sites show that 12 Broadband multispectral vegetation indices (BMVIs) indicated significant differences (p-value < 0.05) between pre- and post-spill observations. The 12 BMVI values at the polluted sites before and after the spill are significantly different with the ones obtained on the spill event date. The result at the non-polluted (control) sites shows that 11 of the 20 BMVI values did not indicate significant change and remained statistically invariant before and after the spill date (p-value > 0.05). Therefore, it can be stated that, in this study, oil spills seem to result in biophysical and biochemical alteration of the vegetation, leading to changes in reflectance signature detected by these indices. Five spectral indices (normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), adjusted resistant vegetation index (ARVI2), green near infrared (G/NIR) and green shortwave infrared (G/SWIR)) were found to be consistently sensitive to the effects of oil pollution on vegetation and hence could be used to map and monitor oil pollution in vegetated areas.

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2150704X.2015.1015656 - Other
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Accepted/In Press date: 31 January 2015
Published date: 26 February 2015
Organisations: Global Env Change & Earth Observation

Identifiers

Local EPrints ID: 374827
URI: https://eprints.soton.ac.uk/id/eprint/374827
ISSN: 2150-704X
PURE UUID: a93ed056-f272-4690-85e0-96ff910e9394
ORCID for Booker Ogutu: ORCID iD orcid.org/0000-0002-1804-6205

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Date deposited: 03 Mar 2015 12:33
Last modified: 06 Jun 2018 12:35

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Contributors

Author: Bashir Adamu
Author: Kevin Tansey
Author: Booker Ogutu ORCID iD

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