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Remote sensing for detection and monitoring of vegetation affected by oil spills

Remote sensing for detection and monitoring of vegetation affected by oil spills
Remote sensing for detection and monitoring of vegetation affected by oil spills
This study is aimed at demonstrating the application of vegetation spectral techniques for detection and monitoring of the impact of oil spills on vegetation. Vegetation spectral reflectance from Landsat 8 data were used in the calculation of five vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), adjusted resistant vegetation index 2 (ARVI2), green-infrared index (G-NIR) and green-shortwave infrared (G-SWIR) from the spill sites (SS) and non-spill sites (NSS) in 2013 (pre-oil spill), 2014 (oil spill date) and 2015 (post-oil spill) for statistical comparison. The result shows that NDVI, SAVI, ARVI2, G-NIR and G-SWIR indicated a certain level of significant difference between vegetation condition at the SS and the NSS in December 2013. In December 2014 vegetation conditions indicated higher level of significant difference between the vegetation at the SS and NSS as follows where NDVI, SAVI and ARVI2 with p-value 0.005, G-NIR – p-value 0.01 and G-SWIR p-value 0.05. Similarly, in January 2015 a very significant difference with p-value <0.005. Three indices NDVI, ARVI2 and G-NIR indicated highly significant difference in vegetation conditions with p-value <0.005 between December 2013 and December 2014 at the same sites. Post-spill analysis shows that NDVI and ARVI2 indicated low level of significance difference p-value <0.05 suggesting subtle change in vegetation conditions between December 2014 and January 2015. This technique may help with the real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in mangrove forests.
0143-1161
3628-3645
Adamu, Bashir
cd2c2985-b01c-4585-a16f-42eca66c05ea
Tansey, Kevin
4c6be51e-d4a0-467c-aaf6-22ced8dc1141
Ogutu, Booker
4e36f1d2-f417-4274-8f9c-4470d4808746
Adamu, Bashir
cd2c2985-b01c-4585-a16f-42eca66c05ea
Tansey, Kevin
4c6be51e-d4a0-467c-aaf6-22ced8dc1141
Ogutu, Booker
4e36f1d2-f417-4274-8f9c-4470d4808746

Adamu, Bashir, Tansey, Kevin and Ogutu, Booker (2018) Remote sensing for detection and monitoring of vegetation affected by oil spills. International Journal of Remote Sensing, 39 (11), 3628-3645. (doi:10.1080/01431161.2018.1448483).

Record type: Article

Abstract

This study is aimed at demonstrating the application of vegetation spectral techniques for detection and monitoring of the impact of oil spills on vegetation. Vegetation spectral reflectance from Landsat 8 data were used in the calculation of five vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), adjusted resistant vegetation index 2 (ARVI2), green-infrared index (G-NIR) and green-shortwave infrared (G-SWIR) from the spill sites (SS) and non-spill sites (NSS) in 2013 (pre-oil spill), 2014 (oil spill date) and 2015 (post-oil spill) for statistical comparison. The result shows that NDVI, SAVI, ARVI2, G-NIR and G-SWIR indicated a certain level of significant difference between vegetation condition at the SS and the NSS in December 2013. In December 2014 vegetation conditions indicated higher level of significant difference between the vegetation at the SS and NSS as follows where NDVI, SAVI and ARVI2 with p-value 0.005, G-NIR – p-value 0.01 and G-SWIR p-value 0.05. Similarly, in January 2015 a very significant difference with p-value <0.005. Three indices NDVI, ARVI2 and G-NIR indicated highly significant difference in vegetation conditions with p-value <0.005 between December 2013 and December 2014 at the same sites. Post-spill analysis shows that NDVI and ARVI2 indicated low level of significance difference p-value <0.05 suggesting subtle change in vegetation conditions between December 2014 and January 2015. This technique may help with the real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in mangrove forests.

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More information

Accepted/In Press date: 14 February 2018
e-pub ahead of print date: 8 March 2018
Published date: 2018

Identifiers

Local EPrints ID: 418853
URI: https://eprints.soton.ac.uk/id/eprint/418853
ISSN: 0143-1161
PURE UUID: 40b05d3a-7df9-4338-8ab4-338c575a53b4
ORCID for Booker Ogutu: ORCID iD orcid.org/0000-0002-1804-6205

Catalogue record

Date deposited: 23 Mar 2018 17:30
Last modified: 14 Mar 2019 01:38

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