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

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journal contribution
posted on 2018-04-24, 10:34 authored by Bashir Adamu, Kevin Tansey, Booker Ogutu
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.

History

Citation

International Journal of Remote Sensing, 2018, 39 (11), pp. 3628-3645

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment/GIS and Remote Sensing

Version

  • VoR (Version of Record)

Published in

International Journal of Remote Sensing

Publisher

Taylor & Francis

issn

0143-1161

eissn

1366-5901

Acceptance date

2018-02-14

Copyright date

2018

Available date

2018-04-24

Publisher version

https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1448483

Language

en

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