Preprint Article Version 1 NOT YET PEER-REVIEWED

Remote Sensing for Detection and Monitoring of Vegetation Affected by Oil Spills

  1. Department of Geography, University of Leicester, Leicester LE1 7RH, UK
  2. Department of Geography, Modibbo Adama University of Technology, Yola, Nigeria
  3. Department of Geography, University of Southampton, Southampton SO17 1BJ, UK
Version 1 : Received: 22 September 2016 / Approved: 23 September 2016 / Online: 23 September 2016 (06:19:49 CEST)

How to cite: Adamu, B.; Ogutu, B.; Tansey, K. Remote Sensing for Detection and Monitoring of Vegetation Affected by Oil Spills. Preprints 2016, 2016090081 (doi: 10.20944/preprints201609.0081.v1). Adamu, B.; Ogutu, B.; Tansey, K. Remote Sensing for Detection and Monitoring of Vegetation Affected by Oil Spills. Preprints 2016, 2016090081 (doi: 10.20944/preprints201609.0081.v1).

Abstract

This study is aimed at demonstrating application of vegetation spectral techniques for detection and monitoring of 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 (NSS) sites 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 certain level difference between vegetation condition at the SS and the NSS were significant with p-value <0.5 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 GSWIR 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 show 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 is essential for real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in the mangrove forest.

Subject Areas

spectral reflectance; vegetation indices; vegetation; remote sensing; oil spill; mangrove forest; oil pollution; Landsat 8

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