Mapping and Analysis of Vegetation Spectral Reflectance in Oil and Gas Seepage Polluted Zones Using Six Vegetation Indices

Chukwudi Andy Okereke, Princecharles C. Anyadiegwu


The growth and health of vegetation may be adversely influenced by oil and gas pollution or leakage.  Thus, when an environment is contaminated with oil and gas pollution, growing vegetation often exhibit signs of stress.  Satellite remote sensing has proven to be an effective tool and approach to detect and monitor vegetation health and status in oil and gas polluted zones.  Previous studies have adopted vegetation indices which are obtained from remotely sensed satellite data to monitor vegetation health.  This study is aimed at demonstrating the potential of vegetation spectral techniques for detecting and monitoring of oil and gas pollution from Landsat 8 OLI/TIRS remotely sensed data.  To determine the influence of oil and gas pollution on vegetation reflectance, few polluted sites were analyzed and their reflectance were compared in all the TM bands against the non – polluted sites.  The mean and standard deviation reflectance of each of the bands in two groups of sites and t – test are calculated to determine if there are any significant differences between the reflectance from the polluted and non – polluted sites.  Thus, the study shows that in all the spectral bands, the vegetation reflectance from polluted and non – polluted areas exhibit small significant difference with a p-value >0.005. To further analyze the impacts of oil and gas on vegetation, six spectral indices including NDVI, SRI, MSAVI2, SAVI, ARVI2 and EVI2 were utilized.  SRI, SAVI and EVI2 showed no significant relationship between polluted and non-polluted areas with a p-value >0.05 higher than the alpha level of 0.05 and the calculated t - test value is lower than the t-critical value of 2.09 while NDVI, MSAVI2 and ARVI2 showed a significant relationship between the polluted and non-polluted areas.

Keywords: oil and gas pollution, Ground Truthing, Vegetation Indices, Landsat 8 OLI/TIRS, Remote Sensing and Vegetation Cover.

DOI: 10.7176/JEES/9-7-05

Publication date:July 31st 2019

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ISSN (Paper)2224-3216 ISSN (Online)2225-0948

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