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Open AccessArticle

Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery

1
Office National d’Études et de Recherches Aérospatiales (ONERA), 31055 Toulouse, France
2
TOTAL S.A., Pôle d’Études et de Recherches de Lacq, 64170 Lacq, France
3
EcoLab, Université de Toulouse, CNRS, INPT, UPS, 31062 Toulouse, France
4
DynaFor, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France
5
TOTAL S.A., Centre Scientifique et Technique Jean-Féger, 64000 Pau, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2241; https://doi.org/10.3390/rs11192241
Received: 9 September 2019 / Revised: 24 September 2019 / Accepted: 25 September 2019 / Published: 26 September 2019
(This article belongs to the Special Issue Monitoring Soil Degradation by Remote Sensing)
Recent remote sensing studies have suggested exploiting vegetation optical properties for assessing oil contamination, especially total petroleum hydrocarbons (TPH) in vegetated areas. Methods based on the tracking of alterations in leaf biochemistry have been proposed for detecting and quantifying TPH under controlled and field conditions. In this study, we expand their use to airborne imagery, in order to monitor oil contamination at a larger scale. Airborne hyperspectral images with very high spatial and spectral resolutions were acquired over an industrial site with oil-contamination (mud pits) and control sites both colonized by Rubus fruticosus L. The method of oil detection exploiting 14 vegetation indices succeeded in classifying the sites in the case of high TPH contamination (overall accuracy ≥ 91.8%). Two methods, based on either the PROSAIL (PROSPECT + SAIL) radiative transfer model or elastic net multiple regression, were also developed for quantifying TPH. Both methods were tested on reflectance measurements in the field, at leaf and canopy scales, and on the image, and achieved accurate predictions of TPH concentrations (RMSE ≤ 3.28 g/kg−1 and RPD ≥ 1.90). The methods were validated on additional sites and open up promising perspectives of operational application for oil and gas companies, with the emergence of new hyperspectral satellite sensors. View Full-Text
Keywords: hyperspectral remote sensing; vegetation; soil contamination; total petroleum hydrocarbons; radiative transfer model; pigment; elastic net regression hyperspectral remote sensing; vegetation; soil contamination; total petroleum hydrocarbons; radiative transfer model; pigment; elastic net regression
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MDPI and ACS Style

Lassalle, G.; Elger, A.; Credoz, A.; Hédacq, R.; Bertoni, G.; Dubucq, D.; Fabre, S. Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery. Remote Sens. 2019, 11, 2241.

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