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

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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-10, Vol.11 (19), p.2241
Main Authors: Lassalle, Guillaume, Elger, Arnaud, Credoz, Anthony, Hédacq, Rémy, Bertoni, Georges, Dubucq, Dominique, Fabre, Sophie
Format: Article
Language:English
Subjects:
Contamination
elastic net regression
Engineering Sciences
False alarms
Hyperspectral imaging
hyperspectral remote sensing
Image acquisition
Industrial pollution
Leaves
Methods
Oil
Oil pollution
Oil spills
Optical properties
Petroleum hydrocarbons
Petroleum production
Physics
pigment
Radiative transfer
radiative transfer model
Remote sensing
Soil contamination
Spectral resolution
total petroleum hydrocarbons
Vegetation
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11192241