Abstract
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.
Highlights
Since the beginning of the 20th century, oil and gas supply has constantly increased to satisfy a growing demand worldwide [1,2]
During the past 50 years, oil production residues were accumulated in mud pits, which resulted in contamination of soil by total petroleum hydrocarbons (TPH)
We propose to assess its robustness on field measurements performed at canopy scale and on the airborne images, using the PROSAIL model
Summary
Since the beginning of the 20th century, oil and gas supply has constantly increased to satisfy a growing demand worldwide [1,2]. Oil spills and leakages are of major concern in the onshore domain. They are likely to occur at every step of the production process (i.e., oil extraction, refining, and transportation) contaminating the soil and groundwater and remaining as mud pits after their cessation [6,7,8]. The resulting soil contamination causes important ecological alterations (e.g., landscape fragmentation and habitat loss) [9,10,11,12]. To avoid such consequences, fast and accurate detection and quantification of oil contamination are, necessary
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