Detecting onshore hydrocarbon is a major topic for both environmental monitoring and exploration. In this work, a hyperspectral image acquired nearby an old oil extraction site in tropical area is analyzed. The area of interest includes a pit filled with bio-degraded heavy oil, surrounded by herbaceous vegetation and many lagoons.
First, we focused on methodologies that can detect oil pollution in an unsupervised manner. Based on the assumption that such oil pits are rare events in the image, statistical approach for anomalies detection, derived from the Reed-Xiaoli detector, is used. In order to decrease the number false alarms, some a priori knowledge about the spectral signature of the pits and about the background is introduced. This approach succeeds in detecting the pit with very few false alarms.
Hydrocarbon pollution can have an impact on vegetation and leads to change in vegetation (bio)physical parameters (pigments, water content, …), according to species, pollutant type and exposition time . In order to map the polluted area without any a priori knowledge, several un-supervised classification, including an original method of automatic classification combining unmixing approach and SVM (support Vector Machine) are applied and compared. The results are compared with a partial “ground truth map” that has been derived from visual observations on the field, and with areas of stressed vegetation that have been mapped using combination of specific spectral indices. The classification results are consistent with the ground truth map and the retrieved stressed vegetation areas.
Recent studies aim to exploit vegetation hyperspectral signature as an indicator of pipeline leakages and natural oil seepages by detecting changes in reflectance induced by oil exposure. In order to assess the feasibility of the method at larger spatial scale, a study has been carried out in a greenhouse on two tropical (Cenchrus alopecuroides and Panicum virgatum) and a temperate (Rubus fruticosus) species. Plants were grown on contaminated soil during 130 days, with concentrations up to 4.5 and 36 g.kg-1 for heavy metals and C10-C40 hydrocarbons respectively. Reflectance data (350-2500 nm) were acquired under artificial light from 1 to 60 days. All species showed an increase of reflectance in the visible (VIS, 400-750 nm) and short-wave infrared (SWIR, 1300-2500 nm) under experimental contaminants exposure. However, the responses were contrasted in the near-infrared (NIR, 750-1300 nm). 47 normalized vegetation indices were compared between treatments, and the most sensitive to contamination were retained. Same indices showed significant differences between treatments at leaf and plant scales. Indices related to plant pigments, plant water content and red-edge reflectance were particularly sensitive to soil contamination. In order to validate the selection of indices, hyperspectral measurements were performed outdoor at plant scale at the end of the experiment (130 days). Leaf samples were also collected for pigment analysis. Index selected at day 60 were still sensitive to soil contamination after 130 days. Significant changes in plant pigment composition were also observed. This study demonstrates the interest of hyperspectral data for oil exploration and environmental diagnosis.
On-shore, hyperspectral imagery is currently used to detect and measure remotely oil spill extension for environmental purpose and hydrocarbon seepage for petroleum exploration. In this study, variations of hyperspectral signatures of vegetal species have been analyzed at the laboratory scale to detect indirectly the potential impacts on the plants of crude oil seepage and spills in the soil. Experimental study has been performed under greenhouse to simulate the exposure of two species of plants to a co-contamination of hydrocarbons and heavy metals contained in sludge from mud pit. Maize and bramble have been selected for this study since they are cultivated and spontaneous species respectively located in the region of interest. Five levels of exposure were performed over a period of 100 days. Reflectance evolution of each plant was measured with a spectroradiometer from 350 nm to 2500 nm with a dedicated leaf clip. Net morphological impacts were observed for maize with a global reduction of plants and leaves sizes correlated to the level of cocontamination. Hyperspectral measurement on maize revealed a higher reflectance in the absorption wavelength of water at 1450 and 1900 nm due to contamination and water stress. Reflectance in the visible increased at 600 nm (red interval) for bramble plants exposed to co-contamination. Then, the level of reflectance in the NIR decreased between 700 and 800 nm (red-edge) and absorption of water also decreased at 1450 and 1900 nm as described previously for the maize.
Airborne remote sensing appears useful for monitoring oil spill accident or detecting illegal oil discharges. In that context, hyperspectral imagery in the SWIR range shows a high potential to describe oil spills. Indeed reflectance spectra of an oil emulsion layer show a wide variety of shapes according to its thickness or emulsion rate. Although based on laboratory measurements, it seems that these two parameters are insufficient to completely describe them. It appears that the way emulsion is performed leads to different reflectance spectra. Hence this paper will present a model which tends to simulate reflectance spectra of an oil emulsion layer over the sea water. To derive an analytical expression, some approximations and assumptions will be done. The result of this model shows high similarities with laboratory measurements and seems able to simulate most of the shapes of reflectance spectra. It also shows that a key parameter to define the shape of the reflectance spectra is the statistical distribution of water bubbles size in the emulsion. The description of this distribution function, if measurable, should be integrated into the methodology of elaboration of spectral libraries in the future.