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A Spatial-Spectral Approach for Visualization of Vegetation Stress Resulting from Pipeline Leakage
AbstractHydrocarbon leakage into the environment has large economic and environmental impact. Traditional methods for investigating seepages and their resulting pollution, such as drilling, are destructive, time consuming and expensive. Remote sensing is an efficient tool that offers a non-destructive investigation method. Optical remote sensing has been extensively tested for exploration of onshore hydrocarbon reservoirs and detection of hydrocarbons at the Earth’s surface. In this research, we investigate indirect manifestations of pipeline leakage by way of visualizing vegetation anomalies in airborne hyperspectral imagery. Agricultural land-use causes a heterogeneous landcover; variation in red edge position between fields was much larger than infield red edge position variation that could be related to hydrocarbon pollution. A moving and growing kernel procedure was developed to normalzie red edge values relative to values of neighbouring pixels to enhance pollution related anomalies in the image. Comparison of the spatial distribution of anomalies with geochemical data obtained by drilling showed that 8 out of 10 polluted sites were predicted correctly while 2 out of 30 sites that were predicted clean were actually polluted.
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Van derWerff, H.; Van der Meijde, M.; Jansma, F.; Van der Meer, F.; Groothuis, G.J. A Spatial-Spectral Approach for Visualization of Vegetation Stress Resulting from Pipeline Leakage. Sensors 2008, 8, 3733-3743.View more citation formats
Van derWerff H, Van der Meijde M, Jansma F, Van der Meer F, Groothuis GJ. A Spatial-Spectral Approach for Visualization of Vegetation Stress Resulting from Pipeline Leakage. Sensors. 2008; 8(6):3733-3743.Chicago/Turabian Style
Van derWerff, Harald; Van der Meijde, Mark; Jansma, Fokke; Van der Meer, Freek; Groothuis, Gert J. 2008. "A Spatial-Spectral Approach for Visualization of Vegetation Stress Resulting from Pipeline Leakage." Sensors 8, no. 6: 3733-3743.
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