Detection and discrimination of various oil-contaminated soils using vegetation reflectance.

The use of hyperspectral spectroscopy for oil detection recently sparked a growing interest for risk assessment over vegetated areas. In a perspective of image applications, we conducted a greenhouse experiment on a brownfield-established species, Rubus fruticosus L. (bramble), to evaluate the potential of vegetation reflectance to detect and discriminate among various oil-contaminated soils. The species was grown for 32 days on four different soils with mixtures of petroleum hydrocarbons and heavy metals. Additional plants were grown on either uncontaminated control or water-deficient soils for comparison. Repeated reflectance measurements indicated modified spectral signatures under both oil and water-deficit exposure, from leaf to multi-plant scales. The amplitude of the response varied with mixture composition, exposure time, acquisition scale and spectrum region. Reflectance changes were linked to alterations in chlorophyll, carotenoid and water contents using vegetation indices. These indices were used to catch spectral similarities among acquisition scales and to discriminate among treatments using Kendall's coefficient of concordance (W) and regularized logistic regression. Of the 33 vegetation indices tested, 14 were concordant from leaf to multi-plant scales (W > 0.75, p < 0.05) and strongly related to leaf biochemistry (R2 > 0.7). The 14 indices allowed discriminating between each mixture and the control treatment with no or minor confusions (≤5%) at all acquisition scales, depending on exposure time. Some of the mixtures remained difficult to discriminate among them and from the water-deficit treatment. The approach was tested at the canopy scale under natural conditions and performed well for identifying bramble exposed to either one of the experimentally-tested mixtures (90% accuracy) or to uncontaminated soil (83% accuracy). This study provided better understanding of vegetation spectral response to oil mixtures and opens up promising perspectives for future applications.

Homogenization and scattering from heterogeneous media based on finite-difference-time-domain Monte Carlo computations.

We present a method to study the scattering by heterogeneous media based on the two-dimensional (2D), finite-difference-time-domain method and a Monte Carlo algorithm. The inhomogeneities may reach wave-length size and their optical constants are in the visible and infrared domain. The algorithm is used to determine an effective propagation constant in a monodisperse medium from the observation of the energy decay in the medium. The result is compared over a large domain of volume fraction with the Keller and the Foldy-Twersky 2D models to determine the domain of their validity. Then the same approach is applied to homogenize the smallest particles in a bidisperse case and determine when such process is adequate.