Rapid identification of oil-contaminated soils using visible near-infrared diffuse reflectance spectroscopy.

In the United States, petroleum extraction, refinement, and transportation present countless opportunities for spillage mishaps. A method for rapid field appraisal and mapping of petroleum hydrocarbon-contaminated soils for environmental cleanup purposes would be useful. Visible near-infrared (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy (DRS) is a rapid, nondestructive, proximal-sensing technique that has proven adept at quantifying soil properties in situ. The objective of this study was to determine the prediction accuracy of VisNIR DRS in quantifying petroleum hydrocarbons in contaminated soils. Forty-six soil samples (including both contaminated and reference samples) were collected from six different parishes in Louisiana. Each soil sample was scanned using VisNIR DRS at three combinations of moisture content and pretreatment: (i) field-moist intact aggregates, (ii) air-dried intact aggregates, (iii) and air-dried ground soil (sieved through a 2-mm sieve). The VisNIR spectra of soil samples were used to predict total petroleum hydrocarbon (TPH) content in the soil using partial least squares (PLS) regression and boosted regression tree (BRT) models. Each model was validated with 30% of the samples that were randomly selected and not used in the calibration model. The field-moist intact scan proved best for predicting TPH content with a validation r2 of 0.64 and relative percent difference (RPD) of 1.70. Because VisNIR DRS was promising for rapidly predicting soil petroleum hydrocarbon content, future research is warranted to evaluate the methodology for identifying petroleum contaminated soils.

Evaluating terrestrial carbon sequestration options for Virginia.

Changes in forest and agricultural land management practices have the potential to increase carbon (C) storage by terrestrial systems, thus offsetting C emissions to the atmosphere from energy production. This study assesses that potential for three terrestrial management practices within the state of Virginia, USA: afforestation of marginal agricultural lands; afforestation of riparian agricultural lands; and changing tillage practices for row crops; each was evaluated on a statewide basis and for seven regions within the state. Lands eligible for each practice were identified, and the C storage potential of each practice on those lands was estimated through a modeling procedure that utilized land-resource characteristics represented in Geographic Information System databases. Marginal agricultural lands' afforestation was found to have the greatest potential (1.4 Tg C yr(-1), on average, over the first 20 years) if applied on all eligible lands, followed by riparian afforestation (0.2 Tg C yr(-1) over 20 years) and tillage conversion (0.1 Tg C yr(-1) over 14 years). The regions with the largest potentials are the Ridge and Valley of western Virginia (due to extensive areas of steep, shallow soils) and in the Mid-Atlantic Coastal Plain in eastern Virginia (wet soils). Although widespread and rapid implementation of the three modeled practices could be expected to offset only about 3.4% of Virginia's energy-related CO(2) emissions over the following 20 years (equivalent to about 8.5% of a Kyoto Treaty-based target), they could contribute to achievement of C-management goals if implemented along with other mitigation measures.

Time-to-Collision estimation from motion based on primate visual processing.

A population coded algorithm, built on established models of motion processing in the primate visual system, computes the time-to-collision of a mobile robot to real-world environmental objects from video imagery. A set of four transformations starts with motion energy, a spatiotemporal frequency based computation of motion features. The following processing stages extract image velocity features similar to, but distinct from, optic flow; "translation" features, which account for velocity errors including those resulting from the aperture problem; and finally, estimate the time-to-collision. Biologically motivated population coding distinguishes this approach from previous methods based on optic flow. A comparison of the population coded approach with the popular optic flow algorithm of Lucas and Kanade against three types of approaching objects shows that the proposed method produces more robust time-to-collision information from a real world input stimulus in the presence of the aperture problem and other noise sources. The improved performance comes with increased computational cost, which would ideally be mitigated by special purpose hardware architectures.