Marleen F. Noomen

Hyperspectral indices for detecting changes in canopy reflectance as a result of underground natural gas leakage

Natural gas leakage from underground pipelines is known to affect vegetation adversely, probably by displacement of the soil oxygen needed for respiration. This causes changes in plant and canopy reflectance, which may serve as indicators of gas leakage. In this study, a covariance analysis was performed between reflectance indices of maize (Zea mays) and wheat (Triticum aestivum) canopies and oxygen concentrations in a simulated natural gas leak. Twenty‐nine days after oxygen shortage occurred, the reflectance indices had the highest correlation with oxygen concentrations in the soil, for both species. The effect was consistent within species but the absolute values varied between the species. Normalization by adding a constant value to the control index of one species resulted in significant linear regression models for several indices. The indices with the highest regression coefficients were used to predict the oxygen concentration in the soil. This showed that the gas leakage caused reflectance changes up to 0.5 m from the source. As it could not be proven that oxygen shortage was the cause of the reflectance changes, further work is needed to study the side‐effects of gas leakage, such as bacterial oxygen depletion, on plant growth and reflectance.

The effects of high soil CO2 concentrations on leaf reflectance of maize plants

Carbon dioxide gas at higher concentrations is known to kill vegetation and can also lead to asphyxiation in humans and animals. The objective of this study is to test whether soil CO2 concentrations ranging from 2% to 50% can be detected using vegetative spectral reflectance. A greenhouse experiment was performed to measure the reflectance of maize plants growing in soil contaminated with high concentrations of CO2. The correlation between leaf chlorophyll and reflectance in both the red edge and the yellow region was studied using different methods. The method that resulted in the strongest correlation between leaf reflectance and chlorophyll was subsequently used to study the effects of CO2 on plant health. The results showed that the method developed by Cho and Skidmore (2006) was the most accurate in predicting leaf chlorophyll (R 2 of 0.72). This index in combination with a new index proposed in this study—named the yellow edge position or YEP—showed that an increase in CO2 concentration corresponds to a decrease in leaf chlorophyll. Two first derivative water absorption features at 1400 and 1900 nm indicate that a concentration of 50% CO2 decreased leaf water content. Although upscaling to canopy reflectance is necessary, this experiment shows that leaf reflectance can be used to detect high soil CO2 concentrations, particularly halfway through the growing season.

Proximal spectral sensing to monitor phytoremediation of metal-contaminated soils.

Assessment of soil contamination and its long-term monitoring are necessary to evaluate the effectiveness of phytoremediation systems. Spectral sensing-based monitoring methods promise obvious benefits compared to field-based methods: lower cost, faster data acquisition and better spatio-temporal monitoring. This paper reviews the theoretical basis whereby proximal spectral sensing of soil and vegetation could be used to monitor phytoremediation of metal-contaminated soils, and the eventual upscaling to imaging sensing. Both laboratory and field spectroscopy have been applied to sense heavy metals in soils indirectly via their intercorrelations with soil constituents, and also through metal-induced vegetation stress. In soil, most predictions are based on intercorrelations of metals with spectrally-active soil constituents viz., Fe-oxides, organic carbon, and clays. Spectral variations in metal-stressed plants is particularly associated with changes in chlorophyll, other pigments, and cell structure, all of which can be investigated by vegetation indices and red edge position shifts. Key shortcomings in obtaining satisfactory calibration for monitoring the metals in soils or metal-related plant stress include: reduced prediction accuracy compared to chemical methods, complexity of spectra, no unique spectral features associated with metal-related plant stresses, and transfer of calibrations from laboratory to field to regional scale. Nonetheless, spectral sensing promises to be a time saving, non-destructive and cost-effective option for long-term monitoring especially over large phytoremediation areas, and it is well-suited to phytoremediation networks where monitoring is an integral part.

Detection of hydrocarbons in clay soils: A laboratory experiment using spectroscopy in the mid- and thermal infrared

Remote sensing has been used for direct and indirect detection of hydrocarbons. Most studies so far focused on indirect detection in vegetated areas. We investigated in this research the possibility of detecting hydrocarbons in bare soil through spectral analysis of laboratory samples in the short wave and thermal infrared regions. Soil/oil mixtures were spectrally measured in the laboratory. Analysis of spectra showed development of hydrocarbon absorption features as soils became progressively more contaminated. The future application of these results airborne seems to be a challenge as present and future sensors only cover the diagnostic regions to a limited extent.

Evaluating the feasibility of multitemporal hyperspectral remote sensing for monitoring bioremediation

Abstract In recent years, several studies focused on the detection of hydrocarbon pollution in the environment using hyperspectral remote sensing. Particularly the indirect detection of hydrocarbon pollution, using vegetation reflectance in the red edge region, has been studied extensively. Bioremediation is one of the methods that can be applied to clean up polluted sites. So far, there have been no studies on monitoring of bioremediation using (hyperspectral) remote sensing. This study evaluates the feasibility of hyperspectral remote sensing for monitoring the effect of bioremediation over time. Benzene leakage at connection points along a pipeline was monitored by comparing the red edge position (REP) in 2005 and 2008 using HyMap airborne hyperspectral images. REP values were normalized in order to enhance local variations caused by a change in benzene concentrations. 11 out of 17 locations were classified correctly as remediated, still polluted, or still clean, with a total accuracy of 65%. When only polluted locations that were remediated were taken into account, the (users) accuracy was 71%.

Spectral changes in the leaves of barley plant due to phytoremediation of metals—results from a pot study

Abstract This research studied the changes in leaf reflectance spectra (350–2500 nm) due to metal phytoextraction into barley plants grown in metal-spiked soils (3 levels of Cd, Pb, As and their metal-mixture treatments). Growth of barley was adversely affected due to 100 mg As kg-1 and metal-mixture (10 Cd+150 Pb+100 As; mg kg-1) treatments. Metal phytoextraction were in order of: root>straw≥leaves >grains. Results of reflectance spectra of leaves show the influence of As-treatment only, causing spectral changes in visible and infrared domains mostly, as apparent from the significant correlation between leaf-As and leaf-spectra. Chlorophyll and water stress indices and band depths analyses showed significant correlations to leaf-As, and can be used to distinguish metal-stressed plants. Finally, regression models demonstrate the potential use of hyperspectral reflectance data to monitor plant health during phytoremediation process and to estimate leaf-As in barley, particularly in this study.

Integrated Remote Sensing and Geophysical Investigations of the Geodynamic Activities at Lake Magadi, Southern Kenyan Rift

The tectonic lineaments and thermal structure of Lake Magadi, southern Kenyan rift system, were investigated using ASTER data and geophysical methods. Five N-S faults close to known hot springs were identified for geoelectric ground investigation. Aeromagnetic data were employed to further probe faults at greater depths and determine the Curie-point depth. Results indicate a funnel-shaped fluid-filled (mostly saline hydrothermal) zone with relatively low resistivity values of less than 1 Ω-m, separated by resistive structures to the west and east, to a depth of 75 m along the resistivity profiles. There was evidence of saline hydrothermal fluid flow toward the surface through the fault splays. The observed faults extend from the surface to a depth of 7.5 km and are probably the ones that bound the graben laterally. They serve as major conduits for the upward heat flux in the study area. The aeromagnetics spectral analysis also revealed heat source emplacement at a depth of about 12 km. The relative shallowness implies a high geothermal gradient evidenced in the surface manifestations of hot springs along the lake margins. Correlation of the heat source with the hypocenters showed that the seismogenetic zone exists directly above the magmatic intrusion, forming the commencement of geodynamic activities.

Remote sensing of onshore hydrocarbon seepage: problems and solutions

Abstract Optical remote sensing has in the last two decades been extensively tested for the detection of hydrocarbons at the Earths surface. The spectral absorption features of seepage-related hydrocarbons can easily be confused with those of man-made bituminous surfaces such as tarred roads. The characteristic low albedo of bituminous surfaces can, at the same time, easily be confused with other dark surfaces such as shade. This paper presents the results of two pixel-based classifications that have been carried out on hyperspectral imagery acquired over seepage areas. The first classification algorithm is a ‘minimum distance to class means’ (MDC), which is sensitive to spectral absorption features as well as albedo differences. The second algorithm is a ‘spectral angle mapper’ (SAM), which is not sensitive to albedo differences. Both algorithms are applied for the detection of crude oil resulting from macroseepage and an anomalous halo of bare soil resulting from microseepage. The results show that, at best, only 48% and 29% of the pixels that respectively contain crude oil and seepage-related bare soil could be detected, with the inclusion of many false anomalies. Confusion mainly results from the physical characteristics of the anomalies, as these are not unique to seepages. It is concluded that remote sensing of natural hydrocarbon seepages can be improved by image processing algorithms that make use of spatial information.