Hyperspectral remote sensing of evaporate minerals and associated sediments in Lake Magadi area, Kenya

Pleistocene to present evaporitic lacustrine sediments in Lake Magadi, East African Rift Valley, Kenya were studied and mapped using spectral remote sensing methods. This approach incorporated surface mineral mapping using space-borne hyperspectral Hyperion imagery together with laboratory analysis, including visible, near-infrared diffuse reflectance spectroscopy (VNIR) measurements and X-ray diffraction for selected rock and soil samples of the study area. The spectral signatures of Magadiite and Kenyaite, which have not been previously reported, were established and the spectral signatures of trona, chert series, volcanic tuff and the High Magadi bed were also analyzed.Image processing techniques, MNF (Minimum Noise Fraction) and MTMF (Mixture Tuned Matched Filtering) using a stratified approach (image analysis with and without the lake area), were used to enhance the mapping of evaporates. High Magadi beds, chert series and volcanic tuff were identified from the Hyperion image with an overall mapping accuracy of 84.3%. Even though, the spatial distribution of evaporites and sediments in Lake Magadi area change in response to climate variations, the mineralogy of this area has not been mapped recently. The results of this study shows the usefulness of the hypersspectral remote sensing to map the surface geology of this kind of environment and to locate promising sites for industrial open-pit trona mining in a qualitative and quantitative manner.     

Troposphere gradients from the ECMWF in VLBI analysis

Modeling path delays in the neutral atmosphere for the analysis of Very Long Baseline Interferometry (VLBI) observations has been improved significantly in recent years by the use of elevation-dependent mapping functions based on data from numerical weather models. In this paper, we present a fast way of extracting both, hydrostatic and wet, linear horizontal gradients for the troposphere from data of the European Centre for Medium-range Weather Forecasts (ECMWF) model, as it is realized at the Vienna University of Technology on a routine basis for all stations of the International GNSS (Global Navigation Satellite Systems) Service (IGS) and International VLBI Service for Geodesy and Astrometry (IVS) stations. This approach only uses information about the refractivity gradients at the site vertical, but no information from the line-of-sight. VLBI analysis of the CONT02 and CONT05 campaigns, as well as all IVS-R1 and IVS-R4 sessions in the first half of 2006, shows that fixing these a priori gradients improves the repeatability for 74% (40 out of 54) of the VLBI baseline lengths compared to fixing zero or constant a priori gradients, and improves the repeatability for the majority of baselines compared to estimating 24-h offsets for the gradients. Only if 6-h offsets are estimated, the baseline length repeatabilities significantly improve, no matter which a priori gradients are used.     

Exploring hydroclimatic change disparity via the Budyko framework

The Budyko framework characterizes landscape water cycles as a function of climate. We used this framework to identify regions with contrasting hydroclimatic change during the past 50 years in Sweden. This analysis revealed three distinct regions: the mountains, the forests, and the areas with agriculture. Each region responded markedly different to recent climate and anthropogenic changes, and within each region, we identified the most sensitive subregions. These results highlight the need for regional differentiation in climate change adaptation strategies to protect vulnerable ecosystems and freshwater resources. Further, the Budyko curve moved systematically towards its water and energy limits, indicating augmentation of the water cycle driven by changing vegetation, climate and human interactions. This finding challenges the steady state assumption of the Budyko curve and therefore its ability to predict future water cycles. Copyright © 2013 John Wiley & Sons, Ltd.     

Early contamination of European flounder (Platichthys flesus) by PCDD/Fs and dioxin-like PCBs in European waters

Contamination levels and profiles of 7 polychlorinated-p-dioxins, 10 polychlorinated furans (PCDD/Fs) and 12 dioxin-like polychlorinated biphenyls (dl-PCBs) were investigated in juvenile European flounder (Platichthys flesus) captured in different nursery areas in the northeastern Atlantic coast across its geographical distribution range. The toxic equivalent concentrations (WHO-TEQ_fish) were also determined in order to evaluate which P. flesus population was more exposed to dioxin-like toxicity. Juveniles caught in the Sørfjord (Norway) showed the lowest WHO-TEQ_fish concentration (0.052 pg WHO-TEQ_fish g⁻¹ wet weight) whereas the highest value was observed in fish from the Wadden Sea (The Netherlands; 0.291 pg WHO-TEQ_fish g⁻¹ ww), mainly due to the greater contribution of 2,3,7,8-tetrachlorodibenzo-p-dioxin, the most toxic congener. Nonetheless, when comparing the results with existent tissue residue-based toxicity benchmarks, no adverse effects resulting from PCDD/Fs and dl-PCBs are expected to occur in flounder from the studied systems.     

Empirical prediction of coseismic landslide dam formation

In this study we develop an empirical method to estimate the volume threshold for predicting coseismic landslide dam formation using landscape parameters obtained from digital elevation models (DEMs). We hypothesize that the potential runout and volume of landslides, together with river features, determine the likelihood of the formation of a landslide dam. To develop this method, a database was created by randomly selecting 140 damming and 200 non‐damming landslides from 501 landslide dams and > 60 000 landslides induced by the M_w 7.9 2008 Wenchuan earthquake in China. We used this database to parameterize empirical runout models by stepwise multivariate regression. We find that factors controlling landslide runout are landslide initiation volume, landslide type, internal relief (H) and the H/L ratio (between H and landslide horizontal distance to river, L). In order to obtain a first volume threshold for a landslide to reach a river, the runout regression equations were converted into inverse volume equations by taking the runout to be the distance to river. A second volume threshold above which a landslide is predicted to block a river was determined by the correlation between river width and landslide volume of the known damming landslides. The larger of these two thresholds was taken as the final damming threshold. This method was applied to several landslide types over a fine geographic grid of assumed initiation points in a selected catchment. The overall prediction accuracy was 97.4% and 86.0% for non‐damming and damming landslides, respectively. The model was further tested by predicting the damming landslides over the whole region, with promising results. We conclude that our method is robust and reliable for the Wenchuan event. In combination with pre‐event landslide susceptibility and frequency–size assessments, it can be used to predict likely damming locations of future coseismic landslides, thereby helping to plan emergency response. Copyright © 2014 John Wiley & Sons, Ltd.     

Stratigraphic filtering and source penetration depth

Seismic exploration underneath highly heterogeneous layers such as basalt flows is possible by lowering the principal source frequency. Unfortunately this also reduces resolution. Wave‐localization theory is a multiple scattering theory that can be used to study stratigraphic filtering in chaotic lithologies. It predicts the apparent attenuation due to scattering of a plane wave traversing a layer with high velocity fluctuations. It can therefore predict the optimum principle source frequency in the trade‐off between loss of resolution and increased penetration depth. We show how this can be done with the help of a few statistical parameters derived from a well‐log analysis; namely, the average background velocity, the expected standard deviation in the velocity fluctuations, the typical scale length of the heterogeneities and the thickness of the basalt layer. In the likely situation that no local well logs exist, a multitude of scenarios can easily be examined at low cost.     

Seismic buffer recognition using wavelet-based features and neural classification

One of the first operations in a seismic signal processing system applied to earthquake data is to distinguish between valid and invalid records. Since valid signals are characterized by a combination of their time and frequency properties, wavelets are natural candidates for describing seismic features in a compact way. This paper develops a seismic buffer pattern recognition technique, comprising wavelet-based feature extraction, feature selection based on the mutual information criterion, and neural classification based on feedforward networks. The ability of the wavelet transform to capture discriminating information from seismic data in a small number of features is compared with alternative feature reduction techniques, including statistical moments. Three different variations of the wavelet transform are used to extract features: the discrete wavelet transform, the single wavelet transform and the continuous wavelet transform. The mutual information criterion is employed to select a relatively small set of wavelets from the time–frequency grid. Firstly, it is determined whether wavelets can capture more informative data in an equal number of features compared with other features derived from raw data. Secondly, wavelet-based features are compared with features selected based on prior knowledge of class differences. Thirdly, a technique is developed to optimize wavelet features as part of the neural network training process, by using the wavelet neural network architecture. The automated classification techniques developed in this paper are shown to perform similarly to human operators trained for this function. Wavelet-based techniques are found to be useful, both for preprocessing of the raw data and for extracting features from the data. It is demonstrated that the definition of wavelet features can be optimized using the classification wavelet network architecture.     

Hydrophobic and electrostatic parameters in bacterial adhesion

Recently it has been shown that the initial stages of bacterial adhesion to a model-surface of sulphated polystyrene can best be described using hydrophobic and electrostatic parameters. In the present study it is tested whether these parameters can generally be applied to predict bacterial adhesion by using (i) glass, as a model for hydrophilic and natural surfaces of silicates and oxides, (ii) polystyrene coated with proteins, as a model for a surface coated with an organic layer, and (iii) river Rhine sediment, as an example of a natural surface. Adhesion to glass was dominated by electrostatic interaction, whereas adhesion to polystyrene coated with various types of proteins depended on the surface characteristics of the bacteria and the type of protein. By relating Van der Waals interactions to hydrophobicity of the interacting species, the adhesion of bacteria to the various surfaces including the river Rhine sediments could be interpreted in terms of the DLVO-theory. It is therefore concluded that the conceptual principles of the DLVO-theory (interplay of Van der Waals and electrostatic interactions) are suitable to describe, at least qualitatively, the initial processes of bacterial adhesion to a wide range of surfaces.     

A study of objective methods for water mass analysis,applied to the Iceland Basin

Ocean Dynamics - Cluster analysis and principal component analysis have been applied to multi-parameter hydrographic data from the Iceland Basin. The results indicate that both objective methods...