Estimation of soil types by non linear analysis of remote sensing data

International audienceThe knowledge of soil type and soil texture is crucial for environmental monitoring purpose and risk assessment. Unfortunately, their mapping using classical techniques is time consuming and costly. We present here a way to estimate soil types based on limited field observations and remote sensing data. Due to the fact that the relation between the soil types and the considered attributes that were extracted from remote sensing data is expected to be non-linear, we apply Support Vector Machines (SVM) for soil type classification. Special attention is drawn to different training site distributions and the kind of input variables. We show that SVM based on carefully selected input variables proved to be an appropriate method for soil type estimation

Automatic extraction of faults and fractal analysis from remote sensing data

Object-based classification is a promising technique for image classification. Unlike pixel-based methods, which only use the measured radiometric values, the object-based techniques can also use shape and context information of scene textures. These extra degrees of freedom provided by the objects allow the automatic identification of geological structures. In this article, we present an evaluation of object-based classification in the context of extraction of geological faults. Digital elevation models and radar data of an area near Lake Magadi (Kenya) have been processed. We then determine the statistics of the fault populations. The fractal dimensions of fault dimensions are similar to fractal dimensions directly measured on remote sensing images of the study area using power spectra (PSD) and variograms. These methods allow unbiased statistics of faults and help us to understand the evolution of the fault systems in extensional domains. Furthermore, the direct analysis of image texture is a good indicator of the fault statistics and allows us to classify the intensity and type of deformation. We propose that extensional fault networks can be modeled by iterative function system (IFS)

Nonlinear analysis of drainage systems to examine surface deformation: an example from Potwar Plateau (Northern Pakistan)

We devise a procedure in order to characterize the relative vulnerability of the Earth's surface to tectonic deformation using the geometrical characteristics of drainage systems. The present study focuses on the nonlinear analysis of drainage networks extracted from Digital Elevation Models in order to localize areas strongly influenced by tectonics. We test this approach on the Potwar Plateau in northern Pakistan. This area is regularly affected by damaging earthquakes. Conventional studies cannot pinpoint the zones at risk, as the whole region is characterized by a sparse and diffuse seismicity. Our approach is based on the fact that rivers tend to linearize under tectonic forcing. Thus, the low fractal dimensions of the Swan, Indus and Jehlum Rivers are attributed to neotectonic activity. A detailed textural analysis is carried out to investigate the linearization, heterogeneity and connectivity of the drainage patterns. These textural aspects are quantified using the fractal dimension, as well as lacunarity and succolarity analysis. These three methods are complimentary in nature, i.e. objects with similar fractal dimensions can be distinguished further with lacunarity and/or succolarity analysis. We generate maps of fractal dimensions, lacunarity and succolarity values using a sliding window of 2.5 arc minutes by 2.5 arc minutes (2.5'×2.5'). These maps are then interpreted in terms of land surface vulnerability to tectonics. This approach allowed us to localize several zones where the drainage system is highly structurally controlled on the Potwar Plateau. The region located between Muree and Muzaffarabad is found to be prone to destructive events whereas the area westward from the Indus seems relatively unaffected. We conclude that a nonlinear analysis of the drainage system is an efficient additional tool to locate areas likely to be affected by massive destructing events affecting the Earth's surface and therefore threaten human activities

How to identify groundwater-caused thermal anomalies in lakes based on multi-temporal satellite data in semi-arid regions

The deduction by conventional means of qualitative and quantitative information about groundwater discharge into lakes is complicated. Nevertheless, at least for semi-arid regions with limited surface water availability, this information is crucial to ensure future water availability for drinking and irrigation purposes. <br><br> Overcoming this lack of discharge information, we present a satellite-based multi-temporal sea-surface-temperature (SST) approach. It exploits the occurrence of thermal anomalies to outline groundwater discharge locations using the example of the Dead Sea. Based on a set of 19 Landsat Enhanced Thematic Mapper (ETM+) images 6.2 (high gain), recorded between 2000 and 2002, we developed a novel approach which includes (i) an objective exclusion of surface-runoff-influenced data which would otherwise lead to erroneous results and (ii) a temporal SST variability analysis based on six statistical measures amplifying thermal anomalies caused by groundwater. <br><br> After excluding data influenced by surface runoff, we concluded that spatial anomaly patterns of the standard deviation and range of the SST data series spatially fit best to in situ observed discharge locations and, hence, are most suitable for detecting groundwater discharge sites

Modified protocol of harvesting, extraction, and normalization approaches for gas chromatography mass spectrometry-based metabolomics analysis of adherent cells grown under high fetal calf serum conditions

A gas chromatography mass spectrometry (GC-MS) metabolomics protocol was modified for quenching, harvesting, and extraction of metabolites from adherent cells grown under high (20%) fetal calf serum conditions. The reproducibility of using either 50% or 80% methanol for quenching of cells was compared for sample harvest. To investigate the efficiency and reproducibility of intracellular metabolite extraction, different volumes and ratios of chloroform were tested. Additionally, we compared the use of total protein amount versus cell mass as normalization parameters. We demonstrate that the method involving 50% methanol as quenching buffer followed by an extraction step using an equal ratio of methanol:chloroform:water (1:1:1, v/v/v) followed by the collection of 6 mL polar phase for GC-MS measurement was superior to the other methods tested. Especially for large sample sets, its comparative ease of measurement leads us to recommend normalization to protein amount for the investigation of intracellular metabolites of adherent human cells grown under high (or standard) fetal calf serum conditions. To avoid bias, care should be taken beforehand to ensure that the ratio of total protein to cell number are consistent among the groups tested. For this reason, it may not be suitable where culture conditions or cell types have very different protein outputs (e.g., hypoxia vs. normoxia). The full modified protocol is available in the Supplementary Materials

Coronal heating in coupled photosphere-chromosphere-coronal systems: turbulence and leakage

Coronal loops act as resonant cavities for low frequency fluctuations that are transmitted from the deeper layers of the solar atmosphere and are amplified in the corona, triggering nonlinear interactions. However trapping is not perfect, some energy leaks down to the chromosphere, thus limiting the turbulence development and the associated heating. We consider the combined effects of turbulence and leakage in determining the energy level and associated heating rate in models of coronal loops which include the chromosphere and transition region. We use a piece-wise constant model for the Alfven speed and a Reduced MHD - Shell model to describe the interplay between turbulent dynamics in the direction perpendicular to the mean field and propagation along the field. Turbulence is sustained by incoming fluctuations which are equivalent, in the line-tied case, to forcing by the photospheric shear flows. While varying the turbulence strength, we compare systematically the average coronal energy level (E) and dissipation rate (D) in three models with increasing complexity: the classical closed model, the semi-open corona model, and the corona-chromosphere (or 3-layer) model, the latter two models allowing energy leakage. We find that: (i) Leakage always plays a role (even for strong turbulence), E and D are systematically lower than in the line-tied model. (ii) E is close to the resonant prediction, i.e., assuming effective turbulent correlation time longer than the Alfven coronal crossing time (Ta). (iii) D is close to the value given by the ratio of photospheric energy divided by Ta (iv) The coronal spectra exibits an inertial range with 5/3 spectral slope, and a large scale peak of trapped resonant modes that inhibit nonlinear couplings. (v) In the realistic 3-layer model, the two-component spectrum leads to a damping time equal to the Kolmogorov time reduced by a factor u_rms/Va_coronaComment: 15 pages, 15 figures, Accepted for publication in A&

Progression-dependent altered metabolism in osteosarcoma resulting in different nutrient source dependencies

Osteosarcoma (OS) is a primary malignant bone tumor and OS metastases are mostly found in the lung. The limited understanding of the biology of metastatic processes in OS limits the ability for effective treatment. Alterations to the metabolome and its transformation during metastasis aids the understanding of the mechanism and provides information on treatment and prognosis. The current study intended to identify metabolic alterations during OS progression by using a targeted gas chromatography mass spectrometry approach. Using a female OS cell line model, malignant and metastatic cells increased their energy metabolism compared to benign OS cells. The metastatic cell line showed a faster metabolic flux compared to the malignant cell line, leading to reduced metabolite pools. However, inhibiting both glycolysis and glutaminolysis resulted in a reduced proliferation. In contrast, malignant but non-metastatic OS cells showed a resistance to glycolytic inhibition but a strong dependency on glutamine as an energy source. Our in vivo metabolic approach hinted at a potential sex-dependent metabolic alteration in OS patients with lung metastases (LM), although this will require validation with larger sample sizes. In line with the in vitro results, we found that female LM patients showed a decreased central carbon metabolism compared to metastases from male patients

Analysis of adherent cell culture lysates with low metabolite concentrations using the Biocrates AbsoluteIDQ p400 HR kit

The AbsoluteIDQ p400 HR kit is a commercial product for targeted metabolomics. While the kit has been validated for human plasma and serum, adherent cell lysates have not yet been evaluated. We have optimized the detection of polar and lipid metabolites in cell lysates using the kit to enable robust and repeatable analysis of the detected metabolites. Parameters optimized include total cell mass, loading volume and extraction solvent. We present a cell preparation and analytical method and report on the performance of the kit with regard to detectability of the targeted metabolites and their repeatability. The kit can be successfully used for a relative quantification analysis of cell lysates from adherent cells although validated only for human plasma and serum. Most metabolites are below the limit of the Biocrates' set quantification limits and we confirmed that this relative quantification can be used for further statistical analysis. Using this approach, up to 45% of the total metabolites in the kit can be detected with a reasonable analytical performance (lowest median RSD 9% and 13% for LC and FIA, respectively) dependent on the method used. We recommend using ethanol as the extraction solvent for cell lysates of osteosarcoma cell lines for the broadest metabolite coverage and 25 mg of cell mass with a loading volume of 20 µL per sample

Selective Earth-Abundant System for CO2 Reduction: Comparing Photo- and Electrocatalytic Processes

The valorization of CO2 via photo- or electrocatalytic reduction constitutes a promising approach toward the sustainable production of fuels or value-added chemicals using intermittent renewable energy sources. For this purpose, molecular catalysts are generally studied independently with respect to the photo- or the electrochemical application, although a unifying approach would be much more effective with respect to the mechanistic understanding and the catalyst optimization. In this context, we present a combined photo- and electrocatalytic study of three Mn diimine catalysts, which demonstrates the synergistic interplay between the two methods. The photochemical part of our study involves the development of a catalytic system containing a heteroleptic Cu photosensitizer and the sacrificial BIH reagent. The system shows exclusive selectivity for CO generation and renders turnover numbers which are among the highest reported thus far within the group of fully earth-abundant photocatalytic systems. The electrochemical part of our investigations complements the mechanistic understanding of the photochemical process and demonstrates that in the present case the sacrificial reagent, the photosensitizer, and the irradiation source can be replaced by the electrode and a weak Brønsted acid. © 2019 American Chemical Society