Using sentinel-1 and sentinel-2 time series for slangbos mapping in the free state province, South Africa

Increasing woody cover and overgrazing in semi-arid ecosystems are known to be the major factors driving land degradation. This study focuses on mapping the distribution of the slangbos shrub (Seriphium plumosum) in a test region in the Free State Province of South Africa. The goal of this study is to monitor the slangbos encroachment on cultivated land by synergistically combining Synthetic Aperture Radar (SAR) (Sentinel-1) and optical (Sentinel-2) Earth observation information. Both optical and radar satellite data are sensitive to different vegetation properties and surface scattering or reflection mechanisms caused by the specific sensor characteristics. We used a supervised random forest classification to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were derived based on expert knowledge and in situ information from the Department of Agriculture, Land Reform and Rural Development (DALRRD). We found that the Sentinel-1 VH (cross-polarization) and Sentinel-2 SAVI (Soil Adjusted Vegetation Index) time series information have the highest importance for the random forest classifier among all input parameters. The modelling results confirm the in situ observations that pastures are most affected by slangbos encroachment. The estimation of the model accuracy was accomplished via spatial cross-validation (SpCV) and resulted in a classification precision of around 80% for the slangbos class within each time step

\u3ci\u3eSenecio Conrathii\u3c/i\u3e N.E.Br. (Asteraceae), a New Hyperaccumulator of Nickel from Serpentinite Outcrops of the Barberton Greenstone Belt, South Africa

Five nickel hyperaccumulators belonging to the Asteraceae are known from ultramafic outcrops in South Africa. Phytoremediation applications of the known hyperaccumulators in the Asteraceae, such as the indigenous Berkheya coddii Roessler, are well reported and necessitate further exploration to find additional species with such traits. This study targeted the most frequently occurring species of the Asteraceae on eight randomly selected serpentinite outcrops of the Barberton Greenstone Belt. Twenty species were sampled, including 12 that were tested for nickel accumulation for the first time. Although the majority of the species were excluders, the known hyperaccumulators Berkheya nivea N.E.Br. and B. zeyheri (Sond. & Harv.) Oliv. & Hiern subsp. rehmannii (Thell.) Roessler var. rogersiana (Thell.) Roessler hyperaccumulated nickel in the leaves at expected levels. A new hyperaccumulator of nickel was discovered, Senecio conrathii N.E.Br., which accumulated the element in its leaves at 1695 ± 637 µg g−1 on soil with a total and exchangeable nickel content of 503 mg kg−1 and 0.095 µg g−1, respectively. This makes it the third known species in the Senecioneae of South Africa to hyperaccumulate nickel after Senecio anomalochrous Hilliard and Senecio coronatus (Thunb.) Harv., albeit it being a weak accumulator compared with the latter. Seven tribes in the Asteraceae have now been screened for hyperaccumulation in South Africa, with hyperaccumulators only recorded for the Arctoteae and Senecioneae. This suggests that further exploration for hyperaccumulators should focus on these tribes as they comprise all six species (of 68 Asteraceae taxa screened thus far) to hyperaccumulate nickel

Metabolomics Unravel Contrasting Effects of Biodiversity on the Performance of Individual Plant Species

In spite of evidence for positive diversity-productivity relationships increasing plant diversity has highly variable effects on the performance of individual plant species, but the mechanisms behind these differential responses are far from being understood. To gain deeper insights into the physiological responses of individual plant species to increasing plant diversity we performed systematic untargeted metabolite profiling on a number of herbs derived from a grassland biodiversity experiment (Jena Experiment). The Jena Experiment comprises plots of varying species number (1, 2, 4, 8, 16 and 60) and number and composition of functional groups (1 to 4; grasses, legumes, tall herbs, small herbs). In this study the metabolomes of two tall-growing herbs (legume: Medicago x varia; non-legume: Knautia arvensis) and three small-growing herbs (legume: Lotus corniculatus; non-legumes: Bellis perennis, Leontodon autumnalis) in plant communities of increasing diversity were analyzed. For metabolite profiling we combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and UPLC coupled to FT-ICR-MS (LC-FT-MS) analyses from the same sample. This resulted in several thousands of detected m/z-features. ANOVA and multivariate statistical analysis revealed 139 significantly changed metabolites (30 by GC-TOF-MS and 109 by LC-FT-MS). The small-statured plants L. autumnalis, B. perennis and L. corniculatus showed metabolic response signatures to increasing plant diversity and species richness in contrast to tall-statured plants. Key-metabolites indicated C- and N-limitation for the non-leguminous small-statured species B. perennis and L. autumnalis, while the metabolic signature of the small-statured legume L. corniculatus indicated facilitation by other legumes. Thus, metabolomic analysis provided evidence for negative effects of resource competition on the investigated small-statured herbs that might mechanistically explain their decreasing performance with increasing plant diversity. In contrast, taller species often becoming dominant in mixed plant communities did not show modified metabolite profiles in response to altered resource availability with increasing plant diversity. Taken together, our study demonstrates that metabolite profiling is a strong diagnostic tool to assess individual metabolic phenotypes in response to plant diversity and ecophysiological adjustment

Unpredictability of metabolism—the key role of metabolomics science in combination with next-generation genome sequencing

Next-generation sequencing provides technologies which sequence whole prokaryotic and eukaryotic genomes in days, perform genome-wide association studies, chromatin immunoprecipitation followed by sequencing and RNA sequencing for transcriptome studies. An exponentially growing volume of sequence data can be anticipated, yet functional interpretation does not keep pace with the amount of data produced. In principle, these data contain all the secrets of living systems, the genotype–phenotype relationship. Firstly, it is possible to derive the structure and connectivity of the metabolic network from the genotype of an organism in the form of the stoichiometric matrix N. This is, however, static information. Strategies for genome-scale measurement, modelling and predicting of dynamic metabolic networks need to be applied. Consequently, metabolomics science—the quantitative measurement of metabolism in conjunction with metabolic modelling—is a key discipline for the functional interpretation of whole genomes and especially for testing the numerical predictions of metabolism based on genome-scale metabolic network models. In this context, a systematic equation is derived based on metabolomics covariance data and the genome-scale stoichiometric matrix which describes the genotype–phenotype relationship

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided

Verfahren zur formgebenden Bearbeitung von Werkstuecken

DE 102007002437 A1 UPAB: 20080729 NOVELTY - The shaping process for a workpiece (1) involves the use of a laser beam (2) angled to at least one axis in at least five degrees of freedom. The machining may take place on several parts of the workpiece at the same time. The surface topography of the workpiece is set in three dimensions, and the position coordinates are used to control the laser machining. USE - For shaping workpieces. ADVANTAGE - Shaping can be done more rapidly and with greater precision

The use of ordination techniques for the evaluation of rehabilitation success of ash dams associated with coal-driven power stations

Ecological monitoring of rehabilitated plant communities can be useful to improve the management of rehabililated areas