Late Cretaceous rift-related upwelling and melting of the Trindade starting mantle plume head beneath western Brazil

High mantle potential temperatures and local extension, associated with the Late-Cretaceous impact of the Trindade mantle plume, produced substantial widespread and voluminous magmatism around the northern half of the Paraná sedimentary basin. Our previous studies have shown that, above the central and eastern portions of the postulated impact zone where lithosphere extension is minimal, heat conducted by the plume caused large-scale melting of the more fusible parts of the subcontinental lithospheric mantle beneath the margin of the São Francisco craton and the surrounding Brasilía mobile belt. Here we combine geochemical data and field evidence from the Poxoreu Igneous Province, western Brazil to show how more intense lithospheric extension above the western margin of the postulated impact zone permitted greater upwelling and melting of the Trindade plume than further east. Laser ⁴⁰Ar/³⁹Ar age determinations indicate that rift-related basaltic magmas of the Poxoreu Igneous Province were emplaced at ? 84 Ma. Our detailed geochemical study of the mafic magmas shows that the parental melts underwent polybaric crystal fractionation within the crust prior to final emplacement. Furthermore, some magmas (quartz-normative) appear to have assimilated upper crust whereas others (nepheline- and hypersthene-normative) appear to have been unaffected by open-system crustal magma chamber processes. Incompatible trace element ratios (e.g. chondrite-normalised La/Nb?=?1) and isotopic ratios (⁸⁷Sr/⁸⁶Sr?=?0.704 and ¹⁴³Nd/¹⁴⁴Nd?=?0.51274) of the Hy-normative basalts resemble those of oceanic islands (OIB). We therefore propose that these “OIB-like” magmas were predominantly derived from convecting-mantle-source melts (i.e. Trindade mantle plume). Inverse modelling of rare-earth element (REE) abundances suggests that the initial melts were predominantly generated within the depth range of ?80–100 km, in mantle with a potential temperature of ?1500 °C.     

Spatially resolved spectroscopy of the E+A galaxies in the z= 0.32 cluster AC 114

We present spatially resolved intermediate-resolution spectroscopy of a sample of 12 E+A galaxies in the   z = 0.32  rich galaxy cluster AC 114, obtained with the FLAMES multi-integral field unit system on the Very Large Telescope (VLT) of the European Southern Observatory. Previous integrated spectroscopy of all these galaxies by Couch & Sharples had shown them to have strong Balmer line absorption and an absence of [O  ii ]λ3727 emission – the defining characteristics of the 'E+A' spectral signature, indicative of an abrupt halt to a recent episode of quite vigorous star formation. We have used our spectral data to determine the radial variation in the strength of Hδ absorption in these galaxies and hence map out the distribution of this recently formed stellar population. Such information provides important clues as to what physical event might have been responsible for this quite dramatic change in star formation activity in the recent past of these galaxies. We find a diversity of behaviour amongst these galaxies in terms of the radial variation in Hδ absorption: four galaxies show little Hδ absorption across their entire extent; it would appear they were misidentified as E+A galaxies in the earlier integrated spectroscopic studies. The remainder show strong Hδ absorption, with a gradient that is either negative (Hδ equivalent width decreasing with radius), flat or positive . By comparison with numerical simulations we suggest that the first of these different types of radial behaviour provides evidence for a merger/interaction origin, whereas the latter two types of behaviour are more consistent with the truncation of star formation in normal disc galaxies with the Hδ gradient becoming increasingly positive with time after truncation. It would seem therefore that more than one physical mechanism is responsible for E+A formation in the same environment.     

Observations of the structure and evolution of solar flares with a soft X-ray telescope

132 soft X-ray flare events have been observed with The Aerospace Corporation/Marshall Space Flight Center S-056 X-ray telescope that was part of the ATM complement of instruments aboard Skylab. Analyses of these data are reported in this paper. The observations are summarized and a detailed discussion of the X-ray flare structures is presented. The data indicated that soft X-rays emitted by a flare come primarily from an intense well-defined core surrounded by a region of fainter, more diffuse emission. Loop structures are found to constitute a fundamental characteristic of flare cores and arcades of loops are found to play a more important role in the flare phenomena than previously thought. Size distributions of these core features are presented and a classification scheme describing the brightest flare X-ray features is proposed. The data show no correlations between the size of core features and: (1) the peak X-ray intensity, as indicated by detectors on the SOLRAD satellite; (2) the rise time of the X-ray flare event, or (3) the presence of a nonthermal X-ray component. An analysis of flare evolution indicates evidence for preliminary heating and energy release prior to the main phase of the flare. Core features are found to be remarkably stable and retain their shape throughout a flare. Most changes in the overall configuration seem to be the result of the appearance, disappearance or change in brightness of individual features, rather than the restructuring or re-orientation of these features. Brief comparisons with several theories are presented.     

Exploring uncertainty in remotely sensed data with parallel coordinate plots

The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels.     

The Creation of a National Agricultural Land Use Dataset: Combining Pycnophylactic Interpolation with Dasymetric Mapping Techniques

Agricultural Census data is summarised over spatially coarse reporting units for reasons of farm confidentiality. This is problematic for research at a local level. This article describes an approach combining dasymetric and volume preserving techniques to create a national land use dataset at 1 km² resolution. The results for an English county are compared with contemporaneous aggregated habitat data. The results show that the accurate estimates of local agricultural land use (Arable and Grass) patterns can be estimated when individual 1 km squares are combined into blocks of > 9 squares, thereby providing local estimates of agricultural land use. This in turn allows more detailed modelling of land uses related to specific livestock and cropping activities. The dataset created by this work has been subject to extensive external validation through its incorporation into a number of other national models: nitrate leaching (e.g. MAGPIE, NEAP‐N), waste, and pathogen modelling related to agricultural activity.     

Variation in tree mortality and regeneration affect forest carbon recovery following fuel treatments and wildfire in the Lake Tahoe Basin, California, USA

Background

Forest fuel treatments have been proposed as tools to stabilize carbon stocks in fire-prone forests in the Western U.S.A. Although fuel treatments such as thinning and burning are known to immediately reduce forest carbon stocks, there are suggestions that these losses may be paid back over the long-term if treatments sufficiently reduce future wildfire severity, or prevent deforestation. Although fire severity and post-fire tree regeneration have been indicated as important influences on long-term carbon dynamics, it remains unclear how natural variability in these processes might affect the ability of fuel treatments to protect forest carbon resources. We surveyed a wildfire where fuel treatments were put in place before fire and estimated the short-term impact of treatment and wildfire on aboveground carbon stocks at our study site. We then used a common vegetation growth simulator in conjunction with sensitivity analysis techniques to assess how predicted timescales of carbon recovery after fire are sensitive to variation in rates of fire-related tree mortality, and post-fire tree regeneration.

Results

We found that fuel reduction treatments were successful at ameliorating fire severity at our study site by removing an estimated 36% of aboveground biomass. Treated and untreated stands stored similar amounts of carbon three years after wildfire, but differences in fire severity were such that untreated stands maintained only 7% of aboveground carbon as live trees, versus 51% in treated stands. Over the long-term, our simulations suggest that treated stands in our study area will recover baseline carbon storage 10?C35?years more quickly than untreated stands. Our sensitivity analysis found that rates of fire-related tree mortality strongly influence estimates of post-fire carbon recovery. Rates of regeneration were less influential on recovery timing, except when fire severity was high.

Conclusions

Our ability to predict the response of forest carbon resources to anthropogenic and natural disturbances requires models that incorporate uncertainty in processes important to long-term forest carbon dynamics. To the extent that fuel treatments are able to ameliorate tree mortality rates or prevent deforestation resulting from wildfire, our results suggest that treatments may be a viable strategy to stabilize existing forest carbon stocks.     

A New Combined Assessment of Mixed Uncertainty in Spatial Models: Conceptualization and Implementation

Uncertainty quantification is not often performed in spatial modeling applications, especially when there is a mixture of probabilistic and non‐probabilistic uncertainties. Furthermore, the effect of positional uncertainty is often not assessed, despite its relevance to geographical applications. Although there has been much work in investigating the aforementioned types of uncertainty in isolation, combined approaches have not been much researched. This has resulted in a lack of tools for conducting mixed uncertainty analyses that include positional uncertainty. This research addresses the issue by first presenting a new, flexible, simulation‐oriented conceptualization of positional uncertainty in geographic objects called F‐Objects. F‐Objects accommodates various representations of uncertainty, while remaining conceptually simple. Second, a new Python‐based framework is introduced, termed Wiggly and capable of conducting mixed uncertainty propagation using fuzzy Monte Carlo simulation (FMCS). FMCS combines both traditional Monte Carlo with fuzzy analysis in a so‐called hybrid approach. F‐Objects is implemented within the Wiggly framework, resulting in a tool capable of considering any combination of: (1) probabilistic variables; (2) fuzzy variables; and (3) positional uncertainty of objects (probabilistic/fuzzy). Finally, a realistic GIS‐based groundwater contamination problem demonstrates how F‐Objects and Wiggly can be used to assess the effect of positional uncertainty.     

Towards Real‐Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

Geodemographic classifications provide discrete indicators of the social, economic and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end, this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.