Penetration of a woven CFRP laminate by a high velocity steel sphere impacting at velocities of up to 1875 m/s

The impact of a woven 6 mm thick CFRP laminate has been subjected to impact by an annealed steel sphere up to velocities of 1875 m/s. It was observed that above a threshold impact energy, the percentage of kinetic energy dissipated by the laminate was constant. Further, the level of damage, as measured by C-Scan and through-thickness microscopy remained roughly constant as the impact energy was increased. However, the size of the hole formed increased. This suggested that the energy transferred to the target in the velocity range of interest became independent of the delamination. Consequently, the main energy transfer mechanism at the high velocities of impact is thought to be due to the cavity expansion and more importantly, the kinetic energy of the particulates

Faunal Response to Fine Sediment Deposition in Urban Rivers

Fine sediment deposition and infiltration into the bed of lotic ecosystems, such as sedimentation, siltation and colmation, has been widely recognised as one of the most important causes of degradation within lotic ecosystems. The impact of increased fine sediment loading as a result of agricultural practices, urban development and channel management activities for flood defence purposes, have been widely acknowledged but poorly quantified. This chapter quantifies the influence of increasing sediment input that is sediment loading on the benthic invertebrate community inhabiting an artificial channel with an impervious concrete bed. This approach provided highly controlled conditions but also reflected channel and habitat characteristics typical of many highly modified and managed urban streams. Significant advances have been made recently in the development of biomonitoring tools which quantify fine sediment impacts on instream communities and which facilitate identification of vulnerable locations within river channels

Mapping reedbed habitats using texture-based classification of QuickBird imagery

Many organisms rely on reedbed habitats for their existence, yet, over the past century there has been a drastic reduction in the area and quality of reedbeds in the UK due to intensified human activities. In order to develop management plans for conserving and expanding this threatened habitat, accurate up-to-date information is needed concerning its current distribution and status. This information is difficult to collect using field surveys because reedbeds exist as small patches that are sparsely distributed across landscapes. Hence, this study was undertaken to develop a methodology for accurately mapping reedbeds using very high resolution QuickBird satellite imagery. The objectives were to determine the optimum combination of textural and spectral measures for mapping reedbeds; to investigate the effect of the spatial resolution of the input data upon classification accuracy; to determine whether the maximum likelihood classifier (MLC) or artificial neural network (ANN) analysis produced the most accurate classification; and to investigate the potential of refining the reedbed classification using slope suitability filters produced from digital terrain data. The results indicate an increase in the accuracy of reedbed delineations when grey-level co-occurrence textural measures were combined with the spectral bands. The most effective combination of texture measures were entropy and angular second moment. Optimal reedbed and overall classification accuracies were achieved using a combination of pansharpened multispectral and texture images that had been spatially degraded from 0.6 to 4.8 m. Using the 4.8 m data set, the MLC produced higher classification accuracy for reedbeds than the ANN analysis. The application of slope suitability filters increased the classification accuracy of reedbeds from 71% to 79%. Hence, this study has demonstrated that it is possible to use high resolution multispectral satellite imagery to derive accurate maps of reedbeds through appropriate analysis of image texture, judicious selection of input bands, spatial resolution and classification algorithm and post-classification refinement using terrain data