909 resultados para Ressource allocation
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Published online first in 10 July 2013
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Density-dependent responses are an important component of the organism life-history, and the resource allocation theory is a central concept to the life-history theory. When resource allocation varies due to environmental changes, a plant may change its morphology or physiology to cope with the new conditions, a process known as phenotypic plasticity. Our study aimed to evaluate how plant density affects Eichhornia crassipes allocation patterns. A total of 214 individuals in high and low density were collected. The density effect was observed in all plant traits examined including biomass accumulation. All traits of E. crassipes demonstrated higher values in high density conditions, except for biomass of leaves. Density exhibited a high influence on vegetative traits of E. crassipes, but did not influence allocation pattern, since a trade-off among the vegetative traits was not found. The morphological plasticity and the absence of trade-offs were discussed as strategies to overcome neighbor plants in competition situations. In high density conditions, there were clear changes in the morphology of the plants which probably allows for their survival in a highly competitive environment.
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Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.
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Dissertação de mestrado integrado em Engenharia Civil
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Tese de Doutoramento em Engenharia Civil.
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Dissertação de mestrado integrado em Engenharia Civil
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Dissertação de mestrado em Estatística
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Magdeburg, Univ., Fak. für Geistes-, Sozial- und Erziehungswiss., Diss., 2011
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Magdeburg, Univ., Fak. für Informatik, Diss., 2011
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Magdeburg, Univ., Fak. für Naturwiss., Diss., 2011
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Magdeburg, Univ., Fak. für Naturwiss., Diss., 2011
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Magdeburg, Univ., Fak. für Naturwiss., Diss., 2010
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Magdeburg, Univ., Fak. für Mathematik, Diss., 2010
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Cylinder head casting; aluminum casting; inorganic bonded cores; vakuum; magnesium sulfate
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Magdeburg, Univ., Fak. für Maschinenbau, Diss., 2011