2 research outputs found

    Furnariidae Species Classification Using Extreme Learning Machines and Spectral Information

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    Automatic bird species classification and identification are issues that have aroused interest in recent years. The main goals involve more exhaustive environmental monitoring and natural resources managing. One of the more relevant characteristics of calling birds is the vocalisation because this allows to recognise species or identify new ones, to know its natural history and macro-systematic relations, among others. In this work, some spectral-based features and extreme learning machines (ELM) are used to perform bird species classification. The experiments were carried on using 25 species of the family Furnariidae that inhabit the Paranaense Littoral region of Argentina (South America) and werevalidated in a cross-validation scheme. The results show that ELM classifierobtains high classification rates, more than 90% in accuracy, and the proposed features overperform the baseline features.Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Vignolo, Leandro Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Sarquis, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto Nacional de Limnología. Universidad Nacional del Litoral. Instituto Nacional de Limnología; ArgentinaFil: Martínez, César Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

    Management to conserve biodiversity is likely to increase soil carbon storage in upland Atlantic oakwoods in the United Kingdom

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    The objective of this research was to determine whether fencing to exclude grazing from upland woodlands to facilitate the natural regeneration of trees is likely to increase soil carbon storage. Permanent sample plots were established along a transect through Young Wood, the highest Atlantic oakwood in England, immediately prior to fencing and exclusion of sheep in autumn 2008. Plots outside the wood contained either heather (Calluna vulgaris), bilberry (Vaccinium myrtillus), gorse (Ulex europaeus), wavy hair grass (Deschampsia flexuousa) or mixtures of these species. The wood is 99% sessile oak (Quercus petraea) with woodland ground flora such as heath bedstraw (Galium saxatile). Soil samples were analysed for carbon and nitrogen content. Results indicated that more carbon is stored in soil under the oaks than in either heather, bilberry, gorse, grass or mixtures of these species. In conclusion, this study showed that fencing and excluding grazing to conserve Atlantic oakwoods at their altitudinal limit in the United Kingdom is likely to have a carbon mitigation benefit as well as protecting and enhancing the biodiversity for which the management was initially intended
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