22 research outputs found

    Bibliometria, hist贸ria e geografia da pesquisa brasileira em eros茫o acelerada do solo

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    Sonic Buoy Mechanical Design

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    A Discus 'wave following' buoy was designed as part of the IOS Meteorological Program The Design philosophy adopted for a Discus Buoy is describe, and details of the implementation are discussed. A complete t set of drawings of the hardware as manufactured for the first deployment in the Auturrm of 1991 is included for completeness and forms the basis for operational use or development of the system. The subsequent modifications, that have been implemented todate, are also included in the set of drawings

    Multiscale analysis of topographic surface roughness in the Midland Valley, Scotland

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    Surface roughness is an important geomorphological variable which has been used in the Earth and planetary sciences to infer material properties, current/past processes, and the time elapsed since formation. No single definition exists; however, within the context of geomorphometry, we use surface roughness as an expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest. Six techniques for the calculation of surface roughness were selected for an assessment of the parameter`s behavior at different spatial scales and data-set resolutions. Area ratio operated independently of scale, providing consistent results across spatial resolutions. Vector dispersion produced results with increasing roughness and homogenization of terrain at coarser resolutions and larger window sizes. Standard deviation of residual topography highlighted local features and did not detect regional relief. Standard deviation of elevation correctly identified breaks of slope and was good at detecting regional relief. Standard deviation of slope (SD(slope)) also correctly identified smooth sloping areas and breaks of slope, providing the best results for geomorphological analysis. Standard deviation of profile curvature identified the breaks of slope, although not as strongly as SD(slope), and it is sensitive to noise and spurious data. In general, SD(slope) offered good performance at a variety of scales, while the simplicity of calculation is perhaps its single greatest benefit
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