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Werner Schneider

Researcher at University of Natural Resources and Life Sciences, Vienna

Publications -  30
Citations -  1093

Werner Schneider is an academic researcher from University of Natural Resources and Life Sciences, Vienna. The author has contributed to research in topics: Leaf area index & Remote sensing (archaeology). The author has an hindex of 12, co-authored 30 publications receiving 1012 citations. Previous affiliations of Werner Schneider include University of Agricultural Sciences, Dharwad.

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Multispectral classification of Landsat-images using neural networks

TL;DR: Three-layer back-propagation networks for classification of Landsat TM data on a pixel-by-pixel basis is reported and it is shown that the neural network is able to perform better than the maximum likelihood classifier.
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Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics

TL;DR: In this article, an unmixing-based data fusion approach was applied to a time series of MERIS FR images acquired over The Netherlands, and the resulting series of fused images were subsequently used to compute two vegetation indices specifically designed for MERIS: the MERIS terrestrial chlorophyll index (MTCI) and the global vegetation index (MGVI).
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Evaluating the ecological sustainability of Austrian agricultural landscapes—the SINUS approach

TL;DR: In this article, the concept of hemeroby, a measure for the naturalness or conversely the human influence on ecosystems, was used for the assessment of ecological sustainability in Austria's cultural landscapes.
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Spatial and Temporal Land Cover Changes in the Simen Mountains National Park, a World Heritage Site in Northwestern Ethiopia

TL;DR: The results showed an increase in the areas of pure forest and shrubland but a decrease in the area of agricultural land over the 20 years, providing the basis for policy/decision makers and resource managers to facilitate biodiversity conservation, including wild animals.
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Evaluation of semi-empirical BRDF models inverted against multi-angle data from a digital airborne frame camera for enhancing forest type classification

TL;DR: In this article, two semi-empirical models of the bidirectional reflectance distribution function (BRDF) were tested to extract spectro-directional information from airborne imagery and explore its use in forest type classification.