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Displaying 286 - 290 of 661Habitat selection in a changing environment: the relationship between habitat alteration and Scops Owl (Aves: Strigidae) territory occupancy
The Scops Owl Otus scops (L., 1758) is a species of European concern, which suffered a noticeable decrease in distribution in the last decades, and changes in agricultural practices have been proposed as a major threat for this owl. We studied the habitat preference of the Scops Owl by assessing the habitat occupancy of 401 territories distributed in a large area in northwest Italy, with a special focus on 98 territories located in a high-density area (Monferrato).
Statistical trend and change-point analysis of land-cover-change patterns in East Africa
This work presents a new four-tier hierarchical change-point algorithm designed to detect land-cover change from satellite data. We tested the algorithm using Global Inventory Modelling and Mapping Studies (GIMMS) data for eastern Africa. Using a unique sequence of four statistical change-point detection methods, we identified significant increases or decreases in normalized difference vegetation index (NDVI), estimated the approximate time of change, and characterized the likely forms of change (i.e. linear trend, abrupt mean and/or variability change, and hockey-stick shaped change).
Edge-directed interpolation-based sub-pixel mapping
Sub-pixel mapping is a technology to obtain the spatial distribution information of land cover within mixed pixels. In this letter, two kinds of edge-directed interpolation algorithms are proposed. These algorithms are applied to coarse spatial resolution images to obtain a high-resolution image with probability information. Then hard classification on a per sub-pixel basis is implemented to achieve sub-pixel mapping. Both methods are demonstrated with three synthetic images.
High-resolution urban land-cover classification using a competitive multi-scale object-based approach
In this study, a two-step classification procedure was used for classifying urban land cover. First, a hierarchy of seven image segmentations of different scales was created for an urban scene, and preliminary classifications were performed for each of the segmentations using a classification algorithm that provides the probability that a segment belongs to a land-cover class in addition to the class assignment. A higher probability for the assigned class indicates that a segment is more likely to have been classified correctly.
GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments
The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote-sensing applications. In fact, one of the most common applications of remote-sensing images is the extraction of land-cover information for digital image base maps by means of classification techniques. The aim of the study was to compare the potential classification accuracy provided by pan-sharpened orthoimages from both GeoEye-1 and WorldView-2 (WV2) VHR satellites over urban environments.