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Displaying 286 - 290 of 661Edge-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.
Habitat 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).
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.
Requirements for labelling forest polygons in an object-based image analysis classification
The ability to spatially quantify changes in the landscape and create land-cover maps is one of the most powerful uses of remote sensing. Recent advances in object-based image analysis (OBIA) have also improved classification techniques for developing land-cover maps. However, when using an OBIA technique, collecting ground data to label reference units may not be straightforward, since these segments generally contain a variable number of pixels as well as a variety of pixel values, which may reflect variation in land-cover composition.