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Integrating RapidEye and ancillary data to map alpine habitats in South Tyrol,Italy
Institution:1. Jilin Key Laboratory of Animal Resource Conservation and Utilization, Northeast Normal University, Jingyue St 2555, Changchun 130024, China;2. College of Animal Science and Technology, Jilin Agricultural University, Xincheng ST 2888, Changchun 130118, China
Abstract:In this paper, we present a two-stage method for mapping habitats using Earth observation (EO) data in three Alpine sites in South Tyrol, Italy. The first stage of the method was the classification of land cover types using multi-temporal RapidEye images and support vector machines (SVMs). The second stage involved reclassification of the land cover types to habitat types following a rule-based spatial kernel. The highest accuracies in land cover classification were 95.1% overall accuracy, 0.94 kappa coefficient and 4.9% overall disagreement. These accuracies were obtained when the combination of images with topographic parameters and homogeneity texture was used. The habitat classification accuracies were rather moderate due to the broadly defined rules and possible inaccuracies in the reference map. Overall, our proposed methodology could be implemented to map cost-effectively alpine habitats over large areas and could be easily adapted to map other types of habitats.
Keywords:Alpine  Habitats  RapidEye  Support vector machines  Land-cover classification  Rule-based kernel
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