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2 results on '"Spindler, Nadine"'

1. Mapping Habitats in Alpine Regions Using Multi-Temporal RapidEye Data. GI_Forum 2013 – Creating the GISociety

Author

Schmidt, Alexander, Polychronaki, Anastasia, Stoinschek, Barbara, Notarnicola, Claudia, Renner, Kathrin, Zebisch, Marc, Spindler, Nadine, Sonnenschein, Ruth, and University of Salzburg

Subjects
912,Geography, 912,Geographie
Abstract

This work aimed to investigate the potential of remote sensing to provide information on the spatial distribution of habitats in the Alpine region. Specifically, the performances of different classification methods, namely Maximum Likelihood (ML), Decision Tree (DT) and Support Vector Machine (SVM) were investigated for land-cover mapping using multitemporal RapidEye data. Results showed that SVM (85 % overall accuracy) outperformed ML (80 % overall accuracy) and DT (79 % overall accuracy). The resulted land-cover classes were subsequently reclassified into habitat classes using a spatial kernel approach. Findings suggest that the inclusion of solar radiation layers in the classification procedure as well as the use of multi-temporal images improves the classification accuracy by 4 % and 10 %, respectively.

Published
2013

2. Integrating RapidEye and ancillary data to map alpine habitats in South Tyrol, Italy.

Author

Polychronaki, Anastasia, Spindler, Nadine, Schmidt, Alexander, Stoinschek, Barbara, Zebisch, Marc, Renner, Kathrin, Sonnenschein, Ruth, and Notarnicola, Claudia

Subjects
*MOUNTAIN plants, *PLANT habitats, *VEGETATION mapping, *SCIENTIFIC observation, *DATA analysis
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. [ABSTRACT FROM AUTHOR]

Published
2015
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