Journal
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
Volume 34, Issue -, Pages 25-34Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jag.2014.06.004
Keywords
Natura 2000; Semi-natural grassland habitats; RapidEye; TerraSAR-X; Time series; Feature selection
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Funding
- German National Space Agency DLR (Deutsches Zentrum fur Luft- und Raumfahrt e.V.) on behalf of the German Federal Ministry of Economy and Technology [50EE0923]
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Remote sensing concepts are needed to monitor open landscape habitats for environmental change and biodiversity loss. However, existing operational approaches are limited to the monitoring of European dry heaths only. They need to be extended to further habitats. Thus far, reported studies lack the exploitation of intra-annual time series of high spatial resolution data to take advantage of the vegetations' phenological differences. In this study, we investigated the usefulness of such data to classify grassland habitats in a nature reserve area in northeastern Germany. Intra-annual time series of 21 observations were used, acquired by a multi-spectral (RapidEye) and a synthetic aperture radar (TerraSAR-X) satellite system, to differentiate seven grassland classes using a Support Vector Machine classifier. The classification accuracy was evaluated and compared with respect to the sensor type - multi-spectral or radar - and the number of acquisitions needed. Our results showed that very dense time series allowed for very high accuracy classifications (>90%) of small scale vegetation types. The classification for TerraSAR-X obtained similar accuracy as compared to RapidEye although distinctly more acquisitions were needed. This study introduces a new approach to enable the monitoring of small-scale grassland habitats and gives an estimate of the amount of data required for operational surveys. (C) 2014 Elsevier B.V. All rights reserved.
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