Land-cover and land-use (LCLU) change reflects environmental as well as social changes. High-mountain environments
such as the Hohe Tauern Range (Austria) and its National Park are very sensitive to these changes. Remotely sensed data
are ideally suited to detect and analyze land-cover and land-use changes on different spatial and temporal scales. In this
study a Landsat Thematic Mapper (TM) time-series from 1988 to 2003, aerial photographs and auxiliary GIS data were
used detect land-cover and land-use changes in the National Park and its surroundings. The objective of the study was to
develop and test a transferable method for change detection in comparable high-mountain environments. After applying
a series of pre-processing steps, object-based image analysis, on the basis of Definiens Developer, was used to extracted
land-cover and land-use information from the Landsat images. To generate meaningful image objects, a multiresolutions
segmentation algorithm was combined with image object merging. The resultant image objects were then classified by
using a hierarchically organized classification method. In a further step the resultant outputs of the land-cover and landuse
classifications build the basis for the post-classification change detection. Moreover the advantages and
disadvantages of the developed method, the first results as well as future adaptations of the method and applications are discussed in this paper.
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