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01.06.2015 | Research Article | Ausgabe 2/2015 Open Access

Earth Science Informatics 2/2015

An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Zeitschrift:
Earth Science Informatics > Ausgabe 2/2015
Autoren:
Daniel Hölbling, Barbara Friedl, Clemens Eisank
Wichtige Hinweise
Communicated by: K. Nikolakopoulus
The original version of this article was revised: The copyright line must read © The Author(s) 20
Published in the Special Issue of Remote Sensing and Geology “Surveying the GEOsphere”; with Guest Editors Dr. Konstantinos Nikolakopoulus, Dr. Cornelia Glaesser and Dr. Nilanchal Patel.
A correction to this article is available online at https://​doi.​org/​10.​1007/​s12145-018-00375-8.

Abstract

Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.

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