Abstract
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.
Highlights
Pixel-based methods have been used for mapping changes based on high resolution (HR) or very high resolution (VHR) satellite imagery (Chen et al 2012; Hussain et al 2013)
We present a semi-automated object-based image analysis (OBIA) approach to tackle the above issues in landslide change detection based on SPOT-5 images and a Digital Elevation Model (DEM)
For a quantitative assessment of the classification accuracy, both classification results were compared to respective landslide inventories that were produced by local landslide experts through visual interpretation of post-event orthophotos with 0.5 m spatial resolution
Summary
Pixel-based methods have been used for mapping changes based on high resolution (HR) or very high resolution (VHR) satellite imagery (Chen et al 2012; Hussain et al 2013). OBIA has a high potential for accurate landslide delineation and change detection from satellite imagery (Blaschke et al 2014b). The coupled analysis of pre- and post-event images allows for the detection of spectral and/or morphologic changes, which can be attributed to new and/or reactivated landslides (Borghuis et al 2007; Mondini et al 2011). Martha et al (2012) suggested an approach based on brightness changes in pre- and post-event panchromatic images for the creation of historical landslide inventories. A semi-automatic object-oriented change detection approach using VHR optical satellite imagery for landslide rapid
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