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dc.contributor.authorVAN DEN EECKHAUT MIETen_GB
dc.contributor.authorKERLE Normanen_GB
dc.contributor.authorPOESEN Jeanen_GB
dc.contributor.authorHERVAS DE DIEGO Franciscoen_GB
dc.date.accessioned2013-01-10T01:01:53Z-
dc.date.available2013-01-09en_GB
dc.date.available2013-01-10T01:01:53Z-
dc.date.created2012-12-11en_GB
dc.date.issued2012en_GB
dc.date.submitted2012-06-29en_GB
dc.identifier.citationProceedings of the 4th GEOBIA p. 211-216en_GB
dc.identifier.urihttp://mtc-m18.sid.inpe.br/col/sid.inpe.br/mtc-m18/2012/05.17.14.41/doc/061.pdfen_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC72627-
dc.description.abstractLight Detection and Ranging (LiDAR) and its derivative products have become a powerful tool in landslide research, particularly for landslide identification and landslide inventory mapping. In contrast to the many studies that use expert-based analysis of LiDAR derivatives to identify landslides only few studies, all pixel-based, have attempted to develop computer-aided methods for extracting landslides from LiDAR. It has not been tested whether object-oriented analysis (OOA) could be an alternative. Therefore, this study investigates the application of OOA using 2 m resolution slope gradient, roughness, curvature, and openness maps calculated from single pulse LiDAR data, without the support of any spectral information. More specifically, the focus is on the possible use of these derivatives for segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not facilitate accurate landslide identification. A semi-quantitative method based on support vector machines (SVM) was developed for a test area in the Flemish Ardennes (Belgium). The procedure was then applied without further modification to a validation area in the same region. The results show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of deep-seated landslides on soil-covered hillslopes such as those in the Flemish Ardennes, because approximately 70% of the landslides of an expert-based inventory were also included in the object-oriented inventory. For mountain areas with bedrock, on the other hand, it is expected more difficult to create a transferable model.en_GB
dc.description.sponsorshipJRC.H.7-Climate Risk Managementen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherINPEen_GB
dc.relation.ispartofseriesJRC72627en_GB
dc.titleIdentification Of Vegetated Landslides Using Only A Lidar-Based Terrain Model And Derivatives In An Object-Oriented Environmenten_GB
dc.typeArticles in periodicals and booksen_GB
JRC Directorate:Sustainable Resources

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