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dc.contributor.authorLiu, Jin-Kingen_US
dc.contributor.authorHsu, Wei-Chenen_US
dc.contributor.authorYang, Mon-Shiehen_US
dc.contributor.authorShieh, Yu-Chungen_US
dc.contributor.authorShih, Tian-Yuanen_US
dc.date.accessioned2014-12-08T15:38:25Z-
dc.date.available2014-12-08T15:38:25Z-
dc.date.issued2010en_US
dc.identifier.isbn978-1-4244-9566-5en_US
dc.identifier.urihttp://hdl.handle.net/11536/26299-
dc.identifier.urihttp://dx.doi.org/10.1109/IGARSS.2010.5651666en_US
dc.description.abstractThe deliverables of an airborne LiDAR survey usually include all points, ground points, digital surface models (DSM) and digital elevation models (DEM). Indices of point clouds tested in this study include density of all points, density of ground points, density of only returns, and density of multiple returns. Shallow landslides are the most common landslides triggered by torrential rainfalls and explicit fresh scars after rainfall events. Multiple returns in forest area give the possibility of differentiating landslide scars from vegetated lands. Classification results from the indices derived from these four kinds of densities are verified by the result obtained by manual interpretation of the derived nDSM images. The experiment is carried out using the dataset obtained in I-Lan County after Typhoon Kalmaegi on 17 July 2008. The results show that a proper definition of the parameters for the indices is most critical for the detection of shallow landslides.en_US
dc.language.isoen_USen_US
dc.subjectNatural disasteren_US
dc.subjectremote sensingen_US
dc.subjectImage shape analysisen_US
dc.subjectObject recognitionen_US
dc.titleLANDSLIDE DETECTION BY INDICES OF LIDAR POINT-CLOUD DENSITYen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/IGARSS.2010.5651666en_US
dc.identifier.journal2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUMen_US
dc.citation.spage3960en_US
dc.citation.epage3963en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000287933804029-
Appears in Collections:Conferences Paper


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