Landslide detection by indices of LiDAR point-cloud density

Jin King Liu*, Wei Chen Hsu, Mon Shieh Yang, Yu Chung Shieh, Tian-Yuan Shih

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The 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.

Original languageEnglish
Title of host publication2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Pages3960-3963
Number of pages4
DOIs
StatePublished - 1 Dec 2010
Event2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 - Honolulu, HI, United States
Duration: 25 Jul 201030 Jul 2010

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
CountryUnited States
CityHonolulu, HI
Period25/07/1030/07/10

Keywords

  • Image shape analysis
  • Natural disaster
  • Object recognition
  • Remote sensing

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