Landslide detection by indices of LiDAR point-cloud density

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

doi:10.1109/IGARSS.2010.5651666

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

Department of Civil Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Pages	3960-3963
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS.2010.5651666
State	Published - 1 Dec 2010
Event	2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 - Honolulu, HI, United States Duration: 25 Jul 2010 → 30 Jul 2010

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Country	United States
City	Honolulu, HI
Period	25/07/10 → 30/07/10

Keywords

Image shape analysis
Natural disaster
Object recognition
Remote sensing

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title = "Landslide detection by indices of LiDAR point-cloud density",

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.",

keywords = "Image shape analysis, Natural disaster, Object recognition, Remote sensing",

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Liu, JK, Hsu, WC, Yang, MS, Shieh, YC & Shih, T-Y 2010, Landslide detection by indices of LiDAR point-cloud density. in 2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010., 5651666, International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3960-3963, 2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010, Honolulu, HI, United States, 25/07/10. https://doi.org/10.1109/IGARSS.2010.5651666

Landslide detection by indices of LiDAR point-cloud density. / Liu, Jin King; Hsu, Wei Chen; Yang, Mon Shieh; Shieh, Yu Chung; Shih, Tian-Yuan.

2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010. 2010. p. 3960-3963 5651666 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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