Landslides are one of the critical natural hazards that cause human, infrastructure, and economic losses. Risk of catastrophic losses due to landslides is significant given sprawled urban development near steep slopes and the increasing proximity of large populations to hilly areas. For reducing these losses, a high-resolution digital terrain model (DTM) is an essential piece of data for a qualitative or a quantitative investigation of slopes that may lead to landslides. Data acquired by a terrestrial laser scanning (TLS), called a point cloud, has been widely used to generate a DTM, since a TLS is appropriate for detecting small- to large-scale ground features on steep slopes. For an accurate DTM, TLS data should be filtered to remove non-ground points, but most current algorithms for extracting ground points from a point cloud have been developed for airborne laser scanning (ALS) data and not TLS data. Moreover, it is a challenging task to generate an accurate DTM from a steep-slope area by using existing algorithms. For these reasons, we developed an algorithm to automatically extract only ground points from the point clouds of steep terrains. Our methodology is focused on TLS datasets and utilizes the adaptive principal component analysis–triangular irregular network (PCA-TIN) approach. Our method was applied to two test areas and the results showed that the algorithm can cope well with steep slopes, giving an accurate surface model compared to conventional algorithms. Total accuracy values of the generated DTMs in the form of root mean squared errors are 1.84 cm and 2.13 cm over the areas of 5252 m2 and 1378 m2, respectively. The slope-based adaptive PCA-TIN method demonstrates great potential for TLS-derived DTM construction in steep-slope landscapes.
Bibliographical noteFunding Information:
Acknowledgments: The authors are grateful to the Disaster Scientific Investigation (DSI) Division of National Disaster Management Research Institute (NDMI) in Korea for assisting this research and providing the LiDAR data. This research was supported by a grant (17AWMP-B121100-02) from the Water Management Research Program funded by Ministry of Land, Infrastructure, and Transport of Korea government. The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.
© 2017 by the authors.
All Science Journal Classification (ASJC) codes
- Geography, Planning and Development
- Computers in Earth Sciences
- Earth and Planetary Sciences (miscellaneous)