A geomorphological model for landslide detection using airborne lidar data

Jin King Liu*, Kuo Hsin Hsiao, Tian-Yuan Shih


研究成果: Article同行評審

16 引文 斯高帕斯(Scopus)


This study analyzes multi-temporal LiDAR data of high accuracy and high resolution by installing a geomorphometric model for extracting landslides. First, two sets of LiDAR data were acquired for before and after a heavy rainfall event. The landslides which took place from 2005 to 2009 were classified automatically by satellite images, and subsequently the landslides were interpreted and edited manually. Geomorphometric parameters including slope, curvature, OHM, OHM roughness, and topographic wetness index were then extracted using stencils of landslide polygons overlaid on respective thematic maps derived from LiDAR, DEM and DSM. The ranges of every parameter were derived from the statistics of the landslide area. Some selected non-morphometric parameters were also included in a later stage to account for all possible features of landslides, such as vegetation index and geological strength. The ranges of the parameters of landslides were optimized for the model by the statistics of the landslide area. The overall accuracy predicted by the model was 64.9%. When the buffer zones of old landslides and riverside areas were included, the overall accuracy was 64.4%, showing no improvement. When landslides smaller than 50 m2 were filtered, the overall accuracy reached 76.6% and 72.5% for 2005 and 2009, respectively. The results show that the geomorphological model proposed in this research is effective for landslide extraction.

頁(從 - 到)629-638
期刊Journal of Marine Science and Technology (Taiwan)
出版狀態Published - 1 12月 2012


深入研究「A geomorphological model for landslide detection using airborne lidar data」主題。共同形成了獨特的指紋。