Steep topographic reliefs and heavy vegetation severely limit visibility when examining geological structures and surface deformations in the field or when detecting these features with traditional approaches, such as aerial photography and satellite imagery. However, a light detection and ranging (LiDAR)-derived digital elevation model (DEM), which is directly related to the bare ground surface, is successfully employed to map topographic signatures with an appropriate scale and accuracy and facilitates measurements of fine topographic features. This study demonstrates the efficient use of 1-m-resolution LiDAR for tectonic geomorphology in forested areas and to identify a fault, a deep-seated landslide, and the regional cleavage attitude in southern Taiwan. Integrated approaches that use grayscale slope images, openness with a tint color slope visualization, the three-dimensional (3D) perspective of a red relief image map, and a field investigation are employed to identify the aforementioned features. In this study, the previously inferred Meilongshan Fault is confirmed as a NE–SW-trending, eastern dipping thrust with at least a 750 m-wide deformation zone. The site where future paleoseismological studies should be performed has been identified, and someone needs to work further on this site. Signatures of deep-seated landslides, such as double ridges, trenches, main escarpments, and extension cracks, are successfully differentiated in LiDAR DEM images through the use of different visualization techniques. Systematic parallel and continuous lineaments in the images are interpreted as the regional cleavage attitude of cleavage, and a field investigation confirms this interpretation.

Large-scale landslides often cause severe damage to lives and properties; therefore, their identification is essential in order to adopt proper mitigation measures. The objective of this study was to set up a methodological approach to help identify large-scale landslides using Lidar data, aerial photos and field investigation. The selected study areas were the Namasha and Liuoguey Areas in Kaohsiung City, Taiwan, both of which were severely hit by the Typhoon Morakot in 2009. The identification of large-scale landslides was performed based on Lidar high-resolution topographic information. The linear structures were mapped according to the shading map, with aspect in different azimuth to show good details of the structures. The scarps of the landslides were also identified. Validation of the results was done using both aerial photos and field investigations. In addition, stability analyses were performed on designated cases to further validate the results of Lidar identification.

The Tsaoling Landslide in Taiwan has captured attentions of researchers worldwide due to its frequent catastrophic failure and distinctive features. Thanks to the launch of TerraSAR-X/TanDEM-X (TSX/TDX) constellation, retrieval of global DEM with high spatial resolution and accuracy becomes possible, which is extremely useful for the study of natural hazards (e.g., landslides) globally. We attempt here for the first time to track recent landslide movements in Tsaoling Taiwan by analyzing DEM time series reconstructed from TSX/TDX image pairs. Quality improvement of InSAR derived DEM through an iterated differential operation is addressed during the data processing. Five cliffs and the Chingshui River are selected to determine the spatial pattern of morphologic changes of the landslide. The results show that: (a) A large amount of collapses occurred on dip slopes in the period from 2011 to 2014 and on surrounding debris deposits during the rainy seasons; (b) The average recession rate of the Chunqui Cliff decreased from 24.4 m/yr to 19.6 m/yr compared with the result between 1999 and 2009; (c) The Tsaoling Landslide has lost 6.90 ×106 m³ of soil from November of 2011 to April of 2014, which shows a positive correlation of 0.853 with rainfall; (d) The Chingshui River is undergoing a gradual bed erosion with a volumes of 1.84 ×106 m³.