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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

Detecting and Characterizing Active Thrust Fault and Deep-Seated Landslides in Dense Forest Areas of Southern Taiwan Using Airborne LiDAR DEM