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Remote Sens. 2015, 7(11), 15443-15466;

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

Department of Geology, Chinese Culture University, Taipei 111, Taiwan
Department of Earth Sciences, National Cheng-Kung University, Tainan 701, Taiwan
Central Geological Survey, MOEA, Taipei 235, Taiwan
Author to whom correspondence should be addressed.
Academic Editors: James Jin-King Liu, Richard Gloaguen and Prasad S. Thenkabail
Received: 31 May 2015 / Revised: 3 November 2015 / Accepted: 6 November 2015 / Published: 18 November 2015
(This article belongs to the Special Issue Remote Sensing in Geology)
Full-Text   |   PDF [3195 KB, uploaded 18 November 2015]   |  


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. View Full-Text
Keywords: LiDAR-derived DEM; openness visualization; red relief image map (RRIM) LiDAR-derived DEM; openness visualization; red relief image map (RRIM)

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Chen, R.-F.; Lin, C.-W.; Chen, Y.-H.; He, T.-C.; Fei, L.-Y. Detecting and Characterizing Active Thrust Fault and Deep-Seated Landslides in Dense Forest Areas of Southern Taiwan Using Airborne LiDAR DEM. Remote Sens. 2015, 7, 15443-15466.

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