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

Abstract

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

Optimized Neural Architecture for Automatic Landslide Detection from High‐Resolution Airborne Laser Scanning Data

“…The optimal combination of features was selected via ten experiments using a CFS algorithm. Selection began from (1,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,39) of the features. The most relevant feature subsets were obtained after 100 iterations in every experiment; this result is in line with the procedure proposed by Sameen et al [52].…”

Evolution of deep-seated landslide at Putanpunas stream, Taiwan

“…The 12 £ 12 grid produced by DEM proved unable to illustrate the features of the terrain, whereas LiDAR DSM proved highly effective in capturing ground variability. LiDAR-derived high-resolution DEMs reveal the topography of the bare earth, including subtle features that cannot be distinguished in aerial photographs or satellite images (Chen et al 2015). LiDAR DEM has also been applied in the detection and characterization of deep-seated landslides (Chigira & Kiho 1994).…”

“…Singhroy and Molch (2004) used RADAR images to monitor the distribution and movement patterns of landslides in the formulation of guidelines for mapping large-scale deep-seated landslides in forested areas. LiDAR data has also been used in the development of detailed landslide inventory maps (Lin et al 2013;Tseng et al 2015;Chen et al 2015). Moreover, DEM data has been used in combination with spectral information to identify the geomorphologic features of landslides (McKean & Roering 2004;Metternicht et al 2005;Agliardi et al 2013;Crosta et al 2013;Lin et al 2014).…”

Spatio Temporal Detection and Virtual Mapping of Landslide Using High-Resolution Airborne Laser Altimetry (Lidar) in Densely Vegetated Areas of Tropics

“…Recent advances in sensor electronics and data treatment make these techniques affordable. The two major remote sensing techniques that are rapidly developing in landslides investigation are interferometric synthetic aperture radar (InSAR) (Fruneau et al 1996;Colesanti et al 2003;Squarzoni et al 2003;Mantovani et al (2016), Ciampalini et al (2015), Del Ventisette et al (2014), Bianchini et al (2013), Greif and Vlcko (2012), Bateson et al 2015), and light detection and ranging (LIDAR) (Carter et al 2001;Haugerud et al 2003;Slob and Hack 2004;Chigira et al 2004;Schulz, 2007;Booth et al 2009;Guzzetti et al 2012;Hölbling et al 2012;Jaboyedoff, 2012;Pradhan et al 2012;Wang et al 2013;Lin et al 2014;Scaioni et al 2014;Chen et al 2015;Li et al 2015;Mahalingam and Olsen 2015;).…”