LiDAR Data for Characterizing Linear and Planar Geomorphic Markers in Tectonic Geomorphology

Dong, Pinliang

doi:10.4172/2169-0049.1000136

Cited by 7 publications

(4 citation statements)

References 48 publications

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“…Within the last 2 decades, the explosion in sub-meter resolution airborne LiDAR data raises the reliability and practicality of geomorphological mapping (Nissen et al, 2012;Lin et al, 2013;Dong, 2015). Compared with largescale DEM data mentioned above, DEMs derived from LiDAR are of great advantage with respect to the detailed description of microscopic topographic elements related to tectonic activities and surface processes.…”

Section: Automated Geomorphological Mappingmentioning

confidence: 99%

Review on the Application of Airborne LiDAR in Active Tectonics of China: Dushanzi Reverse Fault in the Northern Tian Shan

Wei

Sun

2022

Front. Earth Sci.

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High-resolution topographic data are fundamental for active tectonics studies. Within the last 2 decades, the airborne light detection and ranging (LiDAR) system provided a solution for the accurate and efficient acquisition of detailed geomorphic features. The use of LiDAR data for the identification of microstructural and geomorphic features, fault zone activity analysis, and earthquake disaster assessment remains challenging and has been the focus of active tectonics studies. Based on the LiDAR data of Dushanzi anticline–a reverse fault zone in Xinjiang, China, our group carried out a significant number of active tectonic research studies. By reviewing the specific content of these works, we summarized the main application of LiDAR for a specific structure, the Dushanzi Reverse Fault in the northern Tianshan. In addition, other applications of LiDAR in active tectonics are summarized in this paper. These studies show that high-resolution LiDAR data facilitate detailed studies of the fault activity and paleoseismicity. We hope more researchers can realize the advantages of LiDAR technology in active tectonics research and apply LiDAR technology into their own practical work, so as to promote the development of active tectonics research.

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Section: Automated Geomorphological Mappingmentioning

confidence: 99%

Review on the Application of Airborne LiDAR in Active Tectonics of China: Dushanzi Reverse Fault in the Northern Tian Shan

Wei

Sun

2022

Front. Earth Sci.

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“…and visual assessment of channel reconstruction. While lidar data are being increasingly used in tectonic geomorphology (Lin et al, 2009(Lin et al, , 2013Dong, 2015;Dong and Chen, 2018), few studies have been conducted for the analysis of channel longitudinal profiles derived from lidar data to support morphotectonic studies. This can be partly attributed to the following reasons: (1) Some lidar data for morphotectonic studies are usually acquired in a limited area along active faults and do not include complete drainage areas or channel heads, prohibiting the use of traditional methods for channel extraction; (2) geologists often need to select one major channel and obtain longitudinal profiles and measurements from the main channel efficiently (for example, Ouchi [2005] measured ten offset channels across the San Andreas fault by walking along each channel and taking measurements for analysis of channel development); yet a flexible and accurate tool is not available in current GIS software packages.…”

Section: Software Contributionmentioning

confidence: 99%

“…Light detection and ranging (lidar) has provided unprecedented details of Earth's surface features in the past two decades (Woolard and Colby, 2002;Staley et al, 2006;Arrowsmith and Zielke, 2009;Dong, 2012Dong, , 2015Dong and Chen, 2018). Many studies have been carried out specifically for channel analysis using lidar data (Cavalli et al, 2008;Passalacqua et al, 2010aPassalacqua et al, , 2010bHunt and Royall, 2013;Hooshyar et al, 2015).…”

Section: ■ Introductionmentioning

confidence: 99%

A semi-automated method for extracting channels and channel profiles from lidar-derived digital elevation models

et al. 2020

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With the advent of digital elevation models (DEMs) and geographic information systems (GIS), several methods have been proposed to extract channels from raster DEMs. Light detection and ranging (lidar) can produce high-resolution DEMs and poses new challenges to existing methods for channel extraction. This paper introduces a semi-automated method for extracting stream channels and channel profiles from high-resolution DEMs using image processing techniques. Based on user-specified approximate locations of start and end points and a few simple parameters, the method implements five automated steps: (1) channel detection using a local minimum value search; (2) channel delineation using Bresenham’s line algorithm and mathematical morphological operation; (3) vectorization; (4) profile generation; and (5) accuracy assessment. The method is implemented as an ArcGIS Python add-in toolbar named Channel Extraction. The application of the toolbar is demonstrated using a lidar-derived DEM in a study area along the San Andreas fault in California, USA. The software and test data are freely available for download (see Supplemental Files1). The demonstrated samples suggest that this new semi-automated method for extracting channels and channel profiles is flexible and user-friendly and can produce accurate results to support geomorphic studies.

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“…Light detection and ranging (lidar) can be used to identify submeter-scale geomorphic features (Dong, 2015). Currently, this is done by analyzing individual profiles across fault scarps, manually picking fault components on distance vs. elevation plots, calculating statistics and regressions for each, and then running unique analyses for each profile, which is both tedious and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Short communication: A semiautomated method for bulk fault slip analysis from topographic scarp profiles

et al. 2020

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Abstract. Manual approaches for analyzing fault scarps in the field or with existing software can be tedious and time-consuming. Here, we introduce an open-source, semiautomated, Python-based graphical user interface (GUI) called the Monte Carlo Slip Statistics Toolkit (MCSST) for estimating dip slip on individual or bulk fault datasets that (1) makes the analysis of a large number of profiles much faster, (2) allows users with little or no coding skills to implement the necessary statistical techniques, (3) and provides geologists with a platform to incorporate their observations or expertise into the process. Using this toolkit, profiles are defined across fault scarps in high-resolution digital elevation models (DEMs), and then relevant fault scarp components are interactively identified (e.g., footwall, hanging wall, and scarp). Displacement statistics are calculated automatically using Monte Carlo simulation and can be conveniently visualized in geographic information systems (GISs) for spatial analysis. Fault slip rates can also be calculated when ages of footwall and hanging wall surfaces are known, allowing for temporal analysis. This method allows for the analysis of tens to hundreds of faults in rapid succession within GIS and a Python coding environment. Application of this method may contribute to a wide range of regional and local earthquake geology studies with adequate high-resolution DEM coverage, enabling both regional fault source characterization for seismic hazard and/or estimating geologic slip and strain rates, including creating long-term deformation maps. ArcGIS versions of these functions are available, as well as ones that utilize free, open-source Quantum GIS (QGIS) and Jupyter Notebook Python software.

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LiDAR Data for Characterizing Linear and Planar Geomorphic Markers in Tectonic Geomorphology

Cited by 7 publications

References 48 publications

Review on the Application of Airborne LiDAR in Active Tectonics of China: Dushanzi Reverse Fault in the Northern Tian Shan

Review on the Application of Airborne LiDAR in Active Tectonics of China: Dushanzi Reverse Fault in the Northern Tian Shan

A semi-automated method for extracting channels and channel profiles from lidar-derived digital elevation models

Short communication: A semiautomated method for bulk fault slip analysis from topographic scarp profiles

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