## 1 Introduction  
Geomorphology refers to the formation of a wide variety of undulating forms on the earth's surface since internal and external geological forces are continuously being shaped. It is a vital factor in understanding and analyzing the distribution of geological features of the natural geographic environment and geological structures [1, 2]. The analysis of geomorphic features takes on a critical significance to the research of natural environment change [3], land use type distribution [4], disaster identification [5], and geological tectonic movement [6]. The conventional method of geomorphological surveying and feature analysis is to use GIS to conduct a considerable amount of manual field investigation and statistical analysis [7; 8]. Furthermore, the total station is adopted to observe small areas and lower elevation geomorphological patterns [9]. Alternatively, global navigation satellite system (GNSS) technology has been employed to obtain topographic data, before geomorphic features are classified and mapped with the use of the obtained data [10]. Although the above methods exhibit high measurement accuracy and have been extensively used, they are time-consuming and laborious. Notably, for complex geomorphic features where survey operations are risky and inefficient, detailed information regarding the features may be missing [11], and the survey results may not indicate the current situation. As space-sky-ground remote sensing technology has been updated, and a wide variety of data acquisition methods have been proposed, high spatial resolution and multi-source remote sensing data provide considerable sources for the carrying out of high-quality and efficient geomorphological surveys and the acquiring of detailed geomorphological information [12]. With the fusion of multi-source spectral data, large-scale and long-time series surveys and quantitative evolutionary analysis can be achieved [13]. Synthetic aperture radar (SAR) images are adopted to monitor geographical features (e.g., landslides [14], saline lands [15], and rock glaciers [16]). However, optical remote sensing images and radar images are limited by natural environmental factors (e.g., imaging time, spatial resolution, cloudiness, different orbits, and bands), significantly reducing the regularity and quality of data acquisition [17; 18]. The future development trend is toward more refined and accurate geomorphological feature analysis, so that the question as to how to acquire high-quality geomorphic data takes on critical significance. Over the past few years, UAVs have constructed 3D models using their acquired high-resolution images with SfM-MVS and have identified and extracted geographical features using the derived DEMs [19; 20]. [PERSON], et al. [21] obtained paleoseismic geomorphological images using a UAV and quantitatively analyzed paleoseismic offsets using DEM. [PERSON], et al. [22] obtained high-resolution images of the volcanic and classified landforms by combining the digital orthophoto map (DOM) and the DEM. [PERSON], et al. [23] studied the correlation between the development process of an erosion gully and microscopic topographic factors using obtained multi-period centimeter-level erosion gully DOMs and DEMs. DEM has been widely used as a vital source for a wide range of geomorphological feature analyses. However, in a complex geomorphic environment with high vegetation coverage or large topographic relief, the images obtained by conventional UAV route planning methods cannot indicate the real surface, and the interpolated DEM exhibit sharp points [24], thus decreasing the accuracy of geomorphic feature analysis. LiDAR remote sensing technology uses active laser transmitters and receivers to accurately acquire elevation data [25]. It is noteworthy that terrestrial LiDAR scanning (TLS) has become one of the most accurate and reliable methods of constructing high-resolution DEMs on complex landforms [26; 27].