## 4 Discussion  
### Geomorphological Application of Multi-Source Data  
The 3D model covers real color information, and is capable of visually indicating the structural features. Moreover, it has coordinated projection information, such that the fast and quantitative acquisition of different geomorphic structure feature information can be achieved from multiple angles of points, lines, and surfaces. [PERSON], et al. [64] achieved qualitative and quantitative section interpretations, analysis, and measurements using 3D models of high-slope outcrop landforms. [PERSON], et al. [65] used a 3D model of the dangerous rock mass of high and steep slopes to realize an analysis of the spatial distribution, instability mode, and evolution process. In addition, compared with the dense point-cloud of UAVs, the accuracy of the point-cloud obtained by airborne LiDAR is higher [66].  
Compared with the conventional use of DEM for analysis, through different DEM visualization methods, different geographical features can be differentiated, and detailed information can be highlighted. [PERSON], et al. [67] used the red relief image map (RRIM) processing method to interpret different geological hazards and landforms. [PERSON] and [PERSON] [68] compared the different visualization methods to achieve optimal volcano identification and division of specific areas. [PERSON], et al. [69] used a variety of visualization methods to perform a 3D analysis of DEM and effectively identified different landforms.  
Based on the comparison of a wide variety of visualization methods, the SIM and SVF visualization methods can more effectively indicate the geomorphology and more clearly display features (e.g., steep slopes, bedding, and outcrop landforms). Moreover, the integrated exploration of the pit rim structure with multi-source data was achieved using a 3D model, airborne LiDAR point-cloud, and the results of visual analysis (Figure 17).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multicolumn{4}{c}{**Percentage of TWI Area (\%)**} \\ \cline{2-5}  & **\textless{}5** & **5–10** & **10–15** & **\textgreater{}15** \\ \hline Inside & 347,990.75 & 29,010.75 & 2539.00 & 293.5 \\ Outside & 754,893.75 & 58,443.75 & 6198.00 & 523.00 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Statistical results of the proportions of the inside and outside TWI areas of the pit rim structure.  
Four representative geographical features were selected for analysis based on the 3D model. The DOM and SVF visualization results of the gully inside are presented in Figure 17b,c. The geomorphic of the mountain's right side was distributed in a stepped distribution (Figure 17d). This part primarily comprised the outcrops with a staggered distribution (Figure 17e). With the use of the 3D model, outcrops at different locations were effectively identified, and the length, spacing, and trend of the outcrops was obtained simultaneously [70]. The SVF visualization results (Figure 17f) highlighted the hydrological path formed by soil erosion at the end of the ridges. Combined with the LiDAR point-cloud, the dip angle of each river network was accurately obtained (Figure 17g). The 3D display of the SIM visualization results effectively highlighted the strip-like bedding structure formed in the middle of the mountain (Figure 17h). The strike, dip, and dip directions between beddings were accurately measured (Figure 17f).