## 4 Results and Discussion  
### Ground Deformation and Spatial Distribution  
According to the processing in Section 3.2, the average deformation rate of the LOS in the area from 2014 to 2019, the visibility analysis map, and spatial analysis results were obtained.  
As shown in the velocity map in Figure 5(a), negative values indicate that the deformation motion is away from the satellite, and positive values represent the uplift of the ground and the motion toward the satellite. The deformation velocity of the study area ranges from \(-46.50\) mm/y to \(39.87\) mm/y, and the average rate is \(-6.24\) mm/y. Most values are concentrated in the range of \(-10\) mm/y-10 mm/y, which indicates that the deformation of the study area is relatively stable [45]. A total of 410,047 coherence points were obtained; 120,552 points had a rate greater than 0, and 289,493 points had a rate less than 0. The southern part of the study area had 5 obvious deformation zones, as shown in the orange circles in Figure 5(a), with a velocity of less than \(-35\) mm/y. Results of the visibility analysis of the Sentinel-1A ascending data are shown in Figure 5(b). The area was divided into visible areas and invisible areas. The visible area (green) covers 377.7 km\({}^{2}\), accounting for about 61.7%; it includes the good visible area (214.8 km\({}^{2}\)) and the low sensitive area (162.9 km\({}^{2}\)). The invisible area covers 234.1 km\({}^{2}\), accounting for 38.3% of the total and consists of the foreshortening area (blue) (162.9 km\({}^{2}\)), layover area (yellow) (70.6 km\({}^{2}\)), and shadow area (red) (0.6 km\({}^{2}\)). The invisible areas are located primarily on southwest-facing slopes. Therefore, most of the deformation information was obtained on northeast-facing slopes. The result of the terrain visibility analysis shows that Sentinel-1A ascending data are well suited for ground deformation monitoring in the study area.  
To ensure the accuracy of the monitoring result, we deleted 118,324 deformation points in the invisible area and retained the points in the visible area, as shown in Figure 6(a), for a total of 291,723 deformation points. Hot spot analysis showed that 84.3% of the deformation points had significant spatial aggregation, 3.6% of the points had a certain degree of aggregation, and 1.8% of the deformation points might be a random distribution. In addition, 30,078 points had a value of 0, indicating that these points were randomly distributed, were not statistically significant, and should be eliminated. Therefore, 89.7% of the deformation points were statistically significant and used for kernel density analysis. The result of the kernel density analysis is shown in Figure 6(b). Red represents low-value aggregation, and green represents high-value aggregation. Similar to the velocity results in Figure 6(a), red represents aggregation where the rate is less than 0. As shown in Figure 6(b), 18 deformation clusters were obtained. Most were located on both sides of the river, and some were located in the valley. Google Earth images and the deformation rate and slope were used to interpret the 18 regions in Figure 6(b); 26 potential geological hazards were identified in these regions.  
landslides and collapses. As shown in Table 2, we observed 20 potential landslides and 6 potential collapses on both sides of the Minjiang River.