## 5 Conclusions  
In this research, we proposed SBAS-InSAR technology for processing 38 Sentinel-1A ascending SAR images for monitoring ground deformation from Songpinggou to Feihong in Mao County. Reliable deformation information was obtained from visibility analysis of the study area, improving the accuracy of the monitoring results. The analysis of the reliable deformation data combined with interpretation of Google Earth images quickly provided information on deformation areas, facilitating the identification of potential geological disaster areas. The results were verified by field surveys. The main conclusions are as follows:  
The average deformation rate from 14 October 2014 to 13 August 2019 was between \(-\)46.50 mm/y and 39.87 mm/y. Most of the deformation rates of the 410,047 deformation points fell in the range of \(-\)10 to 10 mm/y. The entire study area was relatively stable, but deformation areas were observed on both sides of the Minjiang River. Visibility analysis indicated that the visible area occupied about 61.7% of the study area, with 291,723 reliable deformation points. Invisible areas were mostly located on southwest-facing slopes, accounted for 38.3% of the total area and included 118,324 unreliable deformation points. The results showed that Sentinel-1A ascending data were highly suitable for ground deformation monitoring in this area. Spatial statistical analysis provided 18 deformation clusters, and 26 potential disaster areas were interpreted with the help of Google Earth images and deformation rate data. Potential landslides were the dominant type of geological disaster in Mao County, accounting for 76.9% of all disasters, whereas potential collapses accounted for 23.1%. The field investigation showed that the occurrence probability of potential landslides at the No. 6 and No. 8 areas was relatively high due to failure characteristics and cracks. The No. 22 potential collapse also exhibited a high probability of occurrence, posing threats to the lives and property of residents. Although the method used in this paper can effectively identify geological hazards, it can only be used as a preliminary method for disaster identification. In the next step, it is necessary to use various methods such as the field investigation, Unmanned Aerial Vehicle (UAV), LiDAR and GNSS to conduct more detailed surface deformation analysis of the landslide, and borehole inclinometers should be installed at different depths to accurately monitor the deep deformation of the landslide and estimate the date of the hazard. In addition, in order to obtain effective deformation points, we deleted points in invisible areas. However, this does not mean that there are no geological disasters in these areas. According the previous research, more accurate and extensive monitoring data can be obtained through SAR data of different band, resolution, and orbital directions and sensors to realize disaster identification in invisible areas. In general, this work proposed an effective preliminary method for monitoring ground deformation in mountainous areas and the rapid identification of geological disasters from Songpinggou to Feihong in Mao County. The approach provides a basis for disaster prevention and mitigation planning by the government. **Author Contributions:** Conceptualization, C.C. and K.Z. ; methodology, C.C., K.Z., P.X. and X.D. ; software, K.Z. and Y.L. ; validation, L.Z. ; formal analysis, C.C. and K.Z. ; investigation, Y.L. ; resources, K.Z. ; data curation, C.C. and K.Z. ; writing--original draft preparation, K.Z. ; writing--review & editing, C.C. ; visualization, K.Z. ; supervision, C.C. ; project administration, P.X. ; funding acquisition, P.X. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was financially supported by the National Key Research and Development Program of China (No. 2017 YFC1501004), the National Nature Science Foundation of China (No. 41807227). **Institutional Review Board Statement:** Not applicable.