## 4 Conclusions  
A landslide monitoring procedure and validation methodology from DInSAR and SBAS data are proposed in this study. The mapped scars from LiDAR DEM shows past landslide event but not the current situation. The active landslides should be identified, and further monitoring and treatment should be carried on if the variance speed is fast. DInSAR can detect the landslide area and map with a displacement map. DInSAR can map landslide scars from fringe and displacement maps with an appropriate threshold once events have occurred during two scenes. However, DInSAR has some issues when doing the pair-to-pair solution, which induces noisy signals and is merely challenging to determine whether the slope is slipping or not. The results from DInSAR are not so precise and easy to use for the following stages. GNSS data can detect three-dimensional displacement and help to explain the landslide behavior. Such as possible sliding surfaces and sliding location in the slope. SBAS has more precise displacement derivation with modeling assumed, and landslide boundary can be thus defined. The SBAS method used in this study adopted parameter optimization resulting in more candidate points in the vegetated area. Several deep-seated landslides were selected for instrumentation to compare with DInSAR and SBAS methods. SBAS results show that that method is more appropriate for creeping landslide mapping and monitoring. The verification from GNSS shows that SBAS can derive high-accuracy results for landslide monitoring. According to the correlation between SBAS and GNSS results,  
Figure 14: Finite element numerical displacement simulation result. ascending DInSAR is suitable for the study area. The judgment of a high-accuracy displacement is not the only issue for SBAS or PS methods. The correlation between SBAS and GNSS should also be considered. A vertical displacement estimation method is also proposed in this research. Many landslide sites have enough data of slope inclinometer, a typical underground surface deformation monitoring method. The slope inclinometer obtains horizontal ground surface displacement, assuming that the bottom of the hole remains in the same position. The estimated vertical displacement can help to provide more vertical deformations despite GNSS data. However, the slope inclinometer was measured several months each time. The result indicates the sliding behavior and initiation time appropriately with SBAS vertical displacement. The slope inclinometer data is very precious and probably has decades of data. These data can have more useful applications. The numerical simulation also examined the sliding behavior of slope, which resulted in different sections of vertical movement. Therefore, large-area landslide monitoring can be performed with such a method, and extended monitoring instrumentation can be selected to install at fast-displacement locations. Moreover, the SBAS method can detect unstable landslides and provide early warning for engineering treatment or monitoring works. Potential Landslide mapping based on the SBAS method is proposed in this research and is consistent with field investigation. The method provides an opportunity to map landslides and rank priority to do engineering treatment to stable landslides. Conceptualization, K.-L.W. ; methodology, K.-L.W. and J.-T.L. ; software, K.-L.W., J.-T.L. and H.-K.C. ; validation, K.-L.W. ; investigation, C.-W.C., C.-H.L., J.-Y.W. and H.-H.L. ; resources, C.-C.C. ; writing--original draft preparation, K.-L.W. ; writing--review and editing, K.-L.W. ; visualization, K.-L.W. All authors have read and agreed to the published version of the manuscript. This research was funded by MOST, grant number 109-2625-M-260 -001 and partially funding from Central Geological Survey, MOEA, Taiwan, grant number 108-5226904000-07-01. Not applicable. Landslide scars open data available online: [[https://data.gov.tw/](https://data.gov.tw/)]([https://data.gov.tw/](https://data.gov.tw/)) (accessed on 1 June 2021).