## 2 Materials and Method
### Satellite Images, Aerial Photographs, GNSS, and DTMs of the Study Area  
The level 3 precision corrected images of SPOT5 satellite with a resolution of 2.5 m acquired on 16 th March 2005 and 11 th March 2006 were provided by the Center for Space and Remote Sensing Research. Aerial photograph taken on 30 th November 1980 with a resolution of 0.25 m was provided by the Forestry Bureau Aerial Survey Office. The 5 m resolution digital terrain model (DTM5) derived from aerial photographs of 2002 were provided by the National Land Surveying and Mapping Center, Ministry of Interior. The accuracy in plane and hilly area was approximately 0.5 m, and that in mountain area and steep ground was about 0.8 m horizontal, and 1-2 m vertical, respectively [20]. Positioning of field investigation points was conducted by Global Navigation Satellite System (GNSS) using Leica Zeno GG03 with post-processing accuracy of 5 mm + 0.5 ppm RMS. The real time accuracy was 1 cm + 2 ppm horizontal, and 2 cm + 2 ppm vertical, respectively. Figure 2: Aerial photo showing location of the deep-seated landslide in the Lantai study area with landslide scars and ground erosion, and the aerial photo was acquired on 27 th September 2017 taken by ADS40 sensor at an elevation of approximately 4086–4091 m above ground level (AGL) (the WGS 84 coordinates were used). The red star marks the location of study area. For the high resolution UAV mounted LiDAR, the Aerospace (AeroVironment now) Vapor 55 UAV was used to administer the LiDAR mission using Riegl VUX-1 UAV LiDAR ranger on 2018. Three flight missions were conducted in different periods to cover the study area of about 90 hectares. The field of view was 120 degrees with scan pulse rate of 380 KHz, and flight height was approximately 120-150 m above ground level to achieve a point cloud density of more than 250 points per square meter. Process of the raw dataset is composed of several components, including laser scanning system information, navigation device information, original laser data, trajectory data, tie objects, etc. Full waveform analysis and geo-referencing laser data were conducted by merging laser data and position data derived from INS/GNSS unit. All data are adjusted considering parameters including: orientation and position offsets of the laser data, the navigation device, and the trajectory, under the RIEGL RIPROCESS software with Scan Alignment Tool and/or with RiPRECI-SION, based mainly on the echo, intensity, and plane-based adjustment. This method searches planar surface patches in all point clouds automatically for each flight-line to find correspondences between patches of different point clouds. The plane-based adjustment uses an iterative approach to calculate the correction values in each flight-line. The data can then be verified visually in 2-Dimension and 3-Dimension using laser signal reflectivity, laser data density, color-encoded height visualization, height differences within raster cells, and others. Quality of matched different data records is assessed by visual inspection or by statistical analysis. If the quality of data alignment is good, the point cloud classification is then processed. In this research, TerraSolid toolsets, including TerraScan, TerraModel, and TerraPhoto are used to improve the quality of point cloud classification. First, the point cloud is automatically classified according to multiple parameters including echo, intensity, maximum building size, terrain angle, iteration angle, and iteration distance, so as to separate ground points from \"default\" points. A similar process is used to classify other kinds of point cloud, such as isolation, vegetation, low points, buildings, etc. Then all automatically classified point clouds are checked and re-edited manually using two mutually perpendicular profiles and proceed successively.