# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products
## 2 Study Sites and Methods
### DEM Processing  
UAV imagery was processed in Agisoft Photoscan 1.4.0 using high-quality settings to generate point clouds following a SfM-MVS workflow (e.g., [21; 22; 23]), which were used to generate DEMs and orthophotos. Software filtering of the sparse point cloud is commonly used to remove outliers (e.g., [32; 33]). In our study, we removed points with a reprojection error >0.6 pixels and with a reconstruction uncertainty >60. The native UAV georeferencing was not used for Tal village AOL. Here, GCPs were identified in the images, and the GCPs and model uncertainties were used to optimise (parameters: f, cx, cy, k1-k3, p1, and p2) the sparse point clouds to reduce the root mean square error (RMSE) (e.g., [34]). The model RMSEs were 1.12 m for Tal village and 6.54 m for the Pisang landslides model, which was processed using the native UAV image georeferencing and no GCPs (Table 2). Open-access DEMs were downloaded for the AOIs, including High Mountain Asia (HMA) 8 m, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM2) 30 m, AW3D30 (version 2.1) 30 m, and the non-void-filled Shuttle Radar Topography Mission (SRTM) 30 m. The HMA DEMs were derived using stereo imagery from DigitalGlobe's satellite constellation collected from 28 January 2002 to 24 November 2016 and corresponding to either a single point in time or a mosaic of DEMs (HMA-Mos) with multiple timestamps to reduce data voids. We used a mosaic tile [35] for deriving the river channel at the Pisang Landslides AOI. A single along-track tile (13 September 2013) [36] was used to calculate the volume of the partial river-blocking landslide above Lower Pisang. The GDEM2 was derived using 15 m resolution stereo ASTER imagery collected from 2000 to 2010 [37]. The AW3D30 was derived from 2.5 m ALOS Panchromatic Remote-Sensing Instrument for Stereo Mapping (PRISM) imagery collected from 2006 to 2011, which was used to produce a 5 m commercial DEM that was resampled to 30 m for public distribution [18]. The SRTM was derived from the radar data collected in February 2000 [38]. The HMA and UAV DEMs used WGS84 reference ellipsoid elevations, which were converted to EGM96 geoid elevations before coregistration to match the GDEM2, AW3D30, and SRTM DEMs. The HMA and SRTM DEMs did not cover Tal village. Additionally, the GDEM2 was excluded from Tal village analysis due to the prevalence of artifacts. The Pisang landslides UAV point cloud was generated without ground control, so we used a HMA DEM (15 March 2014) as the reference DEM for coregistration, which was carried out using iterative closest point (ICP) registration in CloudCompare using an octree subsampled point cloud. For Tal village, we used the UAV DEMs as the reference surface for coregistration. DEMs were coregistered in x, y, and z following the curve-fitting procedure described by [PERSON] and [PERSON] [39], which relates elevation differences due to geolocation errors to slope and aspect. Coregistration shifts were greatest for the GDEM2; however, a z shift of \(-\)142 m was required to shift the Pisang UAV DEM to the HMA DEM despite both being referenced to the WGS84 reference ellipsoid (Table S1). This offset is a common error for the DJI Phantom 4, potentially due to the drone recording an incorrect altitude position on startup due to differences between the inbuilt GNSS receiver and barometer. Comparisons between DEM products were derived by resampling fine-resolution DEMs to the resolution of the coarser-resolution product.