## 2 Materials and Methods
### Photogrammetric Data Processing: Standard Workflow  
Drone images were processed using the SfM method implemented in Agisoft Metashape (version 1.70). For producing the final data, the same \"standard\" workflow was implemented allowing results' comparison (Table 2). In the following, we present the standard workflow used before presenting variations to this workflow (Section 2.4), which was meant to assess controls on measurement quality and to validate our approach.  
After importing images from all flights into a single chunk, image coordinates are converted from initially WGS84 ellipsoid to RGF93-Lambert 93 and NGF-IGN69. Automatic identification of low-quality images is performed using the \"estimate image quality\" tool, whereby each image is associated to a score between 0 and 1 representing sharpness (0 means totally blurred). Following recommendations, images with quality below 0.5 were not processed in order not to negatively influence image alignment. Image alignment is carried out using the \"High\" accuracy setting, a reference pair preselection set to \"source\", key point (tie point) limits set to 60,000 (10,000). After initial image alignment and the reconstruction of a sparse point cloud, photogrammetric targets' coordinates are imported. Each target was manually tagged in at least eight images, progressively improving model georeferencing. To retain only the most reliable tie points, sparse point clouds were systematically cleaned using the \"gradual selection\" tool with the following parameters: minimum image count of 3, maximum reprojection error of 0.4 pixel and maximum reconstruction uncertainty of 5.  
During photogrammetric model optimization, model georeferencing and 3D geometry are adjusted through self-calibrating bundle block adjustment (BBA) requiring external information. Using the standard workflow, BBA is carried out using all camera information available (i.e., position, attitude and associated precisions provided by the drone) and all targets as ChkPts but five (i.e., five targets used as GCPs). For all scenarios tested (explanations below), adaptive camera model fitting was used for camera self-calibration, allowing the complete set of calibration parameters (f, cx, cy, k1-k4, p1-p2 and b1-b2) to be automatically adjusted given user-decided weighting of the external information provided. Following recommendations in Ref. [43], the marker and tie point accuracy were adjusted to match the error in pixel on marker coordinates and the RMS reprojection error (pixel) on tie point coordinates, respectively.  
For producing the final dense models, point cloud densification is carried out using a reconstruction quality set to \"High\" with \"aggressive\" depth filtering. Using \"High\" reconstruction quality setting, dense matching is applied to images resampled at half their resolution, speeding up the process and reducing data size. With this setting, the optimum DEM resolution (i.e., minimum grid spacing) is capped at 2 GSD, equaling 0.028 and 0.052 m at Sillon de Talbert and La Palue, respectively. It was considered sufficient for our application since DEMs analyzed have resolutions of 0.1 m and 1 m. Before producing the DEMs, dense point clouds were cleaned using the \"Filter by confidence\" tool, whereby each model point is graded (1-255) according to how many depth maps the point in question appears in. For this application, points with confidence between 0 and 3 were systematically filtered. Finally, DEMs and orthophotos with a minimum planimetric resolution of 0.1 m are created (interpolation enabled) and exported using same orthogonal grids with integer bounding values.