Use of terrestrial photogrammetry based on structure‐from‐motion for mass balance estimation of a small glacier in the Italian alps

Abstract

Different high-resolution techniques can be employed to obtain information about the three-dimensional (3D) surface of glaciers. This is typically carried out using efficient, but also expensive and logistically demanding, light detection and ranging (LiDAR) technologies, such as airborne scanners and terrestrial laser scanners. Recent technological improvements in the field of image analysis and computer vision have prompted the development of a low-cost photogrammetric approach, which is referred to as 'structure-from-motion' (SfM). Combined with dense image-matching algorithms, this method has become competitive for the production of high-quality 3D models. However, several issues typical of this approach should be considered for application in glacial environments. In particular, the surface morphology, the different substrata, the occurrence of sharp contrast from solar shadows and the variable distance from the camera positions can negatively affect the image texture, and reduce the possibility of obtaining a reliable point cloud from the images. The objective of this study is to test the structure-from-motion multi view stereo (SfM-MVS) approach in a small debris-covered glacier located in the eastern Italian Alps, using a consumer-grade reflex camera and the computer vision-based software PhotoScan. The quality of the 3D models produced by the SfM-MVS process was assessed via the comparison with digital terrain models obtained from terrestrial laser scanning (TLS) surveys that were performed at the same epochs. The effect of different terrain gradients and different substrata (debris, snow and firn) was also evaluated in terms of the accuracy of the reconstruction by SfM-MVS versus TLS. Our results show that the quality of this new photogrammetric approach is similar to the quality of TLS and that point cloud densities are comparable or even higher compared with TLS. However, special care should be taken while planning the SfM survey geometry, to optimize the 3D model quality and spatial coverage.

Panorama Image Sets for Terrestrial Photogrammetric Surveys

“…This approach is lower in instrument costs compared to light detection and ranging technology (LiDAR), and has lower requirements on user expertise to create a geometrical 3D model thanks to the high level of automation in image processing. Additionally, in different fields of the geosciences (James and Robson, 2014;Thoeni et al, 2014;Piermattei et al, 2015;Smith and Vericat, 2015;Stumpf et al, 2015) it was shown that accuracy and resolution obtained are comparable to those obtained by LiDAR. However, as reported in the investigations on error sources and obtained precision by (Wackrow and Chandler, 2011;James and Robson, 2012;Micheletti et al, 2014;Nocerino et al, 2014;Bemis et al, 2014) the quality of the photogrammetric results depends principally upon (i) the camera network geometry and (ii) control data.…”

Technical Aspects Related to the Application of SFM Photogrammetry in High Mountain

“…This is the typical case of Alpine glaciers, see, e.g., Piermattei et al, 2015. In such an environment, image acquisition is much more involved, being difficult -if not impossible -to establish a predefined network geometry as in Cat.…”

Exploiting LSPIV to assess debris-flow velocities in the field

“…The SfM photogrammetry using AgiSoft ® Photoscan (e.g. Westoby et al, 2012;Javernick et al, 2014;Piermattei et al, 2015) was used to generate high-resolution coloured point clouds (1300-2900 pts m −3 ) with 2 cm alignment error (using an iterative closest point algorithm on permanent features) making it a reliable spatial and visual reference. For the LSPIV purposes, the point clouds were rotated to make an approximate horizontal flow plane (5 • rotation) to reduce any added spatial error.…”