Archival photogrammetric analysis of river–floodplain systems using Structure from Motion (SfM) methods. Issue 8 (20th December 2016)

• Record Type: Journal Article
• Authors: Bakker, Maarten; Lane, Stuart N.
• Is Part Of: Earth surface processes and landforms. Volume 42:Issue 8(2017)
• Page Start: 1274
• Page End: 1286
• Publication Date: 2016-12-20
• Subjects: archival aerial imagery -- classical photogrammetry -- Structure from Motion (SfM) methods -- systematic error minimisation -- morphological interpretation; Geomorphology -- Periodicals; 551.4
• DOI: 10.1002/esp.4085
• Languages: English
• ISSNs: 0197-9337

Abstract

Abstract: In this study we evaluate the extent to which accurate topographic data can be obtained by applying Structure from Motion (SfM) photogrammetric methods to archival imagery. While SfM has proven valuable in photogrammetric applications using specially acquired imagery (e.g. from unmanned aerial vehicles), it also has the potential to improve the precision of topographic data and the ease with which can be produced from historical imagery. We evaluate the application of SfM to a relatively extreme case, one of low relative relief: a braided river–floodplain system. We compared the bundle adjustments of SfM and classical photogrammetric methods, applied to eight dates. The SfM approach resulted in data quality similar to the classical approach, although the lens parameter values (e.g. focal length) recovered in the SfM process were not necessarily the same as their calibrated equivalents. Analysis showed that image texture and image overlap/configuration were critical drivers in the tie‐point generation which impacted bundle adjustment quality. Working with archival imagery also illustrated the general need for the thorough understanding and careful application of (commercial) SfM software packages. As with classical methods, the propagation of (random) error in the estimation of lens and exterior orientation parameters using SfM methods may lead to inherent systematic error in the derived point clouds. We have shown that linear errors may be accounted for by point cloud registration based on a reference dataset, which is vital for the further application in quantitative morphological analyses when using archival imagery. Copyright © 2016 John Wiley & Sons, Ltd.