Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography

Abstract

In this study, we present a flexible, cost-effective, and accurate method to monitor landslides using a small unmanned aerial vehicle (UAV) to collect aerial photography. In the first part, we apply a Structure from Motion (SfM) workflow to derive a 3D model of a landslide in southeast Tasmania from multi-view UAV photography. The geometric accuracy of the 3D model and resulting DEMs and orthophoto mosaics was tested with ground control points coordinated with geodetic GPS receivers. A horizontal accuracy of 7 cm and vertical accuracy of 6 cm was achieved. In the second part, two DEMs and orthophoto mosaics acquired on 16 July 2011 and 10 November 2011 were compared to study landslide dynamics. The COSI-Corr image correlation technique was evaluated to quantify and map terrain displacements. The magnitude and direction of the displacement vectors derived from correlating two hillshaded DEM layers corresponded to a visual interpretation of landslide change. Results show that the algorithm can accurately map displacements of the toes, chunks of soil, and vegetation patches on top of the landslide, but is not capable of mapping the retreat of the main scarp. The conclusion is that UAV-based imagery in combination with 3D scene reconstruction and image correlation algorithms provide flexible and effective tools to map and monitor landslide dynamics.

Comparison of pre- and self-calibrated camera calibration models for UAS-derived nadir imagery for a SfM application

“…The results still depend on accurate pre-calibration, as errors will tend to scale with altitude of the UAS. This caveat has likely led to self-calibration being preferred across much of the literature (Turner et al, 2014). Self-calibration proved more effective at systematic error within Micmac, likely due to the use of the 'Four' distortion model that was designed specifically for aerial imagery.…”

Ice-cored moraine degradation mapped and quantified using an unmanned aerial vehicle: A case study from a polythermal glacier in Svalbard

“…SfM photogrammetry has also been integrated with small format, low-level aerial imagery acquired from small UAVs (e.g. Lucieer et al, 2013;Tonkin et al, 2014;Ryan et al, 2015;Smith and Vericat, 2015;Clapyut et al, 2015;Rippin et al, 2015). Here, SfM photogrammetry is used to document the evolution of an ice-cored lateral-frontal moraine over an 11-year study period, based on images obtained with a UAV in 2014 and archive images from a piloted aircraft in 2003.…”

Preliminary results from active landslide monitoring using multidisciplinary surveys

“…The use of UAVs for research purposes has become possible and cost affordable due to technological developments such as autopilot systems, lightweight action cameras, miniature GNSS, advances in carbon fiber airframes and the simultaneous development of new processing methodologies based on computer vision like the Structure from Motion (Lucieer, De Jong, & Turner, 2014;Nikolakopoulos et al, 2015a). Instead of using the analog airphoto stereopairs with overlap along the flight path, the Structure from Motion process starts by acquiring photographs of the object of interest with sufficient overlap (e.g.…”

“…Several recent studies have also demonstrated the power of Structure from Motion algorithms for landslide mapping and monitoring (Niethammer, Rothmund, Schwaderer, Zeman, & Joswig, 2011;Akca, 2013;Lucieer et al, 2014;Nikolakopoulos et al, 2015a;Turner, Lucieer, & De Jong, 2015). Different studies demonstrating the use of UAV data for landslide monitoring have been published (Lin, Tao, Wang, & Huang, 2010;Rau, Jhan, Lob, & Linb, 2011;Turner et al, 2015).…”