EP43E-01:
Rapid-Response or Repeat-Mode Topography from Aerial Structure from Motion

Thursday, 18 December 2014: 1:40 PM
Edwin Nissen1, Kendra Leigh Johnson1, F Scot Fitzgerald2, Matthew Morgan2 and Jon White2, (1)Colorado School of Mines, Golden, CO, United States, (2)Colorado Geological Survey, Denver, CO, United States
Abstract:
This decade has seen a surge of interest in Structure-from-Motion (SfM) as a means of generating high-resolution topography and coregistered texture maps from stereo digital photographs. Using an unstructured set of overlapping photographs captured from multiple viewpoints and minimal GPS ground control, SfM solves simultaneously for scene topography and camera positions, orientations and lens parameters. The use of cheap unmanned aerial vehicles or tethered helium balloons as camera platforms expedites data collection and overcomes many of the cost, time and logistical limitations of LiDAR surveying, making it a potentially valuable tool for rapid response mapping and repeat monitoring applications. We begin this presentation by assessing what data resolutions and precisions are achievable using a simple aerial camera platform and commercial SfM software (we use the popular Agisoft Photoscan package). SfM point clouds generated at two small (~0.1 km2), sparsely-vegetated field sites in California compare favorably with overlapping airborne and terrestrial LiDAR surveys, with closest point distances of a few centimeters between the independent datasets. Next, we go on to explore the method in more challenging conditions, in response to a major landslide in Mesa County, Colorado, on 25th May 2014. Photographs collected from a small UAV were used to generate a high-resolution model of the 4.5 x 1 km landslide several days before an airborne LiDAR survey could be organized and flown. An initial estimate of the mass balance of the landslide could quickly be made by differencing this model against pre-event topography generated using stereo photographs collected in 2009 as part of the National Agricultural Imagery Program (NAIP). This case study therefore demonstrates the rich potential offered by this technique, as well as some of the challenges, particularly with respect to the treatment of vegetation.