Dealing with “too much data”: Automated Structure-from-Motion Processing of Time Lapse Imagery at Nùłàdäy Glacier, Yukon, Canada

Abstract

<p>Recent advances in camera sensors, data storage, and structure-from-motion (SfM) processing are opening new possibilities for monitoring glacier processes through time series imagery. With SfM processing, internal and external camera parameters can be estimated in a bundle adjustment, alleviating problems associated with camera stability in the field. Orienting points in real world coordinates, however, still requires manual intervention in the form of ground control identification in imagery when dealing with two camera systems. We are introducing a new automated method of orienting point clouds from two-camera time lapse set ups to allow for fast processing of large numbers of frames. We accomplish this by leveraging several algorithms developed for computer vision and apply them to an analysis of glacier surface elevation change. Two time lapse systems were installed overlooking Nùłàdäy (Lowell Glacier), Yukon, Canada, on July 13, 2019. Each system consisted of a Nikon D5600 and a DigiSnap Pro, recording images at 2-hour intervals. On July 1, 2019 a manned aircraft flight collected imagery of the glacier using a Nikon D850 with a differential GPS collecting high precision locations for each image. The July 1 imagery was processed using Agisoft Photoscan Professional through the Python API to produce a target point cloud for orientation of unregistered time lapse imagery. Using Photoscan Professional’s 4D capability, a time series of images from each time lapse camera were aligned in a one-step bundle adjustment to produce a series of dense point clouds at each time step. Point clouds from time lapse imagery were coregistered to the target point cloud using a Fast Point Feature Histograms and a color-enhanced point cloud alignment based on Rusu et al. (2009) and Park et al. (2017). The M3C2 algorithm (Lague et al., 2013) was used to difference point clouds in the timeseries and derive a time series of elevation change at Nùłàdäy with an uncertainty of 1.5 m<sup>2</sup>.  All steps in the workflow are executed through Python, allowing for easy automated execution of data processing. With streamlined processing it is possible to integrate more time steps into SfM analysis of glacier surface elevation change and integrate the data into modelling efforts of glacier evolution.</p>