Image-based surface reconstruction in geomorphometry – merits, limits and developments of a promising tool for geoscientists

Abstract

Abstract. Photogrammetry and geosciences are closely linked since the late 19th century. Today, a wide range of commercial and open-source software enable non-experts users to obtain high-quality 3-D datasets of the environment, which was formerly reserved to remote sensing experts, geodesists or owners of cost-intensive metric airborne imaging systems. Complex tridimensional geomorphological features can be easily reconstructed from images captured with consumer grade cameras. Furthermore, rapid developments in UAV technology allow for high quality aerial surveying and orthophotography generation at a relatively low-cost. The increasing computing capacities during the last decade, together with the development of high-performance digital sensors and the important software innovations developed by other fields of research (e.g. computer vision and visual perception) has extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure from motion methods offer algorithms, e.g. robust feature detectors like the scale-invariant feature transform for 2-D imagery, which allow for efficient and automatic orientation of large image sets without further data acquisition information. Nevertheless, the importance of carrying out correct fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors still need to be adapted in the common scientific practice. This review manuscript intends not only to summarize the present state of published research on structure-from-motion photogrammetry applications in geomorphometry, but also to give an overview of terms and fields of application, to quantify already achieved accuracies and used scales using different strategies, to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that the identification of common errors, "bad practices" and some other valuable information in already published articles, scientific reports and book chapters may help in guiding the future use of SfM photogrammetry in geosciences.