| Abstract | This paper demonstrates how multi-scale measures of rugosity, slope and aspect can be derived from fine-scale bathymetric
reconstructions created from geo-referenced stereo imagery. We generate three-dimensional reconstructions over large
spatial scales using data collected by Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs),
manned submersibles and diver-held imaging systems. We propose a new method for calculating rugosity in a Delaunay
triangulated surface mesh by projecting areas onto the plane of best fit using Principal Component Analysis (PCA). Slope
and aspect can be calculated with very little extra effort, and fitting a plane serves to decouple rugosity from slope. We
compare the results of the virtual terrain complexity calculations with experimental results using conventional in-situ
measurement methods. We show that performing calculations over a digital terrain reconstruction is more flexible, robust
and easily repeatable. In addition, the method is non-contact and provides much less environmental impact compared to
traditional survey techniques. For diver-based surveys, the time underwater needed to collect rugosity data is significantly
reduced and, being a technique based on images, it is possible to use robotic platforms that can operate beyond diver
depths. Measurements can be calculated exhaustively at multiple scales for surveys with tens of thousands of images
covering thousands of square metres. The technique is demonstrated on data gathered by a diver-rig and an AUV, on small
single-transect surveys and on a larger, dense survey that covers over 3,750m2. Stereo images provide 3D structure as well
as visual appearance, which could potentially feed into automated classification techniques. Our multi-scale rugosity, slope
and aspect measures have already been adopted in a number of marine science studies. This paper presents a detailed
description of the method and thoroughly validates it against traditional in-situ measurements.
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