Multi-Scale Measures of Rugosity, Slope and Aspect from Benthic Stereo Image Reconstructions

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 . 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.

Accuracy and Precision of Habitat Structural Complexity Metrics Derived from Underwater Photogrammetry

“…Surface rugosity is a metric similar to linear rugosity [37], but which accounts for the three-dimensional structural complexity of the coral [20]. This metric of complexity is commonly used in benthic remote sensing applications to assess bottom complexity, though typically over much larger spatial extents and at much lower resolutions [12].…”

“…The approach of using a plane-of-best-fit when calculating surface roughness estimates such as this is generally favoured over the use of the true, real-world horizontal plane. This is because using plane-of-best-fit de-couples slope and SR, ensuring that change in elevation does not influence surface roughness calculations [20,38]. This approach was completed for five different nominal sizes of virtual quadrats, 0.25 m 2 , 1 m 2 , 2.25 m 2 , 4 m 2 , and 9 m 2 , giving sample sizes (number of non-overlapping quadrat which could be fit on the surface) of 456, 114, 48, 27, and 12 respectively.…”

“…SR is a good choice in that it has a direct analog in traditional linear rugosity [37] which has been used for nearly 50 years to quantify the structural complexity of habitat. It has also been demonstrated to be superior to this traditional approach when applied to modeled habitat as its not sensitive to the specific placement in 2D space of the measured transect [20]. However, other approaches, such as variance in height [17], slope, or even fractal dimension [16,42,43] may also prove useful as descriptors of structural complexity.…”

“…Among these techniques there has always been a tradeoff between area covered and resolution obtained such that very precise and accurate methodologies were often only practical over very small areas. However, recent advances in image processing algorithms and computing power has seen a proliferation of techniques to estimate structural complexity for large reef areas based on digital photogrammetry and application of structure from motion software [16,[19][20][21][22][23].…”

“…Systems have been developed to successfully map and model large areas of benthic habitat (up to~500 m 2 ) [19,31,32] or even km long transects [23] with a variety of off-the-shelf to custom-built hardware and software packages. Photogrammetry-based models derived from these various processes have been used to look at the relative benefits of various metrics of structural complexity used to characterise benthic habitats [16,20].…”