Storlazzi, Curt D., et al. “End of the chain? Rugosity and fine-scale bathymetry from existing underwater digital imagery using structure-from-motion (SfM) technology.” Coral Reefs (2016): 1-6. DOI: 10.1007/s00338-016-1462-8
Introduction
Rugosity is the roughness or smoothness of the sea floor. It is the ratio of the actual distance to the linear distance between two points (figure 1). The abyssal plain is flat and smooth, so it would have a low rugosity value. In comparison, a coral reef (figure 2) is complex and tortuous, so the rugosity would be higher than that of the abyssal plain.
The complex structure, and thus high rugosity of coral reefs, is attributed to the diverse ecosystem it is constructed from. As the complexity of reefs increase with the addition of new members both the surface area available for attachment and the nooks and crannies available for refuge increase too. In addition to having importance to benthic communities, rugosity also can have an influence of ocean motion, like waves and currents. Thus accurately measuring rugosity is important for biological and physical monitoring modeling.
The traditional method for determining rugosity is called ‘chain-and-tape’. Scientists calculate the rugosity based on the difference between the linear distance from point A to B and length of chain draped along the sea floor in transect A-B. This method is beneficial because it is simple and low cost. The downfall is that the rugosity is subjective to where the chain is placed. In a complex area, like a coral reef, a different rugosity may be measured in each transect, so multiple transects must be made to get an accurate representation of rugosity (see figure 1c and 1d from the original article).
Alternative methods to determine rugosity involve remote sensing. Remote sensing is used to capture bathymetry and the spatial distribution of geomorphological features on the seafloor. Scientists use the models created from remote sensing data to calculate rugosity. The technique, however, is expensive and requires the user to have knowledge of complicated software. It also can under estimate the rugosity of an area because it is low-resolution and cannot capture the full complexity of some features.
The authors of this study sought to determine if they could use a third method called ‘Structure from Motion’ (SfM) to determine rugosity. Conceptually SfM works the same way that the human eyes do; it uses overlapping 2D still images and marries them together to make a 3D representation of an area. Traditionally the method has been used to assess community structure and density, but because it is made from actual images, it may be high enough resolution so that an accurate value of rugosity can be calculated. In addition to producing high-resolution images, SfM can be quick and the software is simple and available free on the Internet.
Methods
(source: https://commons.wikimedia.org/wiki/File:Sfm1.jpg)
To determine if SfM is a good method to determine rugosity scientists made a 3D model from video taken during a transect along a reef near Maui, Hawaii. In SfM software the video was split into still frames and every fifth image was used to construct a 3D replication of the reef. Very simply, SfM software works by looking for like pixels in two images that overlap and positions them in a X-Y-Z coordinate system relative to each other. A 3D image is then is created from information collected on each pixel from multiple views (figure 3). To calculate the rugosity the data was taken from the SfM software and put into ArcMap software. To determine the success of the SfM method for measuring rugosity the results were compared to results using remote sensing methods. Check out this video that has a in-depth and clear explanation of SfM.
Findings:
Researchers were successful at using SfM software to create an image that was able to cover a lager area than the chain and tape method and an image of greater resolution than remote sensing methods. They found that the calculated rugosity of the area increased with resolution, which makes sense because greater resolution depicts more detail.
The findings of this work are important because they demonstrate how scientists can map the seafloor efficiently, at a low cost, and with data that is already available. This method has other applications than just rugosity; check out this presentation about how it can be used to monitor the change of reef shape over time (https://vimeo.com/140017194).
Hello, welcome to Oceanbites! My name is Annie, I’m a marine research scientist who has been lucky to have had many roles in my neophyte career, including graduate student, laboratory technician, research associate, and adjunct faculty. Research topics I’ve been involved with are paleoceanographic nutrient cycling, lake and marine geochemistry, biological oceanography, and exploration. My favorite job as a scientist is working in the laboratory and the field because I love interacting with my research! Some of my favorite field memories are diving 3000-m in ALVIN in 2014, getting to drive Jason while he was on the seafloor in 2017, and learning how to generate high resolution bathymetric maps during a hydrographic field course in 2019!
