Poster presented at SUNY Oneonta's Life of the Mind in Fall 2018
Poster presented at 2018 Fall Meeting of AGU in Washington DC
Time series milled pebble animation; actually, this is the same group of pebbles overlain together in CloudCompare from 3 sampling times
Example point cloud animation of a tree throw mound
Data table of point clouds from alluvial and bedrock locations in upstate New York with links to point clouds (work in progress...)
This project began when I read an article on the shape of Mars' alluvial rocks, which related the shape to the transport distance. Well, I thought that if they could do it for Mars, I could certainly make a stab at doing that here in my backyard. I had hundreds of photos of river gravels from the area. I had always looked more at size than shape, but now shapeliness became important. But several pieces of the landscape scale puzzle were missing. Where were streams getting their rock recruits? I don't think that cobbles are mobile everywhere in the channels--some of the trunk streams are choking on cobbles coming in from tributaries. And then there is an issue with glaciers overriding this area more than once. Could measurements of ensembles of particles yield useful information? The more I thought about it, the richer (and perhaps more intractable!) the problem became.
I knew I didn't want to hand count particles. I had learned that sub-millimeter measurements could be made reliably in 3D point clouds derived from structure from motion photogrammetry. So I decided to look into measuring shapes as an ensemble, as in the photograph above. My goal is to learn how to interpret the shape measurements and relate them to both the physical weathering processes and the duration or intensity of the process. These processes could include fragmental destruction (crushing or slow fracturing), chipping from particle impacts, and abrasive wear from suspended sediment in the stream. There could well be others. The weathering effects on particles could lead to sharp edges, size reduction, and for wear, rounding of edges. I think examples of all of these effects can be seen in the photo above. And it's not all process. The rock strength properties must certainly play a role too. And what about the slippery algae growing on these rocks, the slow chemical reactions between rock and water, time dependent permeability and rock strength...This could be an intractable research problem. But once must start somewhere. I started with software that was in place, available, and user friendly: CloudCompare, and point clouds generated from PhotoScan.
CloudCompare, an open source software, has several routines that calculate some aspect of shape. And, CloudCompare computes histograms of the shape. The shapes I document include the relief, local roughness, local dip and dip direction, and point normal change rate. I estimate relief by fitting a plane to the point cloud, then finding the distance from every point in the cloud to the plane. Local roughness routine fits a plane to a small set of points and finds the distance from the points to the plane. Curvature can be measured by taking advantage of normal vectors constructed to planes fit to small sets of points, and finding the spatial change rate of the normal vector direction.
The next step was then to relate histograms and cumulative probability plots to shapes, and shape change. I measured lentils and chickpeas, rounded pebbles, angular pebbles, and then measured before-after shapes of rocks I broke up and tumbled in a small rock polisher. I'm convinced that the ensemble approach can provide very good proxies for size, roughness, and rounding. This is too much fun! So now after a few proofs of concept, undergraduates at SUNY Oneonta are working with me in a more focused effort to understand rock weathering in our area.
Ludmány, B. and Domokos, G. (2018). Identification of Primary Shape Descriptors on 3D Scanned Particles. Periodica Polytechnica Electrical Engineering And Computer Science, 62(2), 59-64. doi:10.3311/ppee.12313
Novák-Szabó, T., Sipos, A. Á., Shaw, S., Bertoni, D., Pozzebon, A., Grottoli, E., Sarti, G., Ciavola, P., Domokos, G. and Jerolmack, D. J. "Universal Characteristics of Particle Shape Evolution by Bed-Load Chipping." Science Advances 4.3 (2018): eaao4946. Web. 17 Aug. 2018. http://advances.sciencemag.org/content/4/3/eaao4946.full
T. Szabó, G. Domokos, J. P. Grotzinger, D. J. Jerolmack, Reconstructing the transport history of pebbles on Mars. Nat. Commun. 6, 8366 (2015). https://www.nature.com/articles/ncomms9366
Blott, S., & Pye, K. (2007). Particle shape: a review and new methods of characterization and classification. Sedimentology (2008) 55, 31–63. doi:10.1111/j.1365-3091.2007.00892.x
Tevis Jacobs, Till Junge, Lars Pastewka, Quantitative characterization of surface topography using spectral analysis, [Tevis D B Jacobs et al 2017 Surf. Topogr.: Metrol. Prop. 5 013001] https://arxiv.org/ftp/arxiv/papers/1607/1607.03040.pdf
Lague, Dimitri, Nicolas Brodu, and Jérôme Leroux. "Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (NZ)." ISPRS journal of photogrammetry and remote sensing 82 (2013): 10-26. https://www.sciencedirect.com/science/article/pii/S0924271613001184
Hodge, Rebecca, James Brasington, and Keith Richards. "In Situ characterization of Grain-Scale Fluvial Morphology Using Terrestrial Laser Scanning." Earth Surface Processes and Landforms (2009): n/a-n/a. Web. 14 Aug. 2018.