Particle Size by Proxy: Decoding the Textural Information in Remotely Sensed Landforms

Particle Size by Proxy: Decoding the Textural Information in Remotely Sensed Landforms

NAU SESES seminar, 25 October 2016

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Daniel Buscombe

October 25, 2016
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  1. 25 October 2016 Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’

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  10. Background • BSc Physical Geography (Environmental Sci, Biological Sci) Lancaster

    University, UK Graduated 2003. Morphodynamics of a Ridge-and-Runnel System on an Intertidal Beach. Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 6/39 6/39
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  14. Background • PhD Nearshore Oceanography University of Plymouth, UK Graduated

    2008. Morphodynamics, Sediment Dynamics and Sedimentation of a Gravel Beach. (Advisors: Gerd Masselink, Mark Davidson) Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 8/39 8/39
  15. Background • PhD Nearshore Oceanography University of Plymouth, UK Graduated

    2008. Morphodynamics, Sediment Dynamics and Sedimentation of a Gravel Beach. (Advisors: Gerd Masselink, Mark Davidson) Concepts in gravel beach dynamics Daniel Buscombe ⁎, Gerhard Masselink School of Geography, University of Plymouth, Plymouth, PL4 8AA, UK Received 3 November 2005; accepted 5 June 2006 Available online 9 August 2006 Abstract The dominant processes in gravel beach dynamics are reviewed, highlighting some common themes which unify the various components of the gravel beach system, the repercussions of which impart on how gravel beach dynamics might be understood conceptually. In particular, gravel beach dynamics are thought to be highly dependent on the temporal and spatial variation in grain size, and the continual adjustments made by an active beach step, both of which act not only as the expression of changing morphodynamic conditions, but also as a controlling influence. Morphodynamics, the notion that the exchanges on beaches between the hydrodynamics, sediment transport, and morphological change takes the form of reciprocal relationships which are mediated through feedback mechanisms (in such a way that they cannot be thought of or studied independently) is not a new one. Yet it appears that for the gravel beach, morphodynamics must be re-defined to describe conditions where variations in sediment size are thought to deserve parity, rather than as merely a sequent entity or boundary condition. ‘Morpho-sedimentary-dynamics’ is a phrase coined to intuit such cause and effect, detailing the co-evolution of morphology, hydro-hydraulics and sediment properties whilst acknowledging causative pluralism, feedbacks and multiplier effects. This is the recommended conceptual framework within which to crystallise thought and organise further research for the gravel beach. Essentially, it increases the minimum number of parameters needed to describe the state of the gravel beach as a physical system. Therefore, it is advised that simplicity will be most expedient in our future modelling efforts, if complexity is to be adequately encapsulated. © 2006 Elsevier B.V. All rights reserved. Keywords: Coastal geomorphology; Gravel beaches; Nearshore sediment transport; Sedimentology 1. Introduction Historically, our insights into shorter term gravel beach (Fig. 1) dynamics have lagged behind our under- standing of littoral environments composed of finer sediments, mainly because of the logistical problems associated with laboratory or field experimentation. Re- cently, however, there has been some revival of interest in gravel beach dynamics, resulting in a spate of exper- imental and modelling efforts (e.g., Blewett et al., 2000; Van Wellen et al., 2000; Holmes et al., 2002, 2003; Clarke et al., 2003; Pedrozo-Acuna et al., 2006; Austin and Masselink, 2006). The intention of this paper is to review shorter-term, process-oriented gravel beach fore- shore and beach face morphodynamics by highlighting the key aspects which most require further study. The aim is to stimulate sustained interest in gravel beach dynamics by acknowledging that most of these morpho- dynamic facets have in the past gone virtually un- studied, due to both conceptual and experimental dif- ficulties, and emphasising the importance of redressing this fact. Indeed, although swash-dominated, gravel Earth-Science Reviews 79 (2006) 33–52 www.elsevier.com/locate/earscirev ⁎ Corresponding author. E-mail address: daniel.buscombe@plymouth.ac.uk (D. Buscombe). 0012-8252/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.earscirev.2006.06.003 Morphological change and sediment dynamics of the beach step on a macrotidal gravel beach Martin J. Austin a,⁎, Daniel Buscombe b a School of Earth, Ocean and Environmental Science, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, UK b School of Geography, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, UK Received 8 October 2006; received in revised form 2 November 2007; accepted 26 November 2007 Abstract The morpho-sedimentary evolution of a pure gravel beach step over a tidal cycle is examined during fairweather conditions using detailed measurements of nearshore hydrodynamics, morphological and sedimentary change, and nearshore sediment transport. The characteristics of the beach step are analysed with specific reference to the concurrent dynamics of the beachface, a departure from previous studies which have treated the step as a feature isolated from the nearshore region as a whole. The step and berm are both accretionary features strongly linked to tidal stage, yet their temporal evolution is independent, the relaxation time of the berm being linked to the spring–neap tidal cycle, and that of the more transient step linked to the semi-diurnal tide. Over this time-scale the beachface is a closed sedimentary unit, although the beach step may be differentiated from the beachface using sedimentary moments. Indeed, despite the location of the step in the region of wave breaking, it has relatively stable sedimentology, remaining characteristically coarser and more leptokurtic than the swash zone. Co-spectral analysis between nearshore bed motion and cross-shore current velocity reveals that significant nearshore sediment transport occurs at sub-incident frequencies in response to wave groups. Motion of the nearshore bed is not a linear function of velocity magnitude or direction, so it is likely that there is a role for the various mechanical properties of the bed. Therefore a better description of nearshore sediment transport in the region of the beach step would require instantaneous sediment size information, allowing the use of a time-variant friction factor. © 2007 Elsevier B.V. All rights reserved. Keywords: beach step; morphodynamics; gravel beach; sediment transport 1. Introduction Beach steps are morphological features commonly associated with steep, coarse-grained beaches, particularly those composed of gravel. Typically located around the elevation of the mean water level (MWL), the step evolves according to the tidal stage and forms an acute discontinuity in the beach profile at the transition between the breaker and swash regions (e.g. Miller and Zeigler, 1958; Bauer and Allen, 1995; Ivamy and Kench, 2006), and is illustrated in Fig. 1. The sedimentology of the beach step is usually skewed towards the coarsest fraction found on the beachface, the fines having been selectively removed. The importance of the beach step is two-fold. Firstly, due to the abrupt change in water depth, the beach step forms a steep hydrodynamic gradient close to the breakpoint, thereby exerting a control on wave breaking (Hughes and Cowell, 1987). This region is strongly associated with sediment convergence; non-linear shoaling waves transport sediment onshore (Hoefel and Elgar, 2003), whilst the backwash erodes sediment from the lower swash and transports it seawards (Miller and Zeigler, 1958; Strahler, 1966). It could therefore be argued that the step is the coarse beach analogy to the breakpoint bar (Dhyr-Nielsen and Sorensen, 1970; Roelvink and Stive, 1989). Secondly, the beach step plays an important role in the morphological response of reflective (steep) beaches to storms. Beaches typically respond to energetic waves by becoming more dissipative; offshore sediment transport, conductive to bar formation, prevails, and the beach gradient is reduced. However, beaches that display a significant beach step Marine Geology 249 (2008) 167–183 www.elsevier.com/locate/margeo ⁎ Corresponding author. E-mail addresses: martin.austin@plymouth.ac.uk (M.J. Austin), daniel.buscombe@plymouth.ac.uk (D. Buscombe). 0025-3227/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.margeo.2007.11.008 Barrier dynamics experiment (BARDEX): Aims, design and procedures J.J. Williams a,⁎, D. Buscombe b, G. Masselink b, I.L. Turner c, C. Swinkels d a ABPmer, Suite B, Town Quay, Southampton SO14 2AQ, UK b School of Marine Science and Engineering, University of Plymouth, Plymouth, PL4 8AA, UK c Water Research Laboratory, School of Civil and Environmental Engineering, University of New South Wales Sydney, NSW 2052, Australia d Deltares, Rotterdamseweg 185, 2629 HD Delft, Postbus 177, 2600 MH Delft, The Netherlands a b s t r a c t a r t i c l e i n f o Article history: Received 21 November 2011 Accepted 9 December 2011 Available online 23 January 2012 Keywords: Gravel barrier Delta flume Waves Tides Prototype scale experiment Although relatively common features in nature, only a handful of laboratory studies have examined the dy- namic response of gravel beaches and barriers to combined tidal and wave forcing and to storm simulations. This paper reports experiments undertaken in the Delta flume during the BARDEX project using a prototype gravel barrier (55 m-long, 5 m-wide and 4 m-high with seaward and lagoon facing slopes of 1 V/8H and 1 V/ 4H, respectively) composed of sub-rounded gravel (D50 =11 mm). Hydrodynamic conditions and beach morphology were measured using buried PTs, ECMs and closely spaced bed location sensors on a scaffold frame spanning the entire barrier. Additional measurements were also obtained from video and from instru- ments on an offshore frame. A series of systematic tests were undertaken using pumps to change water levels on the seaward (hS ) and lagoon (hL ) sides of the barrier. These included: 1) hydraulic conductivity tests where hS and hL levels were varied; 2) tests to assess the impact of waves (hS =2.5 m, variable hL in the range 1 m to 2.5 m, significant wave height, Hs =0.8 m, and peak wave period, Tp =3.0 s, 4.5 s and 6 s); 3) tests examining the effect of tides (varying hS from 1.75 m to 3.25 m, variable hL at high (hL =hS +1 m), me- dium (hL =hS ) and low (hL =hS −1 m) levels, Hs =0.8 m and Tp =4.5 s); and 4) overwash tests (tidal simu- lation, variable hL , Hs =1 m and Tp =4.5 s, 6 s, 7 s and 8 s). The principal objective of the paper is to provide essential information on the design and execution of the BARDEX experiments referred to in the series of pa- pers that follow in this special edition. It also describes the instrumentation used to measure hydrodynamic, morphodynamic and sediment processes. © 2011 Elsevier B.V. All rights reserved. 1. Introduction The BARDEX experiments were motivated by two main consider- ations. (1) Gravel beaches provide effective natural sea defences from flooding at many worldwide locations (e.g. Bradbury and Powell, 1992; Mason and Coates, 2001; Obhrai et al., 2008; Pedrozo-Acuna et al., 2007) and many are currently actively eroding (e.g. Chadwick et al., 2005; Pye and Blott, 2009). This process increases the threat to coastal infrastructure, exacerbates coastal flooding problems and may possibly lead to further loss of important natural habitats. The processes responsible for the formation, maintenance and erosion of gravel beaches and barriers are not fully-understood and require fur- ther work (e.g. Masselink and Buscombe, 2008). (2) Coarse sediment is increasingly used for beach nourishment and recharge in coastal protection schemes (e.g. Lawrence et al., 2002; Moses and Williams, 2008; Riddell and Young, 1992; Van Wellen et al., 2000) and attracts an annual expenditure of c. 60 million Euros in the UK alone (Bradbury and McCabe, 2003). The further development, testing and validation of numerical models to assist with scheme design and to assess the response of gravel coastlines to a range of storm and sea level scenarios is thus desirable from a coastal engineering and man- agement perspective (e.g. Bradbury, 2000). Most of the world's gravel beaches are found in meso- to macro- tidal settings, and thus tidal effects on beach morphodynamics cannot be ignored (Masselink and Short, 1993). Furthermore, owing to the coarse nature of the sediments, beach porosity can also exert a signif- icant influence on morphodynamic behaviour. However, most previ- ous laboratory flume experiments have used a fixed mean water level to study the response of gravel beaches to waves (e.g. Roelvink and Reniers, 1995). Although a few studies have attempted to exam- ine the response of gravel beaches to waves and tides (e.g. Trim et al., 2002), the experiments are subject to scaling problems and the bea- ches used are normally emplaced on impermeable ramps at the end of the test facilities. Such experiments fail, therefore, to replicate some important aspects of natural gravel beach hydrology. Moreover, many gravel beaches (with a hydraulic conductivity greatly exceed- ing that of sand beaches) are barrier beaches which front and protect low-lying coastal areas (lagoons, estuaries, and coastal plains) from coastal flooding. Examples in the UK include Westward Ho!, Porlock, Coastal Engineering 63 (2012) 3–12 ⁎ Corresponding author. Tel.: +44 23 80711840; fax: +44 23 8071 1841. E-mail addresses: jwilliams@abpmer.co.uk (J.J. Williams), g.masselink@plymouth.ac.uk (G. Masselink), ian.turner@unsw.edu.au (I.L. Turner), Cilia.Swinkels@deltares.nl (C. Swinkels). 0378-3839/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.coastaleng.2011.12.009 Contents lists available at SciVerse ScienceDirect Coastal Engineering journal homepage: www.elsevier.com/locate/coastaleng Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 8/39 8/39
  16. Background • 1st Post-doc University of California Santa Cruz &

    USGS 2008–2009. Santa Cruz Seafloor Observatory. (Dave Rubin, Jessie Lacy). Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 9/39 9/39
  17. Background • 1st Post-doc University of California Santa Cruz &

    USGS 2008–2009. Santa Cruz Seafloor Observatory. (Dave Rubin, Jessie Lacy). Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 9/39 9/39
  18. Background • 1st Post-doc University of California Santa Cruz &

    USGS 2008–2009. Santa Cruz Seafloor Observatory. (Dave Rubin, Jessie Lacy). Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 9/39 9/39
  19. Background • 1st Post-doc University of California Santa Cruz &

    USGS 2008–2009. Santa Cruz Seafloor Observatory. (Dave Rubin, Jessie Lacy). Click Here for Full Article A universal approximation of grain size from images of noncohesive sediment D. Buscombe,1,2 D. M. Rubin,3 and J. A. Warrick3 Received 3 August 2009; revised 10 December 2009; accepted 21 January 2010; published 10 June 2010. [1] The two‐dimensional spectral decomposition of an image of sediment provides a direct statistical estimate, grid‐by‐number style, of the mean of all intermediate axes of all single particles within the image. We develop and test this new method which, unlike existing techniques, requires neither image processing algorithms for detection and measurement of individual grains, nor calibration. The only information required of the operator is the spatial resolution of the image. The method is tested with images of bed sediment from nine different sedimentary environments (five beaches, three rivers, and one continental shelf), across the range 0.1 mm to 150 mm, taken in air and underwater. Each population was photographed using a different camera and lighting conditions. We term it a “universal approximation” because it has produced accurate estimates for all populations we have tested it with, without calibration. We use three approaches (theory, computational experiments, and physical experiments) to both understand and explore the sensitivities and limits of this new method. Based on 443 samples, the root‐mean‐squared (RMS) error between size estimates from the new method and known mean grain size (obtained from point counts on the image) was found to be ±≈16%, with a 95% probability of estimates within ±31% of the true mean grain size (measured in a linear scale). The RMS error reduces to ≈11%, with a 95% probability of estimates within ±20% of the true mean grain size if point counts from a few images are used to correct bias for a specific population of sediment images. It thus appears it is transferable between sedimentary populations with different grain size, but factors such as particle shape and packing may introduce bias which may need to be calibrated for. For the first time, an attempt has been made to mathematically relate the spatial distribution of pixel intensity within the image of sediment to the grain size. Citation: Buscombe, D., D. M. Rubin, and J. A. Warrick (2010), A universal approximation of grain size from images of noncohesive sediment, J. Geophys. Res., 115, F02015, doi:10.1029/2009JF001477. 1. Introduction [2] Grain size is of fundamental importance, governing the mechanical, electrical and fluid dynamic properties of sediment. The surface texture of a noncohesive, unlithified sediment bed, as sensed by a photographic device, is the two‐dimensional projection of its three‐dimensional struc- ture. Using photographs to quantify grain size (and other properties) of ancient or modern sediment beds, in an automated fashion, is of considerable interest because it is relatively cheap and rapid, and thus can allow much greater coverage and resolution of grain size measurements com- pared to traditional methods [Rubin, 2004]. This is because measurements from digital images are orders of magnitude faster than physical measurements such as sieving and settling [Barnard et al., 2007]. In addition, measurements are nonintrusive and sample only those grains that are exposed to the flow and are thus subject to transport or winnowing. [3] Images of natural sediment beds are complex, typically composed of at least several hundred individual grains all varying in area, form, angularity, color, etc. In addition, grains overlap and this casts shadows across the surface which are irregular in size and spatially random in color. Existing methods of automated grain size estimation from images rely on calibration [e.g., Rubin, 2004; Carbonneau et al., 2004, 2005; Verdú et al., 2005; Buscombe et al., 2008], or on advanced sequences of image processing to isolate and measure each individual grain [e.g., Graham et al., 2005], or both, which are often sediment population specific. In this contribution, we describe a new method for estimating mean grain size from an image which overcomes both these disadvantages. [4] The problem of accurate and automated grain size estimation from an image of natural sediment can be 1United States Geological Survey, and Institute of Marine Studies, University of California, Santa Cruz, California, USA. 2Now at School of Marine Science and Engineering, University of Plymouth, Plymouth, UK. 3U.S. Geological Survey, Santa Cruz, California, USA. This paper is not subject to U.S. copyright. Published in 2010 by the American Geophysical Union. JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, F02015, doi:10.1029/2009JF001477, 2010 F02015 1 of 17 Currents, drag, and sediment transport induced by a tsunami Jessica R. Lacy,1 David M. Rubin,1 and Daniel Buscombe2 Received 2 February 2012; revised 22 June 2012; accepted 9 August 2012; published 22 September 2012. [1] We report observations of water surface elevation, currents, and suspended sediment concentration (SSC) from a 10-m deep site on the inner shelf in northern Monterey Bay during the arrival of the 2010 Chile tsunami. Velocity profiles were measured from 3.5 m above the bed (mab) to the surface at 2 min intervals, and from 0.1 to 0.7 mab at 1 Hz. SSC was determined from the acoustic backscatter of the near-bed profiler. The initial tsunami waves were directed cross shore and had a period of approximately 16 min. Maximum wave height was 1.1 m, and maximum current speed was 0.36 m/s. During the strongest onrush, near-bed velocities were clearly influenced by friction and a logarithmic boundary layer developed, extending more than 0.3 mab. We estimated friction velocity and bed shear stress from the logarithmic profiles. The logarithmic structure indicates that the flow can be characterized as quasi-steady at these times. At other phases of the tsunami waves, the magnitude of the acceleration term was significant in the near-bed momentum equation, indicating unsteady flow. The maximum tsunami-induced bed shear stress (0.4 N/m2) exceeded the critical shear stress for the medium-grained sand on the seafloor. Cross-shore sediment flux was enhanced by the tsunami. Oscillations of water surface elevation and currents continued for several days. The oscillations were dominated by resonant frequencies, the most energetic of which was the fundamental longitudinal frequency of Monterey Bay. The maximum current speed (hourly-timescale) in 18 months of observations occurred four hours after the tsunami arrived. Citation: Lacy, J. R., D. M. Rubin, and D. Buscombe (2012), Currents, drag, and sediment transport induced by a tsunami, J. Geophys. Res., 117, C09028, doi:10.1029/2012JC007954. 1. Introduction [2] Over the past decade measurements of tsunamis have proliferated, documenting both their propagation across the ocean and conditions at landfall. However, these data are almost exclusively records of water surface elevation, with very few measurements of current speed. Tsunamis traveling across the deep ocean have small amplitudes and negligible currents, but as they move into shallow coastal waters, wave height and current speed increase. While inundation is the most obvious hazard associated with tsunamis, the drag force, which is proportional to velocity squared, carries much greater potential for destruction [Yeh, 2006]. Thus, mea- surement and accurate prediction of the currents generated by tsunamis is an important component of hazard assessment. Previously, Bricker et al. [2007] published tsunami current data measured under relatively small oscillations in water surface elevation (<0.17 m), and Lynett et al. [2012] reported currents generated by the 2011 Tohoku tsunami at two remote locations. In addition, overland current speeds have been estimated from survivor videos taken during the Tohoku and Samoa tsunamis [Fritz et al., 2006, 2012]. [3] Modeling of tsunami propagation across the ocean neglects friction, which is reasonable in deep water, where the bottom boundary layer is a very small fraction of the depth. As the tsunami approaches shore this fraction increases, both because the ambient depth decreases and because cur- rent speed and thus bed friction increase. The potential for bed drag to influence tsunami-generated currents is much greater than for wind waves, because tsunami periods are an order of magnitude longer. Measurements of currents in the bottom boundary layer under tsunamis are critical for evalu- ating the treatment of bed friction in models of tsunamis as they approach shore. They are also needed to determine bed shear stress and estimate sediment mobilization by tsunamis, and can contribute to accurate hindcasting of tsunami currents from characteristics of sedimentary deposits, an important goal of paleo-tsunami research [Huntington et al., 2007]. [4] Tsunamis can cause damage far from their source, and much of the damage is due to currents [Lynett et al., 2012]. The potential for tsunamis to initiate seiching (free-surface oscillations in enclosed basins) in harbors and bays has long been known [Miles, 1974; Murty, 1977]. Coupling between the initial tsunami forcing and local resonance produces variation in tsunami signals along a coast, and can lead to 1U.S. Geological Survey, Santa Cruz, California, USA. 2School of Marine Science and Engineering, University of Plymouth, Plymouth, UK. Corresponding author: J. R. Lacy, U.S. Geological Survey, Pacific Coastal and Marine Science Center, 400 Natural Bridges Drive, Santa Cruz, CA 95060, USA. (jlacy@usgs.gov) This paper is not subject to U.S. copyright. Published in 2012 by the American Geophysical Union. JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, C09028, doi:10.1029/2012JC007954, 2012 C09028 1 of 15 390 Variation in nearshore bed-sediment grain size Nearshore sediment transport determines the fate of seabed nutrients, contaminants, and pathogens; asserts control on the seabed and water column as habitats; and drives changes in seafloor topography which, in turn, affect wave transfor- mation processes, spatial gradients in energy dissipation, and nearshore hydrodynamic circulation patterns. Relatively small changes in grain size have been shown to change the sign (depositional or erosional) of nearshore net sand transport rates (Ribberink and Chen 1993); affect the vertical grain-size distribution in suspension (McFetridge and Nielsen 1985); and the shape of suspended sediment concentration profiles (Con- ley et al. 2008). Laboratory experiments with graded beds sim- ulating very high energy sheet-flow conditions show prefer- ential transport of the coarse fractions in the mixture (e.g., van der Werf et al. 2006), and that the transport of each size- fraction is strongly influenced by the presence of other frac- tions (e.g., Wilcock 1988). Model calculations of suspended-sediment flux have been shown to become highly inaccurate within hours if the effects of variable bed-sediment grain-size are ignored, because waves and currents can modify the spatial distribution of seabed sed- iments in a variety of shelf settings over this time-scale (Har- ris and Wiberg 2002). However, advances in modeling grain- size sorting (spatial segregation) and its underlying selective transport mechanisms are hampered by few observations at sufficient coverage/frequency with which to compare theory. The result is that most nearshore (e.g., Bailard 1981; Larson and Kraus 1995) and regional shelf (e.g., Harris and Coleman 1998; Zhang et al. 1999; Cookman and Flemings 2001) mod- els tend to oversimplify grain-size distribution effects on sedi- ment transport because detailed observations of the behavior of a mixture of size fractions is lacking. A more complete understanding of the role of grain size in the physics of sediment transport requires the collection of grain-size data with more temporal and spatial coverage, and Autonomous bed-sediment imaging-systems for revealing temporal variability of grain size Daniel Buscombe1*, David M. Rubin2, Jessica R. Lacy2, Curt D. Storlazzi2, Gerald Hatcher2, Henry Chezar2, Robert Wyland2, and Christopher R. Sherwood3 1United States Geological Survey, Flagstaff, Arizona, USA 2United States Geological Survey, Santa Cruz, California, USA 3United States Geological Survey, Woods Hole, Massachusetts, USA Abstract We describe a remotely operated video microscope system, designed to provide high-resolution images of seabed sediments. Two versions were developed, which differ in how they raise the camera from the seabed. The first used hydraulics and the second used the energy associated with wave orbital motion. Images were analyzed using automated frequency-domain methods, which following a rigorous partially supervised quality control procedure, yielded estimates to within 20% of the true size as determined by on-screen manual measurements of grains. Long-term grain-size variability at a sandy inner shelf site offshore of Santa Cruz, California, USA, was investigated using the hydraulic system. Eighteen months of high frequency (min to h), high-resolution (μm) images were collected, and grain size distributions compiled. The data constitutes the longest known high-fre- quency record of seabed-grain size at this sample frequency, at any location. Short-term grain-size variability of sand in an energetic surf zone at Praa Sands, Cornwall, UK was investigated using the ‘wave-powered’ system. The data are the first high-frequency record of grain size at a single location of a highly mobile and evolving bed in a natural surf zone. Using this technology, it is now possible to measure bed-sediment-grain size at a time-scale comparable with flow conditions. Results suggest models of sediment transport at sandy, wave-dom- inated, nearshore locations should allow for substantial changes in grain-size distribution over time-scales as short as a few hours. *Corresponding author: E-mail: dbuscombe@usgs.gov Acknowledgments Full text appears at the end of the article. DOI 10.4319/lom.2014.12.390 Limnol. Oceanogr.: Methods 12, 2014, 390–406 © 2014, by the American Society of Limnology and Oceanography, Inc. LIMNOLOGY and OCEANOGRAPHY: METHODS Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 9/39 9/39
  20. Background • 2nd Post-doc University of Plymouth, UK 2009–2012. Turbulence,

    Sediment Stratification and Altered Resuspension Under Waves. (Daniel Conley, Alex Nimmo-Smith) Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 10/39 10/39
  21. Background • 2nd Post-doc University of Plymouth, UK 2009–2012. Turbulence,

    Sediment Stratification and Altered Resuspension Under Waves. (Daniel Conley, Alex Nimmo-Smith) Effective shear stress of graded sediments D. Buscombe1 and D. C. Conley1 Received 17 December 2010; revised 20 March 2012; accepted 20 March 2012; published 3 May 2012. [1] A meta-analysis of fractional mobilization data from 14 sets of experiments, totaling 103 different mixed sand and gravel beds and flow conditions, has been carried out in order to identify an expression for effective shear stress, here defined as the component of bed shear stress that is directly involved in transporting each grain size fraction in graded sediment. In doing so we test the assumption that excess stress should be defined solely in terms of a critical stress rather than effective stress, which exhibits sensitivity to the flow stage. In contrast to the approach which evaluates the size-distribution effects on motion threshold by comparing inferred transport rates, an alternative approach is utilized which is based on the skill of reproducing the measured, mobilized particle size distribution. A simple equation is developed for mobilization of sediment mixtures, based on a well-established transport law, and employing a classical ‘‘hiding function’’ approach to the problem of mitigating the bias toward mobilizing fine material in the mixture. We use inverse methods to find the optimal form of the hiding function which provides the best fit with the data. We find that the hiding function is indeed sensitive to the flow and bed composition. On this basis, a simple deterministic equation is proposed for fraction-specific effective stress, which outperforms the next best existing formula based on critical stress by 34% on aggregate. Citation: Buscombe, D., and D. C. Conley (2012), Effective shear stress of graded sediments, Water Resour. Res., 48, W05506, doi:10.1029/2010WR010341. 1. Introduction [2] It is generally accepted that volumetric sediment transport rate per unit width, q, is related to bed shear stress, , such that q ¼ fðnÞ. If bed shear stress is nondi- mensionalized as,  ¼ u2 à RgD (1) (known as the Shields parameter after Shields [1936]), in which uà ¼ ffiffiffiffiffiffiffiffi = p is shear velocity, R ¼ ðs À Þ= is spe- cific sediment density, and , s are the densities of fluid and sediment, respectively, g is gravitational acceleration, and D is grain diameter, nondimensionalized volumetric sediment transport rate (or sediment flux) is expressed as qà ¼ fðn=2Þ, where qà ¼ q ffiffiffiffiffiffiffiffiffiffiffi RgD3 p : (2) [3] To highest order, a large proportion of such models can be represented as qà ¼ fð3=2Þ [e.g., Meyer-Peter and Müller, 1948; Einstein, 1950; Ashida and Michiue, 1972; Bagnold, 1973; Fernandez-Luque and Van Beek, 1976; Kachel and Sternberg, 1971; Parker and Klingeman, 1982; Wiberg and Smith, 1989; Wong and Parker, 2006] but alternatives exist, and are suggested predominantly for oscillatory flows, such as qà ¼ fð4=2Þ [e.g., Sleath, 1978], qà ¼ fð5=2Þ [e.g., Hanes and Bowen, 1985], and qà ¼ fð6=2Þ [e.g., Madsen and Grant, 1976]. [4] Shields [1936] introduced the concept of a critical normalized shear stress, c , necessary to initiate sediment motion (the ‘‘mobility’’ of the sediment). The existence of such a threshold implies q ¼ 0 when  < c , where c rep- resents a critical value of the Shields parameter, which is the value of the Shields parameter at incipient motion. The critical Shields parameter is commonly given as a function of grain Reynolds number: Reg ¼ uÃD  ; (3) where  is the kinematic viscosity of the fluid. A commonly used sediment transport model, which was one of the first to attempt to account for the implications of threshold con- ditions, is that of Meyer-Peter and Müller [1948] given by: qà ¼ 8ð À 0:047Þ3=2: (4) [5] The fact that their ‘‘threshold’’ is a constant is unsur- prising given that for the majority of their experiments Reg > 102, for which c is a constant %0.047. It has since become common practice in much of deterministic sedi- ment transport modeling to include a critical Shields pa- rameter to provide the mathematical consistency for the 1School of Marine Science and Engineering, University of Plymouth, Plymouth, UK. Copyright 2012 by the American Geophysical Union 0043-1397/12/2010WR010341 W05506 1 of 13 WATER RESOURCES RESEARCH, VOL. 48, W05506, doi:10.1029/2010WR010341, 2012 SCHMIDT NUMBER OF SAND SUSPENSIONS UNDER OSCILLATING GRID TURBULENCE Daniel Buscombe1 and Daniel C. Conley2 In many models of sand suspension under waves, the diffusivity of sediment is related to the diffusivity of momentum by the inverse of the turbulent Schmidt number. The value and parameterization of this number has been the topic of much research, yet a lack of consensus has led to ad hoc adjustments in models of turbulent sediment suspensions, with apparently little physical justification. In order to study sediment diffusivity we conducted laboratory experiments to generate gradient-only sediment diffusion. Concentrations of sand suspended by near-isotropic turbulence generated by an oscillating grid, together with detailed velocity measurements, were used to calculate vertical profiles of the Schmidt number with a range of grain sizes and flow conditions. Initial results suggest that momentum diffusivity is greater than sediment diffusivity, and that the ratio of the two scales with grid Reynolds number. Ongoing work will ascertain whether an apparent grain size dependence could instead be explained by two-way feedbacks between sediment and turbulence. Keywords: oscillating grid turbulence; Schmidt number; sediment diffusivity; suspended sediment; eddy viscosity INTRODUCTION In nearshore (combined wave and current) flows, fluid velocities and sand concentrations vary strongly in time during a wave cycle (Conley and Beach, 2003) which is why so-called (wave) 'phase- resolving' or 'intra-wave' models of suspended sediment transport have gained in popularity in recent years (e.g. Li and Davies, 2001; Holmedal et al., 2004; Henderson et al., 2004; Conley et al., 2008; Ruessink et al., 2009). This type of model predicts the velocity and sand concentration fields in time and space by combining solutions to the basic fluid momentum and continuity equations with an advection-diffusion equation to compute the sediment mass balance. Suspended sand concentrations are obtained by solving a 1DV advection-diffusion equation of the form: ∂C ∂t = ∂ ∂ z (ε s (z) ∂ C ∂ z +w s C ) (1) where C = instantaneous volumetric sand concentration; t = time; z = vertical coordinate; εs = sediment diffusivity; and ws = sand settling velocity. Describing fluid motion requires a corresponding momentum balance equation (e.g. Li and Davies, 2001), the solution of which requires an expression for turbulent eddy viscosity which describes the fluid turbulence. In the approach to modelling turbulent mixing under nearshore waves described above, the simplest treatment of sediment diffusivity is to express it as some fraction of the turbulent momentum diffusivity. The ratio, known as the Schmidt number, in nearshore sediment transport models is the ratio of the turbulent eddy viscosity, νt , to sediment diffusivity, εs : β= ν t ε s (2) Outputs of phase-resolving models of sediment suspension are very sensitive to the Schmidt number (e.g. Davies, 1995; Amoudry et al., 2005; Ruessink et al., 2009). Many models assume β=1 (e.g. Fredsoe et al., 1985, Celik and Rodi, 1988), an assumption which seems safest when the evidence for its value seems so contradictory. Indeed there are approximately as many studies in the literature which have used a value less than 1 as those which have a value greater than 1. An argument commonly stated for β >1 is that particles lose correlation with fluid motion as they settle through turbulent eddies (e.g. Fredsoe and Diegaard, 1992). A counter argument (which leads to β <1) is that centrifugal forces have a larger effect on particles than they do on the surrounding fluid, due to particle inertia, thought to be the case above a rippled bed (e.g. van Rijn, 1984; Davies and Thorne, 2005). Nearshore sediment transport literature reports values between 0.1 and 10. This large variation inadequate parameterization of β, which is therefore allowed to vary with model equations and boundary conditions used. The Schmidt number is often used as a tunable parameter (e.g. Ruessink et al., 2009), which isn't a satisfactory situation. 1 School of Marine Science and Engineering, Plymouth University, Drake Circus, Plymouth, Devon, PL4 8AA, UK 2 School of Marine Science and Engineering, Plymouth University, Drake Circus, Plymouth, Devon, PL4 8AA, UK 1 Evaluating Unsupervised Methods to Size and Classify Suspended Particles Using Digital In-Line Holography EMLYN J. DAVIES,* DANIEL BUSCOMBE,1 GEORGE W. GRAHAM,# AND W. ALEX M. NIMMO-SMITH School of Marine Science and Engineering, Plymouth University, Plymouth, United Kingdom (Manuscript received 13 August 2014, in final form 3 December 2014) ABSTRACT Substantial information can be gained from digital in-line holography of marine particles, eliminating depth- of-field and focusing errors associated with standard lens-based imaging methods. However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing, and classification of particles. These computational challenges are the subject of this paper, in which the authors draw upon data collected using a variety of holographic systems developed at Plymouth University, United Kingdom, from a significant range of particle types, sizes, and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from over- segmentation of particles. A simple unsupervised particle classification system is developed and is capable of successfully differentiating sand grains, bubbles, and diatoms from within the surfzone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classification. The simple method proposed here provides a foundation upon which fur- ther development could lead to reliable identification of more complex particle populations, such as those containing phytoplankton, zooplankton, flocculated cohesive sediments, and oil droplets. 1. Introduction Characterizing particles suspended in seawater has become a critical component in understanding the or- ganic carbon cycle, ocean acidification, oceanic circula- tion, and future climate predictions. Possessing a method to accurately and automatically characterize these parti- cles has therefore become important for many areas of marine science and monitoring. For example, suspended particles serve as passive tracers that aid the un- derstanding of turbulent mixing of plankton, heat, and salinity. The measurement and understanding of sus- pended sediment flux is crucial for the prediction of coastal and estuarine change, the operation of ports and harbors, and the safe passage of shipping. Suspended particles also play a key role in controlling radiative transfer (therefore, the interpretation of satellite ocean color imagery) and primary productivity. Particles also scatter sound—a principle that enables acoustic mea- surements of flow velocities, suspended mineral sedi- ments, and bathymetric mapping. Information on the type (organic, inorganic, photosynthesizing, non- photosynthesizing), size, shape, and concentration of particles in seawater provides the necessary insight re- quired to advance understanding of these fundamental processes within the marine environment. Denotes Open Access content. * Current affiliation: Department of Environmental Technology, SINTEF Materials and Chemistry, Trondheim, Norway. 1 Current affiliation: Grand Canyon Monitoring and Research Center, and Southwest Biological Science Center, U.S. Geological Survey, Flagstaff, Arizona. # Current affiliation: Sir Alister Hardy Foundation for Ocean Science, Plymouth, United Kingdom. Corresponding author address: Emlyn J. Davies, Department of Environmental Technology, SINTEF Materials and Chemistry, P.O. Box 4760 Sluppen, NO-7465 Trondheim, Norway. E-mail: emlyn.john.davies@sintef.no JUNE 2015 D A V I E S E T A L . 1241 DOI: 10.1175/JTECH-D-14-00157.1 Ó 2015 American Meteorological Society Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 10/39 10/39
  22. Background • Research Geologist U.S Geological Survey 2012–2016. Sedimentology and

    geomorphology of large rivers, Acoustic Remote Sensing. (Paul Grams, Matt Kaplinski) Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 11/39 11/39
  23. Background • Research Geologist U.S Geological Survey 2012–2016. Sedimentology and

    geomorphology of large rivers, Acoustic Remote Sensing. (Paul Grams, Matt Kaplinski) Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 11/39 11/39
  24. Background • Research Geologist U.S Geological Survey 2012–2016. Sedimentology and

    geomorphology of large rivers, Acoustic Remote Sensing. (Paul Grams, Matt Kaplinski) Technical Note Automated Riverbed Sediment Classification Using Low-Cost Sidescan Sonar Daniel Buscombe1; Paul E. Grams2; and Sean M. C. Smith3 Abstract: The use of low-cost, low-profile, and highly portable sidescan sonar is on the ascendancy for imaging shallow riverine benthic sediments. A new automated, spatially explicit, and physically-based method for calculating lengthscales of bed texture elements in sidescan echograms (a 2D plot of acoustic intensity as a function of slant range and distance) is suggested. It uses spectral analysis based on the wavelet transform of short sequences of echograms. The recursive application of the transform over small overlapping windows of the echogram provides a robust measure of lengthscales of alternating patterns of strong and weak echoes. This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical representation that integrates over many attributes of bed texture, of which grain size is the most important. The technique is a physically-based means to identify regions of texture within a sidescan echogram, and could provide a basis for objective, automated riverbed sediment classification. Results are evaluated using data from two contrasting riverbed environments: those of the Colorado River in Grand Canyon, Arizona, and the West Branch of the Penobscot River, Maine. DOI: 10.1061/(ASCE)HY.1943-7900 .0001079. This work is made available under the terms of the Creative Commons Attribution 4.0 International license, http:// creativecommons.org/licenses/by/4.0/. Introduction Classifying subaqueous riverbed sediments by grain size provides a means to parameterize important boundary conditions for studies of channel hydraulics, aquatic ecology, sedimentation, geomorphic change, and sediment transport. Sampling riverbed sediment de- posits with sufficient spatial density and coverage is particularly difficult when the water is too turbid or deep to image the bed from aerial platforms, or too swift or deep to wade to obtain physical samples. Conventional underwater imaging systems are limited by light attenuation, turbidity, and the small spatial footprint of im- ages (Buscombe et al. 2014a). Sidescan sonar has the potential to address this technical shortfall (Kaeser and Litts 2010) by recording acoustic signals that can be analyzed in a manner that distinguishes varied substrate textures on the basis of related patterns of echo intensities. Sidescan sonar, deployed from a moving vessel, produces photograph-like images of river and bed texture (Fig. 1). The trans- ducer sends out a high-frequency (typically several hundred kHz) acoustic beam perpendicular to the vessel heading on either side (port and starboard) and records the amplitude of the returning ech- oes from a wide swath (Blondel 2009). One ping constitutes the simultaneous acquisition of data from the two sidescan beams at an instant, returning a swath composed of pixels whose intensity relates to the echo strength, determined by acoustic impedance and reflection at those locations. A small strip of the bed is imaged with each ping, building an echogram that provides near continuous coverage as the vessel moves along-track (up or downstream). Spatial distributions of bottom texture visible within echograms are related to the roughness of the bed, which provides a means to map bed-sediment types (Collier and Brown 2005). Sidescan pixels are typically of order centimeter to decimeter in size, there- fore do not resolve individual sand and gravel clasts but do resolve cobbles and boulders, yet the texture in the sonar echogram for patches of sand and gravel should be distinct and quantifiable. In this paper, texture is defined as the frequency of change in arrangement of small-scale surface roughness, which in turn is a measure of the statistical variation in the distribution of bed sedi- ment grain sizes. Texture is used as a 2D quantity without formal definition but is, in concept, closely related to autocorrelation. Less textured surfaces (i.e., over smooth beds) in sonar echograms show less variation in adjacent sidescan pixels over space; they are less textured and have smaller texture lengthscales. In contrast, rough beds in sonar echograms have more texture because they have relatively large spatial variations in adjacent pixels over space (caused by reflections and shadows): the spacing between textural elements is larger and the rough bed is characterized by larger average texture lengthscales. Several inexpensive sidescan units that are easily mounted to small vessels are commercially available and suitable for substrate imaging. These low-profile systems have a minimal draft require- ment so they are especially suitable for imaging in very shallow water. Lightweight and with low power demands, they can be op- erated by one person in any river or stream navigable by a small boat. Kaeser and Litts (2010) reported that one such system (the sidescan sonar on the Humminbird fishfinder) was of sufficient quality for bed imaging in shallow rocky streams. Kaeser et al. (2012) produced bed-sediment maps by merging and superimpos- ing imagery onto a base map in a Geographical Information System (GIS), identifying regions of similar bed texture by visual inspec- tion. In contrast, multibeam sonar systems are typically heavier, more expensive, with greater computational and power demands, and require a greater draft as well as specialist knowledge to operate. 1Research Geologist, U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, Flagstaff, AZ 86001 (corresponding author). E-mail: dbuscombe@usgs.gov 2Research Hydrologist, U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, Flagstaff, AZ 86001. 3Assistant Professor, School of Earth and Climate Sciences, Univ. of Maine, Orono, ME 04469. Note. This manuscript was submitted on September 9, 2014; approved on July 6, 2015; published online on September 23, 2015. Discussion per- iod open until February 23, 2016; separate discussions must be submitted for individual papers. This technical note is part of the Journal of Hydrau- lic Engineering, © ASCE, ISSN 0733-9429. © ASCE 06015019-1 J. Hydraul. Eng. J. Hydraul. Eng. Downloaded from ascelibrary.org by 24.156.81.170 on 10/02/15. Copyright ASCE. For personal use only; all rights reserved. JournalofGeophysicalResearch: EarthSurface RESEARCH ARTICLE 10.1002/2014JF003191 This article is a companion to Buscombe et al. [2014] doi:10.1002/2014JF003189. Key Points: • Examines relationship between backscatter spectral properties and sediment types • Slope, intercept, and integral of power law spectral form sensitive to sediments • Uses this to successfully classify Colorado River bed sediment in Grand Canyon Correspondence to: D. Buscombe, dbuscombe@usgs.gov Citation: Buscombe, D., P. E. Grams, and M. A. Kaplinski (2014), Charac- terizing riverbed sediment using high-frequency acoustics 2: Scattering signatures of Colorado River bed sed- iment in Marble and Grand Canyons, J. Geophys. Res. Earth Surf., 119, doi:10.1002/2014JF003191. Received 23 APR 2014 Accepted 24 OCT 2014 Accepted article online 29 OCT 2014 This article is a US Government work and, as such, is in the public domain in the United States of America. Characterizing riverbed sediment using high-frequency acoustics: 2. Scattering signatures of Colorado River bed sediment in Marble and Grand Canyons D. Buscombe1, P. E. Grams1, and M. A. Kaplinski2 1Grand Canyon Monitoring and Research Center, Southwest Biological Science Center, U.S. Geological Survey, Flagstaff, Arizona, USA, 2School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, Arizona, USA Abstract In this, the second of a pair of papers on the statistical signatures of riverbed sediment in high-frequency acoustic backscatter, spatially explicit maps of the stochastic geometries (length and amplitude scales) of backscatter are related to patches of riverbed surfaces composed of known sediment types, as determined by georeferenced underwater video observations. Statistics of backscatter magnitudes alone are found to be poor discriminators between sediment types. However, the variance of the power spectrum and the intercept and slope from a power law spectral form (termed the spectral strength and exponent, respectively) successfully discriminate between sediment types. A decision tree approach was able to classify spatially heterogeneous patches of homogeneous sands, gravels (and sand-gravel mixtures), and cobbles/boulders with 95, 88, and 91% accuracy, respectively. Application to sites outside the calibration and surveys made at calibration sites at different times were plausible based on observations from underwater video. Analysis of decision trees built with different training data sets suggested that the spectral exponent was consistently the most important variable in the classification. In the absence of theory concerning how spatially variable sediment surfaces scatter high-frequency sound, the primary advantage of this data-driven approach to classify bed sediment over alternatives is that spectral methods have well-understood properties and make no assumptions about the distributional form of the fluctuating component of backscatter over small spatial scales. 1. Introduction Conventional sampling for grain size of submerged sediment deposits (e.g., grabs, cores, and dredges) is costly and labor intensive. Video and photographic sampling is more cost effective and does not require time-consuming laboratory analyses, which allows sampling at greater frequency and coverage [Rubin et al., 2007; Van Rein et al., 2009; Buscombe et al., 2014a]. However, the use of high-frequency (several hundred kilohertz) acoustic backscatter from swath-mapping systems to characterize sediment and classify by grain size [Anderson et al., 2008; Brown and Blondel, 2009; Brown et al., 2011; Snellen et al., 2013] has the potential to provide near-complete coverage, which photographic sampling could not practically achieve, at least within the same time and with the same positional accuracy. Such density and coverage of sampling are important for characterizing the sedimentary makeup of heterogeneous riverbeds [Nelson et al., 2014], which consist of a patchwork of homogeneous or near-homogeneous sediment over scales ranging from less than one to several tens of square meters [Dietrich and Smith, 1984; Paola and Seal, 1995; Nelson et al., 2009]. Bed sediment grain size between adja- cent patches can vary by an order of magnitude. Capturing such variability with conventional physical or photographic sampling would be prohibitively costly and time consuming. In shallow water, given the lack of robust classification techniques based on the physics of scattering for high-frequency multibeam systems [Amiri-Simkooei et al., 2009], an alternative phenomenological approach based on statistical analysis of backscatter signals [Brown and Blondel, 2009; Snellen et al., 2013] has become popular. Many such methods proposed to date rely on aggregation of data over scales much larger than the typical scales of sediment patchiness on heterogeneous riverbeds. In this contribution, we develop a new statistical method for acoustic sediment classification based on analysis of backscatter, which is both continuous in coverage and of sufficient resolution (order meter) to characterize sediment variability on BUSCOMBE ET AL. Published 2014. American Geophysical Union. 1 Case study Spatially explicit spectral analysis of point clouds and geospatial data Daniel Buscombe U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, Flagstaff, AZ, USA a r t i c l e i n f o Article history: Received 30 April 2015 Accepted 5 October 2015 Available online 23 October 2015 Keywords: Point cloud Spectral analysis Geospatial analysis Roughness Texture Remote sensing a b s t r a c t The increasing use of spatially explicit analyses of high-resolution spatially distributed data (imagery and point clouds) for the purposes of characterising spatial heterogeneity in geophysical phenomena ne- cessitates the development of custom analytical and computational tools. In recent years, such analyses have become the basis of, for example, automated texture characterisation and segmentation, roughness and grain size calculation, and feature detection and classification, from a variety of data types. In this work, much use has been made of statistical descriptors of localised spatial variations in amplitude variance (roughness), however the horizontal scale (wavelength) and spacing of roughness elements is rarely considered. This is despite the fact that the ratio of characteristic vertical to horizontal scales is not constant and can yield important information about physical scaling relationships. Spectral analysis is a hitherto under-utilised but powerful means to acquire statistical information about relevant amplitude and wavelength scales, simultaneously and with computational efficiency. Further, quantifying spatially distributed data in the frequency domain lends itself to the development of stochastic models for probing the underlying mechanisms which govern the spatial distribution of geological and geophysical phe- nomena. The software package PySESA (Python program for Spatially Explicit Spectral Analysis) has been developed for generic analyses of spatially distributed data in both the spatial and frequency do- mains. Developed predominantly in Python, it accesses libraries written in Cython and Cþ þ for effi- ciency. It is open source and modular, therefore readily incorporated into, and combined with, other data analysis tools and frameworks with particular utility for supporting research in the fields of geomor- phology, geophysics, hydrography, photogrammetry and remote sensing. The analytical and computa- tional structure of the toolbox is described, and its functionality illustrated with an example of a high- resolution bathymetric point cloud data collected with multibeam echosounder. Published by Elsevier Ltd. 1. Introduction 1.1. The growing use of high-resolution point clouds in the geosciences Across a broad range of geoscience disciplines, interrogating the information in high-resolution spatially distributed data (point clouds) for the purposes of, for example, facies description and grain size calculation (e.g. Hodge et al., 2009; Nelson et al., 2014), geomorphic feature detection and classification (e.g. Burrough et al., 2000; Glenn et al., 2006; Pirotti and Tarolli, 2010), vegetation structure description (e.g. Antonarakis et al., 2009; Dassot et al., 2011), and physical habitat quantification (e.g. Vierling et al., 2008; Wheaton et al., 2010; Lassueur et al., 2006; Pradervand et al., 2014) has become increasingly widespread. The increasing accessibility and use of high-resolution topographic point clouds obtained using Light Detection and Ranging (LiDAR) (e.g. Buckley et al., 2008; Hilldale and Raff, 2008), Structure from Motion (SfM) pho- togrammetry (e.g. James and Robson, 2012; Westoby et al., 2012; Fonstad et al., 2013; Woodget et al., 2015), and range imaging (e.g. Nitsche et al., 2013) has found widespread application in geo- morphology (Roering et al., 2013; Tarolli, 2014). The use of sin- glebeam and multibeam echosounders for bathymetric point cloud collection is on the ascendancy (Mayer, 2006) in geophysical and geomorphological research, and is becoming viable in in- creasingly shallow water (e.g. Parsons et al., 2005; Wright and Kaplinski, 2011; Buscombe et al., 2014b). 1.2. Spatially explicit analysis of topographic point clouds With these technological developments, the heights of natural surfaces can now be measured with such spatial density that al- most the entire spectrum of physical roughness scales can be characterised, down to the form and even grain scales (Brasington et al., 2012). Such ‘microtopography’ has created a demand for analytical and computational tools for spatially explicit (also known as spatially distributed) statistical characterisation of the Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/cageo Computers & Geosciences http://dx.doi.org/10.1016/j.cageo.2015.10.004 0098-3004/Published by Elsevier Ltd. E-mail address: dbuscombe@usgs.gov Computers & Geosciences 86 (2016) 92–108 Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 11/39 11/39
  25. Today’s Talk Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 12/39

    12/39
  26. Today’s Talk Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 12/39

    12/39
  27. Today’s Talk Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 12/39

    12/39
  28. Today’s Talk Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 12/39

    12/39
  29. Measuring Particle Size • Traditional particle size analysis by direct

    means (from physical samples) at relatively few discrete locations. Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 13/39 13/39
  30. Measuring Particle Size • Traditional particle size analysis by direct

    means (from physical samples) at relatively few discrete locations. • Accurate, high resolution Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 13/39 13/39
  31. Measuring Particle Size • Traditional particle size analysis by direct

    means (from physical samples) at relatively few discrete locations. • Accurate, high resolution • Costly, slow Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 13/39 13/39
  32. Measuring Particle Size • Traditional particle size analysis by direct

    means (from physical samples) at relatively few discrete locations. • Accurate, high resolution • Costly, slow • Intrusive/Destructive Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 13/39 13/39
  33. Measuring Particle Size • Traditional particle size analysis by direct

    means (from physical samples) at relatively few discrete locations. • Accurate, high resolution • Costly, slow • Intrusive/Destructive • Impossible? Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 13/39 13/39
  34. Measuring Particle Size • Traditional particle size analysis by direct

    means (from physical samples) at relatively few discrete locations. • Accurate, high resolution • Costly, slow • Intrusive/Destructive • Impossible? • Unachievable resolution? Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 13/39 13/39
  35. Measuring Particle Size – A Case Study Cuttler, Lowe, Falter,

    Buscombe (2016, in press) Sedimentology Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 14/39 14/39
  36. https://xkcd.com/927/ Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 15/39 15/39

  37. Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 16/39 16/39

  38. • Particle size → settling velocity • Particle size →

    flow velocity at deposition • Particle size → hydraulic roughness • Particle size → habitat suitability • Particle size → moisture retention Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 16/39 16/39
  39. • Tracking big sedimentary changes at unprecedented scales and resolutions

    • Monitoring continuously over space and/or time with lower accuracy. • Inferring particle size from remotely sensed signals • Track changes in particle size as landforms evolve (dependent vs.independent variable) Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 17/39 17/39
  40. Measuring Particle Size ‘by Proxy’ • Particle size analysis from

    by indirect means (usually remotely sensed). • Continuous in space/time Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 18/39 18/39
  41. Measuring Particle Size ‘by Proxy’ • Particle size analysis from

    by indirect means (usually remotely sensed). • Continuous in space/time • Less costly, fast Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 18/39 18/39
  42. Measuring Particle Size ‘by Proxy’ • Particle size analysis from

    by indirect means (usually remotely sensed). • Continuous in space/time • Less costly, fast • Non-intrusive/destructive Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 18/39 18/39
  43. Measuring Particle Size ‘by Proxy’ • Particle size analysis from

    by indirect means (usually remotely sensed). • Continuous in space/time • Less costly, fast • Non-intrusive/destructive • Develop proxies Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 18/39 18/39
  44. Measuring Particle Size ‘by Proxy’ • Particle size analysis from

    by indirect means (usually remotely sensed). • Continuous in space/time • Less costly, fast • Non-intrusive/destructive • Develop proxies • Requisite resolution? Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 18/39 18/39
  45. Particle Size ... 1. ... through scattering of sound ◦

    using high-frequency sound to classify riverbed substrates continuously in space Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 19/39 19/39
  46. Particle Size ... 2. ... through terrain roughness ◦ using

    high-resolution topography to classify terrestrial landcover and substrates continuously in space Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 19/39 19/39
  47. Particle Size ... 3. ... through scattering of light ◦

    using in-line digital particle holography to size and classify surf zone suspensions continuously in time Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 19/39 19/39
  48. 1. Particle Size By Sound Scattering Buscombe et al. (2014a,b)

    JGR - Earth Surface Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 20/39 20/39
  49. 1. Particle Size By Sound Scattering Buscombe et al. (2014a,b)

    JGR - Earth Surface Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 20/39 20/39
  50. 1. Particle Size By Sound Scattering Buscombe et al. (2014a,b)

    JGR - Earth Surface Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 20/39 20/39
  51. 1. Particle Size By Sound Scattering Buscombe et al. (2014a,b)

    JGR - Earth Surface Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 20/39 20/39
  52. 1. Particle Size By Sound Scattering Buscombe et al. (2014a,b)

    JGR - Earth Surface Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 20/39 20/39
  53. Acoustic Riverbed Sediment Classification Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by

    Proxy’ 21/39 21/39
  54. Acoustic Riverbed Sediment Classification Grams, Buscombe et al. (in review)

    Geology Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 21/39 21/39
  55. What’s Next? Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 22/39

    22/39
  56. What’s Next? Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 22/39

    22/39
  57. Landscape-Scale Structure-from-Motion Photogrammetry Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’

    23/39 23/39
  58. Landscape-Scale Structure-from-Motion Photogrammetry Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’

    23/39 23/39
  59. Landscape-Scale Structure-from-Motion Photogrammetry Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’

    23/39 23/39
  60. 2. Particle Size By Roughness Buscombe (2016) Computers & Geosciences

    Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 24/39 24/39
  61. 2. Particle Size By Roughness Buscombe (2016) Computers & Geosciences

    Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 24/39 24/39
  62. Surveying a New Rapid in Grand Canyon Daniel Buscombe. dbuscombe@usgs.gov

    Particle Size ‘by Proxy’ 25/39 25/39
  63. Surveying a New Rapid in Grand Canyon Daniel Buscombe. dbuscombe@usgs.gov

    Particle Size ‘by Proxy’ 25/39 25/39
  64. 2. Particle Size By Roughness Daniel Buscombe. dbuscombe@usgs.gov Particle Size

    ‘by Proxy’ 26/39 26/39
  65. Measuring Particle Size in the Sea (Continuously) can be Tricky

    Surf zone at Perranporth Beach, North Cornwall Microscopic plankton. Image: FAU Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 27/39 27/39
  66. 3. Particle Size By Light Scattering Davies, Buscombe et al

    (2014a,b) J. Atmos. & Oceanographic Tech. Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 28/39 28/39
  67. 3. Particle Size By Light Scattering Davies, Buscombe et al

    (2014a,b) J. Atmos. & Oceanographic Tech. Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 28/39 28/39
  68. Surf Zone Particle Holography Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by

    Proxy’ 29/39 29/39
  69. Surf Zone Particle Holography Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by

    Proxy’ 29/39 29/39
  70. Surf Zone Particle Holography Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by

    Proxy’ 29/39 29/39
  71. Concluding Remarks 1. We can continue to make advances in

    measuring particle size by studying the interactions of particles with sound, light and EM radiation Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 30/39 30/39
  72. Concluding Remarks 2. By trading accuracy/precision for spatial and temporal

    coverage and resolution, we can track changes in particle size as landforms evolve Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 30/39 30/39
  73. Concluding Remarks 3. Tease out the two-way feedbacks between particle

    size, fluid-flows and landform evolution Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 30/39 30/39
  74. Concluding Remarks Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 31/39

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  75. Thanks for Listening dbuscombe dbuscombe-usgs Daniel Buscombe. dbuscombe@usgs.gov Particle Size

    ‘by Proxy’ 32/39 32/39
  76. Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 33/39 33/39

  77. Acoustic Sediment Classification Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’

    34/39 34/39
  78. Roughness Calculation Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 35/39

    35/39
  79. Particle Size from Roughness Ryan Richardson, U. Wyoming Daniel Buscombe.

    dbuscombe@usgs.gov Particle Size ‘by Proxy’ 36/39 36/39
  80. Holographic Particle Classification Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’

    37/39 37/39
  81. Poking Eyeball Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 38/39

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  82. Poking Eyeball Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 39/39

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