A little bit more detail on individual research outputs, field campaigns, comings and goings, and goings on ...
2019 AGU Fall Meeting, San Francisco, December
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EP020 A mixed scaling model for migrating bedform velocities in sand bedded rivers. Guala et al.
NSOO3 Open source tools for sediment classification using compositional backscatter in fluvial environments. Fisher et al.
OS025 Using wave modeling and sedimentary provenance to better understand storage and transport processes in terrestrially-connected submarine canyon heads, King Range coast, CA. Joerger et al.
This clinic will introduce deep learning methods for semantic segmentation of fluvial sedimentary landforms and riparian environments, using high-resolution aerial imagery. Deep neural networks are the current state-of-the-art for discrete classification of remotely sensed imagery from Earth observation platforms. The clinic will guide users through the process of preparing training datasets, training models, and evaluation. A number of different deep convolutional neural network architectures for image feature extraction and pixel-scale classifications will be explored and compared.
More about the Community Surface Dynamics Modeling System (CSDMS) Project can be found here
May 2019. Back to the Lab!
Last September, myself and colleagues from USGS and the St. Anthony Falls Laboratory, University of Minnesota, studied the acoustic backscattering properties of zebra mussels (Dreissena polymorpha) and native mussels, Threeridge (Amblema plicata), under controlled laboratory settings. Experiments were conducted in self-contained tanks at the St. Anthony Falls Laboratory, and findings will be used to determine the optimal acoustic parameters that will maximize the discrimination between mussels and substrates.
In May I went back to conduct experiments to characterize the acoustic properties of the tank, measuring reverberations and sound source levels using a high-frequency hydrophone (made by Bethowave Inc) sampling at 2 MHz!
April 2019. Channel mapping in Marble and Grand Canyon
Thanks to R2Sonic for support!
April 2019. New article published in Remote Sensing
We apply deep convolutional neural networks (CNNs) to estimate wave breaking type (e.g., non-breaking, spilling, plunging) from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. For a plunging breaker, the crest and back face of the wave are most important, which suggests that CNN-based models utilize the distinctive ‘streak’ temperature patterns observed on the back face of plunging breakers for classification
Buscombe, D.; Carini, R.J. A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery. Remote Sens. 2019, 11, 859
Mar 2019: Bronze Badge for Reproducibility
Stagge, J.H., Rosenberg, D.E., Abdallah, A.M., Akbar, H., Attallah, N.A. and James, R., 2019. Assessing data availability and research reproducibility in hydrology and water resources. Scientific Data, 6, p.190030.
Buscombe, D., 2017. Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar. Environmental modelling & software, 89, pp.1-18.
Feb 2019: Multispectral Full Water Column Acoustic Survey
We spent two days sampling above the USGS sediment and discharge gage above Diamond Creek, Colorado River in Grand Canyon.
The objective, in collaboration with U.S. Geological Survey Grand Canyon Monitoring and Research Center, and R2Sonic, makers of multispectral (simultaneous multi-frequency surveys) multibeam echosounder, was to collect a multispectral backscatter data set over mobile sand dunes. More details soon!
2018 AGU Fall Meeting, Washington DC, December
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EP31C-2368 Comprehensive Sediment-Transport Measurements on a Large Sand-Bedded River: Bedform Tracking, Acoustical Suspended-Sediment Measurements, and Physical Sampling on the Lower Chippewa River, WI. Dean et al.
EP31C-2362 Estimating Riverbed Sand Thickness Using CHIRP Sonar: Case Study from the Colorado River in Grand Canyon. Platt et al.
EP33B-04 Flow Alteration, River Valley Morphology, and the Influence of Glen Canyon Dam on Sediment Availability along the Colorado River in Grand Canyon. Kasprak et al.
EP24A-06 Estimating bedload from suspended load measurements on the Colorado River in Grand Canyon National Park. McElroy et al.
Nov 2018. New article published in Geophysical Research Letters
The carving of submarine canyons into the continental shelf is poorly understood relative to river valleys on land. We studied a submarine canyon in Northern California that is connected through littoral transport to river sediment using a combination of seafloor mapping, tracking sediments using their chemical elements, and simulating waves and currents to better understand which processes are responsible for canyon erosion. Our findings suggest that canyon focusing of wave energy and an abundant supply of coarse sediment cause erosion at the canyon’s head. This finding helps explain why submarine canyon channel networks erode toward shore and predicts that canyons near mountain ranges will preferentially remain connected the shoreline.
Oct 2018. New article published in Geosciences
We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments.
Unlike previously proposed discriminative algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of allowable proximity and backscatter similarity are controlled by parameters that are learned from the data. The CRF therefore may prove to be a powerful ‘spatially aware’ alternative to other discriminative classifiers.
Oct 2018. New survey of the Delgada canyon, lost coast, Northern California
In late October 2018 a survey crew consisting of myself, Mike Smith, Matt Kaplinski, and Sarah Joerger, aided by Mike Brissette from R2Sonic, aboard the RV Mussel Point, surveyed the Delgada canyon headwall region for the third time in 10 years. The aim of the survey is to study nearshore canyon headwall processes in a region with active terrestrial sediment supply.
We used an R2Sonic 2026 multispectral multibeam sonar to map the region simultaneously at 3—5 different frequencies, in order to develop an understanding of the seafloor-sound interactions over multiple outgoing acoustic frequencies.
Sept 2018. New article published in Progress in Physical Geography
In river valleys, sediment moves between active river channels, near-channel deposits including bars and floodplains, and upland environments such as terraces and aeolian dunefields. Sediment availability is a prerequisite for the sustained transfer of material between these areas, and for the eco-geomorphic functioning of river networks in general. However, the difficulty of monitoring sediment availability and movement at the reach or corridor scale has hindered our ability to quantify and forecast the response of sediment transfer to hydrologic or land cover alterations. Here we leverage spatiotemporally extensive datasets quantifying sediment areal coverage along a 28 km reach of the Colorado River in Grand Canyon, southwestern USA. In concert with information on hydrologic alteration and vegetation encroachment resulting from the operation of Glen Canyon Dam (constructed in 1963) upstream of our study reach, we model the relative and combined influence of changes in (a) flow and (b) riparian vegetation extent on the areal extent of sediment available for transport in the river valley over the period from 1921 to 2016. In addition, we use projections of future streamflow and vegetation encroachment to forecast sediment availability over the 20 year period from 2016 to 2036.
We find that hydrologic alteration has reduced the areal extent of bare sediment by 9% from the pre- to post-dam periods, whereas vegetation encroachment further reduced bare sediment extent by 45%. Over the next 20 years, the extent of bare sediment is forecast to be reduced by an additional 12%. Our results demonstrate the impact of river regulation, specifically the loss of annual low flows and associated vegetation encroachment, on reducing the sediment available for transfer within river valleys. This work provides an extendable framework for using high-resolution data on streamflow and land cover to assess and forecast the impact of watershed perturbation (e.g. river regulation, land cover shifts, climate change) on sediment connectivity at the corridor scale.
Sept 2018. New article published in Earth Surface Processes and Landforms
Morphological change in river channels is frequently evaluated in the context of mass‐balance sediment budgets. In a closed sediment budget, measurements of sediment influx and efflux are coupled with measured changes in channel topography to provide both spatial and temporal resolution, and independent estimates of the mass balance. For sediment budgets constructed over long river segments (~102 channel widths or greater) and long periods (~2 years or longer), spatial and temporal accumulation of measurement uncertainty, compounded by inadequate sampling frequency or spatial coverage, may produce indeterminate results. The degree of indeterminacy may be evaluated in the context of a signal‐to‐noise ratio (SNR), which is a function of the magnitude of the mass balance and the magnitudes of potential systematic uncertainties associated with measurements and incomplete sampling.
We report on a closed sand budget consisting of measurements of flux and two morphological surveys for a 50‐km segment of a large river over a 3‐year period. Accurate reporting of the magnitude and sign of the change in sand storage was only possible by using of state‐of‐the‐art techniques with high temporal frequency and large spatial extent. Together, a sand‐flux and morphological mass balance revealed that sand evacuation was temporally concentrated (~100% of mass change occurred during 19% of the study period) and highly localized (70% of mass change occurred in 12% of the study segment).
A SNR analysis revealed that uncertainty resulting from undersampling may approach or exceed that caused by measurement uncertainty and that daily sampling of suspended‐sand concentration or repeat mapping of at least 50% of the river segment was required to determine the sand budget with SNR > 1. The approach used here to analyze sand‐budget uncertainty is especially applicable to other river systems with large temporal variability in sediment transport and large spatial variability in erosion and deposition.
Sept 2018. Back to the Lab!
Myself and colleagues at the St. Anthony Falls Laboratory, University of Minnesota, studied the acoustic backscattering properties of zebra mussels (Dreissena polymorpha) and native mussels, Threeridge (Amblema plicata), under controlled laboratory settings. Experiments were conducted in self-contained tanks at the St. Anthony Falls Laboratory, and findings will be used to determine the optimal acoustic parameters that will maximize the discrimination between mussels and substrates.
Aug 2018. Fieldwork in Lake Ontario
Myself and colleagues at USGS Great Lakes Science Center carried out exploratory acoustic surveys at two sites in Lake Ontario in New York state. The aim is to explore the backscattering properties of Cladophora and other important substrates.
July 2018. New "deep learning" article published in Geosciences
The application of deep learning, specifically deep convolutional neural networks (DCNNs), to the classification of remotely-sensed imagery of natural landscapes has the potential to greatly assist in the analysis and interpretation of geomorphic processes. However, the general usefulness of deep learning applied to conventional photographic imagery at a landscape scale is, at yet, largely unproven. If DCNN-based image classification is to gain wider application and acceptance within the geoscience community, demonstrable successes need to be coupled with accessible tools to retrain deep neural networks to discriminate landforms and land uses in landscape imagery. Here, we present an efficient approach to train/apply DCNNs with/on sets of photographic images, using a powerful graphical method called a conditional random field (CRF), to generate DCNN training and testing data using minimal manual supervision. We apply the method to several sets of images of natural landscapes, acquired from satellites, aircraft, unmanned aerial vehicles, and fixed camera installations. We synthesize our findings to examine the general effectiveness of transfer learning to landscape-scale image classification. Finally, we show how DCNN predictions on small regions of images might be used in conjunction with a CRF for highly accurate pixel-level classification of images
July 2018. Deep Learning workshops
MAPPING LAND-USE, HAZARD VULNERABILITY AND HABITAT SUITABILITY USING DEEP NEURAL NETWORKS
Over this summer and fall, I am teaming up with colleagues to
Curate data sets for training and testing of machine/deep learning methods on sets of images of natural environments and processes
Hold two training events for applying deep learning to image classification problems, using python and tensorflow
Develop online materials, examples, and software for applying deep learning to image classification
The project is funded by the USGS Community for Data Integration, For more details, go to: https://sites.google.com/view/usgsdeeplearning/home
May 2018. GeoHab 2018 conference
Tues May 8, 10:30: R2Sonic Booth. Poster and software demo of PriSM (Probabilistic Acoustic Substrate Mapping toolbox)
Tues May 8, 15:15: talk entitled "Probabilistic Substrate Mapping in Rivers and Seas with Conditional Random Fields of Acoustic Backscatter"
Tues May 8, 16:00: poster entitled "Repeat-mapping bathymetric change and substrates in a large river: application to sediment budgeting and physical habitat mapping in the Colorado River in Grand Canyon"
April2018. Fieldwork on the Chippewa River, WI.
We spent a week in southern Wisconsin repeat-mapping dune fields on the bed of the Chippewa River near Durand, WI, and downstream of the I35 bridge above Lake Pepin, where the Chippewa meets the Mississippi river. In collaboration with the USACE and Minnesota Water Science Center, we're working on quantifying total sand load in order to address sedimentation issues downstream.
March 2018. Article published in PLOS-ONE
Side scan sonar in low-cost ‘fishfinder’ systems has become popular in aquatic ecology and sedimentology for imaging submerged riverbed sediment at coverages and resolutions sufficient to relate bed texture to grain-size. Traditional methods to map bed texture (i.e. physical samples) are relatively high-cost and low spatial coverage compared to sonar, which can continuously image several kilometers of channel in a few hours. Towards a goal of automating the classification of bed habitat features, we investigate relationships between substrates and statistical descriptors of bed textures in side scan sonar echograms of alluvial deposits. We develop a method for automated segmentation of bed textures into between two to five grain-size classes. Second-order texture statistics are used in conjunction with a Gaussian Mixture Model to classify the heterogeneous bed into small homogeneous patches of sand, gravel, and boulders with an average accuracy of 80%, 49%, and 61%, respectively. Reach-averaged proportions of these sediment types were within 3% compared to similar maps derived from multibeam sonar.
River Science session at the 2018 Geological Society of America joint Cordilleran/Rocky Mountain section meeting.
The meeting will be May 15-17, 2018 in Flagstaff, Arizona. Abstract submission deadline Feb 20
While our session description emphasizes the decades of river science resulting from river management in the Colorado River basin and Intermountain West, we encourage submissions on all aspects of river science in natural or human-modified river systems. Please contact us with any questions. T3. Advances in River Science in the Intermountain West.
2017 AGU Fall Meeting, New Orleans, LA, 11-15 December
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Grams, P.E., Buscombe, D. Topping, D.J., Mueller, E.R. (2017) Identification of discontinuous sand pulses on the bed of the Colorado River in Grand Canyon.
Leary, K., Buscombe, D., Schmeeckle, M., Kaplinski, M. (2017) Assessing the Importance of Cross-Stream Transport in Bedload Flux Estimates from Migrating Dunes: Colorado River, Grand Canyon National Park.
Platt, A. S., Buscombe, D., Porter, R. C., & Grams, P. (2017), Estimating Sediment Thickness from Riverbed to Bedrock within the Colorado River in the Grand Canyon.
October 2017. Figure from our paper selected for the front cover of the October 2017 edition of the Journal of Geophysical Research - Earth Surface
Figure shows example imagery for each of 10 unique substrate classes easily identifiable by eye from our underwater video system (LOBOS), arranged in two groups of five. The first group are found in sites where the riverbed is completely unvegetated (top four rows). The second group (bottom four rows) are found in partially vegetated riverbeds. The substrate codes shown in the first image in every group are those defined in Table 1 and colored the same as how they are represented in Figure 2 in the following paper:
Buscombe, D., Grams, P. E., & Kaplinski, M. A. (2017). Compositional signatures in acoustic backscatter over vegetated and unvegetated mixed sand-gravel riverbeds. Journal of Geophysical Research: Earth Surface, 122, 1771–1793. https://doi.org/10.1002/2017JF004302
October 2017. Article published in Journal of Geophysical Research - Earth Surface
Multibeam acoustic backscatter has considerable utility for remote characterization and mapping of spatially heterogeneous bed sediment composition over vegetated and unvegetated mixed sand-gravel riverbeds of mixed sand and gravel from a moving vessel.
However, high-frequency, high-resolution acoustic backscatter collected in shallow water is hampered by significant topographic contamination of the signal. In this paper, a two-stage method is proposed to filter out the small-scale topographic influence on acoustic backscatter. This process strengthens relationships between backscatter and sediment composition.
A probabilistic framework is presented for classifying vegetated and unvegetated substrates based on acoustic backscatter at decimeter resolution. This capability is demonstrated using data collected from diverse settings within a 386 km reach of a canyon river whose bed varies among sand, gravel, cobbles, boulders, and submerged vegetation.
September 2017. Presented at the Rivers, Coastal and Estuarine Morphodynamics ("RCEM 2017") Symposium in Padova, Italy.
Many scaling relationships have been proposed to link subaqueous sand dune dimensions with aspects of flow. This is useful for estimating dune dimensions from flow depth and velocity, which can be used to estimate dissipation of flow energy, bottom turbulence generation and bedload sediment transport.
However, when data is compiled from rivers worldwide, one observes a more than order of magnitude variation in dune height or length for a given flow depth. Why? We investigate using a very large data set collected from the Colorado River in Grand Canyon. We find that dune dimensions are controlled by grain size and degree flow constriction.
October 2017. New sensors installed on the Chippewa River, WI, to measure "total load"
A new project has started in collaboration with the USGS Minnesota and Wisconsin Water Science Centers, and the Grand Canyon Monitoring and Research Centers, whose goal is to quantify 'total load' on the Chippewa River. Total load refers to the amount of sand moving both in suspension and as bedload. We have installed acoustic sensors to measure both components of load near Durand, WI. The aim is to continuously measure suspended sediment and track dunes moving along the bed to estimate bedload. The sensors will be in place for 3 years and will be used to estimate the amount of sediment that is entering Lake Pepin, hence the requirements for sediment management in the reservoir.
August 2017. Presented at the American Fisheries Society annual meeting in Tampa, Florida.
In the past few years, low-cost, consumer-grade (hereafter, ‘recreational-grade’, to distinguish from ‘survey’ or ‘scientific’ grade) sidescan sonar platforms, have been developed for leisure activities such as fishing and hobbyist archeology. Recreational-grade sonar lack standardization in (and description of) the acoustic signal processing used, often without high-quality (‘survey grade’) positioning and measured boat attitude (heave, pitch, yaw, etc), and reporting of those quantities. It is not usually possible to process data from such sonars using conventional commercial hydrographic surveying software, to post-process the positioning of the scans or carry out a calibration that corrects for radiative properties of individual transducers. However, these inexpensive, lightweight (portable) sidescan sonar units can be deployed on almost any waterborne craft without the requirement of specialist knowledge of sonar and geodetics, and with little to no experience with acoustic remote sensing. This accessibility is behind the rapid increase in popularity of these sonar systems, among the scientific research community for benthic imaging in a range of aquatic environments, both marine and freshwater, lotic and lentic
A special session was held on using low-cost sonar technology in rivers and streams to support. I presented on the development of PyHum program and the ongoing developments in automated bed sediment characterisation from recreational grade sidescan sonar systems.
April 2017. Channel Mapping in Western Grand Canyon
They said it couldn't be done. 60 miles of channel mapped in 2 weeks, with a crew of 25, 7 boats, 2 multibeam sonars, 1 boat-mounted lidar, 1 sub-bottom profiler, 1 sidescan sonar, 1 underwater video camera system, 4 robotic total stations, and lots and lots of gunners and nams!!
March 2017. Article published in Environmental Modelling and Software
In recent years, lightweight, inexpensive, vessel-mounted ‘recreational grade’ sonar systems have rapidly grown in popularity among aquatic scientists, for swath imaging of benthic substrates. To promote an ongoing ‘democratization’ of acoustical imaging of shallow water environments, methods to carry out geometric and radiometric correction and georectification of sonar echograms are presented, based on simplified models for sonar-target geometry and acoustic backscattering and attenuation in shallow water. Procedures are described for automated removal of the acoustic shadows, identification of bed-water interface for situations when the water is too turbid or turbulent for reliable depth echosounding, and for automated bed substrate classification based on singlebeam full-waveform analysis. These methods are encoded in an open-source and freely-available software package, which should further facilitate use of recreational-grade sidescan sonar, in a fully automated and objective manner. The sequential correction, mapping, and analysis steps are demonstrated using a data set from a shallow freshwater environment.