Geology topics

Daniel D. Buscombe

Publications and source records attributed to Daniel D. Buscombe.

At least 37 records · Page 2Linked to original sources

USGS2022

Human-in-the-Loop segmentation of earth surface imagery

Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time-consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed-pixels, -textures, and -spectral signatures, can be especially error-prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi-automated, method for interactive segmentation of N-dimensional (x, y, N) images into two-dimensional (x, y) label images. It uses human-in-the-loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non-photographic imagery consist of 1- and 3-bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation.

Earth and Space Science↗

USGS2021

Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement

Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.

Earth and Space Science↗

USGS2021

Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation

Introduction Structure from motion (SFM) has become an integral technique in coastal change assessment; the U.S. Geological Survey (USGS) used Agisoft Metashape Professional Edition photogrammetry software to develop a workflow that processes coastline aerial imagery collected in response to storms since Hurricane Florence in 2018. This report details step-by-step instructions to create three-dimensional (3D) spatial products from both singular and repeated collections of shoreline aerial imagery. The products can be used for real-time hazard guidance and future forecasting and recovery endeavors.

Open-File Report↗

USGS2021

Geometry of obstacle marks at instream boulders-Integration of laboratory investigations and field observations

Obstacle marks are instream bedforms, typically composed of an upstream frontal scour hole and a downstream sediment accumulation in the vicinity of an obstacle. Local scouring at infrastructure (e.g. bridge piers) is a well‐studied phenomenon in hydraulic engineering, while less attention is given to the time‐dependent evolution of frontal scour holes at instream boulders and their geometric relations (depth to width, and length ratio). Furthermore, a comparison between laboratory studies and field observations is rare. Therefore, the morphodynamic importance of such scour features to fluvial sediment transport and morphological change is largely unknown. In this study, obstacle marks at boulder‐like obstructions were physically modelled in 30 unscaled process‐focused flume experiments (runtime per experiment ≥ 5760 min) at a range of flows (subcritical, clear‐water conditions, emergent and submerged water levels) and boundary conditions designed to represent the field setting (i.e. obstacle tilting, and limited thickness of the alluvial layer). Additionally, geometries of scour holes at 90 in‐situ boulders (diameter ≥ 1 m) located in a 50‐km segment of the Colorado River in Marble Canyon (AZ) were measured from a 1 m‐resolution digital elevation model. Flume experiments reveal similar evolution of local scouring, irrespective of hydraulic conditions, controlled by the scour incision, whereas the thickness of the alluvial layer and obstacle tilting into the evolving frontal scour hole limit incision. Three temporal evolution phases—(1) rapid incision, (2) decreasing incision, and (3) scour widening—are identified based on statistical analysis of spatiotemporal bed elevation time series. A quantitative model is presented that mechanistically predicts enlargement in local scour length and width based on (1) scour depth, (2) the inclination of scour slopes, and (3) the planform area of the frontal scour hole bottom. The comparison of field observations and laboratory results demonstrates scale invariance of geometry, which implies similitude of processes and form rather than equifinality.

Earth Surface Processes and Landforms↗

USGS2021

Biocrust and the soil surface: Influence of climate, disturbance, and biocrust recovery on soil surface roughness

Biocrust communities promote soil surface roughness, a key functional characteristic for soil ecology. However, the spatial scales at which biocrust communities contribute to surface roughness are not well understood. To refine our understanding of the spatial dynamics between biocrust and soil surface roughness, we used mm-resolution terrestrial LiDAR to measure micro-topographic roughness at seven sub-meter, 3-dimensional kernels (spatial scales) for undisturbed and disturbed biocrusts within the cool Great Basin and the hot Chihuahuan Deserts of western North America. This multi-scalar approach applied within the different desert regions allowed us to explore two objectives: 1) assess the relative importance of climate and disturbance on biocrust roughness, and 2) evaluate how soil surface roughness evolves with biocrust recovery. For objective 1, we found that undisturbed cool desert biocrust was up to three times rougher than hot desert biocrust. Much of the difference in roughness between the two desert biocrust communities appeared to be from climate or other regional factors. However, positive correlations between roughness and biocrust indicators, including soil chlorophyll-a and the field-based Level of Development (LOD) index, suggested that differences in roughness at spatial scales ≤ 10 cm are directly related to biocrust development. Mechanical disturbance aimed at removing biocrust resulted in significant reductions in roughness and removed much of the observed differences in roughness between cool and hot desert soils. We evaluated biocrust recovery within the cool desert study area two years after mechanical disturbance and found that the disturbed soil increased in roughness up-to 300%. The increased surface roughness at spatial scales ≤ 10 cm were positively correlated with increased aggregate stability and indicators of biocrust reestablishment. We found that topographic change area was also an important contributor to roughness at all spatial scales, particularly at spatial scales ≥ 20 cm where it was the most important factor evaluated. These results provide insight into how biocrust interacts with other biophysical processes to influence soil surface roughness and how soil surfaces evolve at time scales relevant to soil restoration activities.

New Mexico, Utah↗

USGS2020

Causes of variability in suspended‐sand concentration evaluated using measurements in the Colorado River in Grand Canyon

Rivers commonly exhibit substantial variability in suspended‐sand concentration, even at constant water discharge. Here we derive an approach for evaluating how much of this variability arises from mean bed‐sand grain size. We apply this approach to the Colorado River in Grand Canyon, where discharge‐independent concentration of suspended sand varies by more than a factor of 23 ( N = 1.4 × 10 6 ). Theory predicts that where concentration is controlled by bed‐sand grain size, concentration and grain size in suspension will be inversely correlated (i.e., coarsening of the bed causes suspended sand to become coarser in grain size and lower in concentration). Although the observed correlation is negative, riverbed grain size accounts for only 40% of the variability in concentration. The residuals vary by an order of magnitude; they arise from other processes, such as changes in topography or distribution of sand that cause shear stress to change at constant discharge, changes in the fine tail of bed‐sand grain sizes or changing bedforms. Both bed sand and the other factors influence concentration for durations from less than 1 day to several years. Predictions of concentration based on bed‐sand grain size ( N = 4 × 10 4 ) are less accurate than predictions based on suspended‐sand grain size, probably because suspended sand is a natural integrator of sand‐transporting processes, giving more weight to those areas of the bed that exchange more sand with the flow. Although the causes of variability vary from one river to another, the approach illustrated here is applicable to any river in which concentration varies at constant water discharge.

Arizona↗

USGS2020

A mixed length scale model for migrating fluvial bedforms

With the expansion of hydropower, in‐stream converters, flood‐protection infrastructures, and growing concerns on deltas fragile ecosystems, there is a pressing need to evaluate and monitor bedform sediment mass flux. It is critical to estimate real‐time bedform size and migration velocity and provide a theoretical framework to convert easily accessible time histories of bed elevations into spatially evolving patterns. We collected spatiotemporally resolved bathymetries from laboratory flumes and the Colorado River in statistically steady, homogeneous, subcritical flow conditions. Wave number and frequency spectra of bed elevations show compelling evidence of scale‐dependent velocity for the hierarchy of migrating bedforms observed in the laboratory and field. New scaling laws were applied to describe the full range of migration velocities as function of two dimensionless groups based on the bed shear velocity, sediment diameter, and water depth. Further simplification resulted in a mixed length scale model estimating scale‐dependent migration velocities, without requiring bedform classification or identification.

Geophysical Research Letters↗

USGS2020

Community for data integration 2018 funded project report

The U.S. Geological Survey Community for Data Integration annually funds small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 10 projects funded in fiscal year 2018, outlining their goals, activities, and accomplishments.

Open-File Report↗

USGS2019

Optical wave gauging using deep neural networks

We develop a remote wave gauging technique to estimate wave height and period from imagery of waves in the surf zone. In this proof-of-concept study, we apply the same framework to three datasets: the first, a set of close-range monochrome infrared (IR) images of individual nearshore waves at Duck, NC, USA; the second, a set of visible (i.e. RGB) band orthomosaics of a larger nearshore area near Santa Cruz, CA, USA; and the third, a set of oblique (unrectified) images from the same site. The network is trained using coincident images and in situ wave measurements. The optical wave gauge (OWG) consists of a deep convolutional neural network (CNN) to extract features from imagery — called a ‘base model’, with additional layers to distill the feature information into lower dimensional spaces, and a final layer of dense neurons to predict continuously varying quantities. Four base models are compared. The OWG is trained for both individual wave height and period, and statistical quantities like significant wave height and peak wave period. The best performing OWG on the IR dataset achieved RMS errors of 0.14 m and 0.41 s for height and period, respectively, capturing up to 98% of the variance in these quantities. The best performing OWG on the visible band rectified dataset achieved RMS errors of 0.08 m and 0.79 s, respectively, for height and period. The same values for the oblique RGB imagery were 0.11 m and 0.81 s for height and period, respectively. Overall, wave height and period accuracy is sensitive to choice of base model; OWGs built upon MobilenetV2 tend to perform worst and those built on Inception-ResnetV2 have the smallest RMS error. The presence or otherwise of residual layers in the model makes little systematic difference to the final OWG accuracy. Smaller batch sizes used in model training tend to result in more accurate OWGs. An out-of-calibration validation, using images associated with wave heights or periods outside the range of values represented in the training data, showed that the ability for OWGs to predict the bottom 5% of low wave heights and the top 5% of high wave heights was reasonably good, but the same was not generally true of wave period. The same framework, not optimized for either dataset, predicts both quantities with high accuracy when trained on imagery, despite the differences in electromagnetic band, perspective, and scale. The OWG estimates wave properties from an image in less than 100 ms on a modestly sized CPU, allowing for the possibility of continuous real-time wave estimates.

California, North Carolina↗

USGS2019

Using oblique imagery to measure hypsometric changes in sandbar volume following controlled floods in the Grand Canyon

Measuring changes in the elevation distribution of sub-aerial fine (< 2 mm ) sediment and estimating sandbar volume multiple times per year can improve sediment budget calculations in fluvial systems. In the Grand Canyon of the Colorado River, effects of dam operations on sandbar size and distribution is of long-term management interest. Bar-building controlled floods have been implemented in 1996, 2004, 2008, 2012, 2013, 2014, 2016, and 2018 to mitigate sandbar erosion. Annual topographic surveys provide a single measurement of sandbar change caused by the integrated effects of all flows in one year (both controlled floods and normal dam releases), but do not measure erosion and deposition caused by specific operations or individual floods. On one sandbar monitoring site in Grand Canyon, we demonstrate that imagery from autonomous digital cameras can be used to provide quantitative measures of sandbar hypsometry several times per year without costly and labor-intensive surveys. We describe methods for measuring changes in the storage of fine sediment at monthly or seasonal timescales by constructing hypsometric (area-elevation relation) curves. These curves are created and updated with sandbar area measurements from georectified images taken multiple times each day. As the water surface elevation fluctuates with daily, seasonal, and monthly discharge patterns, sandbar area and volume can be estimated using known stage-discharge relationships. We present parameters extracted from image-derived hypsometries to estimate sandbar volume and elevation relief ratio, which provides a new way to quantitatively measure monthly or seasonal changes in fine sediment storage.

Arizona↗

USGS2018

Probabilistic substrate classification with multispectral acoustic backscatter: A comparison of discriminative and generative models

We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully-connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. 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. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model (GMM) that doesn’t include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF therefore may prove to be a powerful ‘spatially aware’ alternative to other discriminative classifiers.

Geosciences↗

USGS2018

How many measurements are required to construct an accurate sand budget in a large river? Insights from analyses of signal and noise

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 (~10 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 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.

Arizona↗

USGS2018

Quantifying and forecasting changes in the areal extent of river valley sediment in response to altered hydrology and land cover

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.

Arizona↗

USGS2018

Automated remote cameras for monitoring alluvial sandbars on the Colorado River in Grand Canyon, Arizona

Automated camera systems deployed at 43 remote locations along the Colorado River corridor in Grand Canyon National Park, Arizona, are used to document sandbar erosion and deposition that are associated with the operations of Glen Canyon Dam. The camera systems, which can operate independently for a year or more, consist of a digital camera triggered by a separate data controller, both of which are powered by an external battery and solar panel. Analysis of images for categorical changes in sandbar size show deposition at 50 percent or more of monitoring sites during controlled flood releases done in 2012, 2013, 2014, and 2016. The images also depict erosion of sandbars and show that erosion rates were highest in the first 3 months following each controlled flood. Erosion rates were highest in 2015, the year of highest annual dam release volume. Comparison of the categorical estimates of sandbar change agree with sandbar change (erosion or deposition) measured by topographic surveys in 76 percent of cases evaluated. A semiautomated method for quantifying changes in sandbar area from the remote-camera images by rectifying the oblique images and segmenting the sandbar from the rest of the image is presented. Calculation of sandbar area by this method agrees with sandbar area determined by topographic survey within approximately 8 percent and allows quantification of sandbar area monthly (or more frequently).

Arizona↗

USGS2018

Probabilistic models of seafloor composition using multispectral acoustic backscatter: The benthic detectorists

We describe and compare two probabilistic models for task-specific seafloor characterization based on multispectral backscatter. We examine whether generative or discriminative approaches to supervised seafloor characterization do better at harnessing the greatly increased information about seafloor substrate composition that is encoded in the backscattering response across multiple frequencies. A Gaussian mixture model (GMM) is proposed as a generative model, and a fully-connected conditional random field (CRF) is proposed as a discriminative model. Either model uses input data derived from monospectral or multispectral backscatter without modification. The CRF approach considers both the relative backscatter magnitudes of different substrates as well as their relative proximity, and can be optimized using parameters. The GMM model, in contrast, includes no spatial information in its estimates, being based solely on relative backscatter magnitudes. Both GMM and CRF modeling approaches perform better with multispectral backscatter compared to monospectral, significantly outperforming all three monospectral frequencies. With multispectral backscatter inputs, based on average classification accuracies alone, there was little to choose between the two modeling approaches (classification accuracy of 81% and 83% for GMM and CRF models, respectively, evaluated using 50% of available bed observations to train and 50% to test the models). However, a CRF model that has been optimized with respect to its tunable parameters tends to produce higher posterior probabilities (i.e. greater certainty) for its classifications. Using monospectral backscatter inputs, the CRF model significantly outperformed the GMM model in terms of average classification accuracy. On balance, therefore, based on the evidence presented here, the CRF is suggested to be the superior approach for task-specific seafloor classification. Although further work using additional data is required to further examine this conclusion, the work presented here will guide and focus subsequent research efforts as more areas of the seafloor are mapped with the new technology. In order to facilitate these efforts, the algorithms presented here are encoded in a freely available python toolbox for Probabilistic acoustic Sediment Mapping, called PriSM , that can be used for both monospectral and multispectral backscatter. Finally, we show that application of the CRF model to the outputs of a geoacoustical model of seafloor scattering results in realistic substrate classification boundaries. This hybrid CRF and physics-based approach can predict the physical properties of the seafloor at a finer spatial resolution than is possible using the geoacoustical model alone.

Conference Paper↗

USGS2017

Compositional signatures in acoustic backscatter over vegetated and unvegetated mixed sand-gravel riverbeds

Multibeam acoustic backscatter has considerable utility for remote characterization of spatially heterogeneous bed sediment composition over vegetated and unvegetated riverbeds of mixed sand and gravel. However, the use of high-frequency, decimeter-resolution acoustic backscatter for sediment classification in shallow water is hampered by significant topographic contamination of the signal. In mixed sand-gravel riverbeds, changes in the abiotic composition of sediment (such as homogeneous sand to homogeneous gravel) tend to occur over larger spatial scales than is characteristic of small-scale bedform topography (ripples, dunes, and bars) or biota (such as vascular plants and periphyton). A two-stage method is proposed to filter out the morphological contributions to acoustic backscatter. First, the residual supragrain-scale topographic effects in acoustic backscatter with small instantaneous insonified areas, caused by ambiguity in the local (beam-to-beam) bed-sonar geometry, are removed. Then, coherent scales between high-resolution topography and backscatter are identified using cospectra, which are used to design a frequency domain filter that decomposes backscatter into the (unwanted) high-pass component associated with bedform topography (ripples, dunes, and sand waves) and vegetation, and the (desired) low-frequency component associated with the composition of sediment patches superimposed on the topography. 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.

Colorado River, Grand Canyon, Glen Canyon, Marble ↗

USGS2017

Channel mapping river miles 29–62 of the Colorado River in Grand Canyon National Park, Arizona, May 2009

Bathymetric, topographic, and grain-size data were collected in May 2009 along a 33-mi reach of the Colorado River in Grand Canyon National Park, Arizona. The study reach is located from river miles 29 to 62 at the confluence of the Colorado and Little Colorado Rivers. Channel bathymetry was mapped using multibeam and singlebeam echosounders, subaerial topography was mapped using ground-based total-stations, and bed-sediment grain-size data were collected using an underwater digital microscope system. These data were combined to produce digital elevation models, spatially variable estimates of digital elevation model uncertainty, georeferenced grain-size data, and bed-sediment distribution maps. This project is a component of a larger effort to monitor the status and trends of sand storage along the Colorado River in Grand Canyon National Park. This report documents the survey methods and post-processing procedures, digital elevation model production and uncertainty assessment, and procedures for bed-sediment classification, and presents the datasets resulting from this study.

Arizona↗

USGS2017

Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar

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.

Environmental Modelling and Software↗