Remote measurement of river morphology via fusion of LiDAR topography and spectrally based bathymetry

This study developed and evaluated a hybrid approach to remote measurement of river morphology that combines LiDAR topography with spectrally based bathymetry. Comparison of filtered LiDAR point clouds with surveyed cross‐sections indicated that subtle features on low‐relief floodplains were accurately resolved by LiDAR but that submerged areas could not be detected due to strong absorption of near‐infrared laser pulses by water. The reduced number of returns made the active channel evident in a LiDAR point density map. A second dataset suggested that pulse intensity also could be used to discriminate land from water via a threshold‐based masking procedure. Fusion of LiDAR and optical data required accurate co‐registration of images to the LiDAR, and we developed an object‐oriented procedure for achieving this alignment. Information on flow depths was derived by correlating pixel values with field measurements of depth. Highly turbid conditions dictated a positive relation between green band radiance and flow depth and contributed to under‐prediction of pool depths. Water surface elevations extracted from the LiDAR along the water's edge were used to produce a continuous water surface that preserved along‐channel variations in slope. Subtracting local flow depths from this surface yielded estimates of the bed elevation that were then combined with LiDAR topography for exposed areas to create a composite representation of the riverine terrain. The accuracy of this terrain model was assessed via comparison with detailed field surveys. A map of elevation residuals showed that the greatest errors were associated with underestimation of pool depths and failure to capture cross‐stream differences in water surface elevation. Nevertheless, fusion of LiDAR and passive optical image data provided an efficient means of characterizing river morphology that would not have been possible if either dataset had been used in isolation. Copyright © 2011 John Wiley & Sons, Ltd.     

Bathymetric Structure‐from‐Motion: extracting shallow stream bathymetry from multi‐view stereo photogrammetry

Stream bathymetry is a critical variable in a number of river science applications. In larger rivers, bathymetry can be measured with instruments such as sonar (single or multi‐beam), bathymetric airborne LiDAR (light detection and ranging), or acoustic Doppler current profilers. However, in smaller streams with depths less than 2 m, bathymetry is one of the more difficult variables to map at high‐resolution. Optical remote sensing techniques offer several potential solutions for collecting high‐resolution bathymetry. In this research, I focus on direct photogrammetric measurements of bathymetry using multi‐view stereo photogrammetry, specifically Structure‐from‐Motion (SfM). The main barrier to accurate bathymetric mapping with any photogrammetric technique is correcting for the refraction of light as it passes between the two different media (air and water), which causes water depths to appear shallower than they are. I propose and test an iterative approach that calculates a series of refraction correction equations for every point/camera combination in a SfM point cloud. This new method is meant to address shortcomings of other correction techniques and works within the current preferred method for SfM data collection, oblique and highly convergent photographs. The multi‐camera refraction correction presented here produces bathymetric datasets with accuracies of ~0.02% of the flying height and precisions of ~0.1% of the flying height. This methodology, like many fluvial remote sensing methods, will only work under ideal conditions (e.g. clear water), but it provides an additional tool for collecting high‐resolution bathymetric datasets for a variety of river, coastal, and estuary systems. Copyright © 2016 John Wiley & Sons, Ltd.     

Synergy and fusion of optical and synthetic aperture radar satellite data for underwater topography estimation in coastal areas

A method to obtain underwater topography for coastal areas using state-of-the-art remote sensing data and techniques worldwide is presented. The data from the new Synthetic Aperture Radar (SAR) satellite TerraSAR-X with high resolution up to 1 m are used to render the ocean waves. As bathymetry is reflected by long swell wave refraction governed by underwater structures in shallow areas, it can be derived using the dispersion relation from observed swell properties. To complete the bathymetric maps, optical satellite data of the QuickBird satellite are fused to map extreme shallow waters, e.g., in near-coast areas. The algorithms for bathymetry estimation from optical and SAR data are combined and integrated in order to cover different depth domains. Both techniques make use of different physical phenomena and mathematical treatment. The optical methods based on sunlight reflection analysis provide depths in shallow water up to 20 m in preferably calm weather conditions. The depth estimation from SAR is based on the observation of long waves and covers the areas between about 70- and 10-m water depths depending on sea state and acquisition quality. The depths in the range of 20 m up to 10 m represent the domain where the synergy of data from both sources arises. Thus, the results derived from SAR and optical sensors complement each other. In this study, a bathymetry map near Rottnest Island, Australia, is derived. QuickBird satellite optical data and radar data from TerraSAR-X have been used. The depths estimated are aligned on two different grids. The first one is a uniform rectangular mesh with a horizontal resolution of 150 m, which corresponds to an average swell wavelength observed in the 10 × 10-km SAR image acquired. The second mesh has a resolution of 150 m for depths up to 20 m (deeper domain covered by SAR-based technique) and 2.4 m resolution for the shallow domain imaged by an optical sensor. This new technique provides a platform for mapping of coastal bathymetry over a broad area on a scale that is relevant to marine planners, managers, and offshore industry.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

Spectrally based remote sensing of river bathymetry

This paper evaluates the potential for remote mapping of river bathymetry by (1) examining the theoretical basis of a simple, ratio‐based technique for retrieving depth information from passive optical image data; (2) performing radiative transfer simulations to quantify the effects of suspended sediment concentration, bottom reflectance, and water surface state; (3) assessing the accuracy of spectrally based depth retrieval under field conditions via ground‐based reflectance measurements; and (4) producing bathymetric maps for a pair of gravel‐bed rivers from hyperspectral image data. Consideration of the relative magnitudes of various radiance components allowed us to define the range of conditions under which spectrally based depth retrieval is appropriate: the remotely sensed signal must be dominated by bottom‐reflected radiance. We developed a simple algorithm, called optimal band ratio analysis (OBRA), for identifying pairs of wavelengths for which this critical assumption is valid and which yield strong, linear relationships between an image‐derived quantity X and flow depth d. OBRA of simulated spectra indicated that water column optical properties were accounted for by a shorter‐wavelength numerator band sensitive to scattering by suspended sediment while depth information was provided by a longer‐wavelength denominator band subject to strong absorption by pure water. Field spectra suggested that bottom reflectance was fairly homogeneous, isolating the effect of depth, and that radiance measured above the water surface was primarily reflected from the bottom, not the water column. OBRA of these data, 28% of which were collected during a period of high turbidity, yielded strong X versus d relations (R² from 0·792 to 0·976), demonstrating that accurate depth retrieval is feasible under field conditions. Moreover, application of OBRA to hyperspectral image data resulted in spatially coherent, hydraulically reasonable bathymetric maps, though negative depth estimates occurred along channel margins where pixels were mixed. This study indicates that passive optical remote sensing could become a viable tool for measuring river bathymetry. Copyright © 2009 John Wiley & Sons, Ltd.     

An investigation of seawater and sediment depth using a prototype airborne electromagnetic instrumentation system – a case study in Broken Bay, Australia

Previous studies using commercial airborne electromagnetic equipment that is not optimized for marine surveying have demonstrated the use of airborne electromagnetic methods for measuring water depth and estimating sediment thickness. A new prototype helicopter time-domain airborne electromagnetic system, SeaTEM(0), is now under development for bathymetric surveying. The first sea trial of the SeaTEM(0) system took place over Broken Bay, New South Wales, Australia, in shallow water up to ∼30 m in depth. Broken Bay was chosen because the separate paleodrainage systems for the Hawkesbury River, Brisbane Water and Pittwater, which join in Broken Bay give rise to paleovalleys infilled with unconsolidated sediments, ranging in thickness between 0 m (bedrock outcrop) and ∼200 m. The survey area also included a tombolo with a beach either side, which provided the opportunity to measure water depth through a surf zone. Sediment thickness and water depth is predicted from stitched layered-earth inversion of data based on a simplified two-layer model that represents seawater and sediment overlying a resistive half-space basement (bedrock). The resulting bathymetric profiles show agreement typically to within ∼±1 m and ∼±0.5 m with known water depths in areas less than 20 and 6 m deep respectively. The inverted depth profile of the second (sediment) layer is noisy; however, the profiles reveal coarse topographic features of paleovalleys to depth limits of ∼60 to 80 m below sea level in 20 to 30 m water depth, as well as resolving bedrock ridges and exposed reefs in shallow waters.     

Evaluating the performance of topobathymetric LiDAR to support multi-dimensional flow modelling in a gravel-bed mountain stream

Stream biophysical processes are commonly studied using multi-dimensional numerical modelling that quantifies flow hydraulics from which parameters such as habitat suitability, stream carrying capacity, and bed mobility are derived. These analyses would benefit from accurate high-resolution stream bathymetries spanning tens of kilometres of channel, especially in small streams or where navigation is difficult. Traditional ground-based survey methods are limited by survey time, dense vegetation and stream access, and are usually only feasible for short reaches. Conversely, airborne topobathymetric LiDAR surveys may overcome these limitations, although limited research is available on how errors in LiDAR-derived digital elevation models (DEMs) might propagate through flow models. This study investigated the performance of LiDAR-derived topobathymetry in support of multi-dimensional flow modelling and ecohydraulics calculations in two gravel-bedded reaches (approximately 200 m long), one morphologically complex and one morphologically simple, and at the segment scale (32 km-long stream segment) along a 15 m-wide river in central Idaho, USA. We compared metre and sub-metre-resolution DEMs generated from RTK-GPS ground and Experimental Advanced Airborne Research LiDAR-B (EAARL-B) surveys and water depths, velocities, shear stresses, habitat suitability, and bed mobility modelled with two-dimensional (2D) hydraulic models supported by LiDAR and ground-surveyed DEMs. Residual statistics, bias (B), and standard deviation (SD) of the residuals between depth and velocity predicted from the model supported by LiDAR and ground-survey topobathymetries were up to −0.04 (B) and 0.09 m (SD) for depth and −0.09 (B) and 0.20 m s⁻¹ (SD) for velocity. The accuracy (B = 0.05 m), precision (SD = 0.09 m), and point density (1 point m⁻²) of the LiDAR topobathymetric survey (regardless of reach complexity) were sufficient to support 2D hydrodynamic modelling and derivative stream habitat and process analyses, because these statistics were comparable to those of model calibration with B = 0 m and SD = 0.04 m for water surface elevation and B = 0.05 m s⁻¹ and SD = 0.22 m s⁻¹ for velocity in our investigation. © 2020 John Wiley & Sons, Ltd.     

Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry

Quantifying the topography of rivers and their associated bedforms has been a fundamental concern of fluvial geomorphology for decades. Such data, acquired at high temporal and spatial resolutions, are increasingly in demand for process‐oriented investigations of flow hydraulics, sediment dynamics and in‐stream habitat. In these riverine environments, the most challenging region for topographic measurement is the wetted, submerged channel. Generally, dry bed topography and submerged bathymetry are measured using different methods and technology. This adds to the costs, logistical challenges and data processing requirements of comprehensive river surveys. However, some technologies are capable of measuring the submerged topography. Through‐water photogrammetry and bathymetric LiDAR are capable of reasonably accurate measurements of channel beds in clear water. While the cost of bathymetric LiDAR remains high and its resolution relatively coarse, the recent developments in photogrammetry using Structure from Motion (SfM) algorithms promise a fundamental shift in the accessibility of topographic data for a wide range of settings. Here we present results demonstrating the potential of so called SfM‐photogrammetry for quantifying both exposed and submerged fluvial topography at the mesohabitat scale. We show that imagery acquired from a rotary‐winged Unmanned Aerial System (UAS) can be processed in order to produce digital elevation models (DEMs) with hyperspatial resolutions (c. 0.02 m) for two different river systems over channel lengths of 50–100 m. Errors in submerged areas range from 0.016 m to 0.089 m, which can be reduced to between 0.008 m and 0.053 m with the application of a simple refraction correction. This work therefore demonstrates the potential of UAS platforms and SfM‐photogrammetry as a single technique for surveying fluvial topography at the mesoscale (defined as lengths of channel from c.10 m to a few hundred metres). Copyright © 2014 John Wiley & Sons, Ltd.     

南海北部东沙海域巨型水下沙丘的分布及特征

本文基于多波束测深和高分辨率多道反射地震数据研究了东沙海域深水巨型水下沙丘的特征.巨型水下沙丘发育在230~830m水深的上陆坡范围内,呈斑块状分布.NW-SE向的近海底流体运动不仅冲蚀地层,形成了三条与水下沙丘间隔分布的冲蚀带,为水下沙丘提供了沉积物来源,同时也为水下沙丘的形成提供了动力源.研究区水下沙丘波长(L)范围55~510m,波高(h)范围1.5~20m,二者呈指数关系分布.沙丘的波长随水深增大而增大,波高则在500~700m水深范围内最大.水下沙丘NE—SW向展布的脊线和几何参数关系是与现今水动力条件相平衡的结果.     

Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman,near‐infrared and green signals

Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non‐navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is difficult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near‐infrared (NIR), Raman] of the SHOALS‐1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The first method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classification and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (?4·2 cm verusu ?4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (?3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method. Copyright © 2010 John Wiley & Sons, Ltd.     

Assessing the ability of airborne LiDAR to map river bathymetry

Airborne bathymetric LiDAR was collected for 220 river kilometres in the Yakima and Trinity River Basins in the USA. Concomitant with the aerial data collection, ground surveys of the river bed were performed in both basins. We assess the quality of the bathymetric LiDAR survey from the perspective of its application toward creating accurate, precise and complete streambed topography for numerical modelling and geomorphological assessment. Measurement error is evaluated with respect to ground surveys for magnitude and spatial variation. Analysis of variance statistics indicate that residuals from two independent ground surveys in similar locations do not come from the same population and that mean errors at different study locations also come from different populations. Systematic error indicates a consistent bias in the data and random error falls within values of expected precision. Published in 2007 by John Wiley & Sons, Ltd.     

Improved seawater depth determination using corrected helicopter time-domain electromagnetic data

Two areas within Sydney Harbour were surveyed in 2002 with a helicopter‐borne time‐domain electromagnetic system to test its potential for bathymetric mapping in shallow seawater. As delivered, the data were improperly calibrated. Therefore a re‐calibration was performed to reconcile the measured data with ‘ground truth’. Synthetic electromagnetic transients were computed for two‐layer models representing the seawater and the sediment overlying bedrock at a number of locations within the survey area. The seawater depth in the models varied between 3 m and 70 m. The measured and calculated data were compared at each delay time, and were found to be linearly related. The slope and intercept of the line of best fit were used to correct all the measured data. Inversion of the corrected time‐domain electromagnetic data generally resolved the bathymetry to submetre accuracy down to depths of about 55 m.     

Stable isotopic records of the glacial deep-water properties in the South China Sea

Based on the oxygen and carbon stable isotopic records of benthic foraminifera in nine deep-sea cores of the South China Sea (SCS), the bathymetric profiles of δ¹⁸O and δ¹³C since the last glacial maximum (LGM) are preliminarily established. The bathymetric gradients of deep-water δ¹⁸O and δ¹³C in the SCS are obviously greater during the LGM than during the Holocene, showing the existence of the deep thermocline and nutricline at water depth of about 2 000 m. Particularly, the differences in δ¹⁸O and δ¹³C between the LGM and Holocene, from which the ice-volume effect and the global mean shift have been subtracted respectively, are positive values at water depths of 1 000–2 500 m in the SCS. This indicates the existence of deep-water mass with relatively cool temperature or higher salinity, better ventilation and more δ¹³C within the water depth range of the SCS during the LGM, which is distinctly different from that at present. These changes further confirm the existence of the glacial “North Pacific Deep Water” which, however, is possibly confined to the water depth range of 1 000–2 500 m. Project supported by the National Natural Science Foundation of China (Grant Nos. 49576286 and 49732060).     

The Argo Abyssal Plain

Eighteen geophysical transects were made in the Argo Abyssal Plain to study the magnetic anomalies, bathymetry and seismic structure. Magnetic anomalies were identified as being the Mesozoic anomalies M-10 to M-25, increasing in age from the Java Trench to the northwest continental shelf of Australia. A new bathymetric map shows that the Argo Abyssal Plain is bounded by the 5600-m contour and reaches a maximum depth of 5730 m against the inshore side of the Exmouth Plateau. Joey Rise was found to limit the Argo Abyssal Plain on the southwest. Continuous seismic profiles, sonobuoy data and seismic data from other cruises permit one to contour the depths to oceanic basement. Numerous diapir-like structures were observed, but their nature and origin is obscure.     

Slicer Laser Altimetry In The Eastern Caribbean

Data acquired by the airborne Scanning Lidar Imager of Canopies by EchoRecovery (SLICER) laser altimeter provided high-resolution digital topographicdata over Puerto Rico, the Dominican Republic and several of the Lesser AntillesIslands. The instrument was developed by the NASA-Goddard Space Flight Center.It has the capability of multibeam resolution of ground elevations beneath densecanopy areas. Data, therefore, can be used to generate a more accurate representation of the ground surface by removing the vegetation cover. Although internal precision is high (10 cm to 1 m), absolute accuracy is difficult to evaluate and depends on several factors, including the post-processed kinematic GPS (KGPS) flight path for the aircraft platform and clear identification of ground returns in the SLICER waveform. We compared topographic profiles from USGS 30 m and 1:250K DEMs for Puerto Rico with those generated by SLICER and with spot elevations derived from static and continuous GPS surveys. SLICER and KGPS surveys cross at six points in western Puerto Rico. Agreement between both elevation data sets is excellent and well fit (r = 0.921) by a linear model with a final residual bias of -0.501 m for SLICER ground returns relative to KGPS elevations. The agreement between SLICER and USGS 30 m DEMs is also very good with the largest errors associated with steep slopes and high vegetation cover. Residuals between KGPS and USGS 30 m DEMs are +1 ± 25 m, assuming a fixed uniform offset of +43.23 m between WGS84 and mean sea level.     

Glacial lake depth and volume estimation based on a large bathymetric dataset from the Cordillera Blanca,Peru

Glacial lakes are most often located in remote places making it difficult to carry out detailed bathymetric surveys. Consequently, lake depths and volumes for unmeasured lakes are often estimated using empirical relationships developed mainly from small bathymetric datasets. In this study, we use the bathymetry dataset of the Cordillera Blanca, Peru comprising 121 detailed lake bathymetries, the most extensive dataset in the world. We assess the performance of the most commonly applied empirical relationships for lake mean depth and volume estimation, but also investigate relationships between different geometric lake variables. We find that lake volume estimation performs better when derived from lake mean depth, which in turn is estimated from lake width. The findings also reveal the extreme variability of lake geometry, which depends on glacio-geomorphological processes that empirical–statistical relationships cannot adequately represent. Such relationships involve characteristic uncertainty ranges of roughly ±50%. We also estimate potential peak discharges of outburst floods from these lakes by applying empirical relationships from the literature, which results in discharges varying by up to one-order of magnitude. Finally, the results are applied to the 860 lakes without bathymetric measurements from the inventory dataset of the Cordillera Blanca to estimate lake mean depth, volume and possible peak discharge for all unmeasured lakes. Estimations show that ca. 70% (610) of the lakes have a mean depth lower than 10 m and very few longer than 40 m. Lake volume of unmeasured lakes represent ca. 32% (5.18 × 10⁸ m³) of the total lake volume (1.15 × 10⁹ m³) in the Cordillera Blanca. Approximately, 50% of the lakes have potential peak discharges > 1000 m³/s in case of lake outburst floods, implying a need for additional studies for risk assessment. © 2020 John Wiley & Sons, Ltd.     

Modelling rating curves using remotely sensed LiDAR data

Accurate stream discharge measurements are important for many hydrological studies. In remote locations, however, it is often difficult to obtain stream flow information because of the difficulty in making the discharge measurements necessary to define stage‐discharge relationships (rating curves). This study investigates the feasibility of defining rating curves by using a fluid mechanics‐based model constrained with topographic data from an airborne LiDAR scanning. The study was carried out for an 8m‐wide channel in the boreal landscape of northern Sweden. LiDAR data were used to define channel geometry above a low flow water surface along the 90‐m surveyed reach. The channel topography below the water surface was estimated using the simple assumption of a flat streambed. The roughness for the modelled reach was back calculated from a single measurment of discharge. The topographic and roughness information was then used to model a rating curve. To isolate the potential influence of the flat bed assumption, a ‘hybrid model’ rating curve was developed on the basis of data combined from the LiDAR scan and a detailed ground survey. Whereas this hybrid model rating curve was in agreement with the direct measurements of discharge, the LiDAR model rating curve was equally in agreement with the medium and high flow measurements based on confidence intervals calculated from the direct measurements. The discrepancy between the LiDAR model rating curve and the low flow measurements was likely due to reduced roughness associated with unresolved submerged bed topography. Scanning during periods of low flow can help minimize this deficiency. These results suggest that combined ground surveys and LiDAR scans or multifrequency LiDAR scans that see ‘below’ the water surface (bathymetric LiDAR) could be useful in generating data needed to run such a fluid mechanics‐based model. This opens a realm of possibility to remotely sense and monitor stream flows in channels in remote locations. Copyright © 2012 John Wiley & Sons, Ltd.     

Can recreational echosounder-chartplotter systems be used to perform accurate nearshore bathymetric surveys?

This work intends to determine if low-cost surveying techniques based on recreational echosounders can be used to perform nearshore bathymetry for analysing evolution of coastal sectors. For that purpose, two hydrographic surveying techniques were compared, i.e. (1) a real-time kinematic differential global positioning system (RTK-DGPS) synchronised with a single beam echosounder with real-time tidal elevation correction and (2) a low-cost recreational echosounder-chartplotter system using Global Navigation Satellite Systems (GNSS) with real-time European Geostationary Navigation Overlay Service (EGNOS) augmentation services and depth values post-processed using measured sea level. Two bathymetric data sets were obtained, one by each method, for the same area and survey lines at an ebb tidal delta (Tavira Inlet, Ria Formosa Portugal). Vertical differences were determined assuming no morphological variations between surveys. Results showed that depth elevation differences between bathymetric surfaces were of 0.10?±?0.16 m, slightly higher but within the same order of the error attributable to the used interpolator (0.00?±?0.11 m, triangular surface fitting). The differences between surveys performed with two different equipment sets and using different methodologies for correcting water elevations are very small both quantitative and qualitatively. Those differences can be diminished by improving the tidal level correction and uncertainties associated to different tidal slopes throughout the survey area. Pitch/roll corrections performed with low-cost GPS receivers would be also a valuable addition to the accuracy and precision of the method. It is then concluded that navigation with EGNOS augmentation services and sounding devices ten times cheaper than combined RTK-DGPS with single beam echosounders allow to measure and monitor accurately the nearshore bathymetry.