Bathymetric LiDAR Data Research Articles

Parallel Topology-aware Mesh Simplification on Terrain Trees

We address the problem of performing a topology-aware simplification algorithm on a compact and distributed data structure for triangle meshes, the Terrain trees. Topology-aware operators have been defined to coarsen a Triangulated Irregular Network (TIN) without affecting the topology of its underlying terrain, i.e., without modifying critical features of the terrain, such as pits, saddles, peaks, and their connectivity. However, their scalability is limited for large-scale meshes. Our proposed algorithm uses a batched processing strategy to reduce both the memory and time requirements of the simplification process, and thanks to the spatial decomposition on the basis of Terrain trees, it can be easily parallelized. Also, since a Terrain tree after the simplification process becomes less compact and efficient, we propose an efficient post-processing step for updating hierarchical spatial decomposition. Our experiments on real-world TINs, derived from topographic and bathymetric LiDAR data, demonstrate the scalability and efficiency of our approach. Specifically, topology-aware simplification on Terrain trees uses 40% less memory and half the time compared to the most compact and efficient connectivity-based data structure for TINs. Furthermore, the parallel simplification algorithm on the Terrain trees exhibits a 12× speedup with an OpenMP implementation. The quality of the output mesh is not significantly affected by the distributed and parallel simplification strategy of Terrain trees, and we obtain similar quality levels compared to the global baseline method.

AN OPEN-SOURCE, DATA-LOGGING DEVICE FOR MARINE-BASED SURVEYS

Abstract. Observation, monitoring, and understanding of the marine environment, particularly seafloor mapping, have gained global attention. Underwater photogrammetry is a valuable technique for creating accurate seafloor orthomosaics and digital elevation models (DEMs). However, achieving accurate georeferencing in photogrammetry surveys is challenging in marine environments. To address this, a low-cost and open-source data collection device was developed for underwater photogrammetry projects. The device is affordable, flexible, lightweight, and capable of logging position, motion, and utilizing a laser for seafloor feature identification. This paper presents the validation and assessment of the system, focusing on the performance of the position and laser sensors. The study advances underwater photogrammetry and provides insights into the device's capabilities for marine research and mapping applications. The results show that the Post Processed Kinematic (PPK) technique achieves high accuracy, with RMSE values of 0.294 m (distance), 0.267 m (X-coordinate), and 0.12 3m (Y-coordinate) at the Fremantle car park and 0.278 m (distance), 0.16 8m (X-coordinate), and 0.222m (Y-coordinate) at the Fremantle near boat ramp. PPP exhibits acceptable accuracy, while GPS shows relatively lower accuracy. Echosounder measurements correlate well with bathymetric lidar and RTK Rover reference data, with RMSE values of 45 cm and 28 cm, respectively. The laser distance measurer provides accurate measurements between 25 and 60 cm, showing a good correlation with the echosounder (R = 0.77). After correction for offset and refraction, the laser measurements have an RMSE of 1.8 cm compared to the echosounder. This study further demonstrated the feasibility and effectiveness of low-cost and open-source platforms, like Raspberry Pi, for marine research and mapping applications. Further work will investigate integrating this data into photogrammetry surveys.

Coastal Erosion Monitoring Using SEAHAWK Airborne Bathymetric LIDAR Data on the East Coast of Korea

Lee, J.; Kim, J.; Hur, H., and Wie, G., 2023. Coastal erosion monitoring using SEAHAWK Airborne Bathymetric LIDAR data on the east coast of Korea. Journal of Coastal Research, 39(2), 366–376. Charlotte (North Carolina), ISSN 0749-0208. Coastal erosion is accelerating along the east coast of Korea because of natural and anthropogenic forces. A current constraint in understanding and modeling these changes within large coastal areas is the multitemporal bathymetric data or recursive observations. This is because the main survey techniques for coastal erosion monitoring depend on field and echo-sounding surveys. However, the recent availability of Airborne Bathymetric LIDAR (ABL) systems enables topography mapping over large coastal areas. In particular, the development of the SEAHAWK system, a Korean ABL system, provides more data on the east coast of Korea. The ABL system enables assessing temporal changes along the large coasts without other conventional survey data. However, there are issues with the accuracy of the technique relative to quantitative uncertainties and ability to resolve nearshore spatial patterns of erosion and deposition indicative of geomorphologic change. This study validates the ABL data for coastal erosion monitoring by using the multi-temporal data acquired in 2013 and 2020 on the east coast of Korea and analyzes the results to reveal the quantitative and qualitative effectiveness of this technique.

Water Bottom and Surface Classification Algorithm for Bathymetric LiDAR Point Clouds of Very Shallow Waters

The absence of accurate point classification limits the effective use of airborne bathymetric LiDAR (ABL) data for coastal zone mapping. In this study, we propose a classification approach using a custom waveform decomposition technique with the pseudo-waveform generated from ABL point cloud data. Initially, the input point clouds were organized into a 2D grid. Next, the points that fall into a grid cell were organized into a histogram using Z-values to generate the pseudo-waveform. Subsequently, the pseudo-waveform was decomposed into water bottom, column, surface, and noise components using a custom multiple Gaussian curve fitting method. The proposed approach was evaluated with datasets acquired in Florida, USA, using a Riegl VQ-880-G ABL system. With an optimized parameter set, the proposed approach achieved F1 score of 98.944% for the classification of water bottom and an overall accuracy of 91.234% for all the classes. Further, the proposed approach was evaluated with datasets acquired in South Korea using a Seahawk system and compared against MBES data, demonstrating that the water bottom was successfully classified with a vertical error of 0.049 ± 0.167 m.

Machine-Learning-Method-Based Inversion of Shallow Bathymetric Maps Using ICESat-2 ATL03 Data

The application of empirical methods for satellite-derived bathymetry is limited by the lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> bathymetric data in remote, inaccessible areas. This challenge has been addressed with the launch of ICESat-2 (Ice, Cloud, and land Elevation Satellite-2). This study provides an accurate bathymetric photon extraction process for ICESat-2 ATL03 data, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of the bathymetric photons obtained using this process and airborne bathymetric LiDAR data is up to 99%. Next, based on two types of remote sensing data, ICESat-2 and Sentinel-2, machine learning (ML) models, including linear regression (LR), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were trained to obtain bathymetric maps. The experimental results show that the mean RMSE (root mean square error), mean MAE (mean absolute error), and mean MRE (mean relative error) values of the LR models are less than 3.02 m, 2.38 m, and 86.03%, respectively. The mean RMSE, MAE, and MRE values of the LightGBM and CatBoost models are less than 0.91 m, 0.66 m, and 23.17%, respectively. It is concluded that the proposed denoising process for ICESat-2 ATL03 data is effective, and the results of the bathymetric maps obtained using these data are satisfactory. Thus, the proposed approach is effective, and this strategy can be used to replace conventional bathymetric inversion methods to obtain high-accuracy bathymetric maps.

Keurusselkä impact structure, Finland—Overview, new observations, and renewed interpretation of the size

AbstractThe Keurusselkä structure is one of Finland's 12 meteorite impact structures—one of the world's oldest (1150 ± 10 Ma) and the largest of its kind in Finland. Findings of numerous well‐formed shatter cones in a wide area have helped define and prove Keurusselkä's impact origin. Keurusselkä is deeply eroded, making estimating its size challenging. Thus, various size ranges are based on the distribution of shatter cones. This study provides an overview of the earlier published studies in light of the unpublished data resulting from 2003 to 2019 field surveys. Two shatter cone outcrops 15 km from each other were sampled during a field survey in 2019. Thin section samples from these sites were studied with a polarizing microscope, and shock metamorphic features were identified and measured with the universal stage. We also compared topographic and bathymetric Lidar (light detection and ranging) data with the existing geophysical data and shatter cone occurrences. In situ outcrops—Metsomäki 9.6 km toward the W and Martinniemi 5.7 km toward SE from the crater's center—delimit the maximum radius of shatter cones found so far. Studies resulted in planar fractures parallel to (0001) in Metsomäki shatter cones. We determine the size of 37.5 km as the apparent diameter of the Keurusselkä impact structure, whereas the 25 km in diameter semicircular feature represents faulted rim structures.

Bathymetric LiDAR and multibeam echo-sounding data registration methodology employing a point cloud model

Airborne LiDAR bathymetry (ALB) and multibeam echosounders (MBESs) are the most commonly used technologies for underwater topographical measurements. However, ALB cannot obtain underwater information in deep water. Due to concerns about the safety of the survey vessels, it is difficult to take measurements in shallow water using an MBES. To maximize the complementary advantages of these two technologies, this paper proposes a point cloud registration algorithm for ALB and MBES data based on azimuth angle features. First, the topographical curvature features of ALB and the MBES point cloud data are extracted by using a cylindrical neighborhood model and a quadric surface fitting algorithm. Then, the azimuth angle similarity of the curvature feature points is used to match the corresponding points. Finally, a truncated least-squares-based iterative nearest-adjacent point (TrICP) method is utilized to realize multisource data registration. To verify the performance of the proposed registration algorithm, four areas with typical ALB and MBES point cloud data from Xisha Island, South China Sea, are used as the study areas. Compared to the traditional and typical curvature-based registration (CR) algorithm for point cloud data registration, the proposed algorithm obtains average RMSE values of 0.278 m, 0.252 m, 0.214 m and 0.177 m in the four typical study areas. The corresponding registration accuracy is improved by 91%, 73%, 84% and 49%, respectively. The experimental results show that the proposed algorithm is more reliable and effective for ALB and MBES point cloud registration than the previous algorithms.

Refined Geometric Modeling of Laser Pulse Propagation in Airborne LiDAR Bathymetry

To achieve a geometrically accurate representation of the water bottom, airborne LiDAR bathymetry (ALB) requires the correction of the raw 3D point coordinates due to refraction at the air–water interface, different signal velocity in air and water, and further propagation induced effects. The processing of bathymetric LiDAR data is based on a geometric model of the laser bathymetry pulse propagation describing the complex interactions of laser radiation with the water medium and the water bottom. The model comprises the geometric description of laser ray, water surface, refraction, scattering in the water column, and diffuse bottom reflection. Conventional geometric modeling approaches introduce certain simplifications concerning the water surface, the laser ray, and the bottom reflection. Usually, the local curvature of the water surface and the beam divergence are neglected and the travel path of the outgoing and the returned pulse is assumed to be identical. The deviations between the applied geometric model and the actual laser beam path cause a coordinate offset at the water bottom, which affects the accuracy potential of the measuring method. The paper presents enhanced approaches to geometric modeling which are based on a more accurate representation of water surface geometry and laser ray geometry and take into account the diffuse reflection at the water bottom. The refined geometric modeling results in an improved coordinate accuracy at the water bottom. The impact of the geometric modeling methods on the accuracy of the water bottom points is analyzed in a controlled manner using a laser bathymetry simulator. The findings will contribute to increase the accuracy potential of modern ALB systems.

Combined geometric-radiometric and neural network approach to shallow bathymetric mapping with UAS imagery

There is a pressing need for shallow bathymetry in regions around the world, including in fragile marine ecosystems, such as coral reefs. Unfortunately, mapping these shallow-water areas using in situ or boat-based methods is expensive, time consuming, and potentially dangerous, due to the need to put personnel and/or boats in high-energy nearshore areas, which may contain rocks, reefs, and other submerged hazards. For this reason, passive remote sensing methods of shallow bathymetric mapping have been rapidly growing in interest and usage. Two general categories of approaches using passive imaging can be distinguished as: (1) those based on geometry (stereo-photogrammetry); (2) those based on radiometry (attenuation of light in the water column). These two broad categories of approaches have been tested on imagery collected using aircraft and satellites in numerous previous studies. Recent work within the geometric category includes processing unmanned aircraft system (UAS) imagery using Structure from Motion and Multi-View Stereo (SfM-MVS) photogrammetry, while radiometric methods encompass the broad range of spectral bathymetry retrieval algorithms. Each broad approach category has advantages and disadvantages. Fortunately, the geometric and radiometric approaches are highly complementary. The geometric approaches tend to work best in areas of high bottom texture, which facilitates feature matching in the SfM-MVS software. Meanwhile, the radiometric approaches work best in relatively homogeneous bottom types. To leverage the strengths of each type of approach and overcome their respective weaknesses, this work develops and tests a combined geometric-radiometric bathymetric mapping approach designed for shallow-water mapping from UAS imagery. Four radiometric models of varying complexity are tested, ranging from a color-based lookup table approach to neural networks. Two UAS flights on Buck Island, off St Croix in the U.S. Virgin Islands (USVI), are used to assess the accuracy of the methodology when compared to aerial bathymetric lidar data. The results show that the combined geometric-radiometric approach provides an increase in spatial coverage of up to 61% and improvement (i.e. decrease) in 95th percentile error of up to 49% when compared to traditional refraction-corrected SfM-MVS bathymetry.

Assessing the Utility of Open-Access Bathymetric Data for Shipwreck Detection in the United States

Investigation of submerged cultural heritage is an important area of archeological focus. However, the expense of acquiring the necessary data to conduct studies of underwater landscapes is often prohibitive to many researchers. Within the United States, highly resolved bathymetric data are openly available from governmental agencies, and yet little to no marine archaeological exploration has occurred using this information. Here, we investigate the archaeological utility of freely available bathymetric datasets from the National Oceanic and Atmospheric Administration (NOAA) in the United States. These datasets have not previously been utilized for archaeological publications, and include swath bathymetric and topographic LiDAR data, which are widely used by marine archaeologists. We present three case studies from Long Island, New York, coastal Massachusetts (on the Eastern coast of North America), and New Orleans (on the Gulf Coast of North America) to demonstrate the potential of this open-access information for locating shipwreck sites. Results indicate that shipwrecks at varying levels of preservation can be identified at depths up to 160 m, and that even in extremely turbid waters, bathymetric LiDAR can detect some wreckage. Following this assessment, we develop an automated shipwreck detection procedure using an inverse depression analysis. Our results are promising for automated detection methods in marine archaeology research. We argue that archaeologists in the United States should take advantage of these freely available data, as it is possible that these bathymetric data can be used for detection and conservation of cultural and environmental resources even without large funding acquisitions.

Confidence Levels, Sensitivity, and the Role of Bathymetry in Coral Reef Remote Sensing

Remote sensing is playing an increasingly important role in the monitoring and management of coastal regions, coral reefs, inland lakes, waterways, and other shallow aquatic environments. Ongoing advances in algorithm development, sensor technology, computing capabilities, and data availability are continuing to improve our ability to accurately derive information on water properties, water depth, benthic habitat composition, and ecosystem health. However, given the physical complexity and inherent variability of the aquatic environment, most of the remote sensing models used to address these challenges require localized input parameters to be effective and are thereby limited in geographic scope. Additionally, since the parameters in these models are interconnected, particularly with respect to bathymetry, errors in deriving one parameter can significantly impact the accuracy of other derived parameters and products. This study utilizes hyperspectral data acquired in Hawaii in 2000–2001 and 2017–2018 using NASA’s Classic Airborne Visible/Infrared Imaging Spectrometer to evaluate performance and sensitivity of a well-established semi-analytical inversion model used in the assessment of coral reefs. Analysis is performed at several modeled spatial resolutions to emulate characteristics of different feasible moderate resolution hyperspectral satellites, and data processing is approached with the objective of developing a generalized, scalable, automated workflow. Accuracy of derived water depth is evaluated using bathymetric lidar data, which serves to both validate model performance and underscore the importance of image quality on achieving optimal model output. Data are then used to perform a sensitivity analysis and develop confidence levels for model validity and accuracy. Analysis indicates that derived benthic reflectance is most sensitive to errors in bathymetry at shallower depths, yet remains significant at all depths. The confidence levels provide a first-order method for internal quality assessment to determine the physical extent of where and to what degree model output is considered valid. Consistent results were found across different study sites and different spatial resolutions, confirming the suitability of the model for deriving water depth in complex coral reef environments, and expanding our ability to achieve automated widespread mapping and monitoring of global coral reefs.

Combining Ikonos and Bathymetric LiDAR Data to Improve Reef Habitat Mapping in the Florida Keys

Mapping reef habitat at a detailed level is a challenge when using single-source remote sensing data such as Ikonos imagery. In this study, Ikonos and bathymetric LiDAR data were combined to improve reef habitat mapping in the Florida Keys, USA. An object-based ensemble approach was applied to integrate two remote sensing data sources for generating accurate and informative reef maps of geomorphological structure types and detailed habitats. LiDAR can significantly increase reef classification by providing important bathymetry, reef habitat elevation, and intensity information. A synergy of Ikonos imagery and all LiDAR-derived features could effectively classify reef structure (3-class) with an overall accuracy (OA) of 93% and Kappa value of 0.8, and map detailed reef habitats (10-class) with an OA of 79% and Kappa value of 0.7. Ensemble analysis of three machine learning outputs did not significantly improve the classification but generated a complementary uncertainty map to identify regions with a robust classification and areas difficult to map. Fusion of Ikonos and bathymetry LiDAR is a promising alternative to traditional in-situ and manual interpretation methods for detailed reef habitat mapping.

조간대 고해상도 지형정보 구축을 위한 항공수심라이다와 다중빔음향측심기 데이터 통합에 관한 연구

조간대는 지반이 견고하지 않고 육지와 물이 공존하는 환경적 특성 때문에 전통적인 측량방법으로는 고해상도의 지형정보를 구축하기가 어렵다. 만조 시에도 선박이 접근하기 어려운 수심이 얕은 지역이 넓게 분포하기 때문에 해양환경에서 일반적으로 활용되는 음향측심 기술도 적용하기가 어렵다. 항공수심라이다(airborne bathymetric LiDAR, ABL) 측량시스템은 근적외선 및 녹색 레이저를 사용하여 지형 및 수심에 대한 관측을 동시에 수행할 수 있으므로 조간대에 매우 효과적으로 적용이 가능한 측량기술이다. 또한 다중빔음향측심기(multi-beam echo-sounder, MBES)로부터 취득된 근해의 해저 지형자료와 상호보완적으로 통합된다면 연속적인 고해상도 지형자료 제작에 활용이 가능하다. 본 연구에서는 이를 위해 이종 데이터 간의 통합 시 발생하는 상대 오차를 검증하고 통합 데이터의 활용가능성을 분석하였다. 특히 데이터 취득 시의 환경적 요인과 수직기준면 변환과정으로부터 발생하는 오차의 영향을 최소화하기 위해 타원체고 기반으로 MBES 데이터를 취득하여 정확도 평가를 수행하였다. 정확도 평가 결과 RMSE값이 ±0.136m로 IHO에서 규정한 특등급 수심별 허용 오차범위를 만족하는 것으로 나타났다. 이는 항공수심라이다를 활용하여 조간대 지역에 대해 연속성 있는 고해상도 지형정보의 구축이 가능함을 시사한다.

Detection of Seabed Rock Using Airborne Bathymetric Lidar and Hyperspectral Data in the East Sea Coastal Area

The distribution of seabed rock in the coastal area is relevant to navigation safety and development of ocean resources where it is an essential hydrographic measurement. Currently, the distribution of seabed rock relies on interpretations of water depth data or point based bottom materials survey methods, which have low efficiency. This study uses the airborne bathymetric Lidar data and the hyperspectral image to detect seabed rock in the coastal area of the East Sea. Airborne bathymetric Lidar data detected seabed rocks with texture information that provided 88% accuracy and 24% commission error. Using the airborne hyperspectral image, a classification result of rock and sand gave 79% accuracy, 11% commission error and 7% omission error. The texture data and hyperspectral image were fused to overcome the limitations of individual data. The classification result using fused data showed an improved result with 96% accuracy, 6% commission error and 1% omission error.

Spatial abundance patterns and recruitment of a virus‐affected commercial mollusc fishery

AbstractInfectious pathogens figure prominently among those factors threatening marine wildlife. Mass mortality events caused by pathogens can fundamentally alter the structure of wild fish stocks and depress recruitment rates and yield. In the most severe instances, this can precipitate stock collapses resulting in dramatic economic losses to once valuable commercial fisheries. An outbreak of a herpes‐like virus among commercially fished abalone populations in the south‐west fishery of Victoria, Australia, during 2006–2007, has been associated with high mortality rates among all cohorts. Long‐term records from fishery‐independent surveys of blacklip abalone Haliotis rubra (Leach) enabled abundance from pre‐ and post‐viral periods to be analysed to estimate stock density and biomass. The spatial distribution of abundance in relation to physical habitat variables derived from high‐resolution bathymetric LiDAR data was investigated. Significant differences were observed in both measures between pre‐ and post‐viral periods. Although there was some limited evidence of gradual stock improvement in recent years, disease‐affected reefs have remained below productivity rates prior to the disease outbreak suggesting a reduction in larval availability or settlement success. This was corroborated by trends in sublegal sized blacklip abalone abundance that has yet to show substantial recovery post‐disease. Abundance data were modelled as a function of habitat variables using a generalised additive model (GAM) and indicated that high abundance was associated with complex reef structures of coastal waters (&lt;15 m). This study highlights the importance of long‐term surveys to understand abalone recovery following mass mortality and the links between stock abundance and seafloor variability.

Data-Driven Approach to Benthic Cover Type Classification Using Bathymetric LiDAR Waveform Analysis

A data-driven method for describing the benthic cover type based on full-waveform bathymetric LiDAR data analysis is presented. The waveform of the bathymetric LiDAR return pulse is first modeled as a sum of three functions: a Gaussian pulse representing the surface return, a function modeling the backscatter and another Gaussian pulse modeling the return from the bottom surface. Two sets of variables are formed: one containing features describing the bottom return and the other describing various conditions, such as water quality and the depth of the seabed. Regression analysis is used to eliminate the effect of the condition variables on the features, after which the features are mapped onto a cell lattice using a self-organizing map (SOM). The cells of the SOM are grouped into seven clusters using the neighborhood distance matrix method. The clustering result is evaluated using the seabed substrate map based on sonar measurements, as well as delineation of photic zones in the study area. High correspondence between the clusters and the substrate type/photic zone has been obtained indicating that the proposed clustering method adequately describes the benthic cover in the study area. The bottom return pulse waveforms corresponding to the clusters and a cluster map of the study area are also presented. The method can be used for clustering full waveform bathymetric LiDAR data acquired from large areas to discover the structure of benthic cover types and to focus the field studies accordingly.

The role of oxbow lakes in the off-channel storage of bed material along the Ain River, France

Incidents of chute cutoff redistribute floodplain sediment into rivers, causing downstream bar growth while simultaneously creating accommodation space for the storage of sediment within the floodplain in the form of oxbow lakes. Oxbows may be able to sequester enough sediment to balance the amount produced by chute incision, but the long-term consequences of chute cutoff on reach-scale sediment budgets have so far remained unclear. This has been due to a relative paucity of field observations that quantify the exchange of coarse sediment between the channel and floodplain. Here, we take advantage of a unique opportunity to document the sediment budget of a reach of the Ain River, France, that has experienced three recent incidents of chute cutoff. Monitoring of the river prior to chute incision allowed us to precisely quantify the rates of bed-material transfer over a thirteen-year period using a combination of bathymetric surveys, LiDAR data, and aerial photographs. The abandoned channels under study sequestered between 17 and 40% of the sediment introduced to the channel, with most of the rest of the sediment being stored within the river itself. Aggradation of the abandoned channels was not evenly distributed, instead occurring by the growth of point bars and thus implying that the abandoned channel planform may be an important control on aggradation rates. Our results make clear that although oxbows may provide a significant sink for bed material, the amount of sediment sequestered within them cannot compensate for the loading caused by chute incision.

Measuring the Morphology and Dynamics of the Snake River by Remote Sensing

The Snake River is an essential feature of Grand Teton National Park, and this dynamic fluvial system maintains diverse habitats while actively shaping the landscape. The complex, ever-changing nature of the river make effective characterization difficult, however; traditional field methods are illsuited for this task. Remote sensing provides an appealing alternative that could facilitate resource management while providing novel insight on the controls of channel form and behavior. This study continued our ongoing assessment of the potential to measure the morphology and dynamics of large, complex rivers such as the Snake via remote sensing (Figure 1). More specifically, we acquired hyperspectral images and bathymetric LiDAR data in August 2012 and are now comparing the depth retrieval capabilities of these sensors; in situ observations of water column optical properties inform this analysis as well. In addition to bathymetry, we are investigating the feasibility of using these data to infer bottom reflectance and hence delineate various substrates, such as gravel and submerged aquatic vegetation. Another new aspect of our research focuses on estimating flow velocities from the hyperspectral images and high-resolution digital aerial photography acquired simultaneously. Extensive field measurements of velocity will help us develop this approach. Similarly, measurements of sediment grain size on exposed bar surfaces will be used to assess whether particle size can be inferred from the highresolution photography. Remotely sensed data also are being used to identify areas of erosion and deposition and hence quantify the sediment flux associated with changes in channel morphology. Additional hyperspectral and bathymetric LiDAR data will be acquired in 2013, along with field measurements of depth, velocity, and bottom type.

Geostatistical estimations of bathymetric LiDAR errors on rivers

AbstractThe geometry of river channels is a key descriptive element for hydromorphology, hydraulics and hydroecology. Gravel bed rivers usually have a mean water depth of ∼0·5 m. For such shallow waters, the accuracy of bathymetric LiDAR data has to be precisely assessed. Alongside this accuracy investigation, methodological questions arise: How to assess the data quality of elevation LiDAR when comparing reference topographic points on river beds to laser beam footprints of several square metres at different locations? What are the consequences of uncertainties and scaling in accuracy estimations? In this study, we designed a methodology to assess the quality of LiDAR topographical data within rivers using a specific geostatistical method that conducts upscaling as well as interpolation of reference data that takes into account uncertainties. This method uses an anisotropic block kriging from DGPS points on LiDAR footprint areas within a channel‐fitted coordinate system. This assessment focused on a 1·5 km long reach of the Gardon gravel bed river, in the south of France. DGPS points pseudo‐regularly located along the river were acquired at the same time as the LiDAR survey with the HawkEyeII system. LiDAR accuracy results for river bottom elevation show a negative bias for high depth. Added to that bias, a random error with 0·32 m standard deviation was found by considering upscaling and uncertainties in reference data, and a 0·20 m standard deviation was found if they were not considered. Consequently, if LiDAR bias can be corrected, measuring a water depth less than 32 cm, i.e. for 28% of the river area, is unrealistic.However, this experiment shows that LiDAR provides an accurate representation of the riverbed forms. It also provided a useful, continuous, topographic surface from the underwater river bed up to riparian areas. Copyright © 2010 John Wiley and Sons, Ltd.

Morphological characteristics of the Basque continental shelf (Bay of Biscay, northern Spain); their implications for Integrated Coastal Zone Management

This contribution integrates and analyzes data from high-resolution multibeam, seismic profiles, bathymetric LiDAR, and surficial sediment data for the geomorphological seascapes characterisation and process-description of the Basque inner and middle continental shelf (northern Spain). From the data obtained, the Basque shelf is characterised by a heterogeneous seafloor where, on a small spatial scale, different morphologies and sedimentary processes can be observed. Tectonic activity and sea-level changes, together with present processes of sediment supply and climatic conditions, have a critical influence on the present configuration of the continental shelf and the distribution of seafloor types. On the basis of all of the datasets, seafloor classification, bedform analysis, long-term sea-level change-induced seafloor features and anthropogenic features over the seafloor, are described. As a result, three distinct zones have been identified for the Basque shelf, related to the main geomorphological features and seascapes, as summarised below. (i) A western part, with a northwestern orientation, which coincides with the prevailing wave direction. This zone is characterised by a predominantly rocky substrate, except within the mouth of the Nervión estuary, where sandy sediments appear to infill a paleo-channel. (ii) A central part, which is oriented towards the northeast and receives less wave energy. Within this zone, a sedimentary seabed is predominant. Sorted bedforms are well developed and can be detected in water depths in excess of 90 m. (iii) An eastern zone, which is characterised by sedimentary seafloor and shore terraces; here, a flat rocky seafloor is covered by a thin layer of sediments. Throughout the study area, the bathymetry and sedimentary features of the shoreface and inner shelf are controlled mainly by climatic conditions, coastal dynamics and the underlying geological framework. The integration of these data sets, together with their interpretation, is a valuable source of information for Integrated Coastal Zone Management (ICZM) and constitutes a useful tool for implementing various European Directives.