Mapping river bathymetries: Evaluating topobathymetric LiDAR survey

Tonina, Daniele; McKean, J. A.; Benjankar, Rohan; Wright, C. Wayne; Goode, Jaime R.; Chen, Qiuwen; Reeder, W. J.; Carmichael, Richard A.; Edmondson, Michael R.

doi:10.1002/esp.4513

Cited by 64 publications

(71 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this Section, we provide a summary of related work in the context of UAV-based optical remote sensing of coastal and inland water areas, focusing on the derivation of bathymetry. For the sake of completeness, we first briefly discuss passive image-based techniques, both via multimedia photogrammetry [10] and spectrally based depth estimation [11], but mainly concentrate on bathymetric LiDAR [1] in general and small-footprint topo-bathymetric LiDAR for inland water applications in particular [12,13]. Furthermore, we present recent validation and comparison studies on subject matters as our contribution also focuses on sensor evaluation.…”

Section: Related Workmentioning

confidence: 99%

Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor

Mandlburger

Pfennigbauer²,

Schwarz³

et al. 2020

Remote Sensing

100

View full text Add to dashboard Cite

We present the sensor concept and first performance and accuracy assessment results of a novel lightweight topo-bathymetric laser scanner designed for integration on Unmanned Aerial Vehicles (UAVs), light aircraft, and helicopters. The instrument is particularly well suited for capturing river bathymetry in high spatial resolution as a consequence of (i) the low nominal flying altitude of 50-150 m above ground level resulting in a laser footprint diameter on the ground of typically 10-30 cm and (ii) the high pulse repetition rate of up to 200 kHz yielding a point density on the ground of approximately 20-50 points/m 2 . The instrument features online waveform processing and additionally stores the full waveform within the entire range gate for waveform analysis in post-processing. The sensor was tested in a real-world environment by acquiring data from two freshwater ponds and a 500 m section of the pre-Alpine Pielach River (Lower Austria). The captured underwater points featured a maximum penetration of two times the Secchi depth. On dry land, the 3D point clouds exhibited (i) a measurement noise in the range of 1-3 mm; (ii) a fitting precision of redundantly captured flight strips of 1 cm; and (iii) an absolute accuracy of 2-3 cm compared to terrestrially surveyed checkerboard targets. A comparison of the refraction corrected LiDAR point cloud with independent underwater checkpoints exhibited a maximum deviation of 7.8 cm and revealed a systematic depth-dependent error when using a refraction coefficient of n = 1.36 for time-of-flight correction. The bias is attributed to multi-path effects in the turbid water column (Secchi depth: 1.1 m) caused by forward scattering of the laser signal at suspended particles. Due to the high spatial resolution, good depth performance, and accuracy, the sensor shows a high potential for applications in hydrology, fluvial morphology, and hydraulic engineering, including flood simulation, sediment transport modeling, and habitat mapping.Remote Sens. 2020, 12, 986 2 of 28 UAV-based 3D data acquisition was first accomplished using light-weight camera systems, where advancements in digital photogrammetry and computer vision-enabled automatic data processing workflows for the derivation of dense 3D point clouds based on Structure-from-Motion (SfM) and Dense Image Matching (DIM). Due to advancements in UAV-platform technology and ongoing sensor miniaturization, today compact LiDAR sensors are increasingly integrated on both multi-copter and fixed-wing UAVs, enabling 3D mapping with unprecedented spatial resolution and accuracy. The tackled applications include topographic mapping, geomorphology, infrastructure inspection, environmental monitoring, forestry, and precision farming. While UAV-borne laser scanning (ULS) can already be considered state-of-the-art for mapping tasks above the water table, UAV-based bathymetric LiDAR still lacks behind, mainly due to payload restrictions.The established techniques for mapping bathymetry are single-or multi-beam echo sounding (SBES/MBES), incl...

show abstract

Section: Related Workmentioning

confidence: 99%

Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor

Mandlburger

Pfennigbauer²,

Schwarz³

et al. 2020

Remote Sensing

100

View full text Add to dashboard Cite

show abstract

“…Alternatively, green wavelength ALS can collect bathymetry over lengths from one to tens of kilometres (Hilldale & Raff, ; Kinzel, Legleiter, & Nelson, ; Kinzel, Wright, Nelson, & Burman, ), yet footprint size that reduces accuracy and point density are limiting factors (Tonina et al, ). Despite these methods being available, the extra challenge in obtaining them makes bathymetric analysis less prominent in the literature.…”

Section: River Corridor Remote Sensingmentioning

confidence: 99%

Remote sensing of river corridors: A review of current trends and future directions

Tomsett

Leyland

2019

River Research & Apps

109

View full text Add to dashboard Cite

River corridors play a crucial environmental, economic, and societal role yet also represent one of the world's most dangerous natural hazards, making monitoring imperative to improve our understanding and to protect people. Remote sensing offers a rapidly growing suite of methods by which river corridor monitoring can be performed efficiently, at a range of scales and in difficult environmental conditions. This paper aims to evaluate the current state and assess the potential future of river corridor monitoring, whilst highlighting areas that require further investigation. We initially review established methods that are used to undertake river corridor monitoring, framed by the context and scales upon which they are applied. Subsequently, we review cutting edge technologies that are being developed and focussed around unmanned aerial vehicle and multisensor system advances. We also "horizon scan" for future methods that may become increasingly prominent in research and management, citing examples from within and outside of the fluvial domain. Through review of the literature, it has become apparent that the main gap in fluvial remote sensing lies in the trade-off between resolution and scales. However, prioritising process measurements and simultaneous multisensor data collection is likely to offer a bigger advance in understanding than purely from better surveying methods alone. Challenges regarding the legal deployment of more complex systems, as well as effectively disseminating data into the science community, are amongst those that we propose need addressing. However, the plethora of methods currently available means that researchers and monitoring agencies will be able to identify suitable techniques for their needs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Two morphologically different reaches, a complex and simple reach, of the Lemhi River were selected as study sites ( Figure 1) (Tonina et al, 2018). The complex upstream study reach is 230 m long, 10m wide, and has an average bed slope of 0.75%.…”

Section: Reach Scalementioning

confidence: 99%

“…LiDAR survey The EAARL-B system surveyed the entire Lemhi River from Leadore to the confluence with the Salmon River on the afternoons of 25-27 October 2013 during ideal conditions of clear water and minimum foliage on the riparian vegetation. EAARL-B LiDAR full-waveform returns were processed using a combination of the Airborne Lidar Processing System (ALPS), Applied Imagery's Quick Terrain Modeler, and ESRI's ArcMap 10.2 (Tonina et al, 2018). The resulting filtered point cloud of the EAARL-B system had a point density of approximately 1 pointm À2 over the submerged topography of the entire Lemhi River.…”

Section: Data Collection and Processingmentioning

confidence: 99%

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

Tonina

McKean

Benjankar

et al. 2020

Earth Surf Processes Landf

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Mapping river bathymetries: Evaluating topobathymetric LiDAR survey

Cited by 64 publications

References 54 publications

Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor

Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor

Remote sensing of river corridors: A review of current trends and future directions

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

Contact Info

Product

Resources

About