Evaluation of optical remote sensing-based shallow water bathymetry for recursive mapping

Sam, Lydia; Prusty, G.; Gahlot, Nidhi

doi:10.1080/10106049.2017.1299800

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…A similar pattern was also observed for the centre of the canal base, where data from 2001 were outstandingly different from those from the years 2016 and 2019. These findings agree with those of Montgomery (2007), Osman (2015) and Sam et al (2018), who found that the bed canal for water bodies tends to accumulate more material over time. Figure 6…”

Section: Bathymetric Changes Of Crosssectional Profilesupporting

confidence: 92%

Bathymetry changes caused by sedimentation in an unlined canal of the Chókwè irrigation scheme, Mozambique

Sousa

Raude

Wambua

et al. 2022

Irrigation and Drainage

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Bathymetric changes have been observed over the years in the Chókwè irrigation scheme (CIS) and little is known about their cause. This study assessed bathymetric variations that have occurred since 2001, when the scheme was last rehabilitated. The bathymetry of canal beds and banks was analysed using GPS‐Rover surveying equipment to determine cross‐section and longitudinal profiles. Water level measurements were performed using staff gauges at nine different sampling stations. Data sets for 2001, 2016 and 2019 were considered. Canal depth changes were captured, and density plots generated. Bathymetric changes of cross‐sectional and longitudinal profiles, slope trend analysis models, canal depth changes and water depth variation, were assessed. Results suggest cross‐sectional and longitudinal bathymetric profiles are affected by sediment accumulation. The depths of canal beds varied from 0.5 to 2.0 m over a span of more than 80 km. Trend analysis models indicated good accuracy for fits in all three reaches in 2001, 2016 and 2019. The forecast accuracy predictors MAPE, MAD and MSD were all found to have minimal values within the acceptable range of 1 and 3 for all the years and sectors. The water depth in the canal showed considerable variation within and across each sampling point and station, varying from 3.5 to less than 1.2 m. These findings will play a significant role in the study of water flow and sedimentation processes in the CIS.

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Section: Bathymetric Changes Of Crosssectional Profilesupporting

confidence: 92%

Bathymetry changes caused by sedimentation in an unlined canal of the Chókwè irrigation scheme, Mozambique

Sousa

Raude

Wambua

et al. 2022

Irrigation and Drainage

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“…Regular monitoring of water depths (i.e., bathymetry) is gaining ever more importance in the context of climate change and human interference in the sensitive littoral zone for both coastal areas and inland waters. On the coastal side, monitoring is required for documenting the impact of natural hazard events like earthquake sea waves or hurricanes (Salameh et al 2019;Bergsma et al 2019), changes in benthic habitats (Parrish et al 2016;Eugenio et al 2017;Purkis et al 2019), and sea level rise (Sam et al 2018;Rasquin et al 2020). For inland waters, remote sensing based bathymetric surveys are used to measure the depth of relatively clear and shallow rivers and lakes (Hilldale and Raff 2008;Kasvi et al 2019) as the basis for monitoring flood extents, fluvial morphodynamics, and in-stream habitats (Mandlburger et al 2015;Carrivick and Smith 2019).…”

Section: Introductionmentioning

confidence: 99%

BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

Mandlburger

Kölle

Nübel

et al. 2021

PFG

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Besides airborne laser bathymetry and multimedia photogrammetry, spectrally derived bathymetry provides a third optical method for deriving water depths. In this paper, we introduce BathyNet, an U-net like convolutional neural network, based on high-resolution, multispectral RGBC (red, green, blue, coastal blue) aerial images. The approach combines photogrammetric and radiometric methods: Preprocessing of the raw aerial images relies on strict ray tracing of the potentially oblique image rays, considering the intrinsic and extrinsic camera parameters. The actual depth estimation exploits the radiometric image content in a deep learning framework. 3D water surface and water bottom models derived from simultaneously captured laser bathymetry point clouds serve as reference and training data for both image preprocessing and actual depth estimation. As such, the approach highlights the benefits of jointly processing data from hybrid active and passive imaging sensors. The RGBC images and laser data of four groundwater supplied lakes around Augsburg, Germany, captured in April 2018 served as the basis for testing and validating the approach. With systematic depth biases less than 15 cm and a standard deviation of around 40 cm, the results satisfy the vertical accuracy limit Bc7 defined by the International Hydrographic Organization. Further improvements are anticipated by extending BathyNet to include a simultaneous semantic segmentation branch.

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“…The domain characterized by shallow waters, where the dynamic interplay between the sea and land is particularly pronounced, constitutes a critically significant region for a multitude of hydrographic, oceanographic, and topographic survey applications. This encompassing domain is instrumental in supporting various endeavors including coastal construction, marine safety assurance, resource assessment and development, fisheries and marine industries, marine transportation and shipping logistics, environmental preservation, and management, as well as research pertaining to the coastal zones of islands or peninsulas [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Innovative Maritime Uncrewed Systems and Satellite Solutions for Shallow Water Bathymetric Assessment

Constantinoiu,

Tavares,

Cândido

et al. 2024

Inventions

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Shallow water bathymetry is a topic of significant interest in various fields, including civil construction, port monitoring, and military operations. This study presents several methods for assessing shallow water bathymetry using maritime uncrewed systems (MUSs) integrated with advanced and innovative sensors such as Light Detection and Ranging (LiDAR) and multibeam echosounder (MBES). Furthermore, this study comprehensively describes satellite-derived bathymetry (SDB) techniques within the same geographical area. Each technique is thoroughly outlined with respect to its implementation and resultant data, followed by an analytical comparison encompassing their accuracy, precision, rapidness, and operational efficiency. The accuracy and precision of the methods were evaluated using a bathymetric reference survey conducted with traditional means, prior to the MUS survey and with cross-comparisons between all the approaches. In each assessment of the survey methodologies, a comprehensive evaluation is conducted, explaining both the advantages and limitations for each approach, thereby enabling an inclusive understanding for the reader regarding the efficacy and applicability of these methods. The experiments were conducted as part of the Robotic Experimentation and Prototyping using Maritime Unmanned Systems 23 (REPMUS23) multinational exercise, which was part of the Rapid Environmental Assessment (REA) experimentations.

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Evaluation of optical remote sensing-based shallow water bathymetry for recursive mapping

Cited by 11 publications

References 35 publications

Bathymetry changes caused by sedimentation in an unlined canal of the Chókwè irrigation scheme, Mozambique

Bathymetry changes caused by sedimentation in an unlined canal of the Chókwè irrigation scheme, Mozambique

BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

Innovative Maritime Uncrewed Systems and Satellite Solutions for Shallow Water Bathymetric Assessment

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