Hydrographic data modeling methods for determining precise seafloor topography

Aykut, Nedim Onur; Akpınar, Burak; Aydın, Ömer

doi:10.1007/s10596-013-9347-1

Cited by 13 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Big data problems are closely related to the idea of single-beam echosounders measurements [31] and Light Detection and Ranging (LiDAR) [32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

View full text Add to dashboard Cite

Autonomous navigation is an important task for unmanned vehicles operating both on the surface and underwater. A sophisticated solution for autonomous non-global navigational satellite system navigation is comparative (terrain reference) navigation. We present a method for fast processing of 3D multibeam sonar data to make depth area comparable with depth areas from bathymetric electronic navigational charts as source maps during comparative navigation. Recording the bottom of a channel, river, or lake with a 3D multibeam sonar data produces a large number of measuring points. A big dataset from 3D multibeam sonar is reduced in steps in almost real time. Usually, the whole data set from the results of a multibeam echo sounder results are processed. In this work, new methodology for processing of 3D multibeam sonar big data is proposed. This new method is based on the stepwise processing of the dataset with 3D models and isoline maps generation. For faster products generation we used the optimum dataset method which has been modified for the purposes of bathymetric data processing. The approach enables detailed examination of the bottom of bodies of water and makes it possible to capture major changes. In addition, the method can detect objects on the bottom, which should be eliminated during the construction of the 3D model. We create and combine partial 3D models based on reduced sets to inspect the bottom of water reservoirs in detail. Analyses were conducted for original and reduced datasets. For both cases, 3D models were generated in variants with and without overlays between them. Tests show, that models generated from reduced dataset are more useful, due to the fact, that there are significant elements of the measured area that become much more visible, and they can be used in comparative navigation. In fragmentary processing of the data, the aspect of present or lack of the overlay between generated models did not relevantly influence the accuracy of its height, however, the time of models generation was shorter for variants without overlay.

show abstract

“…Big data problems are closely related to the idea of single-beam echosounders measurements [31] and Light Detection and Ranging (LiDAR) [32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The isopach data points were converted into grid using the Kriging interpolation method. The Kriging method was reported by Khaba and Griffiths (); Dost and Mannaerts () and Aykut, Akpinar, and Aydin () to provide more reliable grid results, especially when a large volume of data is being handled. A regularly spaced grid was created and used to establish the sediment distribution pattern, thickness and calculation of sediment volume.…”

Section: Methodsmentioning

confidence: 99%

Sediment distribution and accumulation in Lake Naivasha, Kenya over the past 50 years

et al. 2019

View full text Add to dashboard Cite

Although surface waterbodies are water sources for socio‐economic activities and ecosystems, their functions are threatened by sedimentation. Sedimentation of lakes and reservoirs can result in a loss of storage capacity and altered water quality. The present study assessed the sedimentation status of Lake Naivasha, Kenya, based on sediment distribution and accumulation over the past 50 years, using a Bathymetric Survey System (BSS). The BSS uses multi‐frequency Acoustic Profiling System (APS) to map recently deposited sediments. Sediment core samples were collected with a vibe‐ coring device and dated. Sediment layers corresponding to a period of the past 20 and 50 years were identified. Sediment cores and acoustic images were subsequently used to determine sediment thickness within the lake. The collected depth data from multi‐frequency APS, and dated cores were processed in DepthPic and Surfer software. The sediment depth was extracted in DepthPic, while the sediment volume and distribution were generated from Surfer software. The results from present study indicated that sediment distribution varied from one part of the lake to another for the past 20 and 50 years. High sediment thickness observed in the south‐west and eastern parts of the lake. Between 1996–2016 and 1966–2016 periods, the maximum accumulated sediment thickness was found to be about 0.55 and 1.9 m, with an average sediment thickness of 0.25 and 0.56 m, respectively. The mean sediment load corresponding to the 1966–1996 and 1996–2016 periods was 2.78 × 105 and 4.61 × 105 t/year, respectively. It was found that sediment load into Lake Naivasha has been increasing in the recent past. Based on the present the study, it was found that combined use of BSS, sediment cores and dating can be adopted in many lakes and reservoirs to determine sediment thicknesses even where no prior bathymetric surveys exist for comparison.

show abstract

“…The next step involved creating a grid file from the point data through interpolation. For the present study, ordinary Kriging interpolation method was used because it creates smoother contours than those generated from the Triangulated Irregular Network (TIN) model, as reported by Dost and Mannaerts () and Aykut, Akpinar, and Aydin ().…”

Section: Methodsmentioning

confidence: 99%

Bathymetric survey of Lake Naivasha and its satellite Lake Oloiden in Kenya; using acoustic profiling system

et al. 2018

View full text Add to dashboard Cite

Lakes and reservoirs play important roles as freshwater sources for domestic, industrial, agricultural, fisheries and recreational purposes. However, for the lakes to be sustainably exploited, there is need to understand their bathymetric characteristics by conducting bathymetric surveys. This aids in generating information that can guide lakes stakeholders and managers in establishing the volume of available water. It is recommended, therefore, that bathymetric surveys be conducted at ten‐year intervals. Such continuous bathymetric information is lacking in many lakes, especially in developing countries. One example is Lake Naivasha in Kenya, which is largely exploited for various socio‐economic purposes. Despite its importance, its most recent published bathymetric data were collected in 1991. The goal of the present study, therefore, was to conduct a bathymetric survey of Lake Naivasha and its satellite Lake Oloiden, using an Acoustic Profiling System (APS) to generate Depth–Area–Volume relationships for the lakes. The survey results indicate the in the year 2016 mean depth, volume and surface area of the lake were 4.68 m, 722 × 106 m3 and 154.17 × 106 m2, respectively. Because of limited information from the 1991 survey, the 2016 survey results were comparable with those of 1983. The difference in the lakes mean, and maximum depth for the 1983 and 2016 survey was less by 0.23 and 2 m, respectively. This could be an indicator the lake is being affected by anthropogenic activities or environmental changes. The established Depth–Area–Volume relationships are crucial since they provide invaluable information to lake and water resources managers for making informed decisions regarding management of the lake's water resources.

show abstract

Hydrographic data modeling methods for determining precise seafloor topography

Cited by 13 publications

References 11 publications

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Sediment distribution and accumulation in Lake Naivasha, Kenya over the past 50 years

Bathymetric survey of Lake Naivasha and its satellite Lake Oloiden in Kenya; using acoustic profiling system

Contact Info

Product

Resources

About