Vessel-Based, Shallow Water Mapping with a Phase-Measuring Sidescan Sonar

Author(s):  
Mark Borrelli ◽  
Theresa L. Smith ◽  
Stephen T. Mague
2021 ◽  
Author(s):  
Mikkel Skovgaard Andersen ◽  
Lars Øbro Hansen ◽  
Zyad Al-Hamdani ◽  
Signe Schilling Hansen ◽  
Manfred Niederwieser ◽  
...  

<p>Bubbling reefs are submarine structures formed by aggregating carbonate resulting from leaking gases. The reef formations can form pillars rising several meters above the sea floor. They support a high diversity of benthic communities, and in the EU Habitat Directive they are specifically mentioned as a natural habitat type that require conservation.</p><p>Knowledge about the presence, locations and shape of bubbling reefs are usually obtained by geophysical surveying using multibeam echosounder (MBES), sidescan sonar and/or seismic acquisition systems, combined with ground truth verification. However, this traditional survey method is time consuming, especially for full coverage surveys in shallow water. Full coverage surveys are a requirement to capture the bubbling reefs due to their relatively small spatial extent. Besides, traditional geophysical vessel borne surveys have their limitations in shallow water due to low spatial coverage and vessel draft.</p><p>In recent years, airborne topobathymetric (green wavelength) lidar has emerged as a new possible surveying method in shallow water (e.g. Andersen et al., 2017). Compared to vessel borne MBES, full coverage lidar surveys can be conducted within hours instead of days/weeks, while also including full coverage in the shallow water and a seamless transition between land and water. Thus, topobathymetric lidar may be a good choice for carrying out full coverage surveys in large shallow water areas. However, the accuracy and the resolution of the collected dataset are important in these surveys, not least when mapping small scale features such as bubbling reefs.</p><p>In this study, we investigated the potential of mapping bubbling reefs in shallow water (<10 m) using topobathymetric lidar. The main objective was to assess the performance of airborne topobathymetric lidar to detect and resolve small scale objects, i.e. bubbling reefs, by comparison to MBES data. Both MBES and lidar data were acquired in spring 2019 in a designated Natura 2000 area close to Hirsholmene in the northern Kattegat region in Denmark. The comparison of the two datasets included a quantification of the accuracy, and an assessment of the performance for mapping bubbling reefs.</p><p> </p><p>Reference:</p><p>Andersen M.S., Gergely A., Al-Hamdani Z., Steinbacher F., Larsen L.R., Ernstsen V.B. (2017). Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment. Hydrology and Earth System Sciences, 21: 43-63, DOI: 10.5194/hess-21-43-2017.</p>


Author(s):  
F. Steinbacher ◽  
M. Pfennigbauer ◽  
M. Aufleger ◽  
A. Ullrich

2010 ◽  
Vol 2 (1) ◽  
Author(s):  
Vincentius Siregar

<p>The objective of this study was to explore the capability of high resolution satellite data of QuicBird to map the characteristics of the bottom shallow water (habitat) using the transformation method of two bands (blue and green) by implementing "depth invariant index" algorithm i.e., Y = ln Band 1 - (ki/kj) ln Band 2. The result provide more detail information on the characteristic of the bottom shallow water comparing to the used of original band (RGB). The classification of the transformed image showed 6 classes of bottom substrats i.e., Live coral, Death, Coral, Sand mix coral, Sand mix algae, and<br />Macro algae with Sand. The accuracy test of the map derived from the classification was about 79%.</p><p>Keywords: bottom shallow water, Quick Bird image, depth invariant index, classification</p>


Geosciences ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 390 ◽  
Author(s):  
Rönn ◽  
Schwarzer ◽  
Reimers ◽  
Winter

Stones and boulders in shallow waters (0–10 m water depth) form complex geo-habitats, serving as a hardground for many benthic species, and are important contributors to coastal biodiversity and high benthic production. This study focuses on limitations in stone and boulder detection using high-resolution sidescan sonar images in shallow water environments of the southwestern Baltic Sea. Observations were carried out using sidescan sonars operating with frequencies from 450 kHz up to 1 MHz to identify individual stones and boulders within different levels of resolution. In addition, sidescan sonar images were generated using varying survey directions for an assessment of range effects. The comparison of images of different resolutions reveals considerable discrepancies in the numbers of detectable stones and boulders, and in their distribution patterns. Results on the detection of individual stones and boulders at approximately 0.04 m/pixel resolution were compared to common discretizations: it was shown that image resolutions of 0.2 m/pixel may underestimate available hard-ground settlement space by up to 42%. If methodological constraints are known and considered, detailed information about individual stones and boulders, and potential settlement space for marine organisms, can be derived.


Author(s):  
Erin Beck ◽  
William Kirkwood ◽  
David Caress ◽  
Todd Berk ◽  
Paul Mahacek ◽  
...  
Keyword(s):  

Author(s):  
Vincentius Siregar

The objective of this study was to explore the capability of high resolution satellite data of QuicBird to map the characteristics of the bottom shallow water (habitat) using the transformation method of two bands (blue and green) by implementing "depth invariant index" algorithm i.e., Y = ln Band 1 - (ki/kj) ln Band 2. The result provide more detail information on the characteristic of the bottom shallow water comparing to the used of original band (RGB). The classification of the transformed image showed 6 classes of bottom substrats i.e., Live coral, Death, Coral, Sand mix coral, Sand mix algae, andMacro algae with Sand. The accuracy test of the map derived from the classification was about 79%.Keywords: bottom shallow water, Quick Bird image, depth invariant index, classification


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