Seabed geomorphic features in a glaciated shelf of the Baltic Sea

Kaskela, Anu; Kotilainen, Aarno; Al-Hamdani, Zyad; Leth, Jørgen; Reker, Johnny

doi:10.1016/j.ecss.2012.01.008

Cited by 33 publications

(23 citation statements)

References 36 publications

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“…A total of 33 years of continuous measurements of the water level at Havneby on Rømø, 25 km south of the study area, showed a mean low water level of −0.94 m (DVR90) and a mean high water of 0.94 m (DVR90) (Klagenberg et al, 2008). Although the tidal range in Knudedyb was probably slightly different, it was the best estimate for the study site.…”

Section: Morphological Classification Analysismentioning

confidence: 99%

“…The classification classes were decided based on previous studies using the BTM classification tool with success (Diesing et al, 2009;Lundblad et al, 2006). The thresholds for the fine-and broad-scale BPIs were in previous studies often defined as 1 SD (Lundblad et al, 2006;Verfaillie et al, 2007); however, thresholds of 0.5 SD have also previously been applied (Kaskela et al, 2012). We used a low threshold of 0.5 SD due to the generally very gentle variations in the terrain geometry of the tidal inlet system.…”

Section: Geomorphometric Classification Analysismentioning

confidence: 99%

“…for geomorphological mapping. Previous studies classifying morphology in either terrestrial or marine environments have been performed numerous times (Al-Hamdani et al, 2008;Cavalli and Marchi, 2008;Hogg et al, 2016;Höfle and Rutzinger, 2011;Ismail et al, 2015;Kaskela et al, 2012;Lecours et al, 2016;Sacchetti et al, 2011). These classification studies generally focus on either the marine or the terrestrial environment and do not cover the fine-scale morphology in the shallow water, due to the challenging environmental conditions.…”

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confidence: 99%

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Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment

Andersen

Gergely

Al-Hamdani

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The transition zone between land and water is difficult to map with conventional geophysical systems due to shallow water depth and often challenging environmental conditions. The emerging technology of airborne topobathymetric light detection and ranging (lidar) is capable of providing both topographic and bathymetric elevation information, using only a single green laser, resulting in a seamless coverage of the land-water transition zone. However, there is no transparent and reproducible method for processing green topobathymetric lidar data into a digital elevation model (DEM). The general processing steps involve data filtering, water surface detection and refraction correction. Specifically, the procedure of water surface detection and modelling, solely using green laser lidar data, has not previously been described in detail for tidal environments. The aim of this study was to fill this gap of knowledge by developing a step-by-step procedure for making a digital water surface model (DWSM) using the green laser lidar data. The detailed description of the processing procedure augments its reliability, makes it user-friendly and repeatable. A DEM was obtained from the processed topobathymetric lidar data collected in spring 2014 from the Knudedyb tidal inlet system in the Danish Wadden Sea. The vertical accuracy of the lidar data is determined to ±8 cm at a 95 % confidence level, and the horizontal accuracy is determined as the mean error to ±10 cm. The lidar technique is found capable of detecting features with a size of less than 1 m 2 . The derived high-resolution DEM was applied for detection and classification of geomorphometric and morphological features within the natural environment of the study area. Initially, the bathymetric position index (BPI) and the slope of the DEM were used to make a continuous classification of the geomorphometry. Subsequently, stage (or elevation in relation to tidal range) and a combination of statistical neighbourhood analyses (moving average and standard deviation) with varying window sizes, combined with the DEM slope, were used to classify the study area into six specific types of morphological features (i.e. subtidal channel, intertidal flat, intertidal creek, linear bar, swash bar and beach dune). The developed classification method is adapted and applied to a specific case, but it can also be implemented in other cases and environments.

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Section: Morphological Classification Analysismentioning

confidence: 99%

Section: Geomorphometric Classification Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment

Andersen

Gergely

Al-Hamdani

et al. 2017

Hydrol. Earth Syst. Sci.

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“…This is reflected in the seabed of the coast such that the sea floor is patchy and composed of mainly quite small irregular basins separated from each other by thresholds of islands, peninsulas or submarine ridges. According to Kaskela et al (2012) the seafloor in the northern Gulf of Finland is characterized by sea valleys, holes, sea troughs, and elevations at areas with exposed rock. This rugosity affects near bottom currents such that deposition of suspended particles is restricted to certain areas only and the accumulation has in general a patchy nature, which in fact has been reported also from the off shore Gulf of Finland (Vallius, 1999).…”

Section: Introductionmentioning

confidence: 99%

Sediment and carbon accumulation rates off the southern coast of Finland

Vallius¹

2015

Baltica

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Sediment and carbon accumulation rates off the southern coast of Finland Henry ValliusValliusAbstract The southern coast of Finland encompasses about one third of the coasts of the Gulf of Finland. It is a mosaic of hundreds if not thousands of islands, peninsulas and bays, which also are reflected in the seabed of the coast. The sea floor is composed of a patchy and fragmented mosaic of mainly quite small basins separated from each other by thresholds of islands, peninsulas or submarine ridges. This affects transport and near bottom currents such that deposition of suspended particles is restricted to certain areas only. Linear sediment accumulation rates along the southern coast of Finland were studied from 28 cores of a sampling campaign in 2000-2004 through gamma spectrometry of 137 Cs. Sediment accumulation in this environment with such diverse character was found very patchy and net sedimentation rates varying from less than 0.5 cm/a to values of nearly 3 cm/a as well as mass accumulation rates from 0.5 kg/m 2 /a to 8.8 kg/m 2 /a were found. The sediment accumulation rates were observed to be higher in shallower water in coastal sheltered or semi-sheltered bays. Total carbon concentrations varied from 1.3 % to 12.6 % and carbon accumulation rates from 20 g/m 2 /a to 355 g/m 2 /a such that the highest carbon concentrations were usually found in deeper water with some distance from the coast, while the highest carbon accumulation rates were found in the coastal shallow basins where sediment accumulation was found strongest.

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“…The flexibility of the continuous scale allows the incorporation of seafloor complexity into both discrete and continuous (or multivariate) habitat models. Discrete habitat models use a habitat classification scheme (e.g., Huang et al ; Kaskela et al ) to partition the seascape into distinct habitat types ( see Brown et al for review). For example, in our study, Jordan Basin had an overall greater seafloor complexity (and variability in seafloor complexity) than Georges Basin.…”

Section: Discussionmentioning

confidence: 99%

Using object-based image analysis to determine seafloor fine-scale features and complexity

Lacharité

Meta×as

Lawton

2015

Limnol. Oceanogr. Methods

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Autonomous and remotely operated underwater vehicles equipped with high-definition video and photographic cameras are used to perform benthic surveys. These devices record fine-scale (< 1 m) seafloor features (seafloor complexity) and their local (10-100s m) variability (seafloor heterogeneity). Here, we introduce a methodology to efficiently process this optical imagery using object-based image analysis, which reduces the pixels in high-resolution digital images into a collection of "image-objects" of homogeneous color and/or luminosity. This approach uses intuitive user-defined parameters and reproducible computer code, which aims to facilitate comparisons between habitats and geographic regions. We test this methodology with 511 images taken on the seafloor of a glaciated continental shelf (Gulf of Maine, northwest Atlantic), and describe three applications: (1) estimating percent cover of conspicuous epibenthic fauna by building a Random Forest binary classifier assigning an identity to image-objects; (2) correlating image complexity (number of image-objects) with mean particle grain size; and (3) estimating seafloor heterogeneity from local variability in image complexity within and between two physiographic regions. Percent cover of epibenthic fauna estimated by the Random Forest binary classifier was in close agreement with the human visual assessment. Mean particle grain size (/ scale) was inversely correlated with image complexity (maximum Spearman's q 5 20.89, p < 0.01) with images dominated by pebbles, cobbles, boulders (low on / scale) yielding high image complexity. Predictive relationships of sediment composition were established using polynomial regression. Lastly, our approach could differentiate habitats within and between physiographic regions by using mean seafloor complexity and local variability along transects.Benthic habitat structure is composed of two factors: complexity, the absolute abundance of structural components (e.g., rocks, mounds); and heterogeneity, the variation in complexity due to changes in the relative abundance of these structural components (McCoy and Bell 1991;Sebens 1991). On sedimented substratum, complexity arises from the distribution of sediment grain size and small-scale topographic features (e.g., pits and burrows) resulting from local hydrodynamics and bioturbation. On hard substratum, in rocky intertidal and subtidal habitats, complexity is most often measured with a surface to area ratio [e.g., fractal dimensions (Kostylev et al. 2005), and the chain-and-tape approach (Risk 1972)], which is influenced by the presence of crevices, rock walls or biological structures. Seafloor complexity influences larval settlement (e.g., Walters and Wethey 1996), the abundance and diversity of invertebrate assemblages (e.g., Kostylev et al. 2005;Matias et al. 2010), predator-prey interactions (e.g., Grabowski 2004, and the concentration of organic matter (e.g., Abelson et al. 1993).Among other factors, spatial variability in fine-scale (< 1 m) seafloor complexity (i.e., sea...

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Seabed geomorphic features in a glaciated shelf of the Baltic Sea

Cited by 33 publications

References 36 publications

Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment

Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment

Sediment and carbon accumulation rates off the southern coast of Finland

Using object-based image analysis to determine seafloor fine-scale features and complexity

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