Dynamics of Stone Habitats in Coastal Waters of the Southwestern Baltic Sea (Hohwacht Bay)

Rönn, Gitta Ann von; Krämer, Knut; Franz, Markus; Schwarzer, Klaus; Reimers, Hans-Christian

doi:10.3390/geosciences11040171

Cited by 6 publications

(10 citation statements)

References 58 publications

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“…However, the timescale in their study was of decades. The study area in this work has similar bed sediments and wave heights as in [48], but boulders could be more susceptible to sediment transport in the present study area due to the shallow water depth conditions in Rødsand lagoon. Yet, it is unlikely to find boulders exposed after 2015, though two boulders that were identified only on orthophotos after 2015 may have been unexposed prior to 2015.…”

Section: Evaluation Of Methods To Identify Boulders and Verified Boul...mentioning

confidence: 80%

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Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data

et al. 2021

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Boulders on the seabed in coastal marine environments provide key geo- and ecosystem functions and services. They serve as natural coastal protection by dissipating wave energy, and they form an important hard substrate for macroalgae, and hence for coastal marine reefs that serve as important habitats for fish. The aim of this study was to investigate the possibility of developing an automated method to classify boulders from topo-bathymetric LiDAR data in coastal marine environments. The Rødsand lagoon in Denmark was used as study area. A 100 m × 100 m test site was divided into a training and a test set. The classification was performed using the random forest machine learning algorithm. Different tuning parameters were tested. The study resulted in the development of a nearly automated method to classify boulders from topo-bathymetric LiDAR data. Different measure scores were used to evaluate the performance. For the best parameter combination, the recall of the boulders was 57%, precision was 27%, and F-score 37%, while the accuracy of the points was 99%. The most important tuning parameters for boulder classification were the subsampling level, the choice of the neighborhood radius, and the features. Automatic boulder detection will enable transparent, reproducible, and fast detection and mapping of boulders.

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Section: Evaluation Of Methods To Identify Boulders and Verified Boul...mentioning

confidence: 80%

“…Ref. [48] concluded that exposure of cobbles and boulders from glacial till by erosion is the main driver for stone habitat dynamics. However, the timescale in their study was of decades.…”

Section: Evaluation Of Methods To Identify Boulders and Verified Boul...mentioning

confidence: 99%

Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data

et al. 2021

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“…In this approach, we considered the whole sand fraction as part of the littoral sediment budget vs, which may lead to an overestimation of the budget volume by the varying amount of the fine-grained particles (0.063-0.2 mm).At the same time, there is the risk of an underestimation of the budget volume by the coarser sediment fraction: Besides the sandy sediments, larger stones and boulders are mobilized from the Pleistocene cliff deposits and contribute to the beach and nearshore sediment [11,24]. Due to issues of sample representativity and difficulties in the analyses of the coarse grain size fractions [67], the literature lacks quantitative data of those fractions.…”

Section: Sediment Fraction Nmentioning

confidence: 99%

“…For grain sizes >64 mm (cobbles, boulders), no quantitative data were available. Although stones and boulders have been proven to be partly mobile in the nearshore system [11,24], they are underrepresented in this study's budget estimation.…”

Section: Sediment Fraction Nmentioning

confidence: 99%

“…The remaining siliciclastic sediment is sorted into different grain size fractions according to their behavior under hydrodynamic impact [3]: less mobile stones and boulders remain near the source area or relocate within short distances [3,24,25]. The finest sediments-mostly clay and silt-are transported in suspension and deposited in sheltered areas or deeper waters [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Cliff Retreat Contribution to the Littoral Sediment Budget along the Baltic Sea Coastline of Schleswig-Holstein, Germany

Averes

Hofstede

Hinrichsen

et al. 2021

JMSE

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Mobile coastal sediments, such as sand and gravel, build up and protect wave-dominated coastlines. In sediment-starved coastal environments, knowledge about the natural sources and transport pathways of those sediments is of utmost importance for the understanding and management of coastlines. Along the Baltic Sea coast of Schleswig-Holstein (Germany), the retreat of active cliffs—made of cohesive Pleistocene deposits—supplies a wide size range of sediments to the coastal system. The material is reworked and sorted by hydrodynamic forcing: the less mobile stones and boulders remain close to the source area; the finest sediments, mostly clay and silt, are transported offshore into areas of low energy; and the fractions of sand and fine gravels mostly remain in the nearshore zone, where they make up the littoral sediment budget. They contribute to the morphodynamic development of sandy coastlines and nearshore bar systems. Exemplarily for this coastal stretch and based on an extensive review of local studies we quantify the volume of the potential littoral sediment budget from cliff retreat. At an average retreat rate of 0.24 m yr−1 (<0.1–0.73 m yr−1), the assessment indicates a weighted average sediment volume of 1.5 m3 yr−1 m−1 (<0.1–9.5 m3 yr−1 m−1) per meter active cliff. For the whole area, this results in an absolute sediment budget Vs,total of 39,000–161,000 m3 yr−1. The accuracy of the results is limited by system understanding and data quality and coverage. The study discusses uncertainties in the calculation of littoral sediment budgets from cliff retreat and provides the first area-wide budget assessment along the sediment-starved Baltic Sea coastline of Schleswig-Holstein.

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How can Sentinel‐2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters?

Kuhwald

Deimling

Schubert

et al. 2021

Remote Sens Ecol Conserv

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Seagrass meadows are one of the most important benthic habitats in the Baltic Sea. Nevertheless, spatially continuous mapping data of Zostera marina, the predominant seagrass species in the Baltic Sea, are lacking in the shallow coastal waters. Sentinel‐2 turned out to be valuable for mapping coastal benthic habitats in clear waters, whereas knowledge in turbid waters is rare. Here, we transfer a clear water mapping approach to turbid waters to assess how Sentinel‐2 can contribute to seagrass mapping in the Western Baltic Sea. Sentinel‐2 data were atmospherically corrected using ACOLITE and subsequently corrected for water column effects. To generate a data basis for training and validating random forest classification models, we developed an upscaling approach using video transect data and aerial imagery. We were able to map five coastal benthic habitats: bare sand (25 km²), sand dominated (16 km²), seagrass dominated (7 km²), dense seagrass (25 km²) and mixed substrates with red/ brown algae (3.5 km²) in a study area along the northern German coastline. Validation with independent data pointed out that water column correction does not significantly improve classification results compared to solely atmospherically corrected data (balanced overall accuracies ~0.92). Within optically shallow waters (0–4 m), per class and overall balanced accuracies (>0.82) differed marginally depending on the water depth. Overall balanced accuracy became worse (<0.8) approaching the border to optically deep water (~ 5 m). The spatial resolution of Sentinel‐2 (10–20 m) allowed delineating detailed spatial patterns of seagrass habitats, which may serve as a basis to retrieve spatially continuous data for ecologically relevant metrics such as patchiness. Thus, Sentinel‐2 can contribute unprecedented information for seagrass mapping between 0 and around 5 m water depths in the Western Baltic Sea.

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Dynamics of Stone Habitats in Coastal Waters of the Southwestern Baltic Sea (Hohwacht Bay)

Cited by 6 publications

References 58 publications

Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data

Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data

Cliff Retreat Contribution to the Littoral Sediment Budget along the Baltic Sea Coastline of Schleswig-Holstein, Germany

How can Sentinel‐2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters?

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