On mapping and monitoring geodiversity and benthic habitats in a dynamic shallow water coastal environment: example from Rødsand lagoon, western Baltic Sea

Author(s):  
Verner Brandbyge Ernstsen ◽  
Signe Schilling Hansen ◽  
Lars Øbro Hansen ◽  
Manfred Niederwieser ◽  
Ramona Baran ◽  
...  

<p>Shallow water coastal environments can be highly dynamic and comprise a range of dynamic geodiversity variables as well as a range of benthic habitats. It is challenging to map such dynamic shallow water coastal environments and their geodiversity variables and benthic habitats in high-resolution, high precision and full coverage, which is necessary in order to evaluate impact on the seabed and the benthic habitats from e.g. climate change (e.g. changing wind climate) or human disturbance (e.g. construction of wind parks, pipelines, etc.).</p><p>We have conducted successive high-resolution, high-precision airborne topobathymetric lidar surveys in combination with seabed groundtruthing (e.g. seabed sampling and diver observations) along existing monitoring lines in Rødsand lagoon, Denmark, in the western Baltic Sea. The coastal lagoon is a Natura 2000 site, located near the planned fixed connection between Germany and Denmark.</p><p>Here, we present high-resolution, high-precision mapping of geodiversity variables with a focus on seabed morphology and seabed sediments that constitute the abiotic structures of the benthic habitats. We demonstrate the role of the interaction between the dynamic coastal processes and the drowned underlying glacial landscape in relation to the spatial distribution of the seabed morphology and sediments as well as the benthic habitats. Finally, we discuss how to optimise the monitoring of dynamic geodiversity variables and abiotic benthic habitat structures in such dynamic shallow water coastal environments.</p><p> </p><p>Acknowledgements</p><p>This work was carried out as part of “WP4 – In situ remote sensing of geodiversity for habitat mapping” within the project “ECOMAP – Baltic Sea environmental assessments by opto-acoustic remote sensing, mapping, and monitoring” funded by the BONUS EEIG and the Innovation Fund Denmark.</p>

Author(s):  
Sabah Aljenaid ◽  
Eman Ghoneim ◽  
Mohammed Abido ◽  
Khalil AlWedhai ◽  
Ghadeer Khadim ◽  
...  

2020 ◽  
Author(s):  
Quang Nguyen ◽  
Michal Malinowski ◽  
Piotr Krzywiec ◽  
Christian Huebscher

<p>Geological structure and tectonics of the Phanerozoic sedimentary cover within the transition zone between the Precambrian and Paleozoic platform in the Polish sector of the Baltic Sea was imaged using new 2D high-resolution multi-channel seismic reflection data. The new seismic data were acquired in 2016 during the course of RV Maria S. Merian expedition MSM52 within the framework of the BALTEC project. Eight profiles (with the total length of ca. 850km) covered the tectonics blocks located within the Polish Exclusive Economic Zone, stretching from the East European Craton (EEC) to the Paleozoic platform across the Teisseyre-Torquist Zone (TTZ).</p><p>Our in-house seismic processing workflow focused on removing multiples contaminating this shallow-water data, both water bottom and interbed related. Various demultiple techniques such as SRME, TAU-P domain deconvolution, high resolution parabolic Radon demultiple and SWDM (Shallow water demultiple) have been tested. Combination of all those techniques at different stages of the processing with some modifications based on a particular seismic profile proved to be the most effective. Consequently, multiples obscuring seismic sections were efficiently reduced. Data were processed up to Kirchhoff pre-stack time migration.</p><p>The longest seismic profile (line BGR16-212, ca. 240 km long) crosses almost perpendicularly majority of Precambrian and Paleozoic fault systems bordering the tectonic blocks of the EEC basement, so fault systems could be easily interpreted. EEC Precambrian basement is characterized by a regional flexure towards the TTZ. Cambrian-Ordovician exhibits similar geometry and is characterized by a relatively constant thickness related to deposition on the Tornquist Ocean passive margin. Thick Silurian succession is characterized by a regional divergent pattern caused by deposition within the Caledonian foredeep basin. Structural pattern within the W part of the study area is much more complex as this area underwent Late Paleozoic extension/transtension, Variscan inversion, Permo-Mesozoic subsidence and Late Cretaceous inversion.</p><p>This study was funded by the Polish National Science Centre grant no UMO-2017/27/B/ST10/02316.</p>


Oceanology ◽  
2012 ◽  
Vol 52 (6) ◽  
pp. 803-809 ◽  
Author(s):  
Ele Vahtmäe ◽  
Tiit Kutser ◽  
Jonne Kotta ◽  
Merli Pärnoja ◽  
Tiia Möller ◽  
...  

2021 ◽  
Vol 13 (11) ◽  
pp. 2052
Author(s):  
Dongchuan Yan ◽  
Guoqing Li ◽  
Xiangqiang Li ◽  
Hao Zhang ◽  
Hua Lei ◽  
...  

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.


Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2639 ◽  
Author(s):  
Francisco Eugenio ◽  
Javier Marcello ◽  
Javier Martin ◽  
Dionisio Rodríguez-Esparragón

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