Comparison of Short-Term Changes Caused by Storms along Natural and Protected Sections of the Dziwnow Spit, Southern Baltic Coast

Bugajny, Natalia; Furmańczyk, Kazimierz

doi:10.2112/jcoastres-d-16-00055.1

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Variable backscatter by particles and absorption by dissolved organic matter disturb all targets registered by the scanner: the combined use of bottom spectral properties and our knowledge of these features has resulted in the possibility of mapping habitats in large coastal systems [70,71]. In the Baltic Sea, there are threats to infrastructure and the coastline from flooding and geological conditions [72][73][74][75]. The first step in determining these threats is using the DTM from the ALB technology, or from data achieved by combining terrestrial measurements with echo sounding, which can accurately reflect the spatial situation.…”

Section: Resultsmentioning

confidence: 99%

Bringing Bathymetry LiDAR to Coastal Zone Assessment: A Case Study in the Southern Baltic

Tysiąc

2020

Remote Sensing

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One of the major tasks in environmental protection is monitoring the coast for negative impacts due to climate change and anthropopressure. Remote sensing techniques are often used in studies of impact assessment. Topographic and bathymetric procedures are treated as separate measurement methods, while methods that combine coastal zone analysis with underwater impacts are rarely used in geotechnical analyses. This study presents an assessment of the bathymetry airborne system used for coastal monitoring, taking into account environmental conditions and providing a comparison with other monitoring methods. The tests were carried out on a section of the Baltic Sea where, despite successful monitoring, coastal degradation continues. This technology is able to determine the threat of coastal cliff erosion (based on the geotechnical analyses). Shallow depths have been reported to be a challenge for bathymetric Light Detection and Ranging (LiDAR), due to the difficulty in separating surface, water column and bottom reflections from each other. This challenge was overcome by describing the classification method used which was the CANUPO classification method as the most suitable for the point cloud processing. This study presents an innovative approach to identifying natural hazards, by combining analyses of coastal features with underwater factors. The main goal of this manuscript is to assess the suitability of using bathymetry scanning in the Baltic Sea to determine the factors causing coastal erosion. Furthermore, a geotechnical analysis was conducted, taking into account geometrical ground change underwater. This is the first study which uses a coastal monitoring approach, combining geotechnical computations with remote sensing data. This interdisciplinary scientific research can increase the awareness of the environmental processes.

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Section: Resultsmentioning

confidence: 99%

Bringing Bathymetry LiDAR to Coastal Zone Assessment: A Case Study in the Southern Baltic

Tysiąc

2020

Remote Sensing

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“…Since the mid-twentieth century, increased cyclonal activity has been observed in winter periods in the North Atlantic due to global climate change [2,3]. The number of extreme storm surges in the Baltic Sea is increasing steadily [4,5], which along with milder winters and limited ice cover, further exacerbates coastal erosion [6]. The observed rates of change in coastlines, particularly in case of erosion, have been a significant concern for communities inhabiting littoral zones.…”

Section: Introductionmentioning

confidence: 99%

Determining Long-Term Land Cover Dynamics in the South Baltic Coastal Zone from Historical Aerial Photographs

et al. 2021

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Coastal regions are dynamic environments that have been the main settlement destinations for human society development for centuries. Development by humans and environmental changes have resulted in intensive land cover transformation. However, detailed spatiotemporal analyses of such changes in the Polish Baltic coastal zone have not been given sufficient attention. The aim of the presented work is to fill this gap and, moreover, present a method for assessing indicators of changes in a coastal dune environment that could be an alternative for widely used morphological line indicators. To fulfill the main aim, spatial and temporal variations in the dune areas of the Pomeranian Bay coast (South Baltic Sea) were quantified using remote sensing data from the years 1938–2017, supervised classification, and a geographic information system post-classification change detection technique. Finally, a novel quantitative approach for coastal areas containing both sea and land surface sections was developed. The analysis revealed that for accumulative areas, a decrease in the land area occupied by water was typical, along with an increase in the surface area not covered by vegetation and a growth in the surface area occupied by vegetation. Furthermore, stabilized shores were subject to significant changes in tree cover area mainly at the expense of grass-covered terrains and simultaneous slight changes in the surface area occupied by water and the areas free of vegetation. The statistical analysis revealed six groups of characteristic shore evolutionary trends, of which three exhibited an erosive nature of changes. The methodology developed herein helps discover new possibilities for defining coastal zone dynamics and can be used as an alternative solution to methods only resorting to cross sections and line indicators. These results constitute an important step toward developing a predictive model of coastal land cover changes.

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“…Soft cliff coasts experience storms strongly, and they can retreat relatively fast. However, most monitoring systems, analyses, and models have been implemented along dune coasts [2][3][4][5][6], largely because of the technical difficulties in registering the morphological changes on cliff coasts. Despite such difficulties, mainly connected with accessibility of high cliffs, the factors influencing cliff erosion have been investigated through quantitative numerical methods.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis

et al. 2019

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Cliff coasts are dynamic environments that can retreat very quickly. However, the short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a period of almost two years to generate a time series of thirteen topographic surveys. Digital elevation models constructed for those surveys allowed the extraction of several geomorphological indicators describing coastal dynamics. Combined with observational and modeled datasets on hydrological and meteorological conditions, descriptive and statistical analyses were performed to evaluate cliff coast erosion. A new statistical model of short-term cliff erosion was developed by using a non-parametric Bayesian network approach. The results revealed the complexity and diversity of the physical processes influencing both beach and cliff erosion. Wind, waves, sea levels, and precipitation were shown to have different impacts on each part of the coastal profile. At each level, different indicators were useful for describing the conditional dependency between storm conditions and erosion. These results are an important step toward a predictive model of cliff erosion.

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Comparison of Short-Term Changes Caused by Storms along Natural and Protected Sections of the Dziwnow Spit, Southern Baltic Coast

Cited by 5 publications

References 9 publications

Bringing Bathymetry LiDAR to Coastal Zone Assessment: A Case Study in the Southern Baltic

Bringing Bathymetry LiDAR to Coastal Zone Assessment: A Case Study in the Southern Baltic

Determining Long-Term Land Cover Dynamics in the South Baltic Coastal Zone from Historical Aerial Photographs

Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis

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