Explore the vast range of titles available.

Polish coastal zone is thought to be one of the most exposed to sea level rise in Europe. With mean sea levels expected to increase between 28 and 98 cm by the end of the century, and storms increasing in severity, accurate estimates of the consequences of those phenomena are needed. Recent advances in quality and availability of spatial data in Poland made possible the reassessment of previous estimates of inundation caused by sea level rise. Up-to-date, detailed information on land use, population and buildings was used here to calculate their exposure to floods at a broad range of scenarios. Inclusion of a high-resolution digital elevation model contributed to a further improvement in estimates. The results revealed that even by using a static “bathtub fill” approach, the amount of exposed land, population or assets is significantly smaller than indicated in previous assessments. In the perspective of the twenty-first century, direct damages caused by sea level rise will be small and adaptation costs will not be significant. However, the increase in the frequency of storm surges could elevate the risk to the population and economy, but cost-effective flood protection measures would be able to mitigate the risk. The exposure of different kinds of assets and sectors of the economy varies to a large extent, though the structural breakdown of potential losses is remarkably stable between scenarios.

The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism [1]. [...]a novel quantitative approach for coastal areas containing both sea and land surface sections was developed. [...]a moisture estimation model was developed that eliminated the effects of the incidence angle and distance. Different geomatic techniques, such as: orthophotography, photogrammetric flights, LiDAR surveys, Unmanned Aerial Vehicle (UAV) surveys, and terrestrial laser scanner datasets, were used to find volumetric differences in the beach and sea cliff, attributing them to storms.

The following paper focuses on evaluating the quality of image prediction in the context of searching for plants of a single species, using the example of Heracleum sosnowskyi Manden, in a given area. This process involves a simplified classification that ends with a segmentation step. Because of the particular characteristics of environmental data, such as large areas of plant occurrence, significant partitioning of the population, or characteristics of a single individual, the use of standard statistical measures such as Accuracy, the Jaccard Index, or Dice Coefficient does not produce reliable results, as shown later in this study. This issue demonstrates the need for a new method for assessing the betted prediction quality adapted to the unique characteristics of vegetation patch detection. The main aim of this study is to provide such a metric and demonstrate its usefulness in the cases discussed. Our proposed metric introduces two new coefficients, M+ and M−, which, respectively, reward true positive regions and penalise false positive regions, thus providing a more nuanced assessment of segmentation quality. The effectiveness of this metric has been demonstrated in different scenarios focusing on variations in spatial distribution and fragmentation of theoretical vegetation patches, comparing the proposed new method with traditional metrics. The results indicate that our metric offers a more flexible and accurate assessment of segmentation quality, especially in cases involving complex environmental data. This study aims to demonstrate the usefulness and applicability of the metric in real-world vegetation patch detection tasks.

Automatic delineation of coastline in coastal zones is an essential task for various applications including protection of coastal regions, disaster management, and planning. The lack of availability of manually annotated high resolution datasets tailored for AI in coastal research remains a concern. Therefore, we created an open source, UAV captured and high resolution RGB dataset named MADRID (Manually Annotated DRone Imagery Dataset). It was recorded during six-separate UAV fly-over flight paths in two different types of coasts in Poland, Miedzyzdroje - cliff coast and in Mrzezyno - dune coast. The dataset comprises of 3691 high-resolution images and each image is accompanied by manually provided coastline annotations utilizing novel polyline annotation technique for the first time. The data is pre-split into training and test data subsets suitable for semantic segmentation tasks. The output of this study has implications for protection of coastal regions and marine ecosystems, delivering valuable insights into the overall situation in the Southern Baltic Sea. All data is organized and structured to comply with FAIR principles.

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.

Coastal zone areas are important parts of the environment, very often subject to constant change [...]

Terefenko, P.; Giza, A.; Paprotny, D., and Walczakiewicz, Sz., 2020. Characteristic of winter storm Xavier and its impacts on coastal morphology: Results of a case study on the polish coast. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 684-688. Coconut Creek (Florida), ISSN 0749-0208. Winter storms are an important factor affecting the Polish coast. Short-term water-level variations caused by winter storms significantly alter the coast in the non-tidal Baltic Sea, where extreme water levels depend on the volume of water flowing in from the North Sea. If the Baltic does not have the time to “fill in,” high wind speeds and wave heights are insufficient to cause major damage. Although this phenomenon has been widely discussed in literature, no studies focused on western Poland have comprehensively documented the evolution of both the foreshore and nearshore coastal profile. This paper describes the meteorological and hydrological conditions of the severe storm Xavier, which traversed the Polish coast from December 5–7, 2013, as well as its impacts on the bathymetric and topographic profile of the coast along the pier in Międzyzdroje. Shoreline changes caused by the Xavier storm are compared with the effects of smaller events that occurred in 2014. The results show that winter storm Xavier completely rebuilt the profile of the coast. The foreshore was eroded and beach elevation was reduced by an average of 0.5 m. Accumulation was observed in the vicinity of the water line down to a depth of 1 m. Furthermore, erosion processes dominated in the nearshore section with an average value of 0.27 m and a maximum of 0.62 m, with a small exception at a depth of 2 m. Although the erosion rates caused by Xavier were significant, in some sections they were comparable to the effects of shorter and weaker storms that occurred in the area both before and after Xavier.

Assessing long-term trends in flood losses and attributing them to climatic and socioeconomic changes requires comprehensive and systematic collection of historical information. Here, we present flood impact data for Europe that are part of the HANZE (Historical Analysis of Natural HaZards) database. The dataset covers riverine, pluvial, coastal, and compound floods that have occurred in 42 European countries between 1870 and 2020. The data were obtained by extensive data collection from more than 800 sources ranging from news reports through government databases to scientific papers. The dataset includes 2521 events characterized by at least one impact statistic: area inundated, fatalities, persons affected. or economic loss. Economic losses are presented both in the original currencies and price levels and with the inflation and exchange rate adjusted to the 2020 value of the euro. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union's Nomenclature of Territorial Units for Statistics (NUTS) level 3. Daily start and end dates, information on causes of the events, notes on data quality issues or associated non-flood impacts, and full bibliography of each record supplement the dataset. Apart from the possibility of downloading the data (https://doi.org/10.5281/zenodo.8410025; Paprotny, 2023a), the database can be viewed, filtered, and visualized online at https://naturalhazards.eu (last access: 4 November 2024). The dataset is designed to be complementary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset values) and be easily usable in statistical and spatial analyses, including multi-hazard studies.

Financial knowledge is the main element of financial literacy, which is important for the sustainable development of individuals and society. Sustainability is a complex concept that spans many fields, including financial knowledge for all ages. Financial knowledge requires significant scientific research showing its impact on individuals and the economy, including non-cash payments. Consumer payment knowledge and its association with consumer financial behavior have long been a matter of widespread interest by researchers, but no in-depth, empirically based scientific research has been completed for Poland. The objective of this study was to examine factors associated with cashless payment behavior with an emphasis on the role of consumer financial knowledge. A total of 1100 interviews were carried out with Polish nationals aged 15 and above. The collected data were analyzed with the use of statistical methods, including analysis of variances (ANOVA), in order to examine consumers’ financial knowledge by basic economic and non-economic factors. Additionally, a data-mining method known as Random Forests was implemented for finding the variable importance in correlations between consumer financial knowledge and preferred methods of payment. The results revealed the diversity of factors influencing consumer behavior. Among the consumers’ personal traits, financial knowledge was one of the most important determinants of their payment choices. The results have implications for the design of payment processes. The results can be used by central banks to determine the directions of financial inclusion, as well as for stakeholders in the payments market.

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.