Orals: Fri, 2 May | Room -2.92
Meteotsunami or meteorological tsunamis are globally occurring progressive shallow water waves with a period of between 2 to 120 minutes which result from an air-sea interaction. Meteotsunami are initiated by sudden pressure changes and wind stress from moving atmospheric systems. These waves are known to cause destruction to assets with injury and fatality to human life. Currently, there is no research into the impact upon ecological assets.
This presentation outlines the impact of two meteotsunami events (2016 and 2021) on an intertidal saltmarsh ecosystem in the southwestern UK. By utilizing satellite imagery and applying Normalized Difference Vegetation Index (NDVI) an assessment was carried out on vegetation before and after each event against a baseline 10-year mean. Results revealed that the 2016 meteotsunami resulted in a minimal impact upon vegetation, suggesting a potential resilience or adaptive response to a single episodic disturbance. In contrast, the 2021 event, compounded by two significant storms and multiple additional meteotsunami, led to a notable decline in NDVI values, indicating a likely short-term disruption to vegetation. Recovery appeared to be rapid (within one to three months.)
This comparative analysis underscores the complex interactions between meteotsunami events, climatic phenomena, and coastal vegetation dynamics, highlighting the necessity for ongoing monitoring and research to understand the resilience mechanisms of such ecosystems in the face of increasing climatic variability and extreme weather events.
How to cite: Lewis, C., Dale, J., Neumann, J., Smyth, T., and Cloke, H.: How successive meteotsunami and storm activity disrupts saltmarsh vegetation., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-35, https://doi.org/10.5194/egusphere-egu25-35, 2025.
Sea-level rise (SLR) driven by climate change has exacerbated coastal erosion, posing significant challenges for coastal management. Effective management necessitates robust tools to evaluate shoreline dynamics under varying climate scenarios, facilitating the identification of high-risk areas. However, the pixelated nature of coastlines and the limited scope of large-scale coastal projections under diverse climate conditions hinder comprehensive risk assessment. This study addresses these gaps by utilizing medium-resolution Landsat data integrated with a Convolutional Neural NetworkCNN - Random Forest-RF, enhanced by an activation function and five max-pooling, to process training predictors based on spectral indices MNDWI, NDWI, NDVI, GCVI, and SAVI, shorelines detection and demarcation. The analysis applies Bruun Rule to assess shoreline retreat relative to SLR along Pakistan's coast at five-year intervals from 2020 to 2050. SLR and SST are sourced from multiple satellite sensors, including AVHRR and SLSTR, and computed using CMEMS relative to a 2000–2023 baseline. Climate projections are derived from a multi-model ensemble of CMIP6 General Circulation Models (GCMs), spanning Shared Socioeconomic Pathways SSP1-2.6 to SSP5-8.5. The proposed CNN-RF model demonstrated high accuracy, achieving precision, recall, and F1 scores of 95.01%, 96.16%, and 96.91%. Results from historical regression rates, combined with SLR and SST projections, indicate widespread erosion in Indus Delta, with alarming retreat rates of -80.4 ±1.15 m/year between 2000 and 2010, corresponding to SLR values ranging from 0.015 to 0.085 m/year. From 2010 to 2023, SLR accelerated to 0.087–0.15 m/year, with SST increasing from 297.79 K to 300.3 K. Conversely, the Sandspit coast exhibited accretion, gaining 23.24 km² at rates of up to mean 49.45 ±1.16 m/year. Notable warming trends were observed, with central Arabian Sea SSTs exceeding 302.41 K, correlating strongly with SLR (R² = 0.40 by 2023). Under the high-emission scenario SSP5-8.5, projections for 2020–2025 indicate persistent erosion in the Indus Delta, with retreat rates of -25 to -60 m/year, while Gwadar Port up to 10 to 15 m/year. For 2025–2030 and 2030-2050 erosion in the Indus Delta, retreat rates up to -68 m/year and of -101 to -120 m/year, Sonmiani Aquifer may transition erosion up to mean -55.1 and -110 m/year). SST anomalies exhibit variability (0.3°C–0.8°C) and periodic spikes linked to climatic events, with annual increases of 0.02°C–0.05°C and a coefficient of variation of 12%–25%. Pearson’s correlation (R² = 0.6–0.8) suggests a positive relationship between SST and SLR, but highlighted variability, indicating areas for refinement. The impacts of the intrusion on the local coastal community are also analyzed with trends of communities’ migration. Our analysis revealed that erosion also results from reduced sediment flow linked to water infrastructures. Future policy and action plans should prioritize Integrated Coastal Zone Management frameworks (ICZMF), providing critical insights into erosion dynamics and addressing integrated nature-based solutions.
Keywords: Sea-level rise (SLR), Coastal Erosion, CNN-Random Forest (RF), Landsat, CMIP6, Integrated Coastal Zone Management frameworks (ICZMF)
How to cite: Aeman, H., Shu, H., and Nadeem, I.: Integrating Medium Resolution Satellite Data and CNN-RF Machine Learning for Shoreline Dynamics: Assessing Coastal Erosion and Accretion under Climate Change Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-207, https://doi.org/10.5194/egusphere-egu25-207, 2025.
Satellite-Derived Waterlines (SDWs) have become highly valuable assets in coastal studies due to their extensive data availability, offering temporal and spatial resolutions of up to 5 days and 10 m, respectively. Numerous tools for SDW extraction have been developed, being widely used in microtidal beaches with high reliability. However, macrotidal environments present significant challenges due to their large intertidal extensions. The dynamic nature and complex morphology of these areas frequently lead to inaccuracies in shoreline detection by existing tools. Furthermore, the large volume of SDWs makes identifying errors challenging. Understanding misdetection conditions is key to automating error flagging and improving efficiency.
This study aims at improving the understanding of waterline (mis)detection by identifying the environmental conditions that influence incorrect identification of the sand-water interface. SDWs extracted using the SHOREX tool, developed by the Geo-Environmental Cartography and Remote Sensing Group from the Universitat Politècnica de València, were analyzed in Salinas (144 SDWs) and El Puntal (141 SDWs), two macrotidal beaches in northern Spain with complex intertidal topography.
The analysis was undertaken with the aim of taking a step back to understand what beach features are identified as waterline by currently available tools. This involved a detailed visual inspection of SDWs compared to their corresponding RGB imagery, conducted by a validated operator. The beaches were discretized into equally spaced transects, and for each SDW, the operator classified the detected feature in each transect as one of the following: Waterline (sand-water interface), Maximum High Tide Level (dry-wet sand interface), Intertidal Water (boundary of accumulated water in the intertidal zone), Intertidal Morphological Features (dry-wet sand interface due to intertidal bars), Backshore elements, or Clouds. Three analyses were derived: (1) the percentage of transects classified as each indicator per SDW, (2) the confidence level perceived by the operator for each indicator, and (3) the correlation between met-oceanic variables (e.g., wave height, peak period, storm surge, astronomical tide, and tidal stage) and the percentage of Waterline identification per SDW.
The results revealed a strong positive correlation (R=0.56) between the percentage of transects classified as waterline (ideal identification) and a variable combining tidal level and phase (flood/ebb). Better detections during high tides likely occurred due to drier intertidal sand, while wet sand during ebb tides led to detection problems. However, a lack of representation of the highest tidal states was observed in the satellite time series. Wave parameters (Hs and Tp) showed weaker inverse correlations to the percentage of waterline detection (R=−0.15 and −0.28, respectively), likely due to increased sand saturation during the rundown phase of energetic waves. High vertex count correlated positively with waterline identification (R=0.51), indicating improved detection with greater shoreline detail, while strong negative correlation with SDW sinuosity (R=−0.51) suggested misdetection due to complex intertidal features.
This new approach advances understanding of SDW detection in macrotidal beaches, paving the way for improving detection methodologies. Ongoing work includes assessing additional SDW detection tools, extending analyses to diverse beach types (depending on hydro-morphodynamic conditions), and developing methods for automatic error flagging in each environment.
How to cite: Millán Roldán, M. C., Gallego Jiménez, A., Gomes da Silva, P., Pardo-Pascual, J. E., Palomar, J., Cabezas-Rabadán, C., Pellón, E., and Martínez Sánchez, J.: A new method for evaluating satellite-derived waterline detection in macrotidal beaches with complex intertidal morphology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10754, https://doi.org/10.5194/egusphere-egu25-10754, 2025.
The Gulf of Khambhat, a 200 km stretch of coastline in Gujarat, India, is increasingly vulnerable to the impacts of rising sea levels, inundation, and erosion. The region is home to densely populated districts such as Bhavnagar, Surat, Bharuch, and Khambhat, as well as vital ports like Dahej, crucial for global trade and economic growth. However, urbanization, industrialization, and a growing population have placed additional pressure on the region's underground resources, making the soft sediments more prone to subsidence. This, coupled with the environmental pressures from climate change, significantly amplifies the area's vulnerability to coastal hazards. The land use and land cover (LULC) changes between 2017 and 2023 have shown an increase in built-up areas and a decline in vital ecosystems like mangroves. Between 2014 and 2017, approximately 28.66 square kilometers of high tidal mudflats were lost, which not only destroyed critical habitats but also exposed populated areas to tidal flooding. This accelerated erosion further threatens the stability of the coastline. According to the IPCC AR6, the sea level along the Gulf is projected to rise by 0.95 meters by 2100. Tropical cyclones like Tauktae and Biparjoy, which caused significant damage in the region, may further intensify the risks of storm surges and flooding in the future. The combined effects of sea level rise (SLR), tropical cyclones, and vertical land motion (VLM) may further threaten the region’s biodiversity, health, and food security. In this context, this study aims to examine the combined effects of coastal subsidence and sea level rise on the coastal cities along the Gulf of Khambhat. Given the increasing frequency of cyclones in India, the study also assesses the risks of inundation and flooding due to SLR, storm surges, and land subsidence in the 21st century. The approach integrates scenario-based SLR projections from the IPCC AR6 (ranging from SSP1-1.9 to SSP5-8.5), vertical land motion rates, high-resolution Digital Elevation Models (DEMs), and historical storm surge data. The study uses C-band Sentinel-1 satellite data (92 SAR images) from March 2020 to June 2023, processed through the GMTSAR software with an advanced Small Baseline Subset (SBAS)-based Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique. The analysis reveals a subsidence rate of over 5 mm/year in various areas of the Gulf, particularly in locations like Palsana, Volvad, Navetha, and Bhadbhut. Furthermore, the results suggest that if sea level rise continues as projected by the IPCC and if the subsidence rate persists, the inundated area will increase by approximately 1.57% by 2030, 4.70% by 2050, and 18.20% by 2100 under the worst-case scenario (SSP5-8.5). Additionally, a cyclone similar to Tauktae, with the worst 4-meter storm surge height, could further impact over 1,000 square kilometers of the Gulf region under the same scenario. Given these alarming projections, it is essential to develop comprehensive emergency response plans for flood-related disasters to mitigate the growing risks and protect both the environment and local communities.
How to cite: Sharma, S. and Ojha, C.: Coastal Subsidence and Inundation Risk in the Gulf of Khambhat, India: A Geospatial Perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-385, https://doi.org/10.5194/egusphere-egu25-385, 2025.
The problem of coastline erosion is of global concern. Acquisition and processing of useful earth observation data about coastal changes is crucial to accurate change monitoring [1]. With the availability of sophisticated machine learning techniques, it is possible to accurately detect and analyze patterns of changes in coastal regions. One important aspect here is the explainability of the machine learning model used to predict changes and the possibility to incorporate human expertise in the process of detection [2]. In this research, we use an explainable artificial intelligence model to discover data patterns in Sentinel-2 time-series images to describe changes over a 7-year study period. Time-series imagery was acquired every month from January 2018 to September 2023, covering 4,694 cloud-free locations along the North Sea and Baltic Sea coastlines, each spanning 5 km x 5 km. These locations were selected using farthest point sampling to ensure representative coverage. The imagery was further divided into smaller scenes of 1.28 km x 1.28 km, and active learning techniques were employed to minimize labeling efforts. We have used Latent Dirichlet Allocation (LDA), a Bayesian generative model recently established as explainable model [1]. Being a probabilistic model, LDA is able to output certainty score for its predictions. We use the LDA as an unsupervised explainable model to create interpretable intermediate visual outcomes that support model explainability, while certainty scores of each prediction enhances trust. These interpretable outcomes are used by the domain expert to assess quality of the outcomes. Two kinds of visualizations are produced: 1) visual topic maps -LDA retrieved visual topics depicting latent data patterns, often perceived by humans as visual objects 2) change class maps and change signature maps - maps showing which land cover classes (e.g wave-breaking zones, dry sand, inter-tidal area, vegetation) have gone through most changes ( we produce histograms showing percentage of change per class per year, and also over the whole study period ); change signatures describe the nature of change in every class. We conclude the research by validating our results by domain experts.
This work is part of Helmholtz Autocoast project.
Keywords: Explainable AI, Coastal Change Monitoring, Sentinel-2 time-series, Visualizations
References:
- Fejjari, G. Valentino, J. A. Briffa and S. D'Amico, "Detection and Monitoring of Maltese Shoreline Changes using Sentinel-2 Imagery," 2023 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea), La Valletta, Malta, 2023, pp. 52-56, doi: 10.1109/MetroSea58055.2023.10317486.
- Karmakar, C. O. Dumitru, G. Schwarz and M. Datcu, "Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 676-689, 2021, doi: 10.1109/JSTARS.2020.3039012.
- Karmakar, C.O. Dumitru, N. Hughes and M. Datcu, "A Visualization Framework for Unsupervised Analysis of Latent Structures in SAR Image Time Series", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, pp. 5355-5373, 2023.
How to cite: karmakar, C.: Explainable Unsupervised Model for Coastline Change Monitoring with Sentinel-2 Time Series, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16180, https://doi.org/10.5194/egusphere-egu25-16180, 2025.
Offshore rip currents are among the primary causes of drowning incidents for beachgoers. Maritime radar has demonstrated potential for monitoring rip currents. To investigate the characteristics of rip currents as captured in radar imagery, a radar-based monitoring station was established along the southwestern coast of Taiwan. This station acquires nearshore radar echo images every 20 minutes, and the observational experiments have been ongoing for over six months. Rip current features detected in radar images can be categorized into two types.
The first type is the offshore flow channel (channel rip) occurring within the surf zone. The highly irregular surface structures in the surf zone increase radar wave scattering intensity, resulting in strong electromagnetic echoes in radar imagery. Conversely, wave breaking within the offshore flow channel is often reduced compared to the surrounding areas, leading to weakened radar wave scattering.
The second type is the offshore rip head extending beyond the surf zone. Floating debris on the sea surface, influenced by the rip current, is transported offshore, forming a streak-like region. Compared to clean seawater, these floating materials generate stronger sea surface echoes. Additionally, interactions between the offshore-directed rip current and onshore-directed waves increase sea surface roughness, further enhancing radar backscatter intensity.
To better elucidate the rip current features observed in radar images, we conducted supplementary experiments during the radar monitoring period, including field surveys of bathymetry, aerial photography, and drifter experiments. Cross-validation of these diverse datasets aims to clarify the feasibility of microwave radar for detecting rip currents comprehensively.
How to cite: Wu, L.-C. and Lai, J.-W.: Microwave Radar Detection of Rip Currents: Observations and Characterization from a Coastal Monitoring Station in Southwestern Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2112, https://doi.org/10.5194/egusphere-egu25-2112, 2025.
Coastal change assessments have significant socioeconomic, environmental, and infrastructure implications due to the extensive impacts of climate change, such as rising sea levels, the increasing frequency and intensity of storms, as well as the consequences of human intervention. Satellite products have been used to monitor the coast at relatively high resolution (30 m) since the 1970s through the Landsat program. However, the arrival of the EU Copernicus/Sentinels in 2014 introduced a marked increase in coastal applications thanks to the improved spatial and temporal resolutions. The research presented in this study explores the derivation of waterlines from Sentinel 2 imagery and the creation of a novel holistic approach to a validation framework. Specifically, this study aims to: a) explore the inherent waterline errors against reference datasets and begin to establish the overall uncertainty in deriving waterlines from optical satellite imagery; and b) assess the potential of these results and their suitability for coastal change applications. The results indicate an average positional error of approximately 4 meters for Sentinel images in coastal regions by evaluating the Sentinel-2 satellite images with distinct features visible in aerial orthophotography. Subsequently, the horizontal and vertical inaccuracies of the satellite-derived waterlines (SDWL) were further determined by using a GNSS line as a reference dataset. The horizontal assessment was conducted by calculating the average distance between the SDWLs and the GNSS reference lines across eighteen Sentinel-2 images corresponding to the years 2021, 2022, 2023 and 2024. These were analysed, showing a median displacement of 15 meters, and indicating an offshore trend for the satellite-derived waterlines. The vertical assessment, or height error, was computed by comparing the average height of SDWLs (as determined by the average tide gauge heights) with the reference dataset height (as measured by GNSS), resulting in a mean absolute error of 6 cm. The vertical results indicate that the SDWLs’ heights, as measured by the local tide gauges, align well with in situ local height measurements. The results of this study will aid in identifying temporal and spatial scales and resolutions at which Earth Observation products are suitable for coastal management. The initial stages of a validation framework are presented to assess the quality and applicability of satellite-derived waterlines for coastal change monitoring based on specific user requirements. Identifying the sources of error and improving uncertainty models for satellite-derived products enables better decision-making in coastal management. These analyses will demonstrate whether the outcomes remain consistent among satellite images or change according to local environmental conditions. Increasing end-user confidence in the rates of change obtained from available satellite products can provide crucial information in study areas, and at space-time resolutions previously unattainable.
How to cite: Castillo Campo, Y., Monteys, X., Casal, G., and Cahalane, C.: Enhanced monitoring of coastal change: a comprehensive validation framework for satellite imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13902, https://doi.org/10.5194/egusphere-egu25-13902, 2025.
As science and technology continue to advance, the accuracy of coastal and ocean bathymetry mapping continues to improve. Bathymetric mapping of coastal zones usually integrates products from multiple instruments for optical sensing (satellite, LiDAR) and/or acoustic sensing (single beam, multibeam and sidescan sonars) that are of varying accuracy and spatial resolution. Merging of these data from different sources may lead to spatial and temporal inconsistencies in the joint bathymetric data and inhibits their use for reliable assessment of coastal resilience to climate change such as sea level rise. This particularly requires caution since the rate of sea level change is typically on the order of a few mm yr-1, which is much smaller than the accuracy of bathymetric data, e.g. the accuracy ranges from the order of a few cm for LiDAR and multibeam eco sounding data to a few tens of cm for satellite data. In this study, we first demonstrate a problem, which is often overlooked in existing literature, in using coastal bathymetric data derived from state-of-the-art techniques for assessing coastal resilience to sea level rise. Using the Germen Wadden Sea as example, we found that the inconsistency of spatial resolution in the bathymetry mapping, when merged into a uniform gridded dataset, could result in a false trend in the change of the mean elevation of tidal basins, leading to a misconception of coastal resilience to sea level rise. We developed an analytical method to identify inconsistency in gridded bathymetry dataset that can be applied worldwide. Based on the identified inconsistency, we propose two solutions to minimise the associated effect. Our methods are broadly applicable to reduce the error in coastal bathymetry mapping and improve quantitative assessment of coastal resilience to climate change.
How to cite: Miao, B., Arlinghaus, P., Schrum, C., and Zhang, W.: Inconsistency of resolution in bathymetry mapping may lead to misconception of coastal resilience to climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6763, https://doi.org/10.5194/egusphere-egu25-6763, 2025.
Sea level rise is commonly associated with retreating shorelines. However, shoreline evolution is the result of the complex interaction between several groups of processes: Changes in inundation from changing water levels, vertical land motion and morphodynamics. Our goal is to quantify and separate the influence of these processes on the shoreline geometry by using remote sensing data. In a case study for the barrier island of Terschelling (the Netherlands), we found that between 1992 and 2022 morphodynamics had the largest impact on shoreline changes: Inundation by sea level rise, corrected for vertical land motion, accounted for -0.3 m/year shoreline retreat on average while the total average shoreline trend was -3.2 m/year.
These results are very site-specific and cannot be easily transferred to other places. The main limitation for upscaling this method lies in the availability of land elevation data. Local high-quality elevation datasets from airborne LiDAR or ship-based bathymetry are ideal but usually limited to countries which invest in regular observations campaigns. On the other hand, global Digital Elevation Models (DEMs) either lack the required vertical accuracy or horizontal resolution, they often cover only either the topography or the bathymetry, or they mix several data sources resulting in a mean elevation model spanning time periods of several years to decades.
Here we present a technique to derive a time-variable elevation grid that 1) can be applied globally, 2) has a high temporal resolution, 3) covers the intertidal area around the shoreline (foreshore and upper shoreface), and 4) has sufficient vertical accuracy and horizontal resolution. Additionally, we will address the question which accuracies are considered "sufficient" for certain problems.
To create such a time-variable topo-bathymetry model with yearly resolution for the years 1993-present, we combine existing global DEMs (e.g. DeltaDTM or CoastalDEM) with satellite remote sensing observations in a Kalman filter scheme. The observations are yearly 2.5D point clouds (x,y,h) of the intertidal zone that we generate by assigning sea surface heights from coastal altimetry to shoreline contours from optical remote sensing ("waterline method"). First, we incrementally update the global DEMs with these point clouds in a forward Kalman filter. Then, we use a backward smoother to derive the final elevation grid that best represents the topo-bathymetry at one point in time.
For validation, we apply this technique to sandy beaches in the Netherlands, Duck (USA) and Narrabeen (Australia), where high-quality elevation dataset are available. We hope to find that this method increases the accuracy of global DEMs and allows us to study temporal variations in coastal morphology and the role of sea level rise in data-sparse regions worldwide.
How to cite: Aschenneller, B., Rietbroek, R., and van der Wal, D.: A time-variable topo-bathymetry from coastal remote sensing observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3667, https://doi.org/10.5194/egusphere-egu25-3667, 2025.
Satellite-derived bathymetry (SDB) has been developed since the 1970s and has grown exponentially with the popularization of remote sensing technologies. Over the years, several authors have proposed various methods to perform bathymetric inversion from the information contained in the pixels of satellite images, aiming to improve the accuracy and reliability of non-direct methods for estimating depth data in shallow waters.
Despite the potential of remote sensing-based algorithms and global models to monitor multiple parameters of the planet's surface, few studies correlate SDB with water level in satellite images, obtained for the same region under different tidal conditions. Most recent efforts are limited to cluster analyses, separating the images into high-tide and low-tide groups to perform SDB with empirical models in a segmented approach, adjusting the linear coefficients of the regression models, partly for high-tide conditions and partly for low-tide conditions. The present study seeks to integrate tidal variation data with SDB techniques through Machine Learning (ML), particularly through the input channels of a Convolutional Neural Network (CNN).
Previous research employing a simpler ML model, the Multi-Layer Perceptron (MLP), in Babitonga Bay, a microtidal region situated along the southern coast of Santa Catarina, Brazil, was compared to empirical SDB models that rely on the linear interaction of electromagnetic spectrum bands with the water column. The findings demonstrated that the nonlinear inferences generated by deep neural networks can enhance the accuracy of SDB data by more than 100% in optically complex environments, influenced by high concentrations of Colored Dissolved Organic Matter (CDOM) and Suspended Particulate Matter (SPM), such as Babitonga Bay.
The application of more complex neural networks, such as CNN combined with additional input layers incorporating tidal data, has great potential for enhancing the performance of SDB, since CNN models utilize kernels that analyze multiple pixels surrounding a target point, enabling a more robust and context-aware approach, unlike MLP models, which infer depth on a pixel-by-pixel basis. The introduction of tide level variables as input channels in these deep learning neural networks makes these models suitable for universal application across micro-, meso-, and macrotidal environments.
The CNN model applied to Babitonga Bay yielded substantial improvements in SDB accuracy, reducing the mean absolute error (MAE) from 2.9 m (traditional SDB methods) and 1.3 m (MLP) to 0.1 m. These results were obtained using field data collected in 2018 through single-beam echo sounder surveys for training, testing, and validation for both cases, the traditional empirical SBD models, and the machine learning models (MLP and CNN).
How to cite: Mascagni, M. L., Klein, A. H. D. F., Fernandes, A. M. D. R., Coelho, D. K., Santos, A. B. D., and Pool, L.: Machine Learning Approaches for Tidal Data Interpolation in Satellite-Derived Bathymetry Applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10452, https://doi.org/10.5194/egusphere-egu25-10452, 2025.
The scientific, economic and social interest in coastal environments requires the continuous and sufficiently accurate determination of bathymetries of these areas, in particular of estuaries and beaches, at least at shallow depths of around 20 m.
The usual techniques for their determination, which combine the use of space geodesy (GNSS) and multibeam echosounder techniques, are a very accurate methodology, but also very costly in economic terms. The methodologies associated with the use of UAVs, which employ a wide variety of sensors, from RGB optical, thermal or multispectral cameras to Lidar Detection and ranging (LiDAR), are providing spatial information and images of high metric and thematic quality, with very short capture times for large extensions, at significantly lower costs than previous techniques.
Typically, photogrammetric techniques, in particular Structure for Motion (Sfm), have been used for the orientation process of the photogrammetric model, which obtain successful results in emerged areas, but have many limitations, or are simply impossible to apply, in the determination of bathymetries, due fundamentally to two aspects, key to the conventional photogrammetric process. Firstly, due to the difficulty in identifying homologous points that allow Bundle Block Adjustment, due to air-water refraction, and secondly, and equally important, due to the reflective behavior of the sea surface itself.
To overcome the indicated barriers, the first results obtained using a multispectral sensor with 10 bands on board a UAV, ranging from 444 to 842 nanometres (nm), highlighting 4 bands in the blue and green ranges (444, 475, 531 and 560 nm), are presented. With the images obtained, and by applying spectral techniques used in satellite-derived bathymetry, previously normalized radiometrically and mosaicked to the sea surface, we have proceeded to determine the sea depth, in different conditions of turbidity and clarity that can be considered globally unfavorable and that characterize the Cantabrian Sea in the North of Spain.
Essel, B.; Bolger, M.; McDonald, J.; Cahalane, C. Developing a Theoretical Assessment Method for an Assisted Direct Georeferencing Approach to Improve Accuracy when Mapping over Water: The Concept, Potential and Limitations. In Proceedings of the ISPRS 12th International Symposium on Mobile Mapping Technology (MMT), Padua, Italy, 24–26 May 2023.
Román, A.; Heredia, S.; Windle, A.E.; Tovar-Sánchez, A.; Navarro, G. Enhancing Georeferencing and Mosaicking Techniques over Water Surfaces with High-Resolution Unmanned Aerial Vehicle (UAV) Imagery. Remote Sens. 2024, 16, 290. https://doi.org/10.3390/rs16020290.
Windle, A.E.; Silsbe, G.M. Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters. Front. Environ. Sci. 2021, 9, 674247.
How to cite: Sánchez-Espeso, J., Bellido, G., García-Utrilla, I., Faugére, E., Pérez-Díaz, B., Jiménez, M., and Castanedo, S.: Use of high resolution multispectral Unmanned Aerial vehicle (UAV) imagery to retrieval nearshore bathymetry using photogrammetric and spectral techniques in the Cantabrian Sea in Spain., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13317, https://doi.org/10.5194/egusphere-egu25-13317, 2025.
Surface reflectance of remote sensing datasets contribute to various fields such as natural resources management (Liang et al., 2024), agricultural practices (Liu et al., 2020; Stratoulias et al., 2017), ecological monitoring (Liang et al., 2024), and climate studies (Liu et al., 2020), providing critical information about Earth's surface conditions and resources. Nonetheless, inaccuracies in the raw remote surface reflectance data, resulting from both internal sensor anomalies (Hu et al., 2012) and external atmospheric effects (Dash et al., 2011; Vermote et al., 2016), reveal that correction of these datasets is essential. Moreover, Surface Reflectance datasets of coastal and inland waters are significantly affected by cloud coverage (Wang & Chen, 2024) introducing noise (Qing et al., 2021) into the imagery and shadows. This study introduces a methodology to correct and fill in missing data from multispectral Level 2 Surface Reflectance daily time-series, by identifying logical errors and implementing Principal Component Analysis. The study successfully results in continuous two-decade surface reflectance dataset to assure its reliability and utility across various applications.
References
Dash, P., Walker, N. D., Mishra, D. R., Hu, C., Pinckney, J. L., & D’Sa, E. J. (2011). Estimation of cyanobacterial pigments in a freshwater lake using OCM satellite data. Remote Sensing of Environment, 115(12), 3409–3423. https://doi.org/10.1016/j.rse.2011.08.004
Hu, C., Lee, Z., & Franz, B. (2012). Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans, 117(1), 1–25. https://doi.org/10.1029/2011JC007395
Liang, S., Li, Y., Wei, H., Dong, L., Zhang, J., & Xiao, C. (2024). Research on Hyperspectral Surface Reflectance Dataset of Typical Ore Concentration Area in Hami Remote Sensing Test Field. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10(1), 137–144. https://doi.org/10.5194/isprs-annals-X-1-2024-137-2024
Liu, J. L., Cheng, F. Y., Munger, W., Jiang, P., Whitby, T. G., Chen, S. Y., Ji, W. W., & Man, X. L. (2020). Precipitation extremes influence patterns and partitioning of evapotranspiration and transpiration in a deciduous boreal larch forest. Agricultural and Forest Meteorology, 287(January), 107936. https://doi.org/10.1016/j.agrformet.2020.107936
Qing, S., Cui, T., Lai, Q., Bao, Y., Diao, R., Yue, Y., & Hao, Y. (2021). Improving remote sensing retrieval of water clarity in complex coastal and inland waters with modified absorption estimation and optical water classification using Sentinel-2 MSI. International Journal of Applied Earth Observation and Geoinformation, 102, 102377. https://doi.org/10.1016/j.jag.2021.102377
Stratoulias, D., Tolpekin, V., de By, R. A., Zurita-Milla, R., Retsios, V., Bijker, W., Hasan, M. A., & Vermote, E. (2017). A workflow for automated satellite image processing: From raw VHSR data to object-based spectral information for smallholder agriculture. Remote Sensing, 9(10). https://doi.org/10.3390/rs9101048
Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46–56. https://doi.org/10.1016/j.rse.2016.04.008
Wang, J., & Chen, X. (2024). A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data. Science of the Total Environment, 906(July 2023), 167631. https://doi.org/10.1016/j.scitotenv.2023.167631
How to cite: biliani, I. and Zacharias, I.: Satellite Surface Reflectance correction and completion methodology by using Principal Component Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20830, https://doi.org/10.5194/egusphere-egu25-20830, 2025.
Satellite-derived bathymetry is an essential tool for mapping shallow coastal areas with complex reef structures where ship-based surveys are either unsafe or inefficient. The focus of this study is to present a baseline study of the seabed morphology of a coastal region (0-130 m water depth) to support before-and-after analyses of an artificial reef deployment site under the OCEAN CITIZEN project.
OCEAN CITIZEN aims to develop a sustainable and innovative protocol for coastal restoration and biodiversity conservation. Adapted to specific ecozones, yet replicable across regions, this protocol emphasises the expansion of Marine Protected Areas (MPAs), the creation of ecological corridors to support ecosystem interactions, the restoration of biodiversity, the enhancement of blue carbon through innovative techniques, and the establishment of self-sustaining economic models for long-term sustainability.
In this context, the study investigates seafloor morphology using optical and acoustic methods to assess the suitability of satellite-derived data for mapping coastal habitats. Satellite data from Maxar's WorldView-2 satellite sensor were compared with ship-based data acquired with a Norbit iWBMS multibeam system (190 - 400 kHz) to assess their performance in water depths ranging from 10 to 20 m. Ground truthing was carried out using underwater video surveys to validate substrate, classification and biological observations.
Preliminary results show that satellite-derived data effectively capture broad-scale seafloor morphology, with contours closely matching multibeam data. However, small-scale and complex reef structures could only be resolved by ship-based surveys. Comparisons of seafloor reflectance (optical) and backscatter (acoustic) showed different sensitivities: as expected, backscatter distinguished sandy areas between hard outcropping substrate, whereas satellite reflectance is also sensitive to variations in substrate brightness. These differences highlight the need to be aware of the complementary nature of the two methods and their potential to provide additional insight into coastal restoration planning.
How to cite: Schönke, M., Jensen, M., Lohrberg, A., Feldens, P., and Schneider von Deimling, J.: Comparison between satellite derived and ship based seafloor characteristic's in areas with complex seafloor morphology - A Baseline study for artificial reef deployment, Tenerife Island, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12115, https://doi.org/10.5194/egusphere-egu25-12115, 2025.
Climate change is reshaping the world’s coastlines through coupled dynamic processes. The increased importance of monitoring coastal changes over time can be partially addressed using satellite derived bathymetry (SDB), which is more cost effective than traditional methods and allows for monitoring capabilities. In this study we developed a two-step methodology aiming to improve shallow water depth estimates from multi-temporal Sentinel-2 satellite images. The pilot area lies in north-east Ireland in optically complex waters. A threshold criterion was applied to identify 10 suitable Sentinel-2 images over one year time (2021). Lyzenga and Stumpf empirical models were evaluated followed by the application of an empirical generalized linear model (GLM). The performance of atmospherically corrected composite images, created using a reducer function (mean and median), was also evaluated, and compared with the performance of single images. Validation results confirmed the outperformance of the GLM model compared to Lyzenga and Stumpf empirical models. The optimum combination of multi temporal images outperformed the single images regression scores, with a reduction of 45 % in RMSE and a MAE as low as 31 cm in the 0 to 10 m depth. The application of empirical models on the multi-temporal image analysis results in a reduction of error outliers. These results enhance the potential of SDB and Sentinel-2 data in a range of potential coastal monitoring applications, such as repetitive bathymetric changes, ecosystem mapping and environmental management.
How to cite: Isler, T., Monteys, X., Casal, G., and Gallagher, C.: Assessment of a generalized linear model for satellite-derived bathymetry in turbid waters using Sentinel-2 multi-temporal images, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2689, https://doi.org/10.5194/egusphere-egu25-2689, 2025.
Sediment plumes in marine environments significantly impact ecosystems by increasing turbidity, depleting bottom-water oxygen, and transporting pollutants. In general, there is a special plume, called Ieodo plume, which is a characteristic bent plume originating from the Ieodo seamount in the northern East China Sea. While satellite-based remote sensing is commonly used to study such phenomena, its spatiotemporal resolution is often insufficient for monitoring rapidly changing marine dynamics. Thus, it is still challenging to understand their specific behavior, dispersion patterns, and range of influence. This study investigates the behavior and dispersion of the Ieodo plume using integrated UAV (Unmanned Aerial Vehicle) and satellite observations. Continuous UAV-based hovering observations were conducted on the Ieodo Ocean Research Station, adjacent to the plume, utilizing optical and multispectral sensors. Optical sensors were employed to monitor flow at the plume's source, while surface currents derived from Optical Flow algorithm were combined with tide and wind data from real-time in situ observations at the research station to estimate plume dispersion range theoretically, using equations derived from plume dynamics. These theoretical predictions were validated against Sentinel-2 optical satellite imagery. Multispectral sensors were used to derive suspended sediment concentration (SSC) information within the plume based on remote sensing reflectance (Rrs). This study provides a comprehensive understanding of the initial characteristics and dispersion of the Ieodo plume based on theoretical and observational analysis. These results are expected to be applicable to predict plume dispersion caused by riverine outflows, seabed resource extraction, and dredging operations, thereby contributing to better management of such marine phenomena.
How to cite: Hwang, S.-B., Lee, J.-S., Kim, S.-Y., and Jo, Y.-H.: Observation of Sediment Plume Dispersion around Ieodo Ocean Research Station in the East China Sea Using Satellites and UAVs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2977, https://doi.org/10.5194/egusphere-egu25-2977, 2025.
The tidal flats are important in an ecological and economical way, and continuous management is required because it shows very dynamic changes. In the Korean tidal flats, efficient management is more emphasized because the heavily economically active. In this study, we propose a way to create a topographic elevation and area theme map of the tidal flat using multiple satellites and to update it efficiently. The waterline method was used to generate a satellite based digital elevation model (DEM), and the topographic elevation was calculated using the tidal information at the time the satellite image was acquired. The exposure frequency of tidal flats was calculated through time-series images and compared and verified with the unmanned aerial vehicle-based DEM to present an optimal topographic elevation map generation method. For the satellite-based tidal flat area theme map, the tidal flat was classified using the supervised classification method, and compared and verified with the tidal flat area data provided by the Ministry of Oceans and Fisheries. The multi-satellite-based DEM of tidal flat could produce a precise theme with an error of about 21 cm with only 5 months of image collection, and even if the image collection period was longer, the accuracy was not significantly improved. In the case of the satellite-based tidal flat area, the accuracy was about 95% compared to the reference data, and it was analyzed that the tidal flat area, which was missing some surveys, could also be detected. Through the results of this study, it was confirmed that the satellite-based topographic elevation and area map production method can drastically shorten the update cycle while maintaining a level of accuracy similar to the current survey method.
How to cite: Kim, K., Lee, J., Kwak, G.-H., and Hyung, J.: An optimal approach for morphological changes of tidal flat using multi-satellite sensors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5013, https://doi.org/10.5194/egusphere-egu25-5013, 2025.
Continuous sampling and analysis of data from the Atlantic sector of the Southern Ocean is key to monitoring rapid, stochastic ecosystem changes in the region. Antarctic krill (hereafter krill), the subject of this research, is particularly prone to regional warming, with a southward contraction of its habitat forecast. While coastal regions of the Antarctic Peninsula, South Orkney Islands and South Georgia are relatively well surveyed using trawl and acoustic survey methods, offshore environments represent significant data gaps. Many important stages in krill life cycle take place offshore including spawning and grazing, and moreover transit between the Antarctic Peninsula and South Georgia entails over 200+ days of advective oceanic (mainly passive) transport with Antarctic Circumpolar Current and its associated fronts. To fill in this significant data gap and infer patterns in temporal and spatial offshore distribution patterns necessitates the integration of diverse data sources, including primary historical observations of krill abundance from surveys and fishing activity as well as secondary observations from remotely sensed environmental variables.
Ideally, we could detect krill directly using hyperspectral imaging to measure the concentration of astaxanthin pigments in surface waters (Basedow et al. 2019). However, given such methods are still in development, we utilize Species distribution models (SDM) to infer spatiotemporal krill distributions. SDMs are models that relate abundance/ occurrence of species with environmental data for a given set of sample locations (Elith and Leathwick 2009). In this research we use multivariable regression methods to build SDMs to predict krill abundance in relation to both static (geographical area, bathymetry) and dynamic (SST) environmental features, and numerical density of krill as a target variable. We explore the accuracy of several nonlinear methods including Random Forest and Boosted Regression Trees, through comparisons of model accuracy (R2 values, standardized RMSE values and so on) and cross-validation. We then compare these predictions to eddy statistics calculated from satellite altimetry data, and phytoplankton concentrations derived from ocean colour data, with both products accessed through the Copernicus Marine Service. In this way, we will use SDMs for spatiotemporal predictions and use these mapped predictions to explain important relationships e.g. krill density as a function of eddy size.
References:
Elith, Jane, and John R. Leathwick. 2009. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annual Review of Ecology, Evolution, and Systematics 40 (1): 677–97. doi: https://doi.org/10.1146/annurev.ecolsys.110308.120159
Basedow, S.L., McKee, D., Lefering, I. et al. Remote sensing of zooplankton swarms. Sci Rep 9, 686 (2019). doi: https://doi.org/10.1038/s41598-018-37129-x
How to cite: Kelly, C., Daae, R., Ellingsen, I., and Omholt Alver, M.: Filling Data Gaps in the Southern Ocean: Fusion of Remote Sensing Observations with Historic Krill Data to Explain Coastal and Offshore Variability in Krill Abundance, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6296, https://doi.org/10.5194/egusphere-egu25-6296, 2025.
Coastal zones are widely recognized as among the most dynamic and sensitive geomorphological systems, particularly in response to weather and climate conditions. Coastal erosion and deposition alternate cyclically, influenced by fluvial sediment transport, wave and tidal regimes, sea level rise, tectonics, coastal flooding, and anthropogenic pressures. With approximately 2.15 billion people residing in coastal areas - and projections indicating significant population growth in these zones - understanding shoreline morphodynamics is essential for cost-effective and sustainable management strategies.
Within the framework of Italy’s National Recovery and Resilience Plan (PNRR), funded by Next Generation EU, the extended partnership RETURN (multi-Risk sciEnce for resilienT commUnities undeR a changiNg climate) aims to strengthen research on environmental, natural, and anthropogenic risks associated with climate change. Specifically, the Diagonal Spoke (DS) 8, Science underpinning climate services for risk mitigation and adaptation, focuses on developing innovative models to forecast atmospheric, hydrological, and marine impact-oriented indicators, alongside assessing their uncertainties. In this context, shoreline position and morphology emerge as critical indicators for assessing the impacts of climate change on coastal regions. Italian coastline spans approximately 7,500 km, of which 943 km are currently eroding, and 970 km are prograding (ISPRA, 2023), based on comparisons of shorelines between 2006 and 2020. Reconstructing coastal dynamics in specific study areas is therefore pivotal for effective land management and provides a valuable tool for government agencies and stakeholders.
The southern coastal area of the Latium region (Central Italy) represents an ideal case study for investigating shoreline morphodynamics, with a coastline approximately 30 km long. This study utilizes multispectral and multi-mission satellite imagery from Landsat 4, 5, 8, and Sentinel-2, offering an unparalleled dataset for reconstructing coastal changes. The primary objectives of the research are: i) annual reconstruction of the instantaneous waterline, and ii) identification of erosional and depositional sectors with quantified rates. Using the Normalized Difference Water Index (NDWI), 40 instantaneous shorelines were reconstructed for the summer season from 1984 to 2024. The application of the Digital Shoreline Analysis System (DSAS) developed by the USGS revealed maximum shoreline regression rates of approximately 1 m/yr (1.07 m/yr and 1.2 m/yr, respectively LRR and WLR). Additionally, in winter 2024/2025 drone survey, conducted using a Matrice 350 RTK equipped with a multispectral MicaSense RedEdge-P camera, were integrated into the methodology to provide high-resolution and spatially detailed data on shoreline position and morphology, enhancing the accuracy of the reconstructed coastal dynamics and complementing the satellite-based analyses. Finally, the accuracy of the reconstructed shorelines was validated by comparing satellite-derived shorelines from 1998, 2005, and 2019 with ISPRA’s orthophoto-derived shorelines. The results demonstrate strong agreement, with RMSE of 14.44 m, 12.60 m, and 5.83 (1998, 2005 and 2019, respectively), falling within the uncertainty range of Landsat and Sentinel imagery. This study highlights the potential of multi-sensor remote sensing surveys and geospatial techniques in monitoring coastal dynamics, providing critical insights for climate adaptation and risk mitigation strategies in coastal regions.
How to cite: Troiani, F., Iacobucci, G., Torre, D., and Piacentini, D.: Integrated satellite and drone-based multispectral analysis for 40-Year shoreline reconstruction on the Southern Latium Coast, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5796, https://doi.org/10.5194/egusphere-egu25-5796, 2025.
Satellite imagery is crucial for studying shoreline evolution due to its ability to provide consistent, high-resolution, and large-scale data over time (Gomes da Silva et al., 2024); and it plays a crucial role in countries with limited coastal information sources. The aim of this study is to explore the use of satellite imagery to investigate shoreline evolution at three different locations in Mozambique: Morrungulo Beach, Barra Beach, and Tofo Beach in Inhambane Province. These three locations are characterized as exposed and mesotidal beaches and were selected as representative of the typical coastal archetype in the south coast of Mozambique. This study uses satellite imagery provided by Planet Labs, which is explored with the open-source code CoastSat.PlanetScope toolkit to map and analyze in detail shoreline changes in the study sites (Doherty et al., 2022). PlanetScope satellite imagery has a spatial resolution of approximately 3 meters and almost daily temporal resolution, allowing for detailed observation of shoreline features. For the automated extraction of the shoreline, CoastSat.PlanetScope takes into account the beach slope and tide to provide shoreline positions along user-defined transects, determined using a water index and pixel thresholding. The temporal scope of the satellite imagery utilized in this study extends from July 2016 to June 2024, using one image per month, offering a comprehensive dataset for examining monthly to multiannual shoreline dynamics. Shoreline positions have been evaluated using data total of 101 shorelines for Barra Beach, 94 shorelines for Morrungulo Beach, and 108 shorelines for Tofo Beach. Through this analysis, it was also possible to determine the shoreline impacts of tropical cyclones that made landfall in the region. Barra Beach revealed a strong erosion rate of 3.7 m/year as calculated using the End Point Rate (EPR) method, which measures the net shoreline change over time, and a moderate erosion rate of 0.5 m/year based on the Linear Regression (LR) method, suggesting relative stability in shoreline position when more shoreline positions are considered. Morrungulo Beach presented an accretion rate of 1.7 m/yr based on EPR, but evidenced an erosion rate of 0.4 m/yr with LR. Tofo Beach presented more consistent erosion, with a rate of 1.8 m/yr for EPR and 0.7 m/yr for LR. The analysis of shoreline changes across the three selected beaches in Mozambique highlights distinct patterns of erosion and accretion over the study period. Barra Beach demonstrated considerable differences in erosion rate according to the method, while Morrungulo Beach exhibited a mix of accretion and minor erosion, depending on the analysis method used. Conversely, Tofo Beach showed consistent erosion. These findings highlight the need to carefully consider shoreline change metrics, selecting those that better represent the coastal processes of interest to ensure site-specific management strategies along Mozambique’s coastline. This study has been funded by DOORS project (H2020 – 101000518 – DOORS), and co-funded by the FI AGAUR grant (2022 FI_B 00897).
How to cite: Pavo-Fernández, E., Loureiro, C., Solana, G., Gracia, V., Mavimbele, A., and Grifoll, M.: Satellite-derived shoreline evolution in Inhambane province (Mozambique) using high-resolution imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6098, https://doi.org/10.5194/egusphere-egu25-6098, 2025.
Aquaculture facilities are not only an important component of fisheries and local economies, but they also have significant economic and environmental impacts that require efficient and sustainable management. Additionally, knowing the exact locations of aquaculture facilities is crucial for operating ships in nearshore areas, as their presence significantly affects the safe navigation of vessels. However, current aquaculture facility data suffer from slow update cycles due to reliance on field surveys and data processing, and their low spatial resolution does not meet the accuracy requirements in the field. To address this issue, this study takes a first step toward updating aquaculture facility data in quasi-real time using satellite imagery. Specifically, we evaluated the spatial agreement between detected aquaculture facility data and existing data based on Sentinel-1 satellite imagery. While many previous studies on aquaculture facility detection have utilized optical satellites, this study aims to verify the detectability of aquaculture facilities using SAR (Synthetic Aperture Radar) imagery, which can be acquired regardless of weather conditions or time of day. The aquaculture facility data provided by the Korea Hydrographic and Oceanographic Agency is available in both polygon and point formats, with the last update date being December 19, 2024. Accordingly, we analyzed Sentinel-1 data acquired around the same time (December 20, 2024) and compared it with the polygon data. Our analysis revealed significant discrepancies between the two datasets. These findings highlight the need to update current aquaculture facility data and suggest that satellite imagery, with its ability to regularly cover broad areas, could be employed to improve the accuracy and timeliness of aquaculture data updates. This confirms the potential value and utility of satellite imagery as an effective tool for managing aquaculture facilities.
How to cite: Kim, Y. and Choi, H.: Comparison of Aquaculture Facilities with Sentinel-1 Data for Change Detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8120, https://doi.org/10.5194/egusphere-egu25-8120, 2025.
Shallow water bathymetry is vital for understanding coastal ecosystems, managing marine resources, and monitoring environmental changes. However, global mapping remains challenging due to the limited penetration of optical and near-infrared light in water, which is rapidly absorbed and scattered by suspended particles and water molecules. Other electromagnetic frequencies, such as microwaves, do not penetrate deeply enough, rendering photogrammetric methods ineffective for underwater mapping.
High-accuracy methods like airborne LiDAR, sonar, and ICESat-2 (a spaceborne altimetric LiDAR) provide detailed bathymetric measurements but are limited by sparse spatial coverage and infrequent revisits. This study combines the strengths of airborne LiDAR and ICESat-2 data to train Machine Learning models for bathymetry extraction from Sentinel-2 multispectral imagery. Sentinel-2 offers global coverage, 10-meter resolution, and a ~5-day revisit cycle, presenting a scalable solution for large-scale mapping. Atmospheric corrections were applied to Sentinel-2 data, and ICESat-2 data were adjusted for tidal and refraction effects. Using Machine Learning models, we evaluate whether smaller ICESat-2-derived training datasets can achieve comparable accuracy to those trained on airborne LiDAR data, which provide a more comprehensive depth range.
In the past, correlations between the logarithm of Sentinel-2 blue-green band ratios versus depth has been widely used in bathymetric studies. We seek to improve prediction accuracy from optical imagery by incorporating other nonlinear relationships and leveraging additional spectral bands, allowing for more robust modeling across varying environmental and water conditions.
Our research underscores the complementary strengths and limitations of ICESat-2 and airborne LiDAR for bathymetric modeling and highlights the potential of Sentinel-2 for global, repeatable bathymetry. Achieving accurate and frequent mapping could revolutionize coastal monitoring, enabling applications such as disaster impact assessments and change detection after events like oceanic landslides, volcanic eruptions or earthquakes.
Keywords: Shallow water bathymetry, ICESat-2 ATL03, Airborne LiDAR, Sentinel-2, Random Forest, Coastal mapping
How to cite: Hsu, H. J. and Moortgat, J.: Enhancing Shallow Water Bathymetry Using Machine Learning with ICESat-2, Airborne LiDAR, and Sentinel-2 Imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10185, https://doi.org/10.5194/egusphere-egu25-10185, 2025.
Multispectral satellite images can survey the surf zone through discretizing the land-sea interface, at a known water level, to monitor recession and accretion rates along the coastline. This shoreline detection method can be enhanced by utilizing the daily return frequency of PlanetScope data, allowing a higher temporal resolution of the observed shorelines. Similar shoreline detection tools, such as CoastSat(Doherty et al., 2022; Vos et al., 2019), discretize the land-sea interface by thresholding the image using a single index, such as NDWI (normalized difference water index) (McFeeters, 1996) and contouring the image at this threshold. Presented here is an alternative approach. In using several multilayer perceptrons (MLP) acting together, each pixel’s probability of being classed as land or sea is calculated. The final shoreline contour is then probabilistically defined whithout the use of manual threshold. The advantage of this method is that it allows for spatial variability within satellite bands, for regions of shadow and geographical features, to still be correctly discretized. It also allows for further use case beyond just sandy beaches, due to the implementation of multiple indices allowing identification of different classes that could be interfacing with the sea. Characteristically, apart from the usual NDWI and NDVI index, we use the RGB and IR bands as well as 24 further band relationships for a total set of 28 indices to train the MLPs. The root mean squared error (RMSE), the distance between the derived shoreline and a height contour relative to the instantaneous water-level, of this method tested at Seaford UK for cloud cover <90% is ~7m.
Doherty, Y., Harley, M. D., Vos, K., & Splinter, K. D. (2022). A Python toolkit to monitor sandy shoreline change using high-resolution PlanetScope cubesats. Environmental Modelling and Software, 157. https://doi.org/10.1016/J.ENVSOFT.2022.105512
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
Vos, K., Splinter, K. D., Harley, M. D., Simmons, J. A., & Turner, I. L. (2019). CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environmental Modelling & Software, 122, 104528. https://doi.org/10.1016/J.ENVSOFT.2019.104528
How to cite: Hewetson, A., Lawrence, J., and Karmpadakis, I.: An automated shoreline detection method using PlanetScope satellite imagery , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17895, https://doi.org/10.5194/egusphere-egu25-17895, 2025.
Beach litter (BL) poses a significant threat to coastal ecosystems and marine biodiversity. Monitoring its density, composition, and distribution is crucial to develop effective management strategies and support the sustainable use of the beach environments. Traditional in situ visual surveys allow detailed identification and classification of BL items but are often limited in spatial coverage and require significant time investment. Recent advancements, including the use of Unmanned Aerial Vehicles (UAV), have enabled more efficient assessments of coastal litter and related geomorphological features.
This study investigates the characteristics and distribution of BL and its relationship with coastal morphodynamics along two unmanaged beaches located along the Molise coast (southern Italy): Petacciato and Ramitelli beaches. These sites were selected based on their distinct morphodynamic characteristics, and their free beach status.
The monitoring methodology considered international guidelines [1, 2]. UAV surveys were conducted before and after a significant storm event to assess its impact not only on beach morphology but also on BL distribution. Flights were carried out with a Yuneec Typhoon H520 hexacopter using a flight height of 15 meters [2]. High-resolution orthophotos were analysed to manually identify BL larger than 2.5 cm and classify them according to the Joint List for Marine Macrolitter Monitoring [2]. Items were categorized by material type, size, and weight, and unusual objects were documented in detail. Shoreline and dune foot variations along with morphological changes of the beach were also quantified to evaluate the role of coastal processes in BL dispersion and accumulation patterns.
First results reveal significant differences in BL density and composition between the two study sites, but with plastic materials dominating both the collected items, consistent with broader Mediterranean trends [3]. The post-storm survey highlighted the role of weather events in redistributing litter, particularly at the southern limit of the Ramitelli beach which is in contact with the jetty of the Saccione River mouth that drives the accumulation of beach litter on the adjacent shoreline. This study underscores the importance of integrating UAV-based monitoring with geomorphological analyses to better understand the interplay between coastal dynamics and BL distribution. The monitoring of these relationships can provide essential data to support and improve coastal management and design targeted beach clean-up strategies.
Key Words
Remote Sensing, Coastal monitoring, Coastal geomorphology, UAV images, Visual assessment, Litter beach analysis.
[1] Vlachogianni T. (2017) - Methodology for Monitoring Marine Litter on Beaches. Macro-Debris (> 2.5 cm). DeFishGear, 1-16.
[2] Fleet D., Vlachogianni T. & Hanke G. [Eds] (2021) - A joint list of litter categories for marine macrolitter monitoring. JRC Technical Reports, Publications Office of the European Union, Luxembourg, 30348, 52.
[3] Rizzo A., Sozio A., Anfuso G., La Salandra M., Sasso C. (2022) – The use of UAV images to assess preliminary relationships between spatial litter distribution and beach morphodynamic trends: the case study of Torre Guaceto beach (Apulia Region, Southern Italy). Geogr. Fis. Dinam. Quat. 45 (2022). 237-250.
How to cite: Dilauro, G., Di Renzo, L., Anfuso, G., Di Paola, G., Rizzo, A., and Rosskopf, C. M.: UAV monitoring for assessing beach litter pollution and coastal morphodynamics: case studies from the Molise region (central Adriatic coast, Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18784, https://doi.org/10.5194/egusphere-egu25-18784, 2025.
Langeoog is one of the East Frisian Islands located in the German Wadden Sea. The island's coastal morphodynamics are strongly influenced by environmental factors such as tides and currents. As natural sediment supplies cannot always compensate for coastal erosion of sand deposits, coastal protection measures are crucial for preserving the island. Langeoog is a special case in this context, as it is the only island in the barrier island chain that is mostly managed without the use of coastal protection structures such as groynes or revetments, due to its prevailing current and sedimentation processes. The island’s settlement and infrastructure are surrounded by a protective dune and adjacent sandy beach areas to the west and north. Conservation measures to preserve the dune are only necessary in the northern part, in front of the Pirolatal, where sand replenishments are carried out regularly to counteract ongoing beach erosion by restocking the sand deposits in front of the protective dune. To initiate necessary measures and estimate the required sediment volumes, knowledge about the development of this beach section is essential for local coastal protection authorities.
In this study, we investigate the suitability of optical multi-sensor remote sensing data to analyse changes in the sand deposits and their effects on the condition of the protective dune in front of Pirolatal on Langeoog Island from 2018 to 2023. For this purpose, we processed high-resolution (HR) and medium-resolution (MR) optical satellite data, applying index-based threshold methods to estimate several proxies of coastal dynamics, such as the instantaneous waterline, the location and state of the protective dune, and the extent of permanently dry sand areas under regular tidal conditions. We compare the results to elevation data to assess the potential of 2D remote sensing data for monitoring this coastal section. The results show that the state of the beach and the height of the dune’s break-off are strongly influenced by accretion events (sand replenishments) and ongoing erosion, particularly during storm surges in the winter season. The condition of the sand deposit is also crucial for determining the position of the instantaneous waterline.
This study demonstrates the benefit of a multi-sensor optical satellite data approach to support coastal monitoring and applied coastal protection efforts.
How to cite: Holzner, J., Martinis, S., and Plank, S.: Assessing Sand Deposit Dynamics at the Island of Langeoog, Germany by Means of Multi-Sensor Remote Sensing Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8104, https://doi.org/10.5194/egusphere-egu25-8104, 2025.
Monitoring of seagrasses and macroalgae is important both for surveillance of habitat conservation and for quantifying the effects of anthropogenic pressure on marine ecosystems. Satellite remote sensing, allows for global and local scale environmental monitoring with low cost and high revisit time. The availability of agency satellites, such as Sentinel-2, and commercial satellites, such as the Planetscope constellation, makes continuous and long-term monitoring of the underwater vegetation possible. However, the interposition between the underwater vegetation and the marine surface of the water column significantly limits the possibility of carrying out satellite monitoring, which is therefore suitable for shallow coastal areas but not for the open sea. Furthermore, in order to properly and detailly interpret the nature of the monitored vegetation, for example in terms of species, it is necessary to compare satellite data with sea truth.
In this work, some strategies are proposed to delimit areas of marine vegetation and to compare them with the sea truth in order to monitor ecosystems continuously and in the long term, possibly starting from an initial accurate on field assessment. The use of free agency data (with lower spatial resolution) and commercial data (with higher resolution) is combined in such a way as to contain the costs of data acquisition. Furthermore, data obtained from the Copernicus Marine Service are used, together with bathymetry data, to estimate the effects of the water column on the reflectance of underwater vegetation. Multi-temporal analysis approaches are proposed to identify possible changes in vegetation covers that can trigger acquisition campaigns at sea, to directly verify the detected anomalies. The proposed approaches, as mentioned, exploit the availability and large geographical coverage of satellite data, without renouncing the use of (more expensive) resources on field when strictly necessary.
How to cite: Cicala, L., Cianflone, M., De Mizio, M., and Fraschetti, S.: Coastal benthic habitat monitoring using Copernicus and contributing missions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18173, https://doi.org/10.5194/egusphere-egu25-18173, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot 4
EGU25-2237 | Posters virtual | VPS20
Assessment of GOCI-II satellite remote sensing products in Lake TaihuFri, 02 May, 14:00–15:45 (CEST) | vP4.10
The Geostationary Ocean Color Imager-II (GOCI-II), which was launched on February 19, 2020, offers an increased observation times within a day and finer spatial resolution than those of its predecessor, the Geostationary Ocean Color Imager (GOCI), which was launched in 2010. To ensure the reliability of GOCI-II data for practical applications, the accuracy of remote sensing products must be validated. In this study, we employed in situ data from Lake Taihu for validation. We assessed the accuracy of GOCI-II products, including the remote sensing reflectance inverted via two atmospheric correction algorithms (ultraviolet (UV) and near-infrared (NIR) atmospheric correction algorithms), as well as the chlorophyll a (Chl-a) concentration, total suspended matter (TSM) concentration, and phytoplankton absorption coefficient (aph). Our results revealed that the UV atmospheric correction algorithm provided a relatively higher accuracy in Lake Taihu, with average absolute percentage deviations (APDs) of the remote sensing reflectance across different bands of 25.17% (412 nm), 29.69% (443 nm), 22.27% (490 nm), 19.38% (555 nm), 36.83% (660 nm), and 33.0% (680 nm). Compared to the products generated using the NIR atmospheric correction algorithm, the derived Chl-a concentration, TSM concentration, and aph products from the UV algorithm showed improved accuracy, with APD values reduced by 16.92%, 3.32%, and 10.91%, respectively. When using UV correction, the 412 nm band performed better than the 380 nm band, likely due to the lower signal-to-noise ratio of the 380 nm band and smaller extrapolation errors when assuming a zero signal for the 412 nm band. Considering that the NIR algorithm is suitable for open ocean waters while the UV algorithm demonstrates higher accuracy in highly turbid environments, a combined UV-NIR atmospheric correction algorithm may be more suitable for addressing different types of water environments. Additionally, more suitable retrieval algorithms are needed to improve the accuracy of Chl-a concentration and aph in eutrophic waters.
How to cite: Zhao, M., Li, H., Li, H., Zhang, X., Ding, X., and Gong, F.: Assessment of GOCI-II satellite remote sensing products in Lake Taihu, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2237, https://doi.org/10.5194/egusphere-egu25-2237, 2025.
