A Model Integrating Satellite‐Derived Shoreline Observations for Predicting Fine‐Scale Shoreline Response to Waves and Sea‐Level Rise Across Large Coastal Regions

Journal of Geophysical Research Earth Surface, Journal Year: 2023, Volume and Issue: 128(7)

Published: May 29, 2023

Abstract Satellite‐derived shoreline observations combined with dynamic models enable fine‐scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite‐data‐assimilated, “littoral‐cell”‐based, ensemble Kalman‐filter model to predict and uncertainty due waves, sea‐level rise (SLR), other natural anthropogenic processes. We apply the developed entire California coastline (approximately 1,760 km), much which is sparsely monitored traditional survey methods (e.g., Lidar/GPS). Water‐level‐corrected, satellite‐derived (obtained from CoastSat toolbox) offer nearly unbiased representation in situ surveyed shorelines mean elevation contours) at Ocean Beach, San Francisco. demonstrate that calibration satellite during 20‐year hindcast period (1995–2015) provides equivalent forecast accuracy validation (2015–2020) compared monthly Beach. When comparing model‐predicted positions observations, achieves an <10 m RMSE for half period. The calibrated/validated then applied multi‐decadal simulations projected wave conditions, while holding parameters fixed. By 2100, estimates 24%–75% California's beaches may become completely eroded SLR scenarios 1.0–3.0 m, respectively. satellite‐data‐assimilated modeling system presented here generally applicable variety settings around world owing global coverage imagery.

Language: Английский

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New Methodology for Shoreline Extraction Using Optical and Radar (SAR) Satellite Imagery

Journal of Marine Science and Engineering, Journal Year: 2023, Volume and Issue: 11(3), P. 627 - 627

Published: March 16, 2023

Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Erosional trends have unfortunately been intensifying for decades due anthropic factors and an accelerated sea level rise might exacerbate the problem. It is crucial preserve these areas safeguarding not only coastal ecosystems cultural heritage, but also population living there. In this context, monitoring essential geomatics techniques, especially satellite remote sensing imagery, prove very advantageous. paper, a semi-automatic methodology extract shorelines from SAR (Synthetic Aperture Radar) Sentinel-1 optical Sentinel-2 images was developed. An experimental algorithm, called J-Net Dynamic, tested in two pilot sites. The validated with GNSS (Global Navigation Satellite System) reference demonstrated be powerful tool robust extraction of shoreline both images. algorithm able closer on natural beach Castelldefels it less sensitive speckle effects than commonly used Canny Edge Detector. Using urban Somorrostro, detector shoreline, while new could do low accuracy because noise induced by man-made structures. For further investigation, Sentinel-2-extracted were compared ones extracted state-of-the-art tool, CoastSat, beaches using automatic manual thresholds. mean errors obtained Dynamic generally higher CoastSat threshold lower if one. proposed including proves most cases offers great advantage being work This feature allow reduce time lag between derived paving way enhanced management areas.

Language: Английский

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Long-term shoreline changes at large spatial scales at the Baltic Sea: remote-sensing based assessment and potential drivers

Frontiers in Marine Science, Journal Year: 2023, Volume and Issue: 10

Published: June 27, 2023

In this study, we demonstrate how freely available satellite images can be used to understand large-scale coastline developments along the coast of Mecklenburg-Western Pomerania (MWP). By validating resulting dataset with an independent Light Detection and Ranging (LIDAR) dataset, achieved a high level accuracy for calculation rates change (ROC) root mean square error (RMSE) up 0.91 m, highlighting reliability Earth observation data purpose. The study assessed coastal system’s natural evolution from 1984 1990, prior significant human interventions, examined period 1996 2022, which was characterized by regular sand nourishments amounting approximately 16 million m³. results reveal notable changes in area, decline erosive trends increase number stable accreting transects. However, it is important note that may masking true ROC coastline. Furthermore, future supply raises concerns about sustainability developments, particularly context sea rise (SLR). provides valuable insights authorities policymakers, informing decisions on resource allocation need appropriate adaptation strategies address SLR ensure long-term resilience.

Language: Английский

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Shoreline Seasonality of California's Beaches

Journal of Geophysical Research Earth Surface, Journal Year: 2025, Volume and Issue: 130(2)

Published: Jan. 30, 2025

Abstract We report on remote sensing techniques developed to characterize seasonal shoreline cycles from satellite‐derived measurements. These are applied 22‐yr of measurements for over 777 km beach along California's 1,700‐km coast, which the general understanding is that shorelines exhibit winter‐narrow and summer‐recovery seasonality. find approximately 90% transects significant recurring in position. Seasonal excursions twice as large northern central California (17.5–32.2 m) than southern (7.3–15.9 m; interquartile ranges). Clustering analyses were effective at characterizing temporal patterns seasonality, revealing ∼459 (59%) conditions, whereas ∼189 (24%) ∼50 (6.4%) spring‐narrow summer‐narrow respectively. spring‐ conditions most common California, where they represent half total length shoreline. Multivariate reveal wave climate geomorphic setting significantly related magnitude timing cycles. Combinations these variables explain 44% seasonality variance complete data set 85% a subset 93 long (>1 km) continuous beaches. conclude diversity waves cause broad range Combined, this indicates overly generalized “winter‐narrow/summer‐recovery” conventions beaches not expressed universally far more diverse simple canonical rules.

Language: Английский

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Wave driven cross shore and alongshore transport reveal more extreme projections of shoreline change in island environments

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

Coastal erosion, intensified by sea level rise, poses significant threats to coastal communities in Hawaiʻi and similar island communities. This study projects long-term shoreline change on the Hawaiian Island of O'ahu using data-assimilated CoSMoS-COAST model. models four key processes: (1) Alongshore transport, (2) Recession due (3) Cross-shore transport waves, (4) Residual processes represented a linear trend term. marks first application for an oceanic equatorial with narrow beaches dynamic wave climate. The model is informed novel combination data derived from high-resolution imagery Planet, Sentinel-2, Landsat satellites, wave-climate hindcasts specific Hawai'i, regional beach-slope surveys. On northern Oʻahu beach, achieved root mean square error 9.4 m between observations output. predicts that 81% O'ahu's sandy beach coastline could experience loss 2100; 39.8% this happening 2030. represents increase, 43.3%, net landward compared previous erosion forecasts, 0.3 rise (2050). Additionally, such as cross-shore equilibrium alongshore sediment play large contribution gross within next decade, particularly O 'ahu's north west shores. In long term, we find recession residual dominate, but dynamic, wave-driven (longshore transport) still account 34% present 2100. We assert are crucial addition accurate modeling environments. These findings have implications planning development, suggesting updates policies rely upon forecasting, highlights importance incorporating other Pacific islands.

Language: Английский

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Satellite Altimetry for Ocean and Coastal Applications: A Review

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(16), P. 3939 - 3939

Published: Aug. 9, 2023

More than 30 years of observations from an international suite satellite altimeter missions continue to provide key data enabling research discoveries and a broad spectrum operational user-driven applications. These were designed advance technologies answer scientific questions about ocean circulation, heat content, the impact climate change on these Earth systems. They are also valuable resource for needs oceanographic weather forecasting agencies that information shipping fishing vessels offshore operations route optimization safety, as well other decision makers in coastal, water resources, disaster management fields. This time series precise measurements surface topography (OST)—the “hills valleys” surface—reveals changes dynamic topography, tracks sea level variations at global regional scales, provides trends reflecting our warming world. Advancing new systems allows higher spatial resolution ever closer coastlines, where impacts storms, waves, rise coastal communities infrastructure manifest. We review some collaborative efforts space agencies, including NASA, CNES, NOAA, ESA, EUMETSAT, which have contributed collection use cases altimetry decision-support contexts. The extended obtained missions, along with advances technology allowed nearer coasts, has enabled range such resulting body knowledge enables better assessments amongst contributions societal benefit. Although not exhaustive, this overview specific examples important role applications, thus justifying significant made by development missions.

Language: Английский

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Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(4), P. 638 - 638

Published: April 10, 2024

The integration of machine learning (ML) techniques in coastal engineering marks a paradigm shift how processes are modeled and understood. While traditional empirical numerical models have been stalwarts simulating phenomena, the burgeoning complexity computational demands paved way for data-driven approaches to take center stage. This review underscores increasing preference ML methods engineering, particularly predictive tasks like wave pattern prediction, water level fluctuation, morphology change. Although scope this is not exhaustive, it aims spotlight recent advancements capacity harness vast datasets more efficient cost-effective simulations dynamics. However, challenges persist, including issues related data availability quality, algorithm selection, model generalization. entails addressing fundamental questions about quantity determining optimal methodologies specific problems, refining training validation. reviewed literature paints promising picture future where only complements but significantly enhances our ability predict manage intricate dynamics environments.

Language: Английский

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Joining Application of Unmanned Aerial Vehicle Imagery with GIS for Monitoring of Soft Cliff Linear Habitats

Journal of Marine Science and Engineering, Journal Year: 2025, Volume and Issue: 13(1), P. 80 - 80

Published: Jan. 5, 2025

In the coastal zone, two types of habitats—linear and areal—are distinguished. The main differences between both are their shape structure hydro- litho-dynamic, salinity, ecological gradients. Studying linear littoral habitats is essential for interpreting ’coastal squeeze’ effect. study’s objective was to assess short-term behavior soft cliffs as during calm season storm events in example Olandų Kepurė cliff, located on a peri-urban protected seashore (Baltic Sea, Lithuania). approach combined surveillance cliff using unmanned aerial vehicles (UAVs) with data analysis an ArcGIS algorithm specially adjusted habitats. authors discerned forms—cliff base cavities scarp slumps. slumps more widely spread. It particularly noticeable at beginning spring–summer period when difference occurrence forms 3.5 times. contrast, proliferate spring. This phenomenon might be related seasonal Baltic Sea level rise. conclusion that 55 m long cells optimal analyzing UAV GIS.

Language: Английский

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Monitoring Sea Surface Temperature and Sea Surface Salinity Around the Maltese Islands Using Sentinel-2 Imagery and the Random Forest Algorithm

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 929 - 929

Published: Jan. 18, 2025

Marine regions are undergoing rapid evolution, primarily driven by natural and anthropogenic activities. Safeguarding these ecosystems necessitates the ability to observe their physical features control processes with precision in both space time. This demands acquisition of precise up-to-date information regarding several marine parameters. Thus, gain a comprehensive understanding ecosystems, this study employs remote sensing techniques, Machine Learning algorithms traditional situ approaches. Together, serve as valuable tools help comprehend distinctive parametric characteristics mechanisms occurring within Maltese archipelago. An empirical workflow was implemented predict spatial temporal variations sea surface salinity temperature from 2022 2024. achieved leveraging Sentinel-2 satellite platforms, random forest algorithm, data collected gliders floats. Subsequently, numerical generated algorithm were validated different error metrics converted into visual representations illustrate across Islands. The demonstrated strong performance predicting temperature, indicating its capability handle dynamic parameters effectively. Additionally, maps for all three years provided clear changes two

Language: Английский

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A remote monitoring approach for coastal engineering projects

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 23, 2025

High costs and project-based (short-term) financing mean that coastal engineering projects are often undertaken in the absence of appropriate post-construction monitoring programmes. Consequently, performance shoreline-stabilizing structures or beach nourishments cannot be properly quantified. Given high value beaches increase erosion problems responses, managers require as much accurate data possible to support efficient decision-making. This work presents a methodological approach characterise coastline position changes result actions. We describe new, low-cost method based on satellite remote sensing monitor shoreline evolution at temporal spatial resolution pre-, during post-implementation. Initially, satellite-derived waterlines identified extracted from publicly available imagery Landsat 5, 7, 8, 9, Sentinel-2 constellations using automatic extraction tool SHOREX. The waterline positions then compiled, differences over time quantified, matrix is constructed allows easy depiction interpretation patterns erosion/accretion. access comprehension morphological by non-expert. Two examples application Valencian coast Spain different scales demonstrate how response actions can characterised levels detail (from local regional) periods time. These applications evidence utility it analysis pre- post-intervention change offers means overcome widespread lack hence improve practice.

Language: Английский

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