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The multi-scale representation of remote sensing images is crucial for information extraction, data analysis, and image processing. However, traditional methods such as image pyramid and image filtering often result in the loss of image details, particularly edge information, during the simplification and merging processes at different scales and resolutions. Furthermore, when applied to coastal landforms with rich texture features, such as biologically diverse areas covered with vegetation, these methods struggle to preserve the original texture characteristics. In this study, we propose a new method, multi-scale expression of coastal landforms considering texture features (METF-C), based on computer vision techniques. This method combines superpixel segmentation and texture transfer technology to improve the multi-scale representation of coastal landforms in remote sensing images. First, coastal landform elements are segmented using superpixel technology. Then, global merging is performed by selecting different classes of superpixels, with boundaries smoothed using median filtering and morphological operators. Finally, texture transfer is applied to create a fusion image that maintains both scale and level consistency. Experimental results demonstrate that METF-C outperforms traditional methods by effectively simplifying images while preserving important geomorphic features and maintaining global texture information across multiple scales. This approach offers significant improvements in edge preservation and texture retention, making it a valuable tool for analyzing coastal landforms in remote sensing imagery.

Kabuth, A.K.; Kroon, A., and Pedersen, J.B.T., 2014. Multidecadal shoreline changes in Denmark. Multidecadal shoreline changes along ca. 7000 km coastline around Denmark were computed for the time interval between 1862 AD and 2005 AD and were connected with a geomorphological coastal classification. The shoreline data set was based on shoreline positions from historical and modern topographic maps. Coastal landforms were identified on a digital terrain model in combination with aerial photographs. Two shoreline-change computation methods were evaluated at a test site, aiming for optimized time efficiency and accuracy of the countrywide application: a Nearest Neighbor search and a cross-shore transect method based on the ArcGIS-based Digital Shoreline Analysis System (DSAS). The cross-shore transect method was more robust and performed better in the detection of local extremes in shoreline changes, which was crucial for the scope of the mapping. Countrywide shoreline-change distances and rates were, therefore, computed with the DSAS method. Patterns in coastline dynamics were identified through the connection of shoreline-change rates with the occurrence of coastal landforms. Short-term changes and alterations of shoreline evolution through coastal structures were not resolved in this study. Because of the long time span covered, the relative errors originating from data and method are acceptable. The scope of the mapping was to provide a coastal management tool that allows screening for critical sites with respect to coastal erosion. As the first countrywide quantification of historical shoreline changes around Denmark, the mapping can contribute to enhanced adaptation and mitigation strategies in response to increased risks of erosion and flooding under a changing climate.

Because mangroves store greater amounts of carbon (C) per area than any other terrestrial ecosystem, conservation of mangrove forests on a global scale represents a potentially meaningful strategy for mitigating atmospheric greenhouse-gas (GHG) emissions. However, analyses of how coastal ecosystems influence the global C cycle also require the mapping of ecosystem area across the Earth’s surface to estimate C storage and flux (movement) in order to compare how different ecosystem types may mitigate GHG enrichment in the atmosphere. In this paper, we propose a new framework based on diverse coastal morphology (that is, different coastal environmental settings resulting from how rivers, tides, waves, and climate have shaped coastal landforms) to explain global variations in mangrove C storage, using soil organic carbon (SOC) as a model to more accurately determine mangrove contributions to global C dynamics. We present, to the best of our knowledge, the first global mangrove area estimate occupying distinct coastal environmental settings, comparing the role of terrigenous and carbonate settings as global “blue carbon” hotspots. C storage in deltaic settings has been overestimated, while SOC stocks in carbonate settings have been underestimated by up to 50%. We encourage the scientific community, which has largely focused on blue carbon estimates, to incorporate coastal environmental settings into their evaluations of C stocks, to obtain more robust estimates of global C stocks.

Given their relatively small area, mangroves and their organic sediments are of disproportionate importance to global carbon sequestration and carbon storage. Peat deposition and preservation allows some mangroves to accrete vertically and keep pace with sea-level rise by growing on their own root remains. In this study we show that mangroves in desert inlets in the coasts of the Baja California have been accumulating root peat for nearly 2,000 y and harbor a belowground carbon content of 900–34,00 Mg C/ha, with an average value of 1,130 (± 128) Mg C/ha, and a belowground carbon accumulation similar to that found under some of the tallest tropical mangroves in the Mexican Pacific coast. The depth–age curve for the mangrove sediments of Baja California indicates that sea level in the peninsula has been rising at a mean rate of 0.70 mm/y (± 0.07) during the last 17 centuries, a value similar to the rates of sea-level rise estimated for the Caribbean during a comparable period. By accreting on their own accumulated peat, these desert mangroves store large amounts of carbon in their sediments. We estimate that mangroves and halophyte scrubs in Mexico’s arid northwest, with less than 1% of the terrestrial area, store in their belowground sediments around 28% of the total belowground carbon pool of the whole region.

Large earthquakes can instantaneously reshape coastal landforms owing to fault zone ruptures that uplift the Earth’s surface. On January 1, 2024, in the north of the Noto Peninsula, central Japan, an Mj7.6 (Mw7.5) earthquake occurred, triggering coastal uplift of up to 4 m. To measure the resulting shoreline advance, we analyzed orthophotos taken before and after the earthquake, focusing on two bays in the northwest of the Noto Peninsula where the largest uplift occurred. In response to the uplift, the shoreline advanced by up to 200 m, increasing the total area of the coastal plains by 0.46 km 2 . The maximum shoreline extension occurred in the midsection of both bays, while the extension at the edges was less than 20 m, possibly reflecting the shoreface topography and bathymetry existing before the uplift. The uplift exposed previously undersea rocks, forming new coastal plains and extending river channels. Our results indicate that coastal landforms such as sandy beaches, coastal plains, shore platforms, and the sediment budgets of feeding drainage systems were substantially altered by this earthquake, and a long recovery period is anticipated. Our findings serve as a crucial benchmark for tracking future changes in shorelines in response to coastal landform adjustments.

We present an overview of tsunami occurrences based on an analysis of a global database of tsunamis for the period 1900–2020. We evaluate the geographic and statistical distribution of various tsunami source mechanisms, high-fatality tsunamis, maximum water heights (MWHs) of tsunamis, and possible biases in the observation and recording of tsunami events. We enhance a global statistical overview with case studies from Indonesia, where tsunamis are generated from a diverse range of sources, including subduction zones, crustal faults, landslides, and volcanic islands. While 80% of global recorded tsunamis during 1900–2020 have been attributed to earthquake sources, the median MWH of earthquake tsunamis is just 0.4 m. In contrast, the median water height of landslide tsunamis is 4 m. Landslides have caused or contributed to 24% of fatal tsunamis. During 1900–2020, more tsunamis with water heights > 1 m occurred in Indonesia than in any other country. In this region fatal tsunamis are caused by subduction zone earthquakes, landslides, volcanos, and intraplate crustal earthquakes. Landslide and volcano tsunami sources, as well as coastal landforms such as narrow embayments have caused high local maximum water heights and numerous fatalities in Indonesia. Tsunami hazards are increased in this region due to the densely populated and extensive coastal zones, as well as sea level rise from polar ice melt and local subsidence. Interrelated and often extreme natural hazards in this region present both an opportunity and a need to better understand a broader range of tsunami processes.

Strong earthquakes along subduction zones are often devastating events, but sparse records along some tectonic margins limit our understanding of seismic hazards. Constraining shaking intensities is critical, especially in subduction zones with infrequent but large‐magnitude earthquakes like the Cascadia Subduction Zone (CSZ), where the lack of recorded ground motions has led to uncertainty in the severity and potential impacts of future earthquakes. Here we fill this observational gap with a novel inventory of quantitative estimates of past shaking intensities from geotechnical modeling of coastal landforms. One hundred fifty‐four deep‐seated landslides and 65 fragile geologic features constrain minimum and maximum peak ground accelerations, respectively. These estimates are broadly consistent with model predictions of M9 ruptures, suggesting strong shaking of 0.4–0.8 g during past CSZ earthquakes. Local discrepancies between our geologic shaking constraints and earthquake simulations may inform past rupture behavior, leading to better predictions of shaking intensity for future earthquakes. Plain Language Summary Strong subduction zone earthquakes are a major hazard capable of generating damaging ground shaking and landslides across widespread regions. In subduction zones with few or no observations of recent events, such as the Cascadia Subduction Zone (CSZ) offshore the Pacific Northwest U.S. and Canada, the severity of these hazards are particularly uncertain. Here we develop a methodology for estimating shaking intensities from past earthquakes using coastal landslides. Modeling a range of representative coastal hillslopes allows us to identify landslides most likely triggered by past earthquakes, as well as an estimate of the minimum shaking intensity during those events in each location. Minimum shaking intensities from landslides are combined with a complementary set of maximum shaking intensity estimates from intact sea stacks to comprehensively constrain shaking intensities along much of the CSZ. Although these shaking estimates mostly agree with recent earthquake simulations in the region, local discrepancies may indicate variations in past earthquake rupture style. Key Points Modeling of coastal landslides and fragile geologic features constrain shaking intensity from past earthquakes Peak ground accelerations from past Cascadia Subduction Zone earthquakes range from ∼0.4 to 0.8 g across much of the margin Along the central Cascadia coastline, our results suggest stronger shaking has occurred than some earthquake simulations predict

Coastal landforms, particularly sea cliffs and associated wave-cut platforms, preserve key evidence of past sea-level fluctuations, tectonic activity, and paleoclimate variability. In this study, we implement a supervised machine learning approach, trained on an original, expert-labeled geomorphological dataset, to detect and classify inherited and active coastal features - such as paleo-sea cliffs and polycyclic sea cliffs - along the south-Tyrrhenian. Using DTM and morphometric indicators, our model, based on a RandomForestClassifier trained on expert-based cartography and independently validated, accurately identifies the spatial signatures of Quaternary coastal evolution. These results are cross validated against independent geomorphological mapping and sea-level reconstruction datasets. The integration of geomorphological classification with sea level markers enables us to reconstruct coastal morphogenesis in relation to the last interglacial cycle. Our findings highlight the potential of machine learning to automate the identification of coastal paleo-landscapes, providing insight into the imprint of climatic forcing on their morphology. This approach offers a scalable framework for investigating past climate–landscape interactions and for supporting future coastal hazard assessments under changing climate conditions.

Relic coastal landforms (fossil corals, cemented intertidal deposits, or erosive features carved onto rock coasts) serve as sea‐level index points (SLIPs), that are widely used to reconstruct past sea‐level changes. Traditional SLIP‐based sea‐level reconstructions face challenges in capturing continuous sea‐level variability and dating erosional SLIPs, such as tidal notches. Here, we propose a novel approach to such challenges. We use a numerical model of cliff erosion embedded within a Monte Carlo simulation to investigate the most likely sea‐level scenarios responsible for shaping one of the best‐preserved tidal notches of Last Interglacial age in Sardinia, Italy. Results align with Glacial Isostatic Adjustment model predictions, indicating that synchronized or out‐of‐sync ice‐volume shifts in Antarctic and Greenland ice sheets can reproduce the notch morphology, with sea level confidently peaking at 6 m and only under a higher than present erosion regime. This new approach yields insight into sea‐level trends during the Last Interglacial. Plain Language Summary Scientists typically investigate the position of sea level in geological time using the elevation, age, and characteristics of fossil marine organisms living in shallow water (e.g., coral reefs), beach deposits, or erosional features that were formed near the sea level. However, these indicators offer only fragmented, if not only point‐like information in time and not a continuous sea‐level record. To overcome this issue, we use a numerical model that reconstructs the shape of tidal notches (i.e., indentations created close to sea level in carbonate cliffs). We compare model‐generated notch shapes with the real shape of the tidal notch, and we produce a set of continuous sea‐level histories that are more likely to have produced one of the best‐preserved fossil tidal notches in the Orosei Gulf, Sardinia, Italy, carved during the Last Interglacial highstand, 125.000 years ago. Our findings suggest that whether the ice sheets in Antarctica and Greenland melted at the same time or separately, both scenarios could reproduce the actual shape of the tidal notch we observe at present. Our findings indicate that the erosion rate during that period was higher than present and the sea level is very likely to have reached up to 6 m. Key Points Cliff erosion modeling and Monte Carlo analysis indicate tidal notch geometry can offer a continuous record of past sea level variability The geometry of Orosei’s tidal notch, Italy can be replicated through simultaneous or asynchronous Antarctic–Greenland ice melting scenarios The morphology of the Last Interglacial notch is more efficiently replicated using higher‐than‐present erosion rates and a 6 m sea‐level peak

Can the magnitude of a giant earthquake be estimated from paleoseismological data alone? Attempts to estimate the size of the Jogan earthquake of AD 869, whose tsunami affected much of the same coast as the 2011 Tohoku tsunami, offers an excellent opportunity to address this question, which is fundamental to assessing earthquake and tsunami hazards at subduction zones. Between 2004 and 2010, examining stratigraphy at 399 locations beneath paddy fields along 180 km of coast mainly south of Sendai, we learned that a tsunami deposit associated with the AD 869 Jogan earthquake had run inland at least 1.5 km across multiple coastal lowlands, and that one of the lowlands had subsided during the Jogan earthquake and an earlier earthquake as well. Radiocarbon ages just below/above sand deposits left by the pre‐Jogan tsunamis suggested recurrence intervals in the range of 500 to 800 years. Modeling inundation and subsidence, we estimated size of the Jogan earthquake as moment magnitude 8.4 or larger and a fault rupture area 200 km long. We did not consider a longer rupture, like the one in 2011, because coastal landform and absence of a volcanic ash layer make any Jogan layer difficult to identify along the Sanriku coast. Still, Sendai tsunami geology might have reduced casualties by improving evacuation maps and informing public‐awareness campaigns. Key Points Even excellent geology can't lead conclusion of the largest possible earthquake These efforts showed recurrence intervals shorter than previously inferred Geological data for coastal subsidence aids in modeling source of AD869 tsunami