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Bangladesh is a global south hotspot due to climate change and sea level rise concerns. It is a highly disaster-prone country in the world with active deltaic shorelines. The shorelines are quickly changing to coastal accretion and erosion. Erosion is one of the water hazards to landmass sinking, and accretion relates to land level rises due to sediment load deposition on the Bay of Bengal continental shelf. Therefore, this study aimed to explore shoreline status with change assessment for the three study years 1991, 2006, and 2021 using satellite remote sensing and geographical information system (GIS) approaches. Landsat 5, 7 ETM+, and 8 OLI satellite imageries were employed for onshore tasseled cap transformation (TCT) and land and sea classification calculations to create shore boundaries, baseline assessment, land accretion, erosion, point distance, and near feature analysis. We converted 16,550 baseline vertices to points as the study ground reference points (GRPs) and validated those points using the country datasheet collected from the Survey of Bangladesh (SoB). We observed that the delta’s shorelines were changed, and the overall lands were accredited for the land-increasing characteristics analysis. The total accredited lands in the coastal areas observed during the time periods from 1991 to 2006 were 825.15 km2, from 2006 to 2021 was 756.69 km2, and from 1991 to 2021 was 1223.94 km2 for the 30-year period. Similarly, coastal erosion assessment analysis indicated that the results gained for the period 1991 to 2006 and 2006 to 2021 were 475.87 km2 and 682.75 km2, respectively. Therefore, the total coastal erosion was 800.72 km2 from 1991 to 2021. Neat accretion was 73.94 km2 for the 30-year period from 1991 to 2021. This research indicates the changes in shorelines, referring to the evidence for the delta’s active formation through accretion and erosion processes of ‘climate change’ and ‘sea level rise’. This research projects the erosion process and threatens land use changes toward agriculture and settlements in the coastal regions of Bangladesh.

This study analyzes laboratory data of beach response to sea‐level rise (SLR), isolating shoreline changes driven by passive flooding (PF) of the beach and consequent wave‐driven processes. The disequilibrium concept relates shoreline change to instantaneous and equilibrium beach states. While PF shifts the shoreline geometrically, SLR induces disequilibrium that produces wave‐driven changes due to apparent profile changes. For the first time, 24 experiments from wave flumes of different scale (including new high‐low energy cyclic waves experiments) are gathered into a dimensionless data set through a scaling technique to investigate SLR‐induced processes. The data indicate trends (possibly linear) between relative wave power and wave‐driven shoreline changes for a given SLR, highlighting the effects of changing background wave energy. Cyclic wave experiments best represent Bruun model's behavior. Wave‐energy dissipation emerges as a key variable for quantifying SLR‐induced disequilibrium, offering new pathways for future improvements of equilibrium shoreline models under SLR and wave‐climate change.

Coastal communities worldwide rely on shoreline models for risk assessment and management, yet these models often struggle to capture observed variability across different temporal scales. We analyzed 30 years of shoreline observations at Hasaki Beach, Japan, using Discrete Wavelet Transform to separate variation by timescale. Spectral analysis revealed wave‐driven annual and semi‐annual cycles, while long‐term trends contributed significantly to total variance. The ShoreFor model, when calibrated using the full 30‐year data set, severely underestimated seasonal variability. In contrast, 2‐year calibration windows successfully reproduced seasonal variations both within calibration periods and, after DWT‐based detrending, across the entire 30‐year validation period. Our findings demonstrate that short‐window calibration substantially enhances model capability for capturing wave‐driven seasonal shoreline changes, offering a practical solution for coastal risk assessment using limited observational data. This approach is particularly valuable given increasing availability of satellite‐derived shoreline data and the need for accurate seasonal predictions under changing climate conditions.

The potential response of shoreface depositional environments to sea level rise over the present century and beyond remains poorly understood. The shoreface is shaped by wave action across a sedimentary seabed and may aggrade or deflate depending on the balance between time-averaged wave energy and the availability and character of sediment, within the context of the inherited geological control. For embayed and accommodation-dominated coastal settings, where shoreline change is particularly sensitive to cross-shore sediment transport, whether the shoreface is a source or sink for coastal sediment during rising sea level may be a crucial determinant of future shoreline change. While simple equilibrium-based models (e.g. the Bruun Rule) are widely used in coastal risk planning practice to predict shoreline change due to sea level rise, the relevance of fundamental model assumptions to the shoreface depositional setting is often overlooked due to limited knowledge about the geomorphology of the nearshore seabed. We present high-resolution mapping of the shoreface-inner shelf in southeastern Australia from airborne lidar and vessel-based multibeam echosounder surveys, which reveals a more complex seabed than was previously known. The mapping data are used to interpret the extent, depositional character and morphodynamic state of the shoreface, by comparing the observed geomorphology to theoretical predictions from wave-driven sediment transport theory. The benefits of high-resolution seabed mapping for improving shoreline change predictions in practice are explored by comparing idealised shoreline change modelling based on our understanding of shoreface geomorphology and morphodynamics before and after the mapping exercise.

A geospatial analysis of shoreline change pattern is most significant parameter to understand the behavioral interaction between land and sea water. Geospatial analyses using various statistical and quantitative methods which are more applicable, accurate and dependable to measures the spatio-temporal trend of erosion accretion and estimate the change rate of shoreline. Remote sensing and GIS techniques have been used for the identification of shoreline change over the various time scales. To identify the rate Digital Shoreline Analysis System (DSAS) was applied in the current research. The present study aimed to identify the trend of coastal erosion accretion during 43 years (1975–2018) which is divided into four short term period (1975–1988, 1988–2000, 2000–2010 and 2010–2018) between the coastal stretch of Subarnarekha and Rasulpur estuary along Bay of Bengal using multi temporal satellite images. The accurate shoreline position has been delineated by the histogram threshold method using the images of Landsat Multi Spectral Scanner, Thematic Mapper and Enhanced Thematic Mapper. The shoreline change rate has been calculated based on cast transect method through some statistical techniques such as End Point Rate (EPR) and Net Shoreline Movement (NSM) in GIS application. 70.42 km long coastal stretch along Bay of Bengal has been divided into three littoral zones (LZ) to analyze the shoreline shifting on a zone basis. From the analysis it has been observed that maximum erosion occurred between 1988 and 2000 time period in all zones. The result shows that highest rate of net shoreline movement has been found in LZ I (− 1715.71 m) in 1975–1988 and LZ III (− 1719.65 m) in 2000–2010 at Subarnarekha estuary and Junput respectively. The present study reveals that most of the accretive formation is observed in 2000–2010 and 2010–2018. Major accretion is identified in the southern part of Subarnarekha estuary, 23.93 m/year in EPR method. Maximum shades of changes was experienced in LZ I, especially in Subarnarekha estuary area. In the time span of 1975–1988 this area faced the highest erosion and highest accretion with the rate of − 78.54 m/year and 23.93 m/year respectively through EPR method. During 1988–2000, − 37.35 m/year erosional rate was found in the Subarnarekha estuary. The highest erosional rate was − 8.48 m/year in Beguran Jalpai during 1988–2000 by EPR. The maximum rate of accretion has been noticed as 7.7 m/year in LZ II in the time period of 2010–2018.

Erosion poses a significant threat to coastal and estuarine environments worldwide and is further exacerbated by anthropogenic activities and increasing coastal hazards. While conventional engineered structures, such as seawalls and revetments, are commonly employed to protect shorelines from wave impact and erosion, they can also cause detrimental environmental effects. By creating/restoring coastal habitats with engineered structures, hybrid living shorelines offer coastal protection and other co-benefits. Using aerial imagery, we studied the rates of shoreline change before and after living shoreline installation, and between living shorelines and adjacent bare shorelines in three estuaries in New South Wales, Australia. Mangroves had established behind most rock fillets and displayed a trend of increasing canopy cover with fillet age. In the first 3 years since installation, the rates of lateral shoreline change reduced from − 0.20, − 0.16, and − 0.10 m/year to − 0.03, − 0.01, and 0.06 m/year in living shorelines in Hunter, Manning, and Richmond Rivers, respectively. However, when compared to control shorelines, the effectiveness in reducing erosion varied among living shorelines with mean effect sizes of 0.04, − 0.28, and 1.74 across the three estuaries. A more positive rate of shoreline change was associated with an increasing percentage of mangrove canopy area and an increasing length of protected shoreline at wide channels. While hybrid mangrove living shorelines are a promising solution for mitigating erosion and creating habitats at an estuary-wide scale, they may also contribute to downdrift erosion, emphasising the importance of considering site-specific hydrogeomorphology and sediment movement when installing living shorelines.

Coasts are critical ecological, economic and social interfaces between terrestrial and marine systems. The current upsurge in the acquisition and availability of remote sensing datasets, such as Landsat remote sensing data series, provides new opportunities for analyzing multi-decadal coastal changes and other components of coastal risk. The emergence of machine learning-based techniques represents a new trend that can support large-scale coastal monitoring and modeling using remote sensing big data. This study presents a comprehensive multi-decadal analysis of coastal changes for the period from 1990 to 2024 using Landsat remote sensing data series along the eastern and southern coasts of Peninsular Malaysia. These coastal regions include the states of Kelantan, Terengganu, Pahang, and Johor. An innovative approach combining deep learning-based shoreline extraction with the Digital Shoreline Analysis System (DSAS) was meticulously applied to the Landsat datasets. Two semantic segmentation models, U-Net and DeepLabV3+, were evaluated for automated shoreline delineation from the Landsat imagery, with U-Net demonstrating superior boundary precision and generalizability. The DSAS framework quantified shoreline change metrics—including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), and Linear Regression Rate (LRR)—across the states of Kelantan, Terengganu, Pahang, and Johor. The results reveal distinct spatial–temporal patterns: Kelantan exhibited the highest rates of shoreline change with erosion of −64.9 m/year and accretion of up to +47.6 m/year; Terengganu showed a moderated change partly due to recent coastal protection structures; Pahang displayed both significant erosion, particularly south of the Pahang River with rates of over −50 m/year, and accretion near river mouths; Johor’s coastline predominantly exhibited accretion, with NSM values of over +1900 m, linked to extensive land reclamation activities and natural sediment deposition, although local erosion was observed along the west coast. This research highlights emerging erosion hotspots and, in some regions, the impact of engineered coastal interventions, providing critical insights for sustainable coastal zone management in Malaysia’s monsoon-influenced tropical coastal environment. The integrated deep learning and DSAS approach applied to Landsat remote sensing data series provides a scalable and reproducible framework for long-term coastal monitoring and climate adaptation planning around the world.

Aangri, A.; Hakkou, M.; Krien, Y., and Benmohammadi, A., 2022. Predicting shoreline change for the Agadir and Taghazout coasts (Morocco). Journal of Coastal Research, 38(5), 937–950. Coconut Creek (Florida), ISSN 0749-0208. Prediction of the long-term shoreline change under the effect of natural and anthropogenic factors is a fundamental goal for coastal managers. This paper presents a simple model for predicting the shoreline change for the Agadir and Taghazout coasts, which consist of several sandy beaches, in the horizons 2050 and 2100; this coastal area plays a vital economic role in this region. The anticipation of its protection against erosion is necessary in view of the effects of climate change, and it will preserve its potential for tourism and urban development in the long term through the establishment of the construction setback line. The approach proposed here combines the long-term extrapolations of historical shoreline changes (1969–2020) in the future, and the estimate of the shoreline retreat due to sea level rise (SLR) by using Bruun's rule, using the severe RCP 8.5 scenario, and taking into account the contribution of vertical land movements. The model used has been calibrated through incorporating a correction factor (F), calculated by comparing observed and predicted data over a long period. The analysis of the predictions results provided in this work showing a potential risk of erosion threatening all seaside tourist and urban infrastructures along this coast. The average retreat is estimated at 12 m by 2050 and 51 m by 2100.

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into shoreline monitoring. Geospatial shoreline data created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020 were compared to contemporaneous field-surveyed Global Position System (GPS) data. WV-derived shorelines were found to have a mean difference of 2 ± 0.08 m of GPS data, but accuracy decreased at high-wave energy shorelines that were unvegetated, bordered by sandy beach or semi-submergent sand bars. Shoreline change rates calculated from WV imagery were comparable to those calculated from GPS surveys and geospatial data derived from aerial remote sensing but tended to overestimate shoreline erosion at highly erosive locations (greater than 2 m yr−1). High-resolution satellite imagery can increase the spatial scale-range of shoreline change monitoring, provide rapid response to estimate impacts of coastal erosion, and reduce cost of labor-intensive practices.

This study analysed long-term shoreline change, the influence of erosion, and adaptation strategies in coastal Ghana. The change between 1972 and 2021 was analysed using Landsat satellite images and the Digital Shoreline Analysis System (DSAS v.5.0), and adaptation strategies were revealed through field observations. The End Point Rate (EPR), Linear Regression Rate (LRR), and Weighted Linear Regression (WLR) were used to estimate the rate of change, whereas the Net Shoreline Movement (NSM) and Shoreline Change Envelope (SCE) were used to calculate the distances of change. The coefficient of shoreline armouring (K) index was used to evaluate the grade of artificial or human interventions on the coast. The erosion rates for the EPR (− 107.6 m/year, or 95.0%) and LRR (− 75.7 m/year, or 99.3%) were higher than the accretion rates of (28.5 m/year, or 5.0%) and (33.6 m/year, or 0.7%), respectively. The NSM recorded maximum erosion (− 14,080 to − 10,840 m) and accretion (1107 to 2135 m) with an average distance of − 4943.1 m. The SCE estimated a maximum (14,080.5 m) and minimum (813.8 m) distances with an average distance change of 5557.9 m. The central coast experienced erosion at average rates of − 119.0 m/yr, − 89.6 m/yr, and − 94.0 m/yr, according to EPR, LRR, and WLR statistics. The eastern coast observed lower erosion rates than the central coast, with rates of − 75.3 m/yr, − 53.2 m/yr, and − 41.9 m/yr for the EPR, LRR, and WLR statistics, respectively. Since 1972, there has been a significant increase in artificial coastal structures on Ghana's central and eastern coasts. The central coast has reached a maximal level of shoreline armouring index, while the eastern coast has reached a minimal level. Although hard protective measures have been implemented on most parts of the coast for adaptation, improved policy initiatives on soft and nature-based protection measures are encouraged based on their favourable ecological impact and economic effectiveness.