Search Results (224)

This study investigated vegetation frontline dynamics, fractional vegetation cover (FVC), and community succession in the tidal-flat wetlands of the Liaohe Estuary. The eastern bank of the Liaohe River within the Shuangtaihe National Nature Reserve was selected as the study area, and six periods of Landsat and Gaofen-1 (GF-1) imagery from 2000 to 2025 were used. Remote-sensing preprocessing, normalized difference vegetation index (NDVI)-based FVC inversion, vegetation frontline extraction, Digital Shoreline Analysis System (DSAS)-based rate calculation, land-cover classification, and spatial correlation analysis were integrated to characterize wetland spatiotemporal dynamics and succession patterns. The results showed that the linear regression rate (LRR) and end point rate (EPR) effectively captured the long-term trend and five short-term fluctuations in vegetation frontline migration. FVC fluctuated markedly over the 25-year period, whereas the weighted average (WA) of the five FVC classes remained generally stable and effectively summarized overall vegetation growth. Vegetation frontline migration was spatially associated with annual FVC change (ΔFVC); both LRR and ΔFVC showed significant positive spatial autocorrelation and evident spatial clustering. In addition, the conversion among mudflats, Suaeda salsa, Phragmites australis, and water bodies was closely coupled with frontline migration. These findings provide a scientific basis for quantifying coastal wetland sustainability and for designing spatially targeted restoration strategies in the Liaohe Estuary. The proposed coupling analysis framework also offers a transferable remote sensing approach for monitoring wetland sustainability under changing environmental conditions. Full article

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

Land-use and land-cover changes around reservoirs mediate the interface between watershed land systems and managed surface-water resources. In regulated reservoirs, water-level regulation can rapidly expose or inundate shore-zone land, yet evidence remains limited on where these transitions occur, how landscape configuration changes, and how such information can inform watershed and reservoir-margin management. Using 0.5 m Jilin-1 optical imagery from April and September of 2024 and 2025, this study mapped land-use/land-cover change (LUCC) in the Danjiangkou Reservoir shore zone and integrated transition matrices, class-level landscape metrics, shoreline-distance gradients, reach-level zoning, paired hydrological records, and multiscale geographically weighted regression (MGWR). The classification achieved an overall accuracy of 93.1% and a Kappa coefficient of 0.921. The strongest land-cover shift occurred between September 2024 and April 2025, when the water proportion declined from 78.74% to 60.10% and bare land expanded during the lowest observed reservoir stage (151.02 m). Subsequent refill was accompanied by partial re-inundation and increases in grassland, cropland, and forest. The 0–30 m shoreline belt was the principal response zone, indicating that hydrologically driven land-cover replacement was concentrated in the immediate reservoir margin. MGWR showed spatially varying positive associations between change-patch characteristics, distance to permanent water, and elevation, but the low explanatory power requires these results to be interpreted as spatial diagnostics rather than causal attribution. The study links land-cover monitoring with reservoir water-level regulation, identifies priority shoreline belts, and provides spatial information for field verification and reservoir-margin management. Full article

The intertidal beach profile provides a fundamental representation of beach morphology and serves as a key indicator of shoreline morphodynamics. To enable frequent and accurate mapping of intertidal beach profiles, this study proposes an automated reconstruction framework that integrates single-pixel image columns with a stacked bidirectional long short-term memory (Bi-LSTM) network. Time-exposure imagery, commonly referred to as Timex imagery, acquired from a shore-based video monitoring station at Xisha Bay, China, is used as the primary data source, while wave records obtained from a wave buoy are incorporated to assign elevations to the detected waterline breakpoints, thereby enabling automatic beach profile reconstruction. The stacked Bi-LSTM network is trained for land–sea segmentation and waterline breakpoint localization. achieving the best performance among the tested methods, with precision, recall, accuracy, and F1 score values of 0.951, 0.894, 0.978, and 0.903, respectively, and a mean breakpoint localization error of 2.23 pixels. Breakpoint elevations were then estimated using a local slope–wave setup attribution model. Validation against field-measured topographic data from four fixed profiles and three survey periods showed good agreement between the reconstructed and measured profiles, with a period-based root mean square error (RMSE) of 0.212 ± 0.080 m. When all validation points were combined, the reconstructed elevations showed strong agreement with the measured elevations, with a coefficient of determination (R²) of 0.988 and an overall RMSE of 0.24 m. The profile comparisons further showed that the reconstructed profiles generally captured the overall profile shape and cross-shore morphological pattern of the measured profiles, although reconstruction accuracy varied among the four fixed profiles. These differences demonstrate that camera viewing angle, field-of-view position, camera-to-profile distance, and image quality are important factors influencing video-derived beach profile reconstruction. These results indicate that the proposed method can directly reconstruct fixed intertidal beach profiles from shore-based Timex imagery without generating a digital elevation model of the entire intertidal zone. It provides a practical tool for high-frequency monitoring of intertidal profile morphology and supports the quantitative analysis of beach erosion–accretion dynamics. Full article

Semi-mangrove shrubs are important indicators of change in temperate–subtropical coastal ecotones and provide conservation-relevant habitats in shoreline transition zones. On Jeju Island, South Korea, the distribution of two key semi-mangrove species (Hibiscus hamabo and Paliurus ramosissimus) remains incompletely documented despite their monitoring value. Because these shrubs occur as narrow, fragmented patches that are difficult to delineate in satellite imagery, they may be omitted from coarse-resolution inventories. Here, we produced high-resolution semi-mangrove maps from 1 cm UAV RGB orthomosaics using a lightweight Tiny U-Net semantic segmentation model trained on field-confirmed, expert-digitized polygons from nine coastal sites. Model performance was evaluated using a site-wise training, validation, and test split. The final model achieved a pooled semi-mangrove IoU of 0.677, balanced accuracy of 0.921, precision of 0.771, recall of 0.848, and a false-positive rate of 0.007, despite the low semi-mangrove prevalence of 2.59%. On the independent test site, Tiny U-Net also outperformed standard U-Net with fewer parameters and shorter training time (IoU = 0.873 vs. 0.568; 1.9 M vs. 31.4 M parameters; 37 vs. 123 min). Probability outputs also highlighted high-confidence candidate patches outside of the labeled polygons, supporting targeted field verification and iterative inventory refinement. This UAV–deep learning workflow provides a practical baseline for fine-scale habitat assessment and repeat monitoring of vegetation dynamics along Jeju’s temperate–subtropical coast. Full article

Sandy shorelines respond to variability in boundary conditions over a wide range of time and spatial scales. While recent studies show that climate modes may affect shoreline evolution at interannual scales, such relationships remain unclear in the South Atlantic Ocean. Here, we investigate whether climate mode-driven variability in wave climate influences shoreline evolution using Ilha Comprida, a barrier island on the southeastern Brazilian coast, as a case study. Offshore wave conditions from the ERA5 reanalysis were analyzed over the last four decades and propagated to the nearshore using wave modeling. Shoreline change was quantified from satellite-derived shoreline positions, and relationships with interannual climate modes were evaluated using climate indices. Results show that the wave climate is bimodal and dominated by swell, with strong seasonality and no significant long-term trend in storminess. The El Niño–Southern Oscillation (ENSO) influences wave energy and extremes, with La Niña phases associated with higher wave power without a change in wave direction. No significant signal of the Southern Annular Mode (SAM) was found. At the coast, shoreline evolution is controlled by long-term sediment redistribution driven by alongshore transport gradients. ENSO-related shoreline signals are weak and spatially limited, occurring only in lower Empirical Orthogonal Function (EOF) modes of variability. These results suggest that, at Ilha Comprida, ENSO mainly modulates episodic wave-driven events rather than long-term shoreline patterns, emphasizing the need to distinguish between short-term energetic variability and longer-term morphodynamic response. This distinction is important for coastal management because even where climate modes do not produce persistent long-term shoreline trends due to site-specific aspects, they may still modulate event-scale risk, which can vary independently of the long-term average shoreline behavior. Full article

Shoreline change detection from remote sensing imagery remains challenging in environments subject to water level fluctuations, as remotely sensed shoreline positions reflect instantaneous hydrodynamic states rather than true geomorphic change. In the Great Lakes, seasonal and short-term water level variations can produce apparent shoreline shifts unrelated to sediment dynamics. Reliable calibration with bathymetry and water level data can mitigate this effect, but such data are often unavailable or difficult to obtain for many coastal and lacustrine systems worldwide. To address this limitation, we proposed a morphology-based framework that quantifies geometric change between successive shoreline curves using a discrete Fréchet distance, a modified Euclidean distance and a Union distance metric. Rather than relying solely on cross-shore displacements, the approach leverages shape similarity to differentiate water-level-driven shifts from true morphological change. We evaluated the framework across three spatial scales (100 m, 500 m, and 1000 m) along 125 km of southwestern Lake Michigan coastline using 2010 and 2020 aerial imagery, benchmarking against water-level-calibrated DSAS erosion hotspots. The Fréchet distance improved monotonically with scale, achieving strong agreement at 1000 m (F1 = 0.84, Spearman $\rho$ = 0.79) but limited reliability at 100 m. While individual morphology-based metrics appeared competitive with or inferior to uncalibrated DSAS at each scale, the union of both distances substantially outperformed uncalibrated DSAS at management-relevant scales (F1 of 0.64 vs. 0.50 at 500 m and 0.79 vs. 0.42 at 1000 m), reflecting the complementary nature of shape-based and displacement-based detection. The Patient Rule Induction Method (PRIM) further identified gentle nearshore slopes and moderate separation from engineered structures as the geomorphic conditions under which the morphology-based and calibrated erosion indicators converged most closely (in-box F1 = 0.92 at 1000 m and 0.72 at 500 m). These results suggest that the proposed framework, particularly the complementary union of both metrics, provides a practical, calibration-free alternative for multiscale shoreline change screening in lacustrine and microtidal, data-limited environments, while local-scale applications still benefit from explicit water-level correction. Full article

This study examines the design and pedagogical evaluation of Extended Reality (XR) applications, with a primary focus on location-based Augmented Reality (AR). The XR applications were implemented within an environmental education program delivered by the Education Center for the Environment and Sustainability (E.S.E.C.) of Kastoria, aiming to enhance students’ understanding of lake ecosystems and environmental awareness through immersive, situated learning experiences. The development followed the ADDIE instructional design framework and was grounded in principles of experiential and situated learning. The educational intervention was conducted in an authentic field setting along the shoreline of Lake Kastoria and combined location-based AR activities with complementary immersive VR experiences. Evaluation data were collected through a structured questionnaire administered to 271 primary and secondary school students, employing XR-relevant constructs including Challenge/Satisfaction/Enjoyment, Ease of Use, Usefulness/Knowledge, Experiential and Situated Learning, Interaction/Collaboration, and Intention to Reuse. In addition, accompanying teachers provided supplementary qualitative feedback to support the interpretation of the findings under authentic field conditions. Descriptive statistical analysis indicated consistently high scores across all constructs (M = 3.27–4.40, SD = 0.41–0.64). Pearson correlation analysis revealed strong associations between Experiential/Situated Learning and Usefulness/Knowledge (r = 0.737), Experiential/Situated Learning and Challenge/Satisfaction/Enjoyment (r = 0.642), Intention to Reuse and Challenge/Satisfaction/Enjoyment (r = 0.635), as well as Usefulness/Knowledge and Challenge/Satisfaction/Enjoyment (r = 0.619). Multiple regression analyses further supported key relationships, including Usefulness/Knowledge as a predictor of Experiential/Situated Learning (β = 0.57, p < 0.001), Experiential/Situated Learning as a predictor of Challenge/Satisfaction/Enjoyment (β = 0.47, p < 0.001), and Interaction/Collaboration as a predictor of Intention to Reuse (β = 0.31, p < 0.001). Intention to reuse was mainly associated with interaction and collaboration, enjoyment and motivation, perceived usefulness/knowledge, and ease of use. Overall, the findings indicate that XR-supported outdoor learning is positively associated with key experiential, emotional, social, and perceived learning dimensions when embedded within a coherent pedagogical framework. Full article

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring. Full article

Beach nourishment is a widely adopted nature-based solution for coastal erosion; however, its design efficacy and morphodynamic resilience under extreme wave conditions remain inadequately quantified, posing challenges for coastal hazard assessment. This study integrates physical-model experiments and XBeach numerical simulations to investigate the hydrodynamic and morphodynamic behavior of nourished beaches subjected to typhoon-driven extreme wave conditions at a headland-bay beach on Meizhou Island, China. Physical-model experiments were conducted to examine shoreline response and sediment redistribution under extreme waves for three nourishment tests. XBeach simulations resolved wave-induced currents, water-level variations, and sediment transport processes, enabling continuous tracking of nearshore hydrodynamics and beach profile evolution for three nourishment tests during Typhoon Doksuri. Results indicate that nourishment geometry and groin configuration play a dominant role in wave breaking patterns, sediment transport pathways and erosion–deposition distributions. Groin positions strongly influence alongshore sediment transport. Relocating the groin to an accretional zone reduces lee-side erosion and promotes a more stable shoreline. Steeper nourishment foreshore slopes promote offshore wave shoaling and breaking, enhancing fast wave-energy dissipation, shifting erosion seaward and limiting landward erosion extent. Consistent responses from both experimental and numerical results demonstrate that nourishment stability under extreme wave conditions is better characterized by the combined effects of erosion extent, erosion length, erosion depth, erosion volume, and alongshore and cross-shore sediment redistribution. The integrated physical–numerical approach provides a practical framework for assessing beach nourishment stability during coastal hazard events and offers guidance for the design and evaluation of resilient beach nourishment in wave-dominated, typhoon-prone coastal regions. Full article

Suspended sediment concentration (SSC) strongly influences estuarine erosion–deposition processes, navigation safety, and coastal engineering stability. However, conventional remote sensing techniques are limited to surface SSC and cannot characterize vertical sediment structures. In this study, Landsat 8 OLI imagery was combined with in situ SSC profiles from six stations in the Guan River Estuary–Haizhou Bay system to retrieve full-depth sediment distributions. A band-combination inversion model using (B3 + B2)/B1 achieved the highest accuracy (R² = 0.679), and an improved vertical distribution model was developed by incorporating turbulent shear (G) into the Rouse framework. Results indicate that surface SSC ranged from 0.15 to 0.86 kg/m³, while middle- and bottom-layer SSC reached up to 1.20 kg/m³ and 1.77 kg/m³, respectively, exhibiting a consistent east–high and west–low spatial pattern. Settling velocity (SSV) varied from 3 × 10⁻⁶ to 1.49 × 10⁻² m/s and showed a positive correlation with SSC at low concentrations and a negative correlation at high concentrations due to flocculation effects. This integrated framework provides a rapid, low-cost method for full-water-column sediment assessment in estuaries and coastal zones, supporting engineering design, navigation maintenance, and sediment management. A better understanding of sediment transport processes in Haizhou Bay is important for maintaining shoreline stability and ecological balance in this semi-enclosed coastal system. The findings of this study provide a scientific basis for sediment management and environmental regulation, which can contribute to the long-term sustainable development of coastal environments in the Yellow Sea region. Full article

Accurate shoreline positioning is critical for coastal monitoring and management, yet deep learning shoreline products are often evaluated using conventional waterbody segmentation metrics that do not explicitly measure boundary alignment. Using 20,689 NAIP aerial images covering the Great Lakes shoreline from the Coastal Aerial Imagery Dataset (CAID), we benchmark five semantic segmentation models and quantify the inconsistency between image-level segmentation accuracy (pixel accuracy, IoU) and shoreline positioning accuracy measured by the Shoreline Intersection Ratio (SIR) and Average Eulerian Distance (AED). Although segmentation performance is consistently high (pixel accuracy typically >98% and IoU often >90%), shoreline agreement is substantially lower and strongly landscape-dependent, with the poorest results in wetlands and urban scenes. Correlation analyses across coastal types and water-surface conditions show that the correspondence between segmentation metrics and SIR varies with shoreline morphology. Multivariate regressions confirm the shoreline-to-water ratio (SWR) as the dominant predictor of both SIR and AED, while shoreline complexity (SCI) and mean water hue (MWH) have weaker, context-dependent effects. These results demonstrate that high segmentation accuracy does not guarantee precise shoreline delineation and motivate shoreline-aware evaluation protocols. Full article

Coastal erosion is a growing concern along many Mediterranean sandy coasts, particularly where reduced fluvial sediment supply, relative sea-level rise and coastal development coincide. This study uses multi-mission Landsat 5/7/8/9 and Sentinel-2 data in Google Earth Engine to extract long-term shoreline series (1984–2025) from MNDWI-based composites. DSAS-style metrics quantify multi-decadal change, while a supervised linear regression forecasting model—validated against a 2013 orthophoto and an independent 2017–2025 test set using an RMSE-based acceptance criterion—is employed to forecast shoreline positions up to 2050. Using this framework, we reconstruct and forecast shoreline evolution along the ~38 km Ionian coast of Basilicata (southern Italy), a microtidal, sediment-starved littoral that has been affected by significant erosion over the past few decades, threatening natural habitats, infrastructure and economic activities. Results show pervasive erosion over the last four decades, with an average shoreline retreat of ≈47 m along the entire coast, and localized retreats exceeding 400 m, particularly at the mouths of the Agri and Sinni rivers and near the Metaponto sector. Forecasts, under linearity and trend-persistence assumptions, indicate further substantial retreat by 2050 in already critical sectors. Methodologically, this work provides a reproducible framework to inform scenario-based coastal planning in similar Mediterranean environments and the first multi-decadal, spatially continuous satellite-based analysis and machine learning-supported forecast for the Basilicata coast, offering a robust basis for regional coastal management. Full article

Shoreline variations in closed-basin lakes are closely linked to hydrological fluctuations and long-term changes in water balance, making them important indicators of environmental change. This study analyzes historical shoreline dynamics in Lake Van (Türkiye), the world’s largest soda lake, and provides scenario-based shoreline projections for 2032 and 2042 to support hydrological assessment and water-related management. Multi-temporal Landsat satellite images from 1982, 1992, 2002, 2012, and 2022 were processed using the Digital Shoreline Analysis System (DSAS 5.0) to quantify shoreline retreat and accretion, while future shoreline positions were estimated using the Kalman filter model. The results show pronounced spatial variability, with the most significant shoreline retreat observed in the Çelebibağ and Karahan regions, where sediment supplied by major inflowing streams contributes to shoreline instability through reworking and redistribution rather than stable accretion. Net shoreline movement values reached −2580.1 m for erosion and up to 1700 m for accretion. Model projections indicate an increasing trend of shoreline retreat by 2032 and 2042, accompanied by localized accretion zones. These hydrological-driven shoreline changes have potential implications for littoral habitats, water–land interactions, and human use of the shoreline, including fisheries infrastructure. The study demonstrates the value of integrating remote sensing and statistical forecasting for monitoring shoreline dynamics in closed-basin lake systems. Full article

Autonomous ships require reliable and economical navigation; however, their performance is hindered when satellite-based positioning signals become unavailable. In such global navigation satellite system (GNSS)-denied conditions, a backup navigation system integrating a strapdown inertial navigation system (SINS), Doppler velocity logger (DVL), and a compass (SINS/DVL/COMPASS) can provide essential state information, but the accuracy and fault tolerance of such systems are constrained by weak observability of position/heading errors and strong dependence on DVL measurements. This study proposes a fault-tolerant optimization method based on perceptual information compensation. First, radar imagery and electronic chart data are fused at the feature level using a weighted wavelet strategy to enhance the environmental feature saliency for shoreline extraction. Second, characteristic coastline inflection points are detected and tracked using a dual-curvature and distance-constrained procedure, generating external position observations via radar–chart matching. These observations are incorporated into the SINS/DVL/COMPASS framework to improve its state observability and robustness. Simulation results show that under nominal conditions, perceptual compensation mitigates error divergence and promotes the convergence of position errors, improving the positioning stability. In terms of robustness, the proposed method delivered more stable state-error behavior than the baseline under DVL speed faults of +2 m/s, −2 m/s, and +2 m/s injected at 301–330, 701–730, and 1101–1130 s, respectively. Quantitatively, the 3σ bounds of velocity and position-related errors are reduced under fault conditions, indicating improved fault tolerance and suitability for short-term nearshore autonomous navigation during GNSS outages. Full article