Search Results (295)

Seawater intrusion (SWI) represents an increasingly critical challenge for coastal groundwater systems, with particularly pronounced impacts observed along the Indian coastline. Coastal aquifers constitute a vital freshwater resource supporting domestic, agricultural, and industrial demands. This study evaluates SWI vulnerability along the entire Indian coast, extending from Gujarat to West Bengal, covering approximately 7517 km of shoreline and up to 100 km inland. The assessment applies the GALDIT vulnerability framework that combines several hydrogeological and hydrochemical criteria such as groundwater occurrence, aquifer hydraulic conductivity, depth to groundwater, distance from shoreline, hydrochemical data, and groundwater quality data. We also assessed the intrusion of existing seawater, shoreline location, and aquifer thickness. However, conventional vulnerability assessments are inherently static and often fail to capture anthropogenic influences. To address this limitation, the present study integrates multi-temporal land use and land cover (LULC) datasets derived from ESA WorldCover remote sensing data for the period 2017–2024. Incorporating LULC dynamics enables a more comprehensive evaluation of the impacts of urban expansion and agricultural intensification on coastal susceptibility to SWI. Accordingly, a modified GALDIT-LU framework is developed to assess the spatiotemporal evolution of coastal vulnerability. The outcomes suggest that huge parts of the Indian coastline are vulnerable to moderate or very high classes, with the very high vulnerability class growing from 13,295 km² in 2017 to 38,257 km² in 2024, a 188% increase in vulnerability over the course of seven years. Groundwater chloride concentrations from Central Ground Water Board (CGWB) monitoring well locations have been used for validation over the proposed assessment, and show good spatial agreement between areas identified as high vulnerability and the spatial distribution of groundwater salinity for all three assessment periods, lending support to the robustness and predictive power of the proposed groundwater salinity assessment. The findings carry direct implications for the United Nations 2030 Agenda, demonstrating that the identified vulnerability patterns intersect with critical targets related to clean water and sanitation, food security, public health, climate action, and poverty reduction along one of the world’s most densely populated coastlines. Full article

High-resolution Normalized Difference Vegetation Index (NDVI) data are essential for monitoring fine-scale coastal environmental dynamics, yet persistent cloud cover, rapid geomorphic change, and strong spatial heterogeneity limit the availability of temporally continuous observations. Existing spatiotemporal fusion approaches can partially address these limitations, but many rely primarily on data-driven feature learning and do not explicitly incorporate geographic information, leading to boundary blurring, structural inconsistency, and sensitivity to background noise in complex coastal environments. This study presents a geographically constrained Transformer-based framework for 2 m NDVI spatiotemporal reconstruction in coastal landscapes named Coastal-Prior-Embedded Global–Local Fusion Transformer (Coastal-GLFT). The approach integrates high-resolution Gaofen-6 panchromatic and multispectral imagery with high-frequency wide-field-view observations and auxiliary geographic datasets describing elevation, coastline proximity, and land use/land cover. Geographic priors were incorporated as explicit spatial constraints, while a spatiotemporal gating mechanism and global–local fusion architecture were used to improve the representation of temporal variation and multi-scale spatial structure. The method was evaluated using a multi-temporal dataset for the Yellow River Delta comprising 49 high-resolution scenes and 137 coarse-resolution scenes acquired between 2020 and 2025. Compared with representative physics-based, convolutional neural network, generative adversarial network, and Transformer-based fusion methods, the proposed approach reduced reconstruction error by approximately 5–72%, increased signal fidelity by approximately 1–12%, and improved structural similarity by approximately 2–52%. Compared with the strongest Transformer-based baseline, SwinSTFM, Coastal-GLFT reduced RMSE from 0.0896 to 0.0855, increased PSNR from 36.19 dB to 37.09 dB, and improved SSIM from 0.8551 to 0.8742. Qualitative analysis further demonstrated improved preservation of boundary structure, spatial continuity, and heterogeneous coastal features, including aquaculture ponds, tidal creeks, and fragmented wetlands. These results indicate that integrating geographic constraints with multi-scale Transformer-based reconstruction can improve the fidelity and structural consistency of high-resolution NDVI reconstruction in complex coastal environments. The framework provides a basis for fine-scale coastal vegetation monitoring and land-cover analysis, while future work should assess transferability across diverse coastal systems and improve computational scalability. Full article

The tourism sector in the Sultanate of Oman is central to “Oman Vision 2040”, with a strategic focus of the government on its dynamic transformation. Coastal regions, vital to tourism, are affected by changes to the coastline due to flash floods, sea-water flooding, and erosion. Despite its implications for tourism and the economy, this topic remains relatively under-explored, especially as to use of Sentinel-1 satellite images. This study assesses water-level changes due to erosion in the urban coastal region of the Al-Batinah governorate via land cover classification. Using the Support Vector Machine (SVM) classification technique, the overall accuracy is found to be 97.7% and the Kappa coefficient value for the year 2018 is 1.0. Although, when using the Random Forest (RF) classification technique, the accuracy is nearly identical, there is varying precision for the water area. A critical observation is made, showing significant increase of the water area from 2.99% in the year 2017 to 12.36% in the year 2025, suggesting water encroachment. With fixed-effect and combined-effect size meta-analysis models, the confidence levels were identified as 95.0% and 0.37, respectively, indicating a consistent variation in water area that supports the outcomes of image classification. This study offers a valuable insight for policymakers as to managing coastal regions, along with providing assistance to vulnerable coastal communities. The study focuses on a particular governorate, given the satellite images, whereas a broader regional comparison would address the limitation of the generalizability of results. In the future, the research could integrate surveys from coastal communities and businesses for a comprehensive qualitative data perspective on the region’s tourism sector. Full article

High-resolution projections of near-surface winds are crucial for ocean circulation and sea level studies in Southeast Asia, a region characterized by complex coastlines and monsoon variability. This study evaluates the added value of dynamical downscaling using the Weather Research and Forecasting (WRF) model at 9 km resolution, driven by two CMIP6 global climate models (EC-Earth3 and MPI-ESM1-2-HR), to simulate 10 m wind climatology over the Southeast Asian seas. Comparisons were made against ERA5 reanalysis and the parent CMIP6 GCMs, focusing on seasonal mean patterns, interannual variability, and the annual cycle. The WRF simulations demonstrate substantial improvement in capturing the spatial structures of monsoonal winds and regional circulation features. Future wind projections under SSP2-4.5 and SSP5-8.5 scenarios reveal seasonally and spatially heterogeneous trends. The downscaled models project strengthening of winter monsoon winds over the Southeast Asian seas and a weakening of summer monsoon flows, with implications for upper ocean dynamics and regional sea level patterns. The leading modes of variability from EOF analysis indicate basin-wide wind anomalies modulated by periodic signals at ~1 year and ~2–7 years, likely driven by ENSO and the Asian monsoon. The power spectra of principal components reveal that internal variability persists across scenarios, though with increased signal-to-noise ratios (SNRs) in the WRF projections toward the end of the 21st century. Full article

Mangrove forests along China’s coastline serve as vital ecological barriers and blue carbon reservoirs. However, pests and diseases have become the primary biotic threats driving stand decline and diminished carbon sequestration capacity. This review synthesizes current knowledge on the major insect pests and plant diseases affecting Chinese coastal mangroves, focusing on their species profiles, characteristic damage symptoms, occurrence dynamics, and integrated control strategies. Fungal pathogens predominate among the diseases, with outbreaks most common during periods of high temperatures and humidity or low temperatures combined with high humidity; these often interact synergistically with insect pests. The dominant insect pests comprise leaf-feeding Lepidoptera, sap-sucking Hemiptera, and wood-boring Coleoptera, which spread through diverse pathways and can rapidly produce extensive “scorched” damage across mangrove stands during epidemic events. Control efforts follow the principle of “prevention first and integrated management,” incorporating cultural practices, chemical interventions, biological control agents, physical trapping methods, and rigorous quarantine-monitoring protocols. When applied in concert, these measures effectively limit damage to acceptably low levels. Recent studies identify pest–disease interactions and climate change as the foremost challenges in current management. Future priorities should include advancing molecular identification techniques, breeding disease-resistant varieties, and developing environmentally friendly biopesticides to establish precision ecological control systems. Such advances will deliver robust scientific support for mangrove conservation and the achievement of China’s dual-carbon goals. Full article

Background/Objectives: Dolphins inhabiting coastlines can be influenced by anthropogenic factors. As biochemical changes accumulate in blubber over weeks to months, blubber metabolites may be informative biomarkers of molecular adaptations to environmental changes. Methods: We investigated the blubber metabolomic signatures of live free-ranging bottlenose dolphins for the first time. This exploratory study analyzed blubber samples from 35 common bottlenose dolphins (Tursiops truncatus) in South Texas waters using untargeted ultra-high-performance liquid chromatography-Orbitrap metabolomics. Results: Blubber samples exhibited distinct temporal and spatial metabolic patterns. Pathway enrichment analyses comparing detected metabolites (n = 2777) revealed that dolphins sampled in the spring had enhanced lipid quality and immune regulation, while dolphins sampled in the summer showed stress-associated metabolic responses. Dolphins inhabiting areas previously reported to experience heavy vessel traffic and contaminant burdens exhibited enriched immune- and inflammation-associated pathways. Dolphins that visually appeared to have poorer body condition exhibited metabolite profiles suggestive of increased protein catabolism. Dolphins in extreme salinity conditions had more abundant membrane maintenance and endocrine pathways. Conclusions: Dolphins from each system exhibited distinct metabolic signatures that may be associated with differing physiological responses, highlighting the potential utility of blubber biomarkers for assessing physiological adaptations in free-ranging marine mammals. Improved understanding of habitat-specific physiological responses offers critical insights into how cumulative impacts may affect the health and adaptive capacity of vulnerable species in dynamic coastal ecosystems. Full article

The red palm weevil, Rhynchophorus ferrugineus (Olivier, 1791), is among the most destructive pests of date palm (Phoenix dactylifera L.) globally, posing a severe and escalating threat to agricultural productivity across the Arabian Peninsula. Despite its well-documented economic impact, the potential influence of climate change on its future distributional dynamics within this region remains poorly quantified. This study employed Maximum Entropy (MaxEnt) species distribution modelling to assess current and projected habitat suitability for R. ferrugineus across the Arabian Peninsula (~3.2 million km²) under two contrasting Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) for the mid-century (2050) and late-century (2070). The model was calibrated using 52 spatially thinned occurrence records and six non-collinear environmental predictors selected following Variance Inflation Factor (VIF) analysis, with sampling bias corrected through a kernel density-based background weighting approach. Model performance was robust, with mean training and test AUC values of 0.921 ± 0.023 and 0.840 ± 0.052, respectively, and a mean TSS of 0.583 ± 0.046. Precipitation of the coldest quarter (Bio 19) and precipitation seasonality (Bio 15) emerged as the most influential predictors of habitat suitability, followed by elevation. Currently, approximately 727,589.8 km² (26.11%) of the peninsula is classified as suitable habitat, concentrated along the eastern Arabian Gulf coastline and the western Red Sea plain. Under SSP1-2.6, suitable habitat is projected to expand by 16.34% and 31.60% by 2050 and 2070, respectively. Under the high-emission SSP5-8.5 scenario, expansions are considerably more pronounced, reaching 34.11% by 2050 and 60.15% by 2070, with total suitable area approaching 1,158,474.8 km² (41.58%) by late-century. Habitat contraction was negligible across all scenarios, indicating a unidirectional range expansion dynamic. These findings highlight the substantial threat posed by climate-driven habitat expansion of R. ferrugineus and provide spatially explicit projections to inform proactive biosecurity planning and pest management strategies for date palm cultivation across the Arabian Peninsula. Full article

Sandy coastlines are dynamic geomorphological units supporting dense human populations and intensive economic activities. However, their evolution is increasingly dominated by anthropogenic modification rather than natural processes. This study investigates shoreline evolution along the western Liaodong Bay coast, China, where extensive anthropogenic engineering has potentially altered natural dynamics. A 30-year satellite-derived shoreline (SDS) analysis of 23 sandy beaches (Xingcheng–Suizhong, 1995–2024) was conducted using the CoastSeg framework and DSAS statistical methods across three sub-periods (1995–2005, 2005–2015, 2015–2024). Shoreline change rates ranged from −1.35 to +2.12 m/yr; 11 beaches (47.8%) exhibited net erosion and 12 (52.2%) net accretion or stability, with marked spatial heterogeneity within individual beaches. This complex spatio-temporal pattern shows the strongest spatial correspondence with the non-uniform distribution of anthropogenic structures—including ports, breakwaters, and land reclamation—which generate an “engineering proximity effect” that may fragment natural beach continuity and contribute to a regional alternating erosion–accretion mosaic pattern, though direct mechanistic verification awaits future hydrodynamic modeling. Shoreline evolution along the western Liaodong Bay coast has entered a stage of “multi-layered anthropogenic control,” requiring frameworks that integrate multi-scale, multi-process coupling mechanisms and transcend traditional regional-averaging approaches. These findings provide critical insights for spatially differentiated management of engineering-intensive sandy coasts. Full article

Under global climate change, ungulate distributions are generally shifting poleward. However, the dispersal pathways, dynamics of suitable habitat, and environmental drivers of the expanding Milu population in the intensively used coastal wetlands of Jiangsu Province remain poorly understood. To support conservation and management, this study used field occurrence data and environmental variables to predict potentially suitable habitat for Milu under current and future climate scenarios. The Biomod2 ensemble modeling framework was applied to assess spatial changes in habitat suitability, and Geographical Detector was used to identify key environmental drivers. Current potentially suitable habitat showed a belt-like pattern along the coast, with the high suitability area covering 0.035 × 10⁴ km². Under future climate scenarios, potentially suitable habitat for Milu is projected to expand in the central and northern coastal areas of Jiangsu, with a substantial increase in the predicted total suitable habitat area. Dis_coastline, BIO14, BIO4, and Pop_density were identified as key factors influencing the distribution of potential suitable habitat for Milu, among which BIO4 and BIO14 were the principal climatic drivers affecting the northward shift in future suitable areas. These results suggest that Milu habitat suitability is jointly shaped by coastline proximity, temperature and water-availability conditions, and population density. Conservation should prioritize the protection of highly suitable habitats, improve patch connectivity, reduce human disturbance, and strengthen wetland protection and vegetation restoration. Full article

Accurate coastline extraction in muddy and macro-tidal environments is challenging due to tidal variability and complex coastal surfaces. The Jiangsu coast of China, characterized by extensive tidal flats, silty coastlines, and strong land–sea interactions, provides an ideal case for long-term coastline change analysis. This study investigates the spatiotemporal evolution of the Jiangsu coastline from 2018 to 2025 using multi-temporal Sentinel-2 imagery. A tide-independent coastline extraction framework was developed by integrating the Normalized Difference Water Index, Modified Normalized Difference Water Index, and Normalized Difference Vegetation Index for different coastal environments. An annual maximum spectral index composite was applied to approximate the highest water-level conditions without explicit tidal correction. Coastline dynamics were quantified using fractal dimension analysis and a transect-based method. The extracted coastlines yielded an average Root Mean Square Error (RMSE) of 13.14 m and an average Mean Absolute Distance Error (MADE) of 9.39 m. Results show that the total coastline length varied within 5% during the study period, with a maximum of 1079.84 km in 2022 and a minimum of 1004.99 km in 2018. Coastline change was dominated by erosion, accounting for 56.21% of the total coastline length. Land cover analysis revealed that accretion mainly occurred near river mouths and aquaculture areas, whereas erosion was more common at interfaces between forested land and engineered coastlines. The proposed framework provides an efficient and consistent approach for short-term coastline monitoring in muddy coastal environments. Full article

Continuous dynamic monitoring of coastline changes is essential for revealing the evolutionary laws and spatiotemporal characteristics of coastal systems. In this study, we employed AlphaEarth Foundations (AEF) data and Sentinel-2 imagery to investigate coastline and land use changes in the Pearl River Estuary (PRE) region over the period 2017–2023. The Random Forest (RF) algorithm was adopted to extract coastlines and classify coastal land-use types, after which their spatiotemporal evolution was quantitatively analyzed. The results demonstrate that the classification performance of AEF data is significantly better than that of Sentinel-2 imagery, with the average overall accuracy and Kappa coefficient exceeding 92% and 89%, respectively. The PRE coastline shows an evolutionary pattern of “overall contraction accompanied by regional differentiation”: its total length first increased and then decreased, peaking at 1029.05 km in 2019, representing a cumulative net reduction of 7.54 km over the 2017–2023 period. Meanwhile, land use expansion driven by reclamation resulted in a cumulative net increase of 25.26 km². Aquaculture ponds (AP) constitute the dominant type of newly reclaimed land, accounting for more than 50%, while the expansion of impervious surface (IS) accounts for 24.52%. This study provides novel insights and a scientific basis for the refined management of coastlines, sustainable land use planning, and coastal-marine ecological protection in the Pearl River Estuary and similar regions worldwide. Full article

Assessing the contribution of tropical cyclones to regional precipitation variability is essential for understanding the associated hydrometeorological benefits and risks. This study quantifies the contribution of tropical cyclones to annual precipitation in the northernmost part of South America from 2016 to 2025, utilizing data from surface rain gauges. Simulations using the Weather Research and Forecasting (WRF) model, configured with 2 km grid spacing and 38 vertical levels, estimate the influence of relative humidity at 850 hPa and ambient temperature at 500 hPa on precipitation over the continental region when each convective system is nearest to the coastline. During Hurricanes Matthew (2016) and Melissa (2025), contributions to the annual average precipitation reached 51% and 47%, respectively, with the highest values observed near the northern South American coastline. The contributions of Harvey (2017), Iota (2020), Julia (2022), and Beryl (2024) to annual precipitation were 0–26%, 0–18%, 0–12%, and 0–19%, respectively. Precipitation distribution was heterogeneous during the passage of tropical storms. The extent of accumulated precipitation was influenced by the cyclone’s trajectory and proximity to mountainous regions. Patterns of relative humidity at 850 hPa did not correspond to a uniform precipitation distribution. Between 6% and 30% of rain gauges did not record precipitation during the analyzed tropical cyclone events. These findings highlight that tropical cyclone-induced precipitation is strongly influenced by complex interactions between atmospheric dynamics and topography. Future research should assess the contributions of these systems to groundwater and surface reservoirs that support indigenous communities in rural areas. Full article

Wetland landscape classification is fundamental to monitoring changes in ecosystem patterns. This study proposes an ensemble classification approach that integrates Maximum Likelihood Classification (MLC) and Decision Tree (DT) methods with optimized feature selection for long-term wetland monitoring in the Liaohe Delta, China. Based on four periods of Landsat remote sensing images from 1987, 1997, 2007, and 2017, multi-dimensional features including PCA1, TCT, NDVI, and MNDWI were extracted to construct a hierarchical classification system comprising 13 landscape types. The results show that the integrated method achieved an overall classification accuracy of 87.71% and a Kappa coefficient of 0.85, improving by 16.50% and 19.72%, respectively, compared with the single MLC approach. The classification results reveal significant spatiotemporal variations in landscape patterns. Reed wetlands decreased from 1284.44 km² in 1987 to 1006.70 km² in 1997, followed by a recovery to 1275.53 km² in 2017. In contrast, Suaeda communities experienced severe degradation, declining sharply from 227.48 km² in 1987 to 30.52 km² in 2017. Meanwhile, the coastline advanced landward by 263.24 km², with the proportion of artificial shoreline increasing from 12.4% to 38.7%. The changes in wetland landscape types in the Liaohe Delta from 1987 to 2017 were mainly influenced by urbanization processes and ecological restoration policies. These findings indicate that the proposed method can effectively support long-term wetland landscape dynamics analysis and provide a useful reference for coastal wetland management. Full article

This study investigates ecosystem service valuation on Zhoushan Island. Based on Landsat remote sensing images for 2000, 2010, and 2020 acquired through the Google Earth Engine (GEE) platform, six land use types are extracted using the Random Forest method. By integrating land use dynamic degree, transfer matrix, ecosystem service value (ESV) accounting, and five-level land–sea gradient zoning approaches, this study systematically analyzes the spatiotemporal evolution of land use and its effects on ESV over the 20-year period, and reveals the spatial differentiation pattern of land use change and ESV gains and losses along the land–sea gradient. The results indicate that from 2000 to 2020, water bodies and cultivated land on Zhoushan Island experienced continuous decline while construction land expanded rapidly, driven by policy regulation, urbanization, and industrial transformation. Localized coastal areas exhibited a typical chain conversion process of “water body → bare land → construction land,” which is closely associated with reclamation and land reclamation activities. Regional ESV declined continuously, reaching only 56.7% of its 2000 level by 2020, with regulating and provisioning services exhibiting the most pronounced deterioration. Analysis of the ESV net transfer matrix indicates that the primary driver of ESV decline was the large-scale conversion of high-value water bodies to low-value construction land and bare land, the magnitude of which far exceeded the positive ecological gains generated by all other land use conversions. The reduction in cultivated land area, compounded by adjustments in cropping structure, has placed sustained pressure on regional food security, and policy responses have lagged considerably behind the pace of ecological degradation. In terms of spatial differentiation, both the intensity of land use change and ESV loss exhibited a gradient pattern that decreases progressively from the coastal zone moving inland. Zone 1 and Zone 2 in the nearshore area together accounted for approximately 80% of total ESV loss, whereas Zone 4 and Zone 5 maintained relatively stable land use structures and ecological support capacity, owing to higher forestland coverage. Spearman’s rank correlation analysis confirmed a statistically significant monotonically decreasing relationship between land use dynamic degree and coastal distance. Policy regulation served as the primary driver of regional land use pattern evolution: early sea reclamation policies facilitated rapid land transformation along the coastline, while subsequent tightening of controls effectively curbed disorderly expansion. Full article

As coastal communities face escalating climate risks driven by climate change and biodiversity loss, integrating mangrove ecosystems into sustainability-oriented governance frameworks spanning ecological conservation, climate adaptation, and natural capital accounting has become a global priority. However, quantifying their protection values based on spatiotemporal shoreline dynamics under extreme disturbance remains challenging. Focusing on Dongzhai Harbor (China), this study integrates multi-temporal remote sensing (2010–2021), shoreline evolution analysis, and the Replacement Cost Method to assess ecosystem resilience against Super Typhoon Rammasun in 2014. Results show mangroves exhibited substantial post-disturbance resilience, with only 6.10% area loss following Typhoon Rammasun and 46% natural recovery within six years. Bootstrap confidence intervals for the mangrove-shoreline association overlapped zero across all three temporal periods, indicating that the observational data do not support a statistically confirmed causal protection effect at the landscape scale. This finding underscores that spatially co-occurring ecosystem services do not automatically imply causation, reinforcing the need for empirically grounded valuation in sustainable land-use planning. Because mangroves naturally establish in sheltered environments, the observed spatial overlap between mangroves and the shoreline cannot be interpreted as direct evidence of causal shoreline stabilization. Based on this framework, the potential protection value reached 907.65 × 10⁴ CNY yr⁻¹ across 32.57 km of weighted coastline aligned with mangroves. Notably, erosional segments contributed 50.5% of this value despite comprising only 27.3% of the length, indicating that the replacement-cost estimate is concentrated in erosional segments under the assumed parameters. While acknowledging the need for local biophysical validation and uncertainty analysis in scaling, these findings support integrating dynamic nature-based solutions into territorial planning and Gross Ecosystem Product accounting. The resulting valuation framework offers a replicable pathway for advancing multi-dimensional sustainability encompassing climate-adaptive coastal governance, natural capital integration, and evidence-based coastal spatial planning. Full article