Search Results (296)

Monitoring salt marsh vegetation in the Yellow River Delta (YRD) wetland is the basis of wetland research, which is of great significance for the further protection and restoration of wetland ecological functions. In the existing remote sensing technologies for wetland salt marsh vegetation classification, the object-oriented classification method effectively produces landscape patches similar to wetland vegetation and improves the spatial consistency and accuracy of the classification. However, the vegetation classes of the YRD are mixed with uneven distribution, irregular texture, and significant color variation. In order to solve the problem, this study proposes a fine-scale classification of dominant vegetation communities using color-enhanced aerial images. The color information is used to extract the color features of the image. Various features including spectral features, texture features and vegetation features are extracted from the image objects and used as inputs for four machine learning classifiers: random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN) and maximum likelihood (MLC). The results showed that the accuracy of the four classifiers in classifying vegetation communities was significantly improved by adding color features. RF had the highest OA and Kappa coefficients of 96.69% and 0.9603. This shows that the classification method based on color enhancement can effectively distinguish between vegetation and non-vegetation and extract each vegetation type, which provides an effective technical route for wetland vegetation classification in aerial imagery. Full article

Efforts have been underway worldwide to reintroduce seawater to many historically diked salt marshes for restoration of tidal flow and associated estuarine habitat. Seawater restoration to a diked Cape Cod marsh was simulated using the computer program PHREEQC based on previously conducted microcosm experiments to better understand the associated timing and sequence of multiple biogeochemical reactions and their implications to aquatic health. Model simulations show that acidic, reducing waters with high concentrations of sorbed ferrous iron (Fe[II]), aluminum (Al), sulfide (S²⁻), ammonia (NH₄⁺ + NH₃), and phosphate (PO₄³⁻) are released through desorption and sediment weathering following salination that can disrupt aquatic habitat. Models were developed for one-dimensional reactive transport of solutes in diked, flooded (DF) marsh sediments and subaerially exposed, diked, drained (DD) sediments by curve matching porewater solute concentrations and adjusting the sedimentary organic matter (SOM) degradation rates based on the timing and magnitude of Fe(II) and S²⁻ concentrations. Simulated salination of the DD sediments, in particular, showed a large release of Al, Fe(II), NH₄⁺, and PO₄³⁻; the redox shift to reductive dissolution provided higher rates of SOM oxidation. The sediment type, iron source, and seasonal timing associated with seawater restoration can affect the chemical speciation and toxicity of constituents to aquatic habitat. The constituents of concern and their associated complex biogeochemical reactions simulated in this study are directly relevant to the increasingly common coastal marsh salination, either through tidal restoration or rising sea level. Full article

Vegetation succession is a critical indicator of ecosystem structure and function and is often disrupted by the expansion of invasive species. However, ecosystem-scale studies elucidating invasion-driven succession mechanisms remain limited. This research focused on the Yancheng coastal salt marsh and analyzed the distribution variation of invasive species (Spartina alterniflora) and native species (Suaeda salsa and Phragmites australis) from 1987 to 2022 via the Google Earth Engine and random forest method. Logistic/Gaussian models were used to quantify land–sea distribution changes and vegetation succession trajectories. By integrating data on soil salinity, invasion duration, and fractional vegetation cover, generalized additive models (GAMs) were applied to identify the main factors influencing vegetation succession and to explore how Spartina alterniflora invasion affects the succession of salt marsh vegetation. The results indicated that the areas of Spartina alterniflora and Phragmites australis significantly increased by 3787.49 ha and 3452.60 ha in 35 years, respectively, contrasting with Suaeda salsa’s 82.46% decline. The FVC in the area has significantly increased by 42.10%, especially in the coexisted areas of different vegetation communities, indicating intensified interspecific competition. The overall trend of soil salinity was decreasing, with a decrease in soil salinity in native species areas from 0.72% to 0.37%. From the results of GAMs, soil salinity, tidal action, and invasion duration were significant factors influencing the distribution of native species, but salinity was not a significant factor affecting the Spartina alterniflora distribution. The findings revealed that the expansion of Spartina alterniflora changed the soil salinity and interspecific interactions, thereby altering the original plant community structure and establishing a new vegetation succession. This study enhances the understanding of the impacts of invasive species on ecosystems and offers theoretical support for salt marsh restoration. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

This study developed a coupled hydrodynamic-sediment-vegetation model to investigate the effects of Spartina alterniflora management and Suaeda salsa restoration on coastal wetland geomorphological evolution and vegetation distribution. Special attention is paid to the regulatory roles of tidal dynamics, sea-level rise, sediment supply, and sediment characteristics. The study shows that the management of Spartina alterniflora significantly alters the sediment deposition patterns in salt marsh wetlands, leading to intensified local erosion and a decline in the overall stability of the wetland system; meanwhile, the geomorphology of wetlands restored with Suaeda salsa is influenced by tidal range, sediment settling velocity, and suspended sediment concentration, exhibiting different deposition and erosion patterns. Under the scenario of sea-level rise, when sedimentation rates fail to offset the rate of sea-level increase, the wetland ecosystem faces the risk of collapse. This study provides scientific evidence for the ecological restoration and management of coastal wetlands and offers theoretical support for future wetland conservation and restoration policies. Full article

Soil saline–alkaline stress and water stress, exacerbated by anthropogenic activities and climate change, are major drivers of wetland vegetation degradation, severely affecting the function of wetland ecosystems. In this study, we conducted a simulation experiment with three water levels and four saline–alkaline concentration levels as stress factors to assess eight key functional traits of Phragmites australis and Bolboschoenus planiculmis, dominant species in the salt marsh wetlands in the western region of Jilin province, China. The study aimed to evaluate how these factors influence the functional traits of P. australis and B. planiculmis. Our results showed that the leaf area, root biomass, and clonal biomass of P. australis significantly increased, and the leaf area of B. planiculmis significantly decreased under low and medium saline–alkaline concentration treatments, while the plant height, ramet number, and aboveground biomass of P. australis and the root biomass, clonal biomass, and clonal/belowground biomass ratio of B. planiculmis were significantly reduced and the ratio of belowground to aboveground biomass of B. planiculmis significantly increased under high saline–alkaline concentration treatment. The combination of drought conditions with medium and high saline–alkaline treatments significantly reduced leaf area, ramet number, and clonal biomass in both species. The interaction between flooding water level and medium and high saline–alkaline treatments significantly suppressed the plant height, root biomass, and aboveground biomass of both species, with the number of ramets having the greatest contribution. These findings suggest that the effects of water levels and saline–alkaline stress on the functional traits of P. australis and B. planiculmis are species-specific, and the ramet number–plant height–root biomass (RHR) strategy may serve as an adaptive mechanism for wetland clones to environmental changes. This strategy could be useful for predicting plant productivity in saline–alkaline wetlands. Full article

This paper systematically reviews the progress of remote-sensing technology in coastal-ecosystem health assessment. Coastal ecosystems, as transitional zones between land and ocean, play vital roles in maintaining biodiversity, carbon sequestration, and coastal protection, but currently face severe challenges from climate change and human activities. Remote-sensing technology, with its capability for large-scale, long time-series observations, has become a key tool for coastal-ecosystem health assessment. This paper analyzes the technical characteristics and advantages of optical remote sensing, radar remote sensing, and multi-source data fusion in coastal monitoring; constructs a health-assessment framework that includes water-quality indicators, vegetation and ecosystem function indicators, and human disturbance and landscape change indicators; discusses the application of advanced technologies from traditional methods to machine learning and deep learning in data processing; and demonstrates the role of multi-temporal analysis in revealing coastal-ecosystem change trends through typical case studies of mangroves, salt marshes, and coral reefs. Research indicates that, despite the enormous potential of remote-sensing technology in coastal monitoring, it still faces challenges such as sensor limitations, environmental interference, and data processing and validation. Future development should focus on advanced sensor technology, platform innovation, data-processing method innovation, and multi-source data fusion, while strengthening the effective integration of remote-sensing technology with management practices to provide scientific basis for the protection and sustainable management of coastal ecosystems. Full article

Accurate classification of salt marsh vegetation is vital for conservation efforts and environmental monitoring, particularly given the critical role these ecosystems play as carbon sinks. Understanding and quantifying the extent and types of habitats present in Ireland is essential to support national biodiversity goals and climate action plans. Unmanned Aerial Vehicles (UAVs) equipped with optical sensors offer a powerful means of mapping vegetation in these areas. However, many current studies rely on single-sensor approaches, which can constrain the accuracy of classification and limit our understanding of complex habitat dynamics. This study evaluates the integration of Red-Green-Blue (RGB), Multispectral Imaging (MSI), and Hyperspectral Imaging (HSI) to improve species classification compared to using individual sensors. UAV surveys were conducted with RGB, MSI, and HSI sensors, and the collected data were classified using Random Forest (RF), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) algorithms. The classification performance was assessed using Overall Accuracy (OA), Kappa Coefficient (k), Producer’s Accuracy (PA), and User’s Accuracy (UA), for both individual sensor datasets and the fused dataset generated via band stacking. The multi-camera approach achieved a 97% classification accuracy, surpassing the highest accuracy obtained by a single sensor (HSI, 92%). This demonstrates that data fusion and band reduction techniques improve species differentiation, particularly for vegetation with overlapping spectral signatures. The results suggest that multi-sensor UAV systems offer a cost-effective and efficient approach to ecosystem monitoring, biodiversity assessment, and conservation planning. Full article

Extreme environments foster phylogenetically diverse microorganisms and unique community assembly patterns. Plateau saline marsh lakes represent understudied extreme habitats characterized by dual stressors of high salinity and low temperature. Here, we analyzed the soil bacterial and archaeal diversity in three salt marshes of the Qaidam Basin on the Qinghai-Tibetan Plateau, China. While the bacterial and archaeal alpha diversity showed no significant differences among the three salt marshes, the community composition varied significantly. Notably, soil salinity (indicated by electric conductivity, EC) exerted opposing effects on microbial diversity—suppressing bacterial while promoting archaeal communities. Stochastic processes were the predominant mechanism for both bacterial and archaeal community assembly, where the weights were, in descending order, drift, homogeneous selection, and dispersal limitation. Network analysis revealed predominantly positive co-occurrence patterns within both bacterial and archaeal communities. We did not find a direct relationship between any bacterial or archaeal co-occurrence network properties and soil EC, but there was a significant correlation of network complexity to microbial diversity, which was influenced by EC. Our findings indicate distinct responses of bacterial and archaeal diversity to varying salinity levels, while the underlying assembly processes appear to be conserved in driving shifts in community diversity in plateau salt marsh wetlands. Full article

Coastal blue carbon ecosystems serve as vital carbon sinks in global climate regulation, yet their long-term carbon storage dynamics remain poorly quantified at regional scales. This study quantified the spatiotemporal evolution of mangrove and salt marsh carbon storage in Guangdong Province, China, over three decades (1986–2020), by integrating a new mangrove and salt marsh detection framework based on Landsat image time series and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The proposed detection framework provided two coastal vegetation detection methods, exploring the potential of utilizing phenological features to improve the mangrove and salt marsh discrimination accuracy with Landsat data. The overall accuracies of both mangrove and salt marsh detection results exceeded 90%, suggesting good consistency with the validation data. The mangrove extent showed a trend of decreasing from 1986 to 1995, then fluctuated from 1995 to 2005, and presented an upward trend from 2005 to 2020. The overall trend of the salt marsh area was upward, with small fluctuations. The mangrove carbon storage in Guangdong increased from 414.66 × 10⁴ Mg C to 490.49 × 10⁴ Mg C during 1986–2020, with Zhanjiang having the largest mangrove carbon storage increase. The salt marsh carbon storage in Guangdong grew from 8.73 × 10⁴ Mg C in 1986 to 14.39 × 10⁴ Mg C in 2020, with Zhuhai as the salt marsh carbon sequestration hotspot. The temporal dynamics of carbon storage in mangroves and salt marshes could be divided into three stages, namely a decreasing period, a fluctuating period, and a rapid increase period, during which ecological and economic policies played a crucial role. The multi-decadal blue carbon datasets and their temporal-spatial change analysis results here can provide a scientific basis for nature-based climate solutions and decision-support tools for carbon offset potential realization and sustainable coastal zone management. Full article

Salt marsh vegetation significantly changes water motion and sediment transport in coastal wetlands, which further influences the geomorphological evolution of coastal wetlands. Accurate determination of the vegetation drag coefficient (Manning’s roughness coefficient) is critical to vegetation flow resistance research. Previous studies on the vegetation roughness coefficient mainly conducted flume experiments under the one-dimensional steady flow condition, which could not reflect the two-dimensional unsteady flow condition in salt marsh vegetated zones. Through theoretical formula analysis and synchronized field observations in a salt marsh vegetated zone, we propose a novel method for calculating the roughness coefficient of salt marsh vegetation especially under the two-dimensional unsteady flow condition. The results indicate that the vegetation roughness coefficient under the two-dimensional unsteady flow condition can be obtained by integrating the flow resistance equation with the discretized momentum conservation equation. Then, in combination with field observation data, the temporal variations in the vegetation roughness coefficient can be derived. The salt marsh vegetated zone in the Jiuduansha Wetland is dominated by flooding currents, and ebbing currents are of secondary importance. The flow resistance of vegetation on flooding and ebbing currents is remarkable. Moreover, the roughness coefficient shows an inverse power-law relationship with the product of flow velocity and water depth (i.e., U_fh_f) at the control volume center. Under the same U_fh_f scenario, due to the increase in the water-facing area of vegetation, the roughness coefficient during the submerged period is generally greater than that during the non-submerged period. The calculated roughness coefficients and their relationships with U_fh_f are consistent with those shown in previous flume experiments, indicating that our proposed method is reasonable. This new method could help determine vegetation flow resistance accurately (particularly under the two-dimensional unsteady flow condition), and it may provide implications for eco-geomorphological simulations of coastal wetlands. Full article

The intertidal zones of estuarine wetlands serve as critical components in maintaining and promoting the sustainable development of regional ecosystems. Salt marsh vegetation, a crucial element of these zones, is experiencing significant deterioration across multiple scales due to various stressors. Despite considerable attention given to the spatial patterns and temporal evolution of salt marsh vegetation, few studies have quantitatively assessed its dynamic interactions with tidal creeks. Tidal creeks serve as primary conduits for material, energy, and information exchange between intertidal zones and adjacent ecosystems. There is a complex feedback mechanism between the development of the tidal creeks and vegetation communities. We investigated the distribution patterns and successional characteristics of salt marsh vegetation at both landscape and pixel scales, with particular emphasis on coupling dynamics with tidal creeks. Our results revealed a distinct spatial gradient in vegetation distribution across the study area. While the invasion of S. alterniflora exhibited limited direct competitive effects on S. salsa, it demonstrated significant influence on tidal creek geomorphological evolution. Notably, S. salsa exhibited pronounced sensitivity to hydrological conditions, with its growth being substantially constrained by tidal creek development and associated soil modifications. Full article

Coastal erosion and sediment transport dynamics in Iraq’s shoreline are increasingly affected by extreme climate conditions, including rising sea levels and intensified storms. This study introduces a novel fractional-order sediment transport model, incorporating a modified gamma function-based differential operator to accurately describe erosion rates and stabilization effects. The proposed model evaluates two key stabilization approaches: artificial stabilization (breakwaters and artificial reefs) and bio-engineering solutions (coral reefs, sea-grass, and salt marshes). Numerical simulations reveal that the proposed structures provide moderate sediment retention but degrade over time, leading to diminishing effectiveness. In contrast, bio-engineering solutions demonstrate higher long-term resilience, as natural ecosystems self-repair and adapt to changing environmental conditions. Under extreme climate scenarios, enhanced bio-engineering retains 55% more sediment than no intervention, compared to 35% retention with artificial stabilization.The findings highlight the potential of hybrid coastal protection strategies combining artificial and bio-based stabilization. Future work includes optimizing intervention designs, incorporating localized field data from Iraq’s coastal zones, and assessing cost-effectiveness for large-scale implementation. Full article

Coastal wetlands are vital ecosystems at the land–sea interface. They intercept land-based pollutants, regulate microclimates, and mediate carbon cycles. They play a significant role in enhancing carbon sequestration capacity and maintaining ecological structure and functioning. This study proposes an improved method for estimating blue carbon storage in coastal salt marsh wetlands, considering soil thickness, by utilizing an enhanced Soil Land Inference Model (SoLIM) to estimate soil thickness in coastal wetlands with a restricted number of sample points. The wetland soil thickness index is integrated into the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) blue carbon storage estimation model, ultimately enabling the estimation and visualization of blue carbon storage in the Liaohe Estuary coastal wetland. Results indicate the following: (1) The studied area’s soil thickness shows a spatial distribution pattern that becomes progressively thinner from north to south. Soil thickness is more significant in the salt marsh vegetation areas and more minor in the coastal tidal flat areas, with 52% of the region having soil thickness between 40 and 60 cm. (2) In 2023, the blue carbon stock in the study area is estimated at 389.85 × 10⁶ t, with high-value areas concentrated in the northern natural landscapes, and low-value areas in the southern coastal zone, characterized by flat terrain and human influence. The coupled soil thickness–blue carbon storage estimation model provides methodological support for refining the estimation of blue carbon storage in coastal wetlands. It also offers technical support for formulating policies on the ecological restoration, compensation, protection, and management of coastal wetlands. Full article

Salt marsh ecosystems play a critical role in coastal protection, carbon sequestration, and biodiversity preservation. However, they are increasingly threatened by climate change and anthropogenic activities, necessitating precise vegetation mapping for effective conservation. This study investigated the effectiveness of spectral features and machine learning models in separating typical salt marsh vegetation types in the Yellow River Delta using uncrewed aerial vehicle (UAV)-derived multispectral imagery. The results revealed that the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Optimized Soil Adjusted Vegetation Index (OSAVI) were pivotal in differentiating vegetation types, compared with spectral reflectance at individual bands. Among the evaluated models, U-Net achieved the highest overall accuracy (94.05%), followed by SegNet (93.26%). However, the U-Net model produced overly distinct and abrupt boundaries between vegetation types, lacking the natural transitions found in real vegetation distributions. In contrast, the SegNet model excelled in boundary handling, better capturing the natural transitions between vegetation types. Both deep learning models outperformed Random Forest (83.74%) and Extreme Gradient Boosting (83.34%). This study highlights the advantages of deep learning models for precise salt marsh vegetation mapping and their potential in ecological monitoring and conservation efforts. Full article