Search Results (307)

Accurate and detailed mapping of coastal wetlands is essential for effective wetland resource management. However, due to periodic tidal inundation, frequent cloud cover, and spectral similarity of land cover types, reliable coastal wetland classification methods remain limited. To address these issues, we developed an integrated pixel- and object-based hierarchical classification strategy based on multi-source remote sensing data to achieve fine-grained coastal wetland classification on Google Earth Engine. With the random forest classifier, pixel-level classification was performed to classify rough wetland and non-wetland types, followed by object-based classification to differentiate artificial and natural attributes of water bodies. In this process, multi-dimensional features including water level, phenology, variation, topography, geography, and geometry were extracted from Sentinel-1/2 time-series images, topographic data and shoreline data, which can fully capture the variability and dynamics of coastal wetlands. Feature combinations were then optimized through Recursive Feature Elimination and Jeffries–Matusita analysis to ensure the model’s ability to distinguish complex wetland types while improving efficiency. The classification strategy was applied to typical coastal wetlands in central Jiangsu in 2020 and finally generated a 10 m wetland map including 7 wetland types and 3 non-wetland types, with an overall accuracy of 92.50% and a Kappa coefficient of 0.915. Comparative analysis with existing datasets confirmed the reliability of this strategy, particularly in extracting intertidal mudflats, salt marshes, and artificial wetlands. This study can provide a robust framework for fine-grained wetland mapping and support the inventory and conservation of coastal wetland resources. Full article

Wetlands, among the most productive ecosystems on Earth, shelter a diversity of species and help maintain ecological balance. However, they are witnessing growing anthropogenic and climatic threats, which underscores the need for regular and long-term monitoring. This study presents a systematic review of 121 peer-reviewed articles published between January 2015 and 30 April 2025 that applied machine learning (ML) and deep learning (DL) for wetland mapping and bird-habitat monitoring. Despite rising interest, applications remain fragmented, especially for avian habitats; only 39 studies considered birds, and fewer explicitly framed wetlands as bird habitats. Following PRISMA 2020 and the SPIDER framework, we compare data sources, classification methods, validation practices, geographic focus, and wetland types. ML is predominant overall, with random forest the most common baseline, while DL (e.g., U-Net and Transformer variants) is underused relative to its broader land cover adoption. Where reported, DL shows a modest but consistent accuracy over ML for complex wetland mapping; this accuracy improves when fusing synthetic aperture radar (SAR) and optical data. Validation still relies mainly on overall accuracy (OA) and Kappa coefficient ($\kappa$), with limited class-wise metrics. Salt marshes and mangroves dominate thematically, and China geographically, whereas peatlands, urban marshes, tundra, and many regions (e.g., Africa and South America) remain underrepresented. Multi-source fusion is beneficial yet not routine; The combination of unmanned aerial vehicles (UAVs) and DL is promising for fine-scale avian micro-habitats but constrained by disturbance and labeling costs. We then conclude with actionable recommendations to enable more robust and scalable monitoring. This review can be considered as the first comparative synthesis of ML/DL methods applied to wetland mapping and bird-habitat monitoring, and highlights the need for more diverse, transferable, and ecologically/socially integrated AI applications in wetland and bird-habitat monitoring. Full article

Coastal wetlands are a critical buffer between land and water that are threatened by land use and climate change, necessitating improved monitoring for management and resilience planning. The recently launched NASA-ISRO L- and S-band SAR satellite (NISAR) will provide regular collections of fully polarimetric SAR imagery over the Great Lakes, allowing for unprecedented remote monitoring of the large expanses of coastal wetlands in the region. Prior research with polarimetric C-band SAR showed inconsistencies with common polarimetric analysis techniques, including the erroneous misattribution of double-bounce scattering in three-component scattering models. To prepare for NISAR and determine whether SAR-based coastal wetland analysis methods established with the C-band are applicable to the L- and S-bands, the NASA-ISRO airborne system (ASAR) collected imagery over western Lake Erie and Lake St. Clair coincident with a field data collection campaign. ASAR data were analyzed to identify common Great Lakes coastal wetland vegetation species, assess the extent of inundation, and derive biomass retrieval algorithms. Co-polarized phase difference histograms were also analyzed to assess the validity of three-component scattering decompositions. The L- and S-bands allowed for the production of wetland type maps with high accuracies (92%), comparable to those produced using a fusion of optical and SAR data. Both frequencies could assess the extent of flooded vegetation, with the S-band correctly identifying inundated vegetation at a slightly higher rate than the L-band (83% to 78%). Marsh vegetation biomass retrieval algorithms derived from L-band data had the best correlation with field data (R² = 0.71). Three component scattering models were found to misattribute double-bounce scattering at incidence angles shallower than 35°. The L- and S-band results were compared with satellite RADARSAT-2 imagery collected close to the ASAR acquisitions. This study provides an advanced understanding of polarimetric SAR for monitoring wetlands and provides a framework for utilizing forthcoming NISAR data for effective monitoring. Full article

Although wetlands are key habitats for biodiversity conservation, they are also among the most threatened ecosystems in the world. They are mainly affected by human pressures and threats, even when they are included in protected areas. The Padule di Fucecchio area is one of the largest and most significant inland marshes in Italy. It is also a wetland of international importance, as defined by the Ramsar Convention. However, studies of the plant communities it contains are surprisingly scarce and out of date. To address this issue, a phytosociological survey of aquatic and marshy vegetation was conducted. This analysis provided an unparalleled census of the area’s current aquatic and marsh vegetation. Eight different plant community types were reported in the former category and twenty-six in the latter, many of which were previously unknown in this territory. One of these is entirely novel and is described here for the first time. However, a comparison with previous data revealed that significant changes to the vegetation structure have occurred in recent decades. The hydrophyte communities have almost completely disappeared and many of the most sensitive plants in the most sensitive marsh communities have become rarer or disappeared. They have mostly been replaced by more resilient native plants and invasive alien species. Full article

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants with significant ecological and public health implications, particularly in urban wetlands exposed to chronic anthropogenic stress. This study evaluates the occurrence, spatial distribution, seasonal variability, and ecological risk of three low molecular weight PAHs—naphthalene (NAP), phenanthrene (PHEN), and anthracene (ANT)—in surface waters of Caño La Malaria, the main freshwater source of Cucharillas Marsh, Puerto Rico’s largest urban wetland. Surface water samples were collected at four locations during both wet- and dry-season campaigns. Samples were extracted and quantified by GC-MS. NAP was the dominant compound, Σ3PAHs concentrations ranging from 7.4 to 2198.8 ng/L, with higher wet-season levels (mean = 745.79 ng/L) than dry-season levels (mean = 186.71 ng/L); most wet-season samples fell within the mild-to-moderate contamination category. Compositional shifts indicated increased levels of PHEN and ANT during the wet season. No significant spatial differences were found (p = 0.753), and high correlations between sites (r = 0.96) suggest uniform input sources. Diagnostic ratios, inter-species correlations, and principal component analysis (PCA) consistently indicated a predominant pyrogenic origin, with robust PHEN–ANT correlation (r = 0.824) confirming shared combustion-related sources. PCA revealed a clear separation between dry- and wet-season samples, with the latter showing greater variability and stronger associations with NAP and ANT. Ecological risk assessment using hazard quotients (HQ_water) indicated negligible acute toxicity risk across all sites and seasons (<0.01); the highest HQ_water (0.0095), observed upstream during the wet season, remained within this range. However, benchmark exceedances by PHEN and ANT suggest potential chronic risks not captured by the acute ERA framework. These findings support integrated watershed management practices to mitigate PAH pollution and strengthen long-term ecological health in tropical urban wetlands. Full article

Wetland loss and human activities are forcing migratory waterbirds to rely on alternative habitats such as croplands, yet their adaptive habitat use across contrasting landscape contexts remains unclear. The Bar-headed Goose (Anser indicus) is a key indicator species in the wetland ecosystems of the Yunnan–Guizhou Plateau. Comparing differences in its wintering habitat selection and utilization is of great significance for understanding its ecological adaptation mechanisms and formulating regional wetland conservation strategies. In this study, we compared the diurnal habitat use during the wintering period of Bar-headed Geese at three wetlands (Nianhu, Caohai, and Napahai) representing distinct landscape contexts. We used GPS satellite tracking and dynamic Brownian bridge movement modeling, combined with random forest analysis of environmental variables, to quantify diurnal habitat use and selection at each site. Our results revealed significant regional differences in habitat use. In the agriculture-dominated wetlands (Nianhu and Caohai), geese primarily utilized cropland and marsh habitats (Nianhu: cropland 45.88% ± 30.70%, marsh 42.55% ± 33.17%; Caohai: cropland 62.33% ± 12.16%, marsh 28.61% ± 13.62%). In contrast, at Napahai, which is dominated by natural habitats, geese primarily used grassland (65.92% ± 20.01%) and marsh (26.85% ± 21.88%), with minimal use of cropland (4.21% ± 7.00%). Diurnal habitat selection was influenced by multiple environmental factors, with distinct regional differences identified through random forest modeling. In Nianhu, key factors included distance to supplemental feeding site, distance to grassland, distance to woodland, and distance to open water. In Caohai, distance to grassland, distance to nocturnal roost site, distance to settlement, and distance to open water were significant drivers. In Napahai, distance to nocturnal roost site, distance to open water, and distance to marsh were the most influential (all with p < 0.01), reflecting flexible behavioral responses. Based on these findings, we recommend region-specific conservation management strategies. Specifically, supplemental feeding at Nianhu should be strictly regulated. Agricultural planning in farming areas should account for the habitat needs of wintering waterbirds. Grassland and marsh habitats at Napahai should also be more effectively protected. Full article

Using Landsat series imagery and the deep learning model CITNet, this study conducted high-accuracy classification and spatiotemporal change analysis of wetlands on Chongming Island from 2000–2020 and explored the driving mechanisms by integrating climatic and anthropogenic factors. The results demonstrate that the total wetland area decreased by approximately 125.5 km² over the two decades. Among natural wetlands, tidal mudflats and shallow seawater zones continuously shrank, while herbaceous marshes exhibited a “decline recovery” trajectory. Artificial wetlands expanded before 2005 but contracted significantly thereafter, mainly due to aquaculture pond reduction. Wetland transformation was dominated by wetland-to-non-wetland conversions, peaking during 2005–2010. Driving factor analysis revealed a “human pressure dominated, climate modulated” pattern: nighttime light index (NTL) and GDP demonstrated strong negative correlations with wetland extent, while minimum temperature and the Palmer Drought Severity Index (PDSI) promoted herbaceous marsh expansion and accelerated artificial wetland contraction, respectively. The findings indicate that wetland changes on Chongming Island result from the combined effects of policy, economic growth, and ecological processes. Sustainable management should focus on restricting urban expansion in ecologically sensitive zones, optimizing water resource allocation under drought conditions, and incorporating climate adaptation and invasive species control into restoration programs to maintain both the extent and ecological quality of wetlands. Full article

The Liaohe Estuary, characterized by Asia’s largest reed marshes and diverse wetland types, provides critical habitats for endangered bird species and performs vital ecological functions, making it a representative international wetland. Tidal flats, as essential components of estuarine wetlands, dissipate wave energy and stabilize shorelines. However, due to their peripheral location within estuarine systems, quantitative monitoring and risk assessment of the Liaohe Estuary tidal flat remain constrained. In this study, 187 cloud-filtered Landsat TM/ETM+/OLI scenes acquired between 2001 and 2021 were integrated with a waterline-derived DEM framework to quantify sedimentation dynamics in the Liaohe Estuary wetland. During the study period, the tidal-flat area exhibited a declining trend, while interannual surface elevations generally ranged from +2.18 to −1.61 m. The mean surface elevation increased by 25.33 cm, accompanied by a mean slope increase of 0.11‰; the average sedimentation rate was 1.27 cm yr⁻¹, with a net depositional volume of 0.51 km³, indicating an overall depositional regime. Moreover, mean elevation displayed a statistically significant upward trend (Kendall’s tau = 0.636, p = 0.0057), corroborating the significant rise in tidal-flat elevation from 2001 to 2021. The coexistence of elevation gain and spatial contraction suggests limited geomorphic resilience and a shrinking spatial extent of the tidal flat. The proposed approach provides a robust framework for long-term monitoring and supports the formulation of quantifiable sustainability targets for coastal management in the Liaohe Estuary. Full article

Coastal marshes provide essential ecosystem services, yet they are vulnerable to anthropogenic stressors and climate change, particularly sea level rise (SLR). Restoration approaches like marsh terracing have emerged as nature-based strategies to enhance resilience and reduce habitat loss. This study applies the Sea Level Affecting Marshes Model (SLAMM) to assess the potential of marsh terraces to mitigate future losses, while also examining the model’s limitations, including its assumptions and capacity to reflect complex marsh processes. A geospatial approach was used to generate 3D representations of terraces through morphostatic modeling within digital elevation models (DEMs). Under a no-restoration scenario, SLAMM projections show that all marshes analyzed are at risk of total loss by 2100. In contrast, scenarios including terracing demonstrate a delay in net marsh loss, extending the persistence of key marsh habitats by approximately a decade. Although marsh degradation remains likely under high SLR conditions, the results underscore the utility of marsh terraces in prolonging habitat stability. Additionally, the study demonstrates the feasibility of integrating restoration features like terraces into DEMs and wetland models. Despite SLAMM’s simplified erosion and accretion assumptions, the model yields important insights into restoration effectiveness and long-term marsh dynamics, informing more adaptive, forward-looking coastal management strategies. Full article

Wetland ecosystems, particularly marsh wetlands, are vital for carbon cycling, yet the accurate estimation of the fraction of absorbed photosynthetically active radiation (FPAR) in these environments is challenging due to their complex structure and limited field data. This study employs the large-scale remote sensing data and image simulation framework (LESS), a 3D radiative transfer model, to simulate FPAR and vegetation indices (VIs) under controlled conditions, including variations in vegetation types, soil types, chlorophyll content, solar and observation angles, and plant density. By simulating 8064 wetland scenes, we overcame the limitations of field measurements and conducted comprehensive quantitative analyses of the relationship between the FPAR and VI (which is essential for remote sensing-based FPAR estimation). Nine VIs (NDVI, GNDVI, SAVI, RVI, EVI, MTCI, DVI, kNDVI, RDVI) effectively characterized FPAR, with the following saturation thresholds quantified: inflection points (FPAR.inf, where saturation begins) ranged from 0.423 to 0.762 (mean = 0.594) and critical saturation points (FPAR.sat, where saturation is complete) from 0.654 to 0.889 (mean = 0.817). The Enhanced Vegetation Index (EVI) and Soil-Adjusted Vegetation Index (SAVI) showed the highest robustness against saturation and environmental variability for FPAR estimation in reed (Phragmites australis) marshes. These findings provide essential support for FPAR estimation in marsh wetlands and contribute to quantitative studies of wetland carbon cycling. Full article

Wetlands function as crucial transitional zones between land and water ecosystems worldwide, contributing significantly to the stability of local ecosystems. However, there is limited research on landscape changes in Xinjiang’s arid interior regions and the factors driving these changes. This study uses data reanalysis techniques to examine the spatial and temporal evolution and landscape patterns of wetlands, as well as their driving forces, in Xinjiang between 1990 and 2023. The results show that over the past three decades, the wetland area in Xinjiang has grown from 18,427 km² in 1990 to 21,532 km² in 2023, with an annual increase of about 94 km². The greatest growth in wetlands, particularly lakes, marshes, and rivers, has occurred around the periphery of the Tarim Basin and the Ili River Basin, while mountainous areas have seen slight reductions. The distribution pattern shows higher wetland coverage in southern Xinjiang and less coverage in the north, with the largest proportion of wetlands found in the south. Additionally, wetland expansion has led to improvements in the number, density, aggregation, and connectivity of wetland patches, while the complexity of their shapes has decreased. The overall habitat quality of wetlands has also improved over time. Attribution analysis highlights that the rise in runoff due to temperature increases over the past 30 years is a major driver of wetland expansion, with warming accounting for the largest share of expansion in lakes (36%) and in rivers (47.9%). Furthermore, the implementation of large-scale engineering measures, such as ecological water diversion, water-saving irrigation, and reservoir management, has contributed significantly to wetland expansion and ecological restoration. These results provide useful insights for the long-term conservation and management of wetland resources in the arid areas of Xinjiang. Full article

Coastal vegetated ecosystems, including tidal marshes, vegetated dunes, and shrub- and forest-dominated wetlands, can mitigate hurricane impacts such as coastal flooding and erosion by increasing surface roughness and reducing wave energy. Land cover maps can be used as input to improve simulations of surface roughness in advanced hydro-morphological models. Consequently, there is a need for efficient tools to develop up-to-date land cover maps that include the accurate distribution of vegetation types prior to an extreme storm. In response, we developed the RUSH tool (Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models). RUSH delivers high-resolution maps of coastal vegetation for near-real-time or historical conditions via a Jupyter Notebook application and a graphical user interface (GUI). The application generates 3 m spatial resolution land cover maps with classes relevant to coastal settings, especially along mainland beaches, headlands, and barrier islands, as follows: (1) open water; (2) emergent wetlands; (3) dune grass; (4) woody wetlands; and (5) bare ground. These maps are developed by applying one of two seasonal random-forest machine learning models to Planet Labs SuperDove multispectral imagery. Cool Season and Warm Season Models were trained on 665 and 594 reference points, respectively, located across study regions in the North Carolina Outer Banks, the Mississippi Delta in Louisiana, and a portion of the Florida Gulf Coast near Apalachicola. Cool Season and Warm Season Models were tested with 666 and 595 independent points, with an overall accuracy of 93% and 94%, respectively. The Jupyter Notebook application provides users with a flexible platform for customization for advanced users, whereas the GUI, designed with user-experience feedback, provides non-experts access to remote sensing capabilities. This application can also be used for long-term coastal geomorphic and ecosystem change assessments. Full article

Marsh wetland degradation and shrub expansion, driven by human activities and climate change, can impact carbon, nitrogen, and sulfur cycles by soil microorganisms. There is a paucity of systematic and in-depth research on the impact of shrub expansion in temperate wetlands on soil element cycles, which is a pressing scientific issue that demands resolution. This study used metagenomic sequencing and soil analysis methods to investigate the impact of shrub expansion in the Sanjiang Plain wetlands on carbon, nitrogen, and sulfur cycles in temperate wetland soils, as well as on functional microbial communities. Shrub expansion significantly altered soil carbon, nitrogen, and sulfur cycle processes and the composition (β diversity) of associated functional microbial communities, despite minimal changes in overall α diversity. Significant shifts occurred in the abundance of cycle pathways and related functional genes. Ammonia nitrogen, moisture, and total phosphorus were identified as the primary factors influencing these cycles and the functional microbial communities. Changes in the abundance of specific cycling pathways following shrub expansion are key drivers of functional community structure transformation. These changes may significantly reduce the long-term carbon sequestration potential of wetlands and affect regional climate feedback by altering greenhouse gas fluxes. The findings provide a theoretical basis for managing shrub expansion and assessing wetland function. Full article

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

Aquatic fungi serve as core ecological engines in freshwater ecosystems, driving organic matter decomposition and energy flow to sustain environmental balance. Wetlands, with their distinct hydrological dynamics and nutrient-rich matrices, serve as critical habitats for these microorganisms. As an internationally designated Ramsar Site, Yunnan Dashanbao Black-Necked Crane National Nature Reserve in China not only sustains endangered black-necked cranes but also harbors a cryptic reservoir of aquatic fungi within its peat marshes and alpine lakes. This study employed high-throughput sequencing to characterize fungal diversity and community structure across 12 understudied wetland sites in the reserve, while analyzing key environmental parameters (dissolved oxygen, pH, total nitrogen, and total phosphorus). A total of 5829 fungal operational taxonomic units (OTUs) spanning 649 genera and 15 phyla were identified, with Tausonia (4.17%) and Cladosporium (1.89%) as dominant genera. Environmental correlations revealed 19 genera significantly linked to abiotic factors. FUNGuild functional profiling highlighted saprotrophs (organic decomposers) and pathogens as predominant trophic guilds. Saprotrophs exhibited strong associations with pH, total nitrogen, and phosphorus, whereas pathogens correlated primarily with pH. These findings unveil the hidden diversity and ecological roles of aquatic fungi in alpine wetlands, emphasizing their sensitivity to environmental gradients. By establishing baseline data on fungal community dynamics, this work advances the understanding of wetland microbial ecology and informs conservation strategies for Ramsar sites. Full article