Search Results (114)

Accurate classification of salt marsh vegetation is crucial for coastal wetland monitoring, but fine-grained species discrimination remains difficult, particularly when only limited training data are available for deep learning approaches. To address this challenge, this paper presents a transfer learning-based framework for classifying salt marsh vegetation using UAV multispectral imagery, focusing on a seven-class taxonomy representative of dominant species and water surfaces. Multispectral orthophotos acquired with a MicaSense Dual-Camera system (10 spectral bands) are combined with five vegetation indices to create rich multi-channel inputs. A classification architecture inspired by heterogeneous transfer learning is developed, where a feature-encoding branch compresses the 15-channel input into three channels before processing through a VGG-16 Convolutional Neural Network (CNN), pre-trained on RGB imagery. By leveraging transfer learning from VGG-16, the proposed model achieves high classification accuracy even with limited training data. Performance is compared with traditional machine learning classifiers, namely Support Vector Machines (SVMs) and Random Forest (RF). Results show that the deep learning approach significantly outperforms SVM and RF, achieving an overall accuracy of 98.4% when jointly using spectral bands and vegetation indices. These findings demonstrate the potential of integrating multispectral UAV data and CNN-based classification to support accurate mapping of heterogeneous salt marsh communities for ecological monitoring and coastal management. Full article

Effective management of transitional waters requires collaboration between administrative and scientific institutions, in line with the sustainable water management principles established by the Water Framework Directive (WFD, 2000/60/EC). The Cadiz and San Fernando salt marshes, classified as wetlands of international importance, currently exhibit an ecological and chemical status that is “worse than good.” However, there is still a lack of high-resolution, spatially explicit tools to identify where contaminants are most likely to accumulate in highly modified transitional waters, which limits effective monitoring and management strategies. This study aims to fill this gap by combining a high-resolution hydrodynamic model with a Lagrangian-particle-tracking approach to determine areas most vulnerable to contaminant accumulation from wastewater discharges. Simulations across multiple tidal cycles revealed that contamination is concentrated near discharge points and in low-flow channels, with tidal dynamics strongly influencing transport patterns. Key findings indicate that certain marsh sectors consistently experience higher contaminant exposure, highlighting priority areas for monitoring and management. The study provides novel insights by integrating modeling tools to produce a vulnerability classification of high-, medium-, and low-risk zones. These results contribute to the broader scientific understanding of contaminant dynamics in transitional waters and offer a transferable framework for improving wetland management in other heavily modified coastal systems. Full article

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

Unmanned aerial vehicle (UAV) images have increasingly become important data for accurate mapping of tidal marsh vegetation. They are particularly important for determining what spatial resolution is needed because UAV imaging requires a trade-off between spatial resolution and imaging extent. However, there are still insufficient studies for assessing the effects of spatial resolution on the classification accuracy of tidal marsh vegetation. This study utilized UAV images with spatial resolutions of 2 cm, 5 cm, and 10 cm, respectively, to classify seven tidal marsh plots with different vegetation complexities in the Yellow River Delta (YRD), China, using the object-oriented example-based feature extraction with support vector machine approach and the pixel-based random forest classifier, and compared the differences in vegetation classification accuracy. This study indicated the following: (1) Vegetation classification varied at different spatial resolutions, with a difference of 0.95–8.76% between the highest and lowest classification accuracy for different plots. (2) Vegetation complexity influenced classification accuracy. Classification accuracy was lower when the relative dominance and proportional abundance of P. australis and T. chinensis were higher in the plots. (3) Considering the trade-off between classification accuracy and imaging extent, UAV data with 5 cm spatial resolution were recommended for tidal marsh vegetation classification in the YRD or similar vegetation complexity regions. 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

Gentiana pneumonanthe L., a wetland specialist and exclusive host of the Alcon Blue (Phengaris alcon), is highly vulnerable to climate change. This study assessed the future climate suitability of the Iberian Peninsula (IP) for G. pneumonanthe. From 14 bioclimatic variables (ISIMIP3b, processed by CHELSA method at 1 km²) and two topographic variables, four bio-ecological indicators were selected using Pearson correlation and Variance Inflation Factors: Thermicity Index, Ombrothermic Index, Accumulated summer precipitation from June to August, and Maximum of the daily maximum temperature of August. A species distribution model platform (Biomod2) was applied for historical (1995–2014) and future periods (2041–2060, 2081–2100) under two anthropogenic radiative forcing scenarios (SSP3-7.0, SSP5-8.5). The ensemble model created shows a strong predictive performance (BOYCE: 0.98). Historically, 13.4% of the IP was climatically suitable, mainly in mountain areas. Under SSP3-7.0, suitable areas are projected to decline by 74.2% (2041–2060) and 99.3% (2081–2100); under SSP5-8.5, by 75.5% and 99.9%, respectively. While small gains may occur in the Pyrenees, most conservation protected areas (Natura 2000, RAMSAR) may lose suitability for species persistence. Such losses could disrupt ecological ecosystems and directly threaten the survival of P. alcon. These findings highlight the urgent need for climate-informed land-use planning and effective habitat conservation. 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

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

Background/Objectives: Most adult patients require endoscopy and duodenal biopsies to diagnose coeliac disease. However, individuals who are unwilling or unable to undergo conventional endoscopy are left without diagnostic options or a formal diagnosis. We aimed to determine whether the small-sized biopsy forceps used during the more tolerable transnasal endoscopy (TNE) can provide adequate duodenal biopsy specimens for diagnosing coeliac disease. Methods: We prospectively recruited adult patients (≥18 years) with suspected coeliac disease between May and July 2024. All patients underwent peroral endoscopy, with four biopsies taken from the second part of the duodenum (D2) and one from the duodenal bulb (D1) using standard 2.8 mm biopsy forceps. The biopsy protocol was then repeated using smaller 2 mm biopsy forceps. Expert pathologists evaluated all samples for size, quality, and Marsh classification. Results: Ten patients (median age 45 years, 50% female) were included in this study, of whom seven (70%) were diagnosed with coeliac disease. In total, 100 duodenal biopsy specimens were collected and analysed (50 using standard biopsy forceps and 50 using smaller biopsy forceps). The size of D2 biopsies was significantly larger when using standard biopsy forceps compared with smaller forceps (4.5 mm vs. 3 mm, p = 0.001). Similarly, biopsies from D1 were also larger with standard forceps (3 mm vs. 2 mm, p = 0.002). Smaller forceps provided sufficient material for accurate classification in all cases, and the agreement between biopsies obtained using both forceps in D2 and D1 was 100% (k = 1.0). Conclusions: This pilot study demonstrates that small-sized biopsy forceps, used during TNE, can provide adequate tissue for histopathological diagnosis in patients with suspected coeliac disease. These findings pave the way for considering TNE as a more tolerable alternative to conventional endoscopy in diagnosing coeliac disease. 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

Tidal creeks are vital geomorphological features of tidal flats, and their spatial and temporal variations contribute significantly to the preservation of ecological diversity and the spatial evolution of coastal wetlands. Traditional methods, such as manual annotation and machine learning, remain common for tidal creek extraction, but they are slow and inefficient. With increasing data volumes, accurately analyzing tidal creeks over large spatial and temporal scales has become a significant challenge. This study proposes a residual U-Net model that utilizes full-dimensional dynamic convolution to segment tidal creeks in the Yellow River Delta, employing Gaofen-2 satellite images with a resolution of 4 m. The model replaces the traditional convolutions in the residual blocks of the encoder with Omni-dimensional Dynamic Convolution (ODConv), mitigating the loss of fine details and improving segmentation for small targets. Adding coordinate attention (CA) to the Atrous Spatial Pyramid Pooling (ASPP) module improves target classification and localization in remote sensing images. Including dice coefficients in the focal loss function improves the model’s gradient and tackles class imbalance within the dataset. Furthermore, the inclusion of dice coefficients in the focal loss function improves the gradient of the model and tackles the dataset’s class inequality. The study results indicate that the model attains an F1 score and kappa coefficient exceeding 80% for both mud and salt marsh regions. Comparisons with several semantic segmentation models on the mud marsh tidal creek dataset show that ODU-Net significantly enhances tidal creek segmentation, resolves class imbalance issues, and delivers superior extraction accuracy and stability. Full article

Salt marsh vegetation in the Yellow River Delta, including Phragmites australis (P. australis), Suaeda salsa (S. salsa), and Tamarix chinensis (T. chinensis), is essential for the stability of wetland ecosystems. In recent years, salt marsh vegetation has experienced severe degradation, which is primarily due to invasive species and human activities. Therefore, the accurate monitoring of the spatial distribution of these vegetation types is critical for the ecological protection and restoration of the Yellow River Delta. This study proposes a multi-source remote sensing data fusion method based on Sentinel-1 and Sentinel-2 imagery, integrating the temporal characteristics of optical and SAR (synthetic aperture radar) data for the classification mapping of salt marsh vegetation in the Yellow River Delta. Phenological and polarization features were extracted to capture vegetation characteristics. A random forest algorithm was then applied to evaluate the impact of different feature combinations on classification accuracy. Combining optical and SAR time-series data significantly enhanced classification accuracy, particularly in differentiating P. australis, S. salsa, and T. chinensis. The integration of phenological features, polarization ratio, and polarization difference achieved a classification accuracy of 93.51% with a Kappa coefficient of 0.917, outperforming the use of individual data sources. Full article

Tidal marshes play a globally critical role in carbon and hydrologic cycles by sequestering carbon dioxide from the atmosphere and exporting dissolved organic carbon to connected estuaries. These ecosystems provide critical habitat to a variety of fauna and also reduce coastal flood impacts. Accurate characterization of tidal marsh inundation dynamics is crucial for understanding these processes and ecosystem services. In this study, we developed remote sensing-based inundation classifications over a range of tidal stages for marshes of the Mid-Atlantic and Gulf of Mexico regions of the United States. Inundation products were derived from C-band and L-band synthetic aperture radar (SAR) imagery using backscatter thresholding and temporal change detection approaches. Inundation products were validated with in situ water level observations and radiometric modeling. The Michigan Microwave Canopy Scattering (MIMICS) radiometric model was used to simulate radar backscatter response for tidal marshes across a range of vegetation parameterizations and simulated hydrologic states. Our findings demonstrate that inundation classifications based on L-band SAR—developed using backscatter thresholding applied to single-date imagery—were comparable in accuracy to the best performing C-band SAR inundation classifications that required change detection approaches applied to time-series imagery (90.0% vs. 88.8% accuracy, respectively). L-band SAR backscatter threshold inundation products were also compared to polarimetric decompositions from quad-polarimetric Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2) and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) imagery. Polarimetric decomposition analysis showed a relative shift from volume and single-bounce scattering to double-bounce scattering in response to increasing tidal stage and associated increases in classified inundated area. MIMICS modeling similarly showed a relative shift to double-bounce scattering and a decrease in total backscatter in response to inundation. These findings have relevance to the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, as threshold-based classifications of wetland inundation dynamics will be employed to verify that NISAR datasets satisfy associated mission science requirements to map wetland inundation with classification accuracies better than 80% at 1 hectare spatial scales. Full article

The intertidal ecosystem serves as a critical transitional zone between terrestrial and marine environments, supporting diverse biodiversity and essential ecological functions. However, these systems are increasingly threatened by climate change, rising sea levels, and anthropogenic impacts. Accurately mapping intertidal ecosystems and differentiating mangroves, salt marshes, and tidal flats remains a challenge due to inconsistencies in classification frameworks. Here, we present a high-precision mapping approach for intertidal ecosystems using multi-source satellite data, including Sentinel-1, Sentinel-2, and Landsat 8/9, integrated with the Google Earth Engine (GEE) platform, to enable the detailed mapping of intertidal zones across China–ASEAN. Our findings indicate a total intertidal area of 73,461 km² in China–ASEAN, with an average width of 1.16 km. Analyses of patch area, abundance, and perimeter relationships reveal a power-law distribution with a scaling exponent of 1.52, suggesting self-organizing characteristics shaped by both natural and human pressures. Our findings offer foundational data to guide conservation and management strategies in the region’s intertidal zones and present a novel perspective to propel research on global coastal ecosystems. Full article