Search Results (160)

Fine-scale identification of Spartina alterniflora (S. alterniflora) is essential for coastal wetland conservation. However, in tidal-flat environments, lodged S. alterniflora often occurs together with upright S. alterniflora and native vegetation. The two-dimensional spectral features of S. alterniflora are easily affected by senescence, canopy posture, tidal stage and mixed pixels, leading to unstable classification. The integration of UAV multispectral imagery and LiDAR data can effectively address this problem. The study focused on Shangsha Island within Jiuduansha Wetland in the Yangtze Estuary and constructed multidimensional spectral–structural features by integrating the two data sources. The separability of upright S. alterniflora, lodged S. alterniflora, Phragmites australis (P. australis) and Scirpus mariqueter (S. mariqueter) was characterized using point-cloud elevation distributions, vertical organization and canopy density. The results showed that P. australis had a multilayered point-cloud structure with broad vertical extent, S. mariqueter showed a compact and sparse structure, and S. alterniflora was characterized by a continuous and dense single-layer point-cloud structure. Lodged S. alterniflora further showed a more concentrated, single-layered point-cloud structure and stronger grass-layer continuity. Multisource classification achieved an overall accuracy of 98.15% and a Kappa coefficient of 0.97. In the lodging-area comparison experiment, point-cloud fusion increased overall accuracy from 95.18% to 97.99% and Kappa from 0.89 to 0.95, improving boundary continuity and discrimination stability. Experimental results demonstrate that the fusion of UAV multispectral imagery and LiDAR data can improve the identification of lodged S. alterniflora in complex tidal-flat environments. Accurate identification and spatial delineation of S. alterniflora can help reduce field-survey effort and associated costs while supporting more targeted and efficient removal operations. Full article

The Jianghan Plain is one of the important freshwater aquaculture regions in China. Accurate information on the spatial distribution and spatiotemporal dynamics of aquaculture ponds is essential for regional ecological management. However, large-scale and accurate identification remains challenging for aquaculture ponds because they have spectral and seasonal hydrological characteristics similar to those of rice fields, rivers, canals, and lakes. In this study, we developed a framework for mapping inland aquaculture ponds using multi-source remote sensing data from Sentinel-2, Sentinel-1, and PlanetScope. The framework integrated elevation-zoned Otsu thresholding for candidate water extraction, phenological features for rice field removal, and object classification based on a Gradient Boosting Decision Tree (GBDT) model using 12 shape and spatial-context features. It was applied to identify aquaculture ponds in the Jianghan Plain from 2016 to 2025. Overall accuracy exceeded 93%, and the F1-score exceeded 0.93 in all validations. Results from different sensors also showed high spatial consistency. In 2025, aquaculture ponds covered 2365.17 km² in the Jianghan Plain and were mainly concentrated in the central and eastern parts of the plain, especially near Honghu Lake and along the Yangtze and Hanjiang river meanders. Over the ten-year period, the aquaculture pond area fluctuated between 2033.58 and 2383.52 km², showing an initial decline followed by recovery. Lost aquaculture ponds were mainly located around lakes, while newly added ponds were mostly distributed along the margins of existing clusters. The decade dataset generated in this study can support freshwater aquaculture management and wetland conservation. Full article

The Chilean flamingo (Phoenicopterus chilensis) is a widely distributed South American species inhabiting diverse wetland ecosystems from Ecuador and Peru south to Chile and Argentina, and east to Brazil, Uruguay, and Paraguay, ranging from sea level to high-altitude Andean salars. Despite its broad distribution, local populations are increasingly exposed to anthropogenic pressures, raising conservation concerns at regional scales. In this study, we generated mitogenomic resources and developed a molecular framework for species identification using non-invasive samples. We sequenced genomic DNA from P. chilensis and assembled its complete mitogenome, enabling the in silico evaluation of commonly used metabarcoding markers. Subsequently, we collected eight fecal samples from a feeding site in a high-Andean lagoon where P. chilensis co-occurs with Phoenicoparrus andinus and Phoenicoparrus jamesi. As a positive control, nine additional fecal samples were collected from the Zoológico Nacional, Santiago, Chile, where only P. chilensis is maintained. DNA was extracted and initially screened via Sanger sequencing. To robustly assign taxonomic identity, we implemented a probabilistic framework combining maximum likelihood and Bayesian inference to estimate the posterior probability of species membership based on observed genetic variants. This approach revealed the presence of two genetically distinct groups not attributable to P. chilensis, indicating the coexistence and detectability of other flamingo taxa through fecal DNA. Our results demonstrate the value of integrating mitogenomic resources, metabarcoding, and statistical inference for non-invasive species identification in mixed-species systems. This framework provides a scalable tool for biodiversity monitoring and has direct implications for conservation strategies and evidence-based policy development in fragile high-Andean ecosystems. Full article

Coal mining in areas with high groundwater levels often induces land subsidence and water accumulation, leading to the formation of artificial secondary wetlands. Reclaimed wetlands may provide important opportunities for regional biodiversity recovery. Taking Shuoxi Lake Wetland in Huaibei City as a case study, this research aims to reveal the characteristics of waterbird diversity in artificial wetlands after ecological reclamation in a coal mining subsidence area and to identify their key environmental drivers, thereby providing a scientific basis for optimizing the habitat service functions of such wetlands. Based on habitat identification and classification of the reclaimed wetland, waterbird diversity was surveyed, and redundancy analysis (RDA), Mantel tests, and ridge regression models were used to identify the major environmental factors influencing the distribution of different waterbird groups and to quantify their relative contributions. The results showed that after ecological reclamation, a total of 28 waterbird species belonging to 7 families and 6 orders were recorded in the artificial wetland of the coal mining subsidence area. Redundancy analysis (RDA) indicated that wader assemblages were more sensitive to vegetation cover (VC), distance to water bodies (DTW), and distance to buildings (DTB), whereas waterfowl assemblages were mainly affected by distance to buildings (DTB), area (A), and distance to water bodies (DTW), and showed no significant response to vegetation heterogeneity. Mantel tests further confirmed significant spatial correlations between waterbird assemblages and area (A), distance to major roads (DTR), distance to buildings (DTB), water depth (WD), and distance to water bodies (DTW). Ridge regression analysis showed that, under conditions in which anthropogenic disturbance was minimized, vegetation cover (VC) and water depth (WD) were the main positive drivers of wader diversity, whereas perimeter to area ratio (PAR) was the main negative driver. Waterfowl diversity was mainly negatively affected by perimeter to area ratio (PAR) and distance to water bodies (DTW). These findings suggest that appropriately regulating water depth, increasing vegetation cover, and reducing patch fragmentation and anthropogenic disturbance are key measures for enhancing the habitat service functions of artificial secondary wetlands in mining areas. These management strategies provide an important reference for wetland rehabilitation in other coal mining subsidence areas. Full article

Kazakhstan’s Ili River delta nourishes a unique wetland ecosystem in arid Central Asia. The delta is dominated by common reed [Phragmites australis (Cav.) Trin. ex Steud.], an ecologically and economically significant species that is sensitive to water levels. We used machine learning methods, including the Random Forest algorithm, to classify common reed-containing wetland vegetation and water surfaces in the delta based on satellite data from 2000 to 2023. The remote sensing results were integrated with field-based geobotanical studies conducted between 2017 and 2019. These studies, which facilitated identification of three classes of vegetation: meadow, marsh and aquatic, also revealed a good correspondence between predicted and measured common reed biomass per unit area. The long-term dynamics of four wetland communities with a common reed content of ≥25% were analyzed with respect to significant interannual variability in the flow of the Ili River. The highest water inflows and water surface areas in the delta were recorded in 2002, 2010 and 2016, and in each case, a statistically significant expansion of common reed was observed one year later. Common reed areas subsequently declined—rapidly or after a lag of several years. Temporal expansion and contraction of these areas following pulses of water differed substantially from that of overall wetland vegetation as measured previously. The identified patterns have important scientific and practical significance for assessing the stability of the surrounding wetlands, preservation of the delta environment, and sustainable use of common reed. Full article

Xinjiang, northwestern China, encompasses sharply contrasting desert–wetland and montane forest ecosystems, providing a natural setting to study species limits and habitat-associated morphological variation in Agaricus subgen. Pseudochitonia. We examined 303 specimens collected from 2021 to 2025 and generated 905 sequences across three loci (ITS, LSU, tef1). Species delimitation followed an integrative framework incorporating morphology, multilocus phylogeny, pairwise genetic distances, chemical reactions, and habitat data. We recognized ten species from Xinjiang. Among them, Agaricus acanthosquamosus is described as a new species in sect. Bohusia. It is recovered as a sister to A. bohusii and is distinguished by conspicuous squarrose spinose pileus scales, subglobose to broadly ellipsoid basidiospores, a solitary to scattered fruiting habit, and reddening context. We also document newly observed morphological variation in several previously described species. These include irregularly clavate cheilocystidia in A. desjardinii, clavate pleurocystidia in A. subperonatus, non-digitate pleurocystidial apices in A. sinodeliciosus, and habitat-associated macromorphological variation in A. xanthodermus. These observations extend the known morphological range of each taxon. Exploratory PCA, PERMANOVA, and trait comparisons revealed significant assemblage-level morphological structuring between the Ebinur Lake desert–wetland assemblage and the Qiongkushitai montane forest assemblage, notably in basidioma size, robustness, and basidiospore dimensions. In addition, we compiled a comprehensive identification key and species checklist for Agaricus subgen. Pseudochitonia in Xinjiang. These integrative results highlight both the diversity of subgen. Pseudochitonia in arid northwestern China and the value of combining morphological, ecological, and molecular evidence for accurate species delimitation. Full article

The floodplains of the Paraná and Paraguay rivers form the Chaco wetland, one of the most species-rich plant ecosystems in Argentina. Because wild grasses can serve as reservoirs of fungal species that cause disease and mycotoxin contamination of cereal crops, we examined asymptomatic, wild grasses from the Chaco wetlands for the presence of the genus Fusarium, which includes multiple species that cause agriculturally important diseases and/or mycotoxin contamination of crops. We focused our efforts on the identification and characterization of the multispecies lineage known as the Fusarium fujikuroi species complex (FFSC). Using morphological traits and partial DNA sequences of the TEF1 gene, we determined that 58 isolates recovered from the grasses were members of FFSC. Fifty of the isolates were identified as one of six FFSC species, including the economically important plant pathogenic species F. proliferatum, F. subglutinans, and F. verticillioides. To our knowledge, two of the species, F. anthophilum and F. pseudocircinatum, have not been reported previously in Argentina. Our analyses also indicated that eight of the FFSC isolates were a novel species, herein described as Fusarium varsavskyanum. A polymerase chain reaction (PCR) assay and genome sequence data indicate that each isolate of F. varsavskyanum isolate had only one mating type idiomorph (MAT1-1 or MAT1-2), which suggests that the fungus is heterothallic. Genome sequence analysis indicated that F. varsavskyanum has the genetic potential to produce, (i) the emerging mycotoxins fusaric acid and beauvericin (or enniatins); (ii) the pigments bikaverin, carotenoids, and fusarubin; and (iii) the plant hormones auxins, cytokinins, and gibberellins. Thus, asymptomatic grasses from the Chaco wetland can harbor Fusarium species that in some agroecosystems can cause economically important diseases and/or mycotoxin contamination of crops. It remains to be determined whether the genotypes of Fusarium species that occur on the wetland grasses, including F. varsavskyanum genotypes, can negatively impact agriculture. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article

Microplastic contamination in wetland ecosystems is an escalating environmental threat, compromising ecosystem services, biogeochemical cycling and biodiversity conservation. This study assessed the occurrence, distribution and physicochemical characteristics of microplastics in the Ramsar-designated Pallikaranai wetland, southern India. Six representative subsamples were collected from spatially distinct locations and analyzed using density separation, followed by polymer identification via Raman spectroscopy and energy-dispersive X-ray spectroscopy (EDS). Microplastics were ubiquitously detected across both sediment and water matrices, with significantly higher abundances in sediments, indicating their role as a major sink. The dominant polymer types, polyethylene (PE), polypropylene (PP) and polystyrene (PS), along with prevalent morphotypes such as fragments, fibers, beads and foams, reflect diverse and persistent anthropogenic inputs. The compositional profile strongly implicates mismanaged domestic and urban waste as the primary source. The widespread presence and accumulation of microplastics in this ecologically sensitive wetland raise concerns over potential impacts on trophic interactions, habitat quality and long-term ecosystem resilience. These findings underscore the urgent need for targeted waste management strategies, pollution mitigation frameworks and continuous monitoring to safeguard the ecological integrity of the Pallikaranai wetland and similar Ramsar-listed ecosystems. Full article

During 2022–2024, a highly pathogenic avian influenza virus (HPAIV) H5N1 strain, designated A/Seagull/Hebei/qhd6/2024 (H5N1), was isolated from migratory birds in Beidaihe National Wetland Park, North China. Phylogenetic analyses revealed that its hemagglutinin (HA) gene belongs to the 2.3.4.4b clade, while the neuraminidase (NA) gene and internal genes clustered with strains originating from multiple continents, consistent with a transcontinental reassortment event. The virus also exhibited 90.1–98.1% nucleotide homology with human-derived H5N1 isolates. Molecular characterization identified key virulence-associated mutations, including the classic HPAIV HA cleavage site, HA-T160A (associated with enhanced human receptor-binding capacity), and NA-I117T (potentially linked to drug resistance). BALB/c mouse infection experiments confirmed systemic replication and high pathogenicity of strain qhd6, with a 50% lethal dose (LD50) of 0.95 log₁₀EID₅₀/mL. Antigenic analysis revealed good cross-reactivity with the widely used H5-Re14 vaccine strain. This study reports the identification, in Beidaihe National Wetland Park, of an HPAIV H5N1 strain whose genetic characteristics suggest intercontinental reassortment and indicate cross-species transmission risk. It clarifies the genetic characteristics and pathogenicity of this strain, providing an important theoretical and practical basis for precise surveillance, risk early warning, and comprehensive prevention and control of AIV at migratory bird stopover sites in North China. Full article

Water pollution control for wetland lakes has undergone a fluctuating development process. Effective pollution management requires not only scientific water quality monitoring data but also clear identification of pollution sources within the study area. Accordingly, this study investigated Mudong Lake, the core area of the Huixian Wetland, and conducted water quality monitoring in January 2023 (dry season) and June 2023 (wet season). Based on the Water Quality Index (WQI) assessment results, water quality was better in the wet season than in the dry season. To identify pollution sources, the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model was applied. The results showed that pollution in the dry season was mainly derived from aquaculture and agricultural non-point source pollution, anthropogenic point source pollution, and internal release from sediments, while pollution in the wet season exhibited mixed characteristics, driven by agricultural non-point sources, domestic sewage discharge, and natural factors. Source apportionment analysis indicated that composite pollution sources (domestic sewage and aquaculture wastewater), agricultural non-point source pollution, and other unidentified sources contributed 43.71%, 34.11%, and 22.18% of the total pollution load, respectively. The findings of this study can provide a scientific basis for pollution control, emission reduction, and the targeted management of Mudong Lake. Full article

Framed within data-centric artificial intelligence, this study integrates statistics, geotechnologies and AI to improve water quality prediction. The primary objective was to identify the minimum sample size required to train robust and accurate machine learning models. Based on 30 sampling points in a tropical wetland in northern Colombia, ammonium concentration was selected as the target variable, and total dissolved solids, suspended solids, phosphate, dissolved oxygen, nitrate and chemical oxygen demand were chosen as predictors. Because 30 observations are insufficient to train robust models, data augmentation was performed using ordinary kriging (OK) and empirical Bayesian kriging (EBK). From the kriging-interpolated surfaces, 1000 synthetic points (randomly and spatially distributed while preserving the estimated spatial structure) were sampled; from this expanded dataset, subsamples of varying sizes were drawn to train six algorithms: multiple linear regression (MLR), random forest (RF), k-nearest neighbours (k-NN), gradient boosting machines (GBM), multilayer perceptron (MLP) and radial basis function neural network (RBF-NN). The RF, k-NN, MLP, RBF-NN and GBM models trained on the interpolated data exhibited excellent performance: in the testing phase, they achieved adjusted coefficients of determination > 0.95 and symmetric mean absolute percentage errors (SMAPEs) < 10%, and the resulting predictive surfaces showed comparable performance under external validation. According to the criteria of stability, goodness of fit, and external validation, the optimal minimum sample size for most algorithms was 104 observations. These results represent a significant advance in mitigating data scarcity in water quality modelling. The identification of effective data augmentation methods and the determination of appropriate sample sizes, as demonstrated here, support the robust application of AI techniques in water quality prediction. The proposed strategy is transferable to other quantitative, spatially continuous environmental variables and thus contributes to the development of the emerging subdiscipline of geospatial artificial intelligence (GeoAI). Full article

Blue space, as an important natural and social composite feature system in cities, not only provides supporting, regulating, and provisioning services, but also plays a key role in human well-being, recreational experience, and urban sustainable development. The blue space cultural ecosystem service (CES) has gradually attracted the attention of academia in recent years, but there is a lack of systematic integration research in related fields. Therefore, it is necessary to conduct a comprehensive analysis of current studies to clarify how, and to what extent, blue spaces influence CESs. This study adopts a PRISMA-based systematic search combined with qualitative synthesis, aiming to review the research status of CES and its developmental trajectory within blue space studies, and to identify future research trends and critical gaps. A total of 52 studies meeting the inclusion criteria were finally selected through database screening. The research innovatively divides the evolution of blue space CES into three stages (2012–2017/2018–2022/2023–2025), revealing a shift in research focus from single value identification to complex policy support. Secondly, through the mapping of six typical blue space types (such as rivers and wetlands) and 10 CES indicators, combined with a Pearson correlation heatmap, it provides quantitative insights into the coupling mechanisms between indicators, such as the significant synergy between spiritual and educational values. Methodologically, it systematically discriminates between the application boundaries of monetary valuation based on the contingent valuation method and non-monetary valuation represented by social media big data and PPGIS, pointing out that technological progress is driving the evaluation toward high dynamics and refinement. Finally, the study points out current bottlenecks such as uneven geographical distribution and insufficient planning transformation, emphasizing that future research should use artificial intelligence to improve data processing accuracy and transform blue space CESs from “invisible welfare” into “explicit policy assets” to guide sustainable urban renewal and healthy space design. Full article

The northeastern margin of the Qinghai–Tibet Plateau is an ecologically fragile region that faces severe habitat fragmentation, which directly threatens regional biodiversity conservation and ecological security. To address this challenge, this study constructed a hierarchical “source-corridor-node” ecological network for the Gannan Tibetan Autonomous Prefecture by integrating Morphological Spatial Pattern Analysis (MSPA), the Minimum Cumulative Resistance (MCR) model, landscape connectivity assessment, and gravity modeling. The key results are as follows: (1) The Gannan Yellow River Water Source Replenishment Area contains 11 core ecological source regions, which are predominantly located in the southeastern regions of Diebu County and Zhouqu County, covering a total area of 4237.81 km²; (2) Ecological resistance analysis identifies high-resistance zones concentrated in anthropogenically active river valleys and urban belts (e.g., Hezuo urban area, Awanzang Town, and the G213 corridor). Low-resistance zones are predominantly situated in protected ecological enclaves (e.g., Zhagana Geopark and Gahai Wetland Reserve); (3) A total of 55 ecological corridors were identified, with a total length of 4355.77 km. Among these, 26 were classified as key ecological corridors, primarily distributed in Diebu and Zhouqu counties in the eastern part of Gannan Prefecture. These areas feature relatively concentrated ecological sources, and the key corridors play a critical role in connecting isolated ecological patches and maintaining regional ecological connectivity. (4) Across the entire territory of Gannan Prefecture, a total of 81 first-level ecological nodes and 53 second-level ecological nodes were delineated. As the core hub of the regional ecological network in Gannan Prefecture, Diebu County encompasses 60 First-level and 41 Second-level ecological nodes, respectively. The hierarchical “source-corridor-node” ecological network constructed in this study effectively enhances the overall landscape connectivity of the area. This progressive analytical framework—integrating source identification, corridor extraction, and node diagnosis—provides a scientific basis for biodiversity conservation, territorial ecological restoration, and sustainable development in high-altitude ecologically fragile zones. Full article

This study analyses two wetlands within the Medina del Campo groundwater body (Duero River Basin, Spain) to reconstruct flood patterns and quantify the hydrological volumes involved in episodic inundation. We integrate Sentinel satellite imagery (2015–2024), targeted field campaigns (2024–2025), and preliminary water-balance assessments (2015–2022). Calculations were constrained to the inundated cells of each wetland bed to reduce spatial heterogeneity issues. For Laguna de los Lavajares, an initial standing water depth was assumed to estimate infiltration losses more accurately. We discuss the primary sources of uncertainty—particularly the representation of atmospheric losses as evaporation versus evapotranspiration—and recommend computing water balances for wet, average, and dry years to capture interannual variability. Key findings include the identification of distinct hydroperiods for each wetland, the dominant role of infiltration in the water balance of Laguna de los Lavajares, and the critical influence of vegetation-driven evapotranspiration in Laguna Redonda. Full article