Search Results (935)

Accurate mapping of wetland vegetation is essential for ecosystem monitoring and conservation planning. Traditional workflows combining Sentinel-1 SAR, Sentinel-2 optical imagery, and topographic data have advanced vegetation classification but require extensive preprocessing and often yield fragmented boundaries and “salt-and-pepper” noise. In this study, we compare a conventional multi-sensor classification framework with a novel embedding-based approach derived from the AlphaEarth foundation model, using a cluster-guided Random Forest classifier applied to the dynamic wetland system of Narran Lake, New South Wales. Both approaches achieved high accuracy ac with test performance typically in the ranges: OA = 0.985–0.991, Cohen’s κ = 0.977–0.990, weighted F1 = 0.986–0.991, and MCC = 0.977–0.990. Embedding based maps showed markedly improved spatial coherence (lower edge density, local entropy, and patch fragmentation), producing smoother, ecologically consistent boundaries while requiring minimal preprocessing. Differences in class delineation were most evident in fire-affected and agricultural areas, where embeddings demonstrated greater resilience to spectral disturbance and post-fire variability. Although overall accuracies exceeded 0.98, these high values reflect the use of spectrally pure, homogeneous training samples rather than overfitting. The results highlight that embedding-driven methods can deliver cleaner, more interpretable vegetation maps with far less data preparation, underscoring their potential to streamline large-scale ecological monitoring and enhance the spatial realism of wetland mapping. Full article

Wetlands are a critical component of the global biogeochemical cycle and have great potential for carbon sequestration under the changing climate. However, previous studies have mainly focused on the dynamics of soil organic carbon while paying little attention to the vegetation carbon storage in wetlands. Poyang Lake is the largest freshwater lake in China, where intra-annual and inter-annual variations in water levels significantly affect the vegetation carbon storage in the floodplain wetland. Therefore, we assessed the seasonal distribution and carbon storage of six typical plant communities (Arundinella hirta, Carex cinerascens, Miscanthus lutarioriparius, Persicaria hydropiper, Phalaris arundinacea, and Phragmites australis) in Poyang Lake wetlands from 2019 to 2024 based on field surveys, the literature, and remote sensing data. Then, we used 16 preseason meteorological and hydrological variables for two growing seasons to investigate the impacts of environmental factors on vegetation carbon storage based on four correlation and regression methods (including Pearson and partial correlation, ridge, and elastic net regression). The results show that the C. cinerascens community was the most dominant contributor to vegetation carbon storage, occupying 12.68% to 44.22% of the Poyang Lake wetland area. The vegetation carbon storage in the Poyang Lake wetland was significantly (p < 0.01) higher in spring (87.75 × 10⁴ t to 239.10 × 10⁴ t) than in autumn (77.32 × 10⁴ t to 154.78 × 10⁴ t). Water body area emerged as a key explanatory factor, as it directly constrains the spatial extent available for vegetation colonization and growth by alternating inundation and exposure. In addition, an earlier start or end to floods could both enhance vegetation carbon storage in spring or autumn. However, preseason precipitation and temperature are negative to carbon storage in spring but exhibited opposite effects in autumn. These results assessed the seasonal dynamics of dominant vegetation communities and helped understand the response of the wetland carbon cycle under the changing climate. Full article

The Ebinur Lake Basin, a key ecological security barrier for windbreak and sand control in northern Xinjiang, is crucial to the ecological safety of western China and the northern sand-prevention belt. Combining the basin’s geographical characteristics, this study comprehensively evaluated ecosystem service functions from four dimensions: water conservation, soil and water conservation, windbreak and sand-fixation, and biodiversity maintenance. Simultaneously, it conducted an ecological sensitivity assessment from four aspects: soil erosion, desertification, land use, and salinization sensitivity. The assessments of the importance of ecosystem service function and ecological sensitivity results were combined to create a tiered zoning plan for the basin. The basin was divided into four first-level zones: the Ebinur Lake Water Area and Wetland Biodiversity Protection Zone, the Desert Vegetation Windbreak and Sand Fixation Ecological Restoration Zone, the Oasis Agricultural Ecological Function Protection Zone, and the Mountain Water Conservation Zone. Six second-level zones were also delineated: the Ebinur Lake Wetland National Nature Reserve, Gobi Vegetation Distribution and Soil Erosion Sensitive Zone, Desert Vegetation Restoration Zone, Jinghe-Bortala Valley Oasis Agricultural Ecological Function Zone, Mountain Water Conservation and Forest-Grass Protection Zone, and Sayram Lake Water Body. This assessment and zoning plan provide support and scientific basis for the basin’s comprehensive ecological management, integrated protection and governance of mountains, rivers, forests, farmlands, lakes, grasslands and deserts, as well as regional ecological development. Full article

Monitoring water quality changes and identifying their driving factors are essential for the effective management of Dongjiang Lake. However, in-depth research on the spatiotemporal variations in the lake’s water quality and the complex interactions between natural and human factors remain insufficient. In this study, we aimed to characterize water quality trends and key physicochemical indicators in Dongjiang Lake by combining a 14-year water environmental dataset (2011–2024) and a correlation analysis. Our results showed that TN and COD_Mn concentrations displayed increasing trends, whereas the NH₃-N concentration showed a decreasing trend throughout the study period. The TN concentration initially decreased earlier in the year before increasing, with values ranging from 0.56 mg/L in September to 0.78 mg/L in November. The trends in COD_Mn concentration were the opposite to those of TN within the year, which first increased from 0.79 mg/L in January to 1.00 mg/L in June, and then decreased to 0.84 mg/L in December. The water level fluctuated inter-annually from 267.63 to 278.04 m during the study period, with a difference of 10.41 m. pH increased from 7.01 to 8.25, and dissolved oxygen decreased from 9.81 to 7.57. The WT fluctuates between 17.83 °C and 19.49 °C (p < 0.05). COD_Mn showed a highly significant positive correlation with transparency, pH, and water temperature, whereas NH₃-N showed a highly significant negative correlation with transparency, pH, and dissolved oxygen. Considering the importance of Dongjiang Lake as a freshwater resource and tourism hub, this study highlights the urgent need to prioritize pollution source control, while accounting for the lake’s deep-water dynamics and incorporating ecosystem-based restoration measures. Full article

Climate change and related weather extremes are increasingly having an impact on all aspects of life. The main objective of the research was to analyze the impact of the most important meteorological elements and the image data of various water bodies of the Kis-Balaton wetland, Hungary. The primary question was which meteorological elements have a positive or negative influence on vegetational surface cover. Drones have facilitated the visual surveying and monitoring of challenging-to-reach water bodies in the area, including a lake and multiple channels. The individual channels had different flow conditions. Aerial surveys were conducted monthly, based on pre-prepared flight plans. Images captured by a Mavic 3 drone flying at an altitude of 150 m and equipped with a multispectral sensor were processed. The time-series images were aligned and assembled into orthophotos. The image details relevant to the research were segregated and classified using Maximum Likelihood classification algorithm. The reliability of the image data used was checked by Shannon entropy and spectral fractal dimension measurements. The results of the classification were compared with the meteorological data collected by a QLC-50 automatic climate station of Keszthely. The investigations revealed that the surface cover of the examined water bodies was different in the two years but showed a kind of periodicity during the year. In those periods, where photosynthetic organisms multiplied in a higher proportion in the water body, higher monthly average air temperatures and higher monthly global solar radiation sums were observed. Full article

The Yangtze-to-Huaihe Water Diversion (YHWD) project has raised concerns about balancing economic benefits and ecological impacts in Lake Caizi, a nationally protected wetland recognized by the World Wildlife Fund. To assess post-diversion contamination and ecological risks, seasonal variation in polycyclic aromatic hydrocarbons (PAHs) was investigated in surface sediments from Lake Caizi. Total PAH concentrations were 103–565 ng/g dw in the wet season, marginally exceeding the 97.1–526 ng/g dw observed in the dry season. The lowest levels occurred in the western sub-lake (Lake Xizi), showing marked declines relative to a decade ago, attributable to enhanced wastewater treatment, farmland-to-lake restoration, and a 10-year fishing ban. Conversely, PAH concentrations in the main lake, particularly the southeastern and northern sectors of the Caizi route, have increased, reflecting pollutant inflows from Zongyang County via the Yangtze River and accumulation driven by the diversion flows. The diagnostic ratio and positive matrix factorization model indicated biomass burning as the dominant PAH source in Lake Xizi across seasons. In contrast, PAH in the main lake were primarily derived from petroleum combustion and leakage, with coal combustion during the wet season shifting to coal combustion dominance in the dry season due to the seasonal halt of shipping activity. Although overall ecological risk remains low in Lake Caizi, localized hotspots near the Caizi routes and industrial zones pose moderate-to-high risks, necessitating continuous monitoring in the future. Full article

High-precision monitoring of arid wetlands is vital for ecological conservation, yet traditional methods incur prohibitive labeling costs due to complex features. In this study, the wetland of Bosten Lake in Xinjiang is selected as a case area, where Pleiades and PlanetScope-3 multimodal remote sensing data are fused using the Gram–Schmidt method to generate imagery with high spatial and spectral resolution. Based on this dataset, we systematically compare the performance of fully supervised models (FCN, U-Net, DeepLabV3+, and SegFormer) with a weakly supervised learning model, One Model Is Enough (OME), for classifying 19 wetland–cropland mosaic types. Results demonstrate that: (1) SegFormer achieved the best overall performance (98.75% accuracy, 95.33% mIoU), leveraging its attention mechanism to enhance semantic understanding of complex scenes. (2) The weakly supervised OME, using only image-level labels, matched fully supervised performance (98.76% accuracy, 92.82% F1-score) while drastically reducing labeling effort. (3) Multimodal fusion boosted all models’ accuracy, most notably increasing U-Net’s mIoU by 63.39%. (4) Models exhibited complementary strengths: U-Net excelled in wetland vegetation segmentation, DeepLabV3+ in crop classification, and OME in preserving spatial details. This study validates a pathway integrating multimodal fusion with WSL to balance high accuracy and low labeling costs for arid wetland mapping. Full article

National urban wetland parks serve as key platforms for ecological conservation and recreation, yet the synergistic mechanisms between plant color dynamics and public aesthetic perception remain underexplored. Understanding these mechanisms is crucial for evidence-based, climate-resilient landscape design. This study quantifies statistical associations between seasonal color and aesthetic patterns in two national wetland parks (South Dian Lake and Laoyu Lake, Kunming) using Hue–Saturation–Brightness (HSB) color metrics and Scenic Beauty Estimation (SBE) based on year-round monitoring at 24 sample sites. Regression analysis revealed that overall SBE values ranged from −1.027 to 0.756, indicating medium aesthetic quality, with South Dian Lake outperforming Laoyu Lake, particularly in aquatic plant communities. Seasonal trends showed the highest aesthetic preference in winter (orange–yellow dominant, 0.110) and the lowest in early spring (−0.167, yellow dominant), followed by relatively stable values from late spring to mid-autumn (0.007–0.020) and a secondary peak in late autumn (0.029). Higher SBE scores were associated with a dominant hue ratio of 70–75%, balancing color unity and diversity. We identify an operational plant color configuration—70–75% dominant hue, 20% evergreen foliage and 5–7 color types—that corresponds to higher SBE scores. By translating aesthetic responses into quantitative color targets, this study provides guidance for climate-adaptive planting design and seasonal management in subtropical wetland landscapes under global warming. Full article

As a key freshwater wetland in the Beijing–Tianjin–Hebei core area, Baiyangdian Lake’s ecological health is strategically significant for regional ecological security, prompting this study to explore how sediment phosphorus forms drive its phytoplankton communities. The research adopted sequential extraction technology, morphological identification, and multivariate statistics in Baiyangdian Lake. Results showed sediment phosphorus was dominated by highly active exchangeable phosphorus (Ex-P, ~60%, with higher levels around villages of lake center and western areas), with occluded phosphorus (Oc-P, ~23%) as the second most abundant form. Ex-P was the core factor shaping phytoplankton communities, directly increasing biomass density (r = 0.38, p < 0.05) and explaining 17.92% of community variation. Bacillariophyta was the dominant group (43.3%), while calcium-bound phosphorus (Ca-P) maintained diversity and aluminum-bound phosphorus (Al-P) inhibited evenness (r = −0.35, p < 0.05). Active phosphorus directly affected, and inactive phosphorus indirectly regulated, phytoplankton patterns, clarifying the unique phosphorus structure of northern carbonate-type lakes and filling research gaps. It is suggested to include Ex-P and Ca-P in aquatic ecological monitoring and prioritize sediment passivation and riparian restoration in high-Ex-P areas to mitigate algal bloom risks. Full article

To investigate the influence of land use landscape patterns on lake water quality in the basin, the land use and water quality data of the Yilong Lake Basin from 1993 to 2023 were analyzed with a geographic information system, remote sensing, and landscape ecology methods in this research. The results show that (1) the land use landscape pattern and water quality of the Yilong Lake Basin had significant changes: the lake surface area, farmland, and shrubland declined, with grassland showing the sharpest decrease and serving as the main source of conversion to other land types, while forest land expanded and built-up land increased by five times. The landscape pattern analysis showed that the aggregation degree of the core habitat in the basin increased and the landscape had decreased patch density and increased heterogeneity. Regarding water quality, the concentrations of total nitrogen (TN), total phosphorus (TP), and ammonium nitrogen (NH₄⁺-N); permanganate index (I_Mn); and biochemical oxygen demand over 5 days (BOD₅) decreased. Furthermore, the concentration of dissolved oxygen (DO) increased and the concentration of chlorophyll-a (Chl-a) fluctuated for a long time but did not decrease dramatically at the end of the period compared with the beginning. In general, the eutrophication degree of Yilong Lake slightly decreased. (2) The landscape configuration strongly shaped the water quality: the redundancy analysis (RDA) revealed that the edge density (ED), landscape shape index (LSI), largest patch index (LPI), and patch density (PD) were negatively associated with the eutrophication of Yilong Lake (TN, TP, NH₄⁺-N, Chl-a), whereas the contagion index (CONTAG) was positively associated; the Shannon’s diversity index (SHDI) was closely linked with TN and I_Mn but negatively with DO; and the patch cohesion index (COHESION) had a low interpretation power for water quality changes. In particular, larger and more cohesive ecological patches supported a higher DO, while an increased patch density was linked to an elevated I_Mn and reduced DO. These results indicate that the restoration of key ecological patches and enhanced landscape cohesion helped to improve the water quality, whereas increased patch density and landscape heterogeneity negatively affected it. (3) In the past 30 years, the ecological management and protection work on Yilong Lake, such as returning farmland to forests and lakes, wetland restoration, and sewage pipe network construction, achieved remarkable results that were reflected in the change in the relationship between land use landscape pattern and water quality in the basin. However, human activities still affected the dynamic evolution of water quality: the expansion of built-up land increased the patch density, the reduction in shrubland and grassland weakened natural filtration, and the rapid urbanization process introduced more pollution sources. Although the increase in forest land helped to improve the water quality, the effect was not fully developed. These findings provide a scientific basis for the management and ecological restoration of plateau lakes. Strengthening land use planning, controlling urban expansion, and maintaining ecological patches are essential for sustaining water quality and promoting the coordinated development of the ecology and economy in the Yilong Lake Basin. Full article

Wetlands store large amounts of soil organic carbon stock (SOCS), making them crucial for global climate regulation. However, climate change, poor management, and weak protection policies threaten these stocks. To assess the contribution of different wetland types for national and international climate targets and to monitor the effectiveness of protection measures, additional research is required. Therefore, we assessed SOCS and disturbances from climate change, land use/land cover (LULC), and soil chemical composition in saline and eutrophic Ramsar sites in Serbia. Analyzing a total of 96 samples, we accounted for soil depth, reference soil group (RSG), and habitat/vegetation type. Mean SOCS in the saline site ranged from approximately 36 t·ha⁻¹ at 0–30 cm to 26 t·ha⁻¹ at 30–60 cm, whereas values were much higher for the eutrophic sites, ranging from 81 to 82 t·ha⁻¹ at 0–30 cm and 47–63 t·ha⁻¹ at 30–60 cm. Differences between groups for the whole soil columns (0–60 cm) were significant at the 0.1% level. While SOCS generally decreases with depth, it showed notable local variability, including occasional instances at deeper layers, indicating complex environmental and anthropogenic influences. Spatial mapping of soil chemistry parameters (pH, humus, P₂O₅, and K₂O) along with land use/land cover (LULC) data revealed nutrient dynamics influenced by agricultural activities. An analysis of regional climate data revealed temperature increases relative to the reference period of 1971–2000 by 0.5 °C for the decade 2001–2010 and of 1.5 °C for 2011–2020. Climate projections under the RCP4.5 and 8.5 scenarios predict further warming trends, as well as increased rainfall variability and drought risks. The results of our study contribute to quantifying the important, though variable, contribution of wetland sites to global climate regulation and show the influence of geogenic, pedogenic, and anthropogenic factors on SOCS. National policies should be adapted to safeguard these stocks and to limit negative effects from surrounding agricultural areas, as well as to develop strategies to cope with expected regional climate change effects. Full article

Shallow lakes are highly sensitive to hydrological changes and human activities; however, the effect of hydrological extremes on water quality dynamics remains unclear. In this study, we investigated hydroclimatic and water quality changes in Datong Lake (a typical shallow lake within the Yangtze River Basin) over the period 2021–2024, with the objective of detecting the dynamic response of lake water quality to its driving factors during extreme hydrological years. Our analysis suggested that precipitation, water level, and temperature of Datong Lake all fluctuated during the study period. Total nitrogen (TN) concentrations increased to 1.25 mg/L, 1.42 mg/L, and 1.05 mg/L in the lake, inlets, and outlet, respectively, driven largely by external nutrient inputs from agricultural and aquacultural activities. Precipitation and water level were significantly higher in the wet year (1051.15 mm and 27.26 m, respectively) than in the dry year (805.05 mm and 27.05 m, respectively). TN and total phosphorus (TP) concentrations at the river inlet were higher in wet years than in dry years, whereas TN and TP in the lake showed the opposite trend. Notably, both TN and TP were positively correlated with temperature, water level, and turbidity, and negatively correlated with dissolved oxygen and electrical conductivity. Among these drivers, turbidity emerged as key influential variable (R² ranging from 0.18 to 0.41) in modulating lake water quality during extreme hydrological years, followed by temperature (R² ranging from 0.11 to 0.17) and water level (R² ranging from 0.12 to 0.13). These findings reveal that extreme hydrological shifts drive changes in lake water quality, underscoring the necessity of integrated management strategies to alleviate climate change impacts on shallow lake ecosystems. Full article

Anthropogenic disturbances and morphological constraints pose significant threats to lake–wetland functions. However, conventional assessments often overlook the influence of wetland morphology on the spatial realization of ecosystem services, which limits effective ecological restoration. This study presents a multidimensional framework coupled with the InVEST model to evaluate the Integrated Ecosystem Service Capacity (IESC) in the Jianghan Lake Cluster. The assessment focuses on key ecosystem services, such as habitat quality, carbon storage, and water purification. The results reveal significant morphology-driven heterogeneity in IESC. Zonal optimization strategies, including ecological water replenishment, buffer-strip construction, and polder-to-lake conversion, significantly enhance IESC across conservation, regulation, and restoration zones. Model simulations indicate that these targeted interventions can reduce non-point source pollution by approximately 35%, and increase carbon sequestration and biodiversity indices by 15–20% and 30%, respectively. This study elucidates the coupling mechanisms between lake morphology and ecosystem service capacity and provides a spatial framework for restoring “lake–river–polder” composite wetland systems. Full article

Soil salinization in arid regions threatens ecological security and sustainable agriculture. The Ebinur Lake wetland in Xinjiang, situated in an arid climate and subject to human disturbance, suffers from severe salt accumulation and ecological degradation. To overcome the lack of soil depth information and limited spatiotemporal monitoring, this study integrates multi-year field samples and Landsat imagery (1996–2024) to construct a six-layer (0–100 cm) soil salinity inversion framework. Multi-source spectral features were optimized using the Random Frog Leaping Algorithm (RFLA), and models based on Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), and Random Forest (RF) were compared. The results (1) demonstrated that RFLA effectively identified high-contribution features, enhancing efficiency and reducing redundancy; (2) showed that CNN outperformed LSTM and RF in capturing spatial salinity, with R² values of 0.75, 0.59, 0.63, 0.69, 0.57, and 0.56 for the six layers; and (3) revealed salinity migration: surface enrichment, mid-layer buffering, and deep-layer accumulation. In oases, surface salinity declined while deep layers accumulated; in deserts, surface salinity increased. The proposed framework enhances the accuracy of multi-depth salinity retrieval and provides technical support for salinization monitoring, irrigation management, ecological assessment, and control of land degradation in arid regions. Full article