Search Results (62)

The distribution of fresh and salty groundwater is a critical factor affecting the coastal wetlands. However, the dynamics of groundwater flow and salinity in river deltas remain unclear due to complex hydrological settings and impacts of human activities. The uniqueness of the Yellow River Delta (YRD) lies in its relatively short formation time, the frequent salinization and freshening alternation associated with changes in the course of the Yellow River, and the extensive impacts of oil production and underground brine extraction. This study employed a detailed hydrogeological modeling approach to investigate groundwater flow and the impacts of oil field brine leakage in the YRD. To characterize the heterogeneity of the aquifer, a sediment texture model was constructed based on a geotechnical borehole database for the top 30 m of the YRD. A detailed variable-density groundwater model was then constructed to simulate the salinity distribution in the predevelopment period and disturbance by brine extraction in the past decades. Probabilistic particle tracking simulation was implemented to assess the alterations in groundwater flow resulting from brine resource development and evaluate the potential risk of salinity contamination from oil well fields. Simulations show that the limited extraction of brine groundwater has significantly altered the hydraulic gradient and groundwater flow pattern accounting for the less permeable sediments in the delta. The vertical gradient increased by brine pumping has mitigated the salinization process of the shallow groundwater which supports the coastal wetlands. The low groundwater velocity and long travel time suggest that the peak salinity concentration would be greatly reduced, reaching the deep aquifers accounting for dispersion and dilution. Further detailed investigation of the complex groundwater salinization process in the YRD is necessary, as well as its association with alternations in the hydraulic gradient by brine extraction and water injection/production in the oilfield. Full article

Cadmium and petroleum are the main pollutants in coastal wetland ecosystems that affect plant nutrient balance and growth. Scholars have discovered how saline plants adapt to single stresses. How plant ecology and physiology correspond to complex cadmium and petroleum pollution, especially regarding the pollution impacts on carbon (C), nitrogen (N), and phosphorus (P) stoichiometry and biomass allocation in coastal wetland plants, remains unclear, limiting their application in regard to pollution remediation. This study focuses on Suaeda salsa, a popular species used in vegetation restoration in the Yellow River Delta’s coastal wetlands. Through the use of pot experiments, the dynamic changes in plant–soil ecological stoichiometry and biomass allocation were systematically investigated in response to individual and combined cadmium (0, 5, and 10 mg kg⁻¹) and petroleum (0, 6, and 12 g kg⁻¹) treatments. Compared with the control (CK), petroleum stress significantly increased the total nitrogen (TN) and plant total phosphorus (TP) contents, but did not substantially impact the total carbon (TC) content, resulting in 19.7% and 26.6% decreases in the plant C/N and C/P ratios, respectively. The soil organic carbon (SOC) content increased significantly under petroleum stress, whereas the TN and TP contents did not notably change, considerably increasing the soil C/N and C/P ratios, which were 1.5 times and 1.3 times greater than those of the CK, respectively. Cadmium stress alone or with petroleum stress did not significantly affect the C, N, or P stoichiometry or biomass allocation in S. salsa. The soil C/N/P stoichiometry redundancy analysis revealed that the contribution rates (especially the soil C/P and C/N ratios) to the total biomass and its allocation in S. salsa (64.5%) were greater than those of the control group plants (35.5%). The correlation analysis revealed that the total growth biomass of S. salsa was negatively correlated with the soil carbon content, C/N ratio, and C/P ratio, but positively correlated with the plant C/N and C/P ratios. The aboveground biomass proportion in S. salsa was significantly negatively correlated with the soil N/P ratio. The belowground biomass proportion exhibited the opposite trend. Petroleum pollution was the main factor driving S. salsa stoichiometry and growth changes, increasing the soil C/N and C/P ratios, reducing the nitrogen and phosphorus nutrient absorption capacities in plant roots, limiting plant nitrogen and phosphorus nutrients, and inhibiting biomass accumulation. Appropriate N and P supplementation alleviated plant growth inhibition due to petroleum pollution stress, which was conducive to improving vegetation ecological restoration in the Yellow River Delta. Full article

This study explores the spatiotemporal dynamics of SOC and microbial-mediated mechanisms in the Yellow River Delta wetlands. Using redundancy analysis and microbial community profiling, we show that vegetation drives distinct SOC storage patterns: Phragmites australis ecosystems exhibit the highest SOC sequestration, followed by Suaeda salsa and Tamarix chinensis habitats, where salt-tolerant taxa like Desulfobacterota and Halobacteriaota promote short-term carbon storage via anaerobic metabolism. The microbial community structure is shaped by both vegetation-induced microhabitats and environmental gradients: SOC and total nitrogen dominate community assembly, while electrical conductivity, pH, and sulfur/nitrogen nutrients regulate spatiotemporal differentiation. Seasonal turnover drives the reorganization of microbial community structures, shaping the dynamic equilibrium of carbon pools. Seasonal DOC dynamics, linked to tidal fluctuations and exogenous carbon inputs, highlight hydrology’s role in modulating active carbon pools. These findings reveal tight linkages among vegetation, microbial functional guilds, and soil biogeochemistry, critical for wetland carbon sequestration. Full article

Land use and land cover change (LULCC) is a key driver of carbon storage changes, especially in complex coastal ecosystems such as the Yellow River Delta (YRD), which is jointly influenced by climate change and resource development. The compounded effects of sea-level rise (SLR) and land subsidence (LS) are particularly prominent. This study is the first to integrate the dual impacts of SLR and LS into a unified framework, using three climate scenarios (SSP1–26, SSP2–45, SSP5–85) provided in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with LS monitoring data, to comprehensively assess future inundation risks. Building on this, and taking into account land use and ecological protection policies in the YRD, three strategic scenarios—Ecological Protection Scenario (EPS), Natural Development Scenario (NDS), and Economic Growth Scenario (EGS)—are established. The PLUS and InVEST models are used to jointly simulate LULCC and carbon storage changes across these scenarios. Unlike previous studies focusing on single driving factors, this research innovatively develops a dynamic simulation system for LULCC and carbon storage driven by the SLR-LS compound effects, providing scientific guidance for land space development and coastal zone planning in vulnerable coastal areas, while enhancing carbon sink potential. The results of the study show the following: (1) Over the past 30 years, the land use pattern of the YRD has generally extended toward the sea, with land use transitions mainly from grasslands (the largest reduction: 1096.20 km²), wetlands, reservoirs and ponds, and paddy fields to drylands, culture areas, construction lands, salt pans, and tidal flats. (2) Carbon storage in the YRD exhibits significant spatial heterogeneity. Low-carbon storage areas are primarily concentrated in the coastal regions, while high-carbon storage areas are mainly found in grasslands, paddy fields, and woodlands. LULCC, especially the conversion of high carbon storage ecosystems to low carbon storage uses, has resulted in an overall net regional carbon loss of 2.22 × 10⁶ t since 1990. (3) The risk of seawater inundation in the YRD is closely related to LS, particularly under low sea-level scenarios, with LS playing a dominant role in exacerbating this risk. Under the EGS, the region is projected to face severe seawater inundation and carbon storage losses by 2030 and 2060. Full article

Nature reserves are integral to sustaining ecological balance, functioning like a precise ecological regulator, where various species depend on and constrain one another to collectively form a stable ecosystem. Nevertheless, in the wake of economic development, pollutants like heavy metal contamination have insidiously emerged, imperceptibly influencing all these processes. To understand the ecological risk of heavy metals in an estuarine nature reserve, this study focused on the Yellow River Delta Nature Reserve (YRDNNR) and analyzed the distribution, potential environmental risks, and possible sources of heavy metals (Mn, Cu, Zn, Cr, As, Cd, Pb) in the surface sediments of this region. The results indicated that YRDNNR was rich in As and Cd, with Cd presenting the most substantial ecological risk. Further analysis suggested that the high levels of As and Cd could be ascribed to agricultural activities. This study also found that agricultural practices have made a significant contribution to the carcinogenic risk and pose certain risks to the natural environment and human health. More in-depth monitoring and testing of As and Cd levels in YRDNNR should be carried out, and measures should be adopted in accordance with their development. Moreover, the systematic regulation of fertilizer and pesticide use, along with enhancements to farmers’ ecological awareness, is of great significance to alleviating pollution hazards. The findings of this study carry significant implications for the ecological conservation of coastal wetlands, serving as a critical alert to the potential proliferation of heavy metal contamination in other areas of the delta. Full article

Water and sediment fluxes into the sea are the basis for the stability of the ecological pattern of the Yellow River Delta (YRD). As a Ramsar wetland of international importance, the YRD is facing the huge ecological risk of land degradation due to changes in water–sediment fluxes into the sea. In this study, we investigated the spatiotemporal dynamics of the coastline and subaerial delta using annual remote sensing images and revealed more detailed and clear relationships between water–sediment fluxes into the sea and the YRD evolution, including the whole delta and its subregions (e.g., the Qingshuigou and Diaokou regions) from 1976 to 2022. Our results showed that the mean yearly water and sediment fluxes during the study period amounted to 210.50 × 10⁸ m³ yr⁻¹ and 367.81 Mt yr⁻¹, respectively. There was an abrupt change in water and sediment fluxes into the sea in 1999, and both decreased significantly from 1976 to 1999, whereas the water discharge has significantly increased and the sediment flux has stabilized since around 2000. The delta area evolutions of the whole YRD and the Qingshuigou region can be characterized by three stages: a rapid growth stage (1976–1993), a rapid retreat stage (1993–2002), and a gradual recovery stage (2002–2022). The area in the Diaokou region displayed a continuous decreasing trend from 1976 to 2022. The regression analysis indicated that the relationships between cumulative sediment flux and cumulative land accretion area presented spatiotemporal differentiation. The cumulative land accretion area increased with the cumulative sediment flux in the whole YRD and its subregions from 1976 to 1992, decreased with the cumulative sediment flux in the YRD from 1993 to 2002, except for the northeast of Qingshuigou, and then expanded with the cumulative sediment flux in the YRD from 2003 to 2022, except for the southeast of Qingshuigou. 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

Land use/land cover (LULC) change has greatly altered ecosystem carbon storage capacity and may eventually profoundly impact global climate change. Characterizing the LULC change and its impact on wetland ecosystem carbon stock provides useful data and insights that can guide decision-making procedures aimed at achieving sustainable development objectives. The Yellow River Delta (YRD) represents the most intact coastal wetland and is considered to be the most recent wetland ecosystem in China. It exhibits significant carbon stock capacity and ecological value. Based on the LULC data of the YRD in 2002, 2007, 2012, 2017, and 2022, this paper quantitatively evaluates the spatiotemporal changes in LULC and carbon stock in the region and analyzes the response characteristics of carbon stock to LULC change. The results show significant reductions in cropland and tidal flat wetland areas from 2002 to 2022, resulting in a decrease of 1,428,735.77 t and an increase of 139,856.58 t in carbon stock, respectively. The built-up land area expanded considerably, and carbon stock was lost by 1,467,915.82 t. Spatially, the carbon stock exhibited a pattern of “low along the coast, high inland; low in the center, high around the periphery”. In addition, protecting cropland, reducing building, facilitating the conversion of reservoirs and ponds to forest, and transforming tidal flat wetlands into reservoirs and ponds can increase the region’s carbon storage capacity. These findings provide valuable insights for regional carbon management strategies and ecological protection policies, supporting the sustainable development goals of the Yellow River Delta. Full article

Ecosystem service value (ESV) reflects ecosystem functions and benefits; however, the factors influencing ESV and the mechanisms driving it in wetlands and non-wetlands are not yet fully understood. The Yellow River Delta (YRD) is distinguished by the presence of numerous wetland areas that are both Reserve and non-Reserve and thus it was selected as the designated study area. In this study, the spatiotemporal structures of ESV in the YRD between 2000 and 2020 were studied using land cover change analysis and the equivalent factor methodology. In addition, we analyzed the drivers behind the geographical variability in ESV by applying the Geographical Detector method. The results showed that the land structure of the YRD National Nature Reserve was relatively stable, whereas the non-Reserve area exhibited greater fluctuations; that is, wetlands in the YRD non-Reserve area decreased by 11.43% compared with the more stable land structure in Reserve areas, where wetland decreased by 4.93%. Furthermore, disparities in the distribution of land use types gave rise to a discernible spatial distribution of overall ESV, with the northeast exhibiting significantly higher ESV levels compared to the southwest. Additionally, in the past two decades, the center of gravity of the ESV in both regions has shifted towards urban centers, and wetlands have migrated towards the coastline. The Normalized Difference Vegetation Index was identified as the main driver of ESV heterogeneity. The findings of this study are highly relevant to regional ecological conservation and the promotion of economic and social development. Full article

Estuarine wetlands are the transition zone between marine, freshwater, and terrestrial ecosystems and are more ecologically fragile. In recent years, the spread of exotic vegetation, specifically Spartina alterniflora, in the Yellow River estuary wetlands has significantly encroached upon the habitats of native species such as Phragmites australis, Suaeda glauca Bunge, and Tamarix chinensis Lour. With advances in land prediction modeling, predicting wetland vegetation distribution can aid management and decision-making for ecological restoration. We selected the core area as the study object and coupled the hydrological model MIKE 21 with the PLUS model to predict the potential future distribution of invasive and dominant species in the region. (1) Based on the fine classification results from satellite images of GF1/G2/G5, we gained an understanding of the changes in wetland vegetation types in the core area of the reserve in 2018 and 2020. (2) Using public data such as ERA5 and GEO as input for basic environmental data, using MIKE 21 to provide high-spatial-resolution hydrodynamic parameters for the PLUS model as an environmental driver, we modeled the spatial distribution of various wetland vegetation in the Yellow River estuary wetland in Dongying under different artificial restoration measures. (3) We predicted the 2022 distribution of typical vegetation in the region, used the classification results of GF6 as the actual distribution, compared the spatial distribution with the actual distribution, and obtained a kappa coefficient of 0.78; the predicted values of the model are highly consistent with the true values. This study combines the fine classification results of vegetation based on hyperspectral remote sensing, the construction of a coupled model, and the prediction effect of typical species, providing a reference for constructing and optimizing the vegetation prediction model of estuarine wetlands. It also allows scientific and effective decision-making for the management of ecological restoration of delta wetlands. Full article

Ecosystem service connectivity (ESC) is the spatial and functional links among and within ecosystems that support unimpeded service flows, and that could play an important role in ecosystem stability enhancement and regional land planning. Understanding the relationships between landscape patterns and ESC is crucial to achieving certain sustainable development goals, but it has not yet received an adequate amount of attention. Here, we evaluated the changes and connectivity of five key types of ecosystem services from 2000 to 2020 and analyzed the correlations and spatial aggregations between the ESCs and landscape metrics in the wetlands of the Yellow River Delta, China. Various research methods, such as the InVEST model, spatial autocorrelation analysis, Spearman’s correlation, and self-organizing map, were applied. The results showed that water yield, water purification, and habitat quality showed high connectivity, but the overall ESC declined along with the restoration of the wetland area. Meanwhile, the High-High ESC cluster of water yield, water purification, and habitat quality had similar spatial distribution patterns, and both were dominated by tidal flats. Moreover, the ESC and landscape metrics showed significant correlations and spatial heterogeneity, and a potential connectivity between water yield and habitat quality was also found. These findings can assist decision-makers in developing effective ecosystem management strategies and provide a reference for future research on ecosystem service connectivity. 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

Coastal wetlands are critical for global biodiversity and ecological stability, yet the invasive Spartina alterniflora (S. alterniflora) poses severe threats to these ecosystems. This study evaluates the effectiveness of management efforts targeting S. alterniflora in the Yellow River Delta (YRD) using Zhuhai-1 hyperspectral imagery and the U-Net method. The U-Net model, coupled with the Relief-F algorithm, achieved a superior extraction accuracy (Kappa > 0.9 and overall accuracy of 93%) compared to traditional machine learning methods. From 2019 to 2021, S. alterniflora expanded rapidly, increasing from 4055.06 hm² to 6105.50 hm², primarily in tidal flats and water bodies. A clearing project reduced its extent to 5063.62 hm² by 2022, and by 2023, only 0.55 hm² remained. These results underscore the effectiveness of Shandong’s management policies but highlight the risk of regrowth due to the species’ resilience. Continuous monitoring and maintenance are essential to prevent its resurgence and ensure wetland restoration. This study offers critical insights into dynamic vegetation monitoring and informs conservation strategies for wetland health. Full article

Deep learning has garnered increasing attention in human activity detection due to its advantages, such as not relying on expert knowledge and automatic feature extraction. However, the existing deep learning-based approaches are primarily confined to recognizing specific types of human activities, which hinders scientific decision-making and comprehensive environmental protection. Therefore, there is an urgent need to develop a deep learning model to address multiple-type human activity detection with finer-resolution images. In this study, we proposed a new multi-task learning model (named PE-MLNet) to simultaneously achieve change detection and land use classification in GF-6 bitemporal images. Meanwhile, we also designed a pooling enhancement module (PEM) to accurately capture multi-scale change details from the bitemporal feature maps through combining differencing and concatenating branches. An independent annotated dataset at Yellow River Delta was taken to examine the effectiveness of PE-MLNet. The results showed that PE-MLNet exhibited obvious improvements in both detection accuracy and detail handling compared with other existing methods. Further analysis uncovered that the areas of buildings, roads, and oil depots has obviously increased, while the farmland and wetland areas largely decreased over the five years, indicating an expansion of human activities and their increased impacts on natural environments. Full article

Coastal saltmarsh wetlands are vital “blue carbon” ecosystems. Fractional vegetation cover (FVC) is a key indicator revealing the spatial distribution and growth status of vegetation. Remote sensing has proven a vital tool for FVC estimation at regional or landscape scales. Establishing a species-level FVC estimation model usually requires sufficient field measurements as training/validation samples. However, field-based sample collection in wetlands is challenging because of the harsh environment. In this study, we proposed a Fractional Vegetation Cover Wasserstein Generative Adversarial Network (FVC-WGAN) model for FVC sample expansion. We chose the Yellow River Delta as the study area and utilized the time series Sentinel-2 imagery and random forest regression model for species-level FVC estimation with the assistance of FVC-WGAN-generated samples. To assess the efficacy of FVC-WGAN, we designed 13 experimental schemes using different combinations of real and generated samples. Our results show that the FVC-WGAN-generated samples had similar feature values to the real samples. Supplementing 500 real samples with generated samples can achieve good accuracy with an average RMSE < 0.1. As the number of real samples increased, the accuracies of FVC estimation improved. When the number of the generated samples was balanced with the real samples, the accuracy improved in terms of both R², RMSE and the spatial consistency. Full article