Search Results (175)

Flood-season lake spatiotemporal dynamics are vital for ecological security and socioeconomic development, requiring consistent high-resolution monitoring. However, precipitation fluctuations and sediment turbidity significantly alter water quality, while blurred boundaries between water and floodplain wetlands challenge precise monitoring. To address these issues, this study proposes a water body extraction method leveraging polarimetric Synthetic Aperture Radar data. utilizes the maximum between-class variance algorithm for initial segmentation, optimizes the threshold via a genetic algorithm, and employs dynamic morphological operations to refine boundary details. The method was validated using 2015–2025 Sentinel-1 flood-season time series of Dongting Lake on Google Earth Engine. The results demonstrate that the proposed method achieves stable and accurate water extraction across various years and seasons, with an overall accuracy surpassing 0.93, confirming its robustness and broad applicability. Furthermore, the spatiotemporal hydrodynamics and driving mechanisms of Dongting Lake were analyzed by integrating the extracted water areas with multi-source data, including water level, precipitation, discharge, temperature, and sunshine duration. Findings indicate that the flood-season water area exhibited a fluctuating trend, initially increasing and subsequently decreasing, peaking at 2202.26 km² in 2020 and dropping to 614.04 km² in 2025, a pattern primarily driven by extreme meteorological events such as heavy rainfall and prolonged droughts. Spatially, inundation patterns were characterized by deeper water in the north and shallower depths in the south, separated by a topographically higher central region. Regression analysis revealed a robust correlation between water area and water level with an R² of 0.931, providing a quantitative reference for water level estimation in ungauged regions. Additionally, discharge and precipitation were positively correlated with water area, whereas temperature and sunshine duration exerted a negligible influence. This study supports flood regulation in the Dongting Lake basin and provides a robust framework for analyzing lake dynamics using long-term SAR data. Full article

Natural climate variations and human activities have significantly altered the river–lake hydrological regimes in the middle and lower reaches of the Yangtze River, leading to substantial changes in the inundation patterns of the Dongting Lake wetland, which in turn profoundly affect the spatial distribution and landscape patterns of wetland vegetation. Determining the response mechanisms and appropriate thresholds of wetland landscape patterns to hydrological rhythm changes is of great importance for maintaining the health of wetland ecosystems and optimizing the ecological operation of water conservancy projects. Based on long-term measured water level data (1992–2023) and multi-temporal Landsat remote sensing images (1997–2022), combined with a digital elevation model (DEM), this study systematically analyzed the spatiotemporal evolution characteristics of the inundation processes in Dongting Lake before and after the operation of the Three Gorges Project (TGP) and their driving mechanisms on the plant landscape patterns of the floodplain wetland. The results show that after the TGP operation, the inundation pattern of Dongting Lake exhibited a drying trend, with a significant decline in annual mean water level (the largest drop of approximately 0.7 m in East Dongting Lake) and a marked reduction in the lake-wide average inundation duration (T) and inundation frequency (F). From 1997 to 2022, the total area of wetland vegetation in Dongting Lake showed a significant expansion trend, and the succession of the landscape pattern experienced a nonlinear process of stability, fragmentation, and recovery. The stepwise regression model revealed that the three elements of the inundation process explained more than 80% of the landscape pattern variation, among which inundation frequency (F) and inundation duration (T) were the core driving factors. Specifically, inundation frequency primarily regulated landscape diversity (SHDI) and contagion (CONTAG) through an environmental filtering effect, while maximum inundation depth (H) mainly maintained the physical connectivity (COHESION) of the landscape. Furthermore, the study quantified the stable hydrological range of the Dongting Lake wetland ecosystem: when the inundation frequency is maintained at 0.40–0.50 and the annual inundation duration is controlled at 4–5 months, the wetland landscape is in an optimal structural state. Once the warning thresholds are breached (e.g., F < 0.35 or T < 90 days), it may trigger the rapid expansion of cultivated poplar forests under combined hydrological and anthropogenic influences, leading to severe habitat fragmentation. These findings deepen the understanding of the response mechanisms of vegetation landscape patterns in large lake wetlands under altered hydrological rhythms. Full article

Compound extreme climate events have become increasingly frequent under climate change and may alter terrestrial carbon cycling through different atmospheric and soil pathways. Focusing on the Dongting Lake Eco-Economic Zone, this study identified three types of compound extreme events during 2003–2024: atmospheric compound hot–dry events (ACHDs), soil compound hot–dry events (SCHDs), and drought-to-rewetting events (DRWs). We then examined their associations with monthly anomalies of net primary production (NPP), heterotrophic respiration (RH), and net ecosystem exchange (NEE) under different land cover backgrounds. The results showed that ACHDs and SCHDs both increased significantly, whereas DRWs exhibited a slight decreasing trend and a more scattered spatial distribution. During the same period, regional NPP increased significantly, RH decreased slightly, and NEE became more negative, indicating an overall strengthening of net carbon uptake. Different event types were associated with contrasting carbon flux response pathways: ACHDs were mainly associated with reduced NPP and slightly increased RH, thereby shifting NEE toward more positive values and weakening regional net carbon uptake, whereas SCHDs and DRWs were more strongly associated with reduced RH and more negative NEE. In addition, the event–carbon relationships differed among land cover types, with cropland, built-up land, and sparsely vegetated surfaces showing higher sensitivity to ACHDs, whereas the responses to SCHDs and DRWs varied markedly among forest, grassland, wetland, and open water classes. These results highlight that compound atmospheric and soil extremes influence regional carbon cycling through distinct component-specific pathways, and that land use background is an important factor associated with differences in carbon flux sensitivity in humid lake–floodplain systems. Full article

Although numerous studies have explored the effects of various sounds on plants, a comparative understanding of how long-term exposure to low-intensity sound and rhythmic versus non-rhythmic sound impact on plants is still lacking. In this study, we conducted a field experiment to determine the growth, physiological responses, and allelopathic intensity of an invasive plant Alternanthera philoxeroides under prolonged exposure to different sound rhythms and intensities. The results showed that even at low intensity, long-term sound exposure inhibited the growth of A. philoxeroides while activating its defense mechanisms; these responses intensified with increasing sound intensity. However, the plant could not well distinguish between rhythmic and non-rhythmic sound treatments, although its allelopathic intensity also increased with sound intensity. This study extends the knowledge of plant response to acoustic stimuli, highlights the limited ability of plants to discriminate sound rhythms, and underestimates the negative impacts of prolonged low-intensity sound on plant performance. Therefore, the application of acoustic treatment techniques in future horticulture and agriculture requires a careful trade-off between their potential benefits and adverse effects on plants. Full article

Shallow groundwater in the Dongting Lake area is an important resource for domestic, agricultural, and industrial use, and its quality is essential for regional sustainable development and public health. Therefore, effective protection of this resource is urgently needed. In this paper, we integrate Positive Matrix Factorization (PMF) and Self-Organizing Map (SOM) machine-learning algorithms to conduct an in-depth analysis of the distribution, sources, and risks of toxic elements in shallow groundwater along the southern shore of Dongting Lake. The results indicate that Fe and Mn in the groundwater of the study area are at a severe pollution level, while As is at a light pollution level. The model analysis identified four pollution sources: natural sources (Fe, Mn) accounting for 31.33%, agricultural production (Zn) for 18.96%, traffic-mining mixed source (Pb, Cu, Cd) for 32.67%, and mineral dissolution-redox driven (As) for 17.04%. The average concentrations of Fe and Mn exceeded the standard limits. Although the carcinogenic metal Cd did not pose a health risk, the health risk value of As exceeded the maximum acceptable level, which requires serious attention. The PMF model quantified four potential sources of toxic elements, while SOM was used as a complementary nonlinear clustering tool to examine the consistency of the PMF-derived source contribution patterns. The integrated PMF–SOM framework, together with spatial distribution and geochemical evidence, improved the interpretability and robustness of source identification. Full article

Rural sustainable development is a core component of the global Sustainable Development Goals, and building digital villages and enhancing the resilience of rural economies are key pathways for underdeveloped regions to achieve rural sustainable development. The coordination and synergy between these two areas are central to rural revitalization. Taking 122 counties in Hunan Province as research units and using 2013–2023 spatial panel data, this study employs an improved coupling coordination model, spatial autocorrelation analysis and geographically weighted regression to explore their spatiotemporal evolution, clustering patterns and driving factors. The results show that both systems improved steadily: digital villages expanded from core areas, while economic resilience developed more balancedly. The coupling coordination evolved from near-disorder to a pattern characterized by regional equilibrium. The coupling coordination degree displayed significant positive spatial autocorrelation, forming an “High-High (H-H)” cluster in the Changsha-Zhuzhou-Xiangtan-Dongting Lake Plain and an “Low-Low (L-L)” cluster in western Hunan. Driving factors showed marked spatial heterogeneity. These findings provide empirical support for differentiated digital village policies in Hunan. Full article

Dongting Lake, a vital freshwater lake in China with substantial ecological, economic, and social significance, has fractional vegetation coverage (FVC) as a core indicator of regional ecological balance. To characterize the ecosystem’s health and support targeted protection, this study analyzed FVC’s spatio-temporal evolution and associated spatial factors in the Dongting Lake ecological restoration area using 2005–2020 MODIS imagery, integrating the dimidiate pixel model, slope trend analysis, and geographic detector model (noting the latter quantifies spatial explanatory power but not direct ecological causality). Results revealed distinct FVC heterogeneity: 2011 had the poorest vegetation (mean FVC = 0.60), while 2005, 2010, and 2012 showed higher FVC (mean = 0.65); summer exhibited the most vigorous growth due to favorable hydrothermal conditions. Slope was the dominant single factor with the highest spatial explanatory power for FVC (q = 0.50), its distribution strongly associated with soil moisture and erosion. The slope–soil moisture interaction had the strongest joint spatial explanatory power (q = 0.625), reflecting topographic–hydrological synergistic spatial association, implying slope may indirectly modulate vegetation water availability (inferred from spatial correlation, not causality). The slope–DEM interaction (q = 0.534) confirmed combined topographic explanatory effects. Overall, 70.3% of the region saw significant FVC improvement (notably in spring) from 2005 to 2020, with degradation in February, March, and December. Slope emerged as a key factor consistent with interannual and seasonal FVC variations. These findings provide a reliable scientific basis for targeted wetland restoration, emphasizing enhanced vegetation management in summer, autumn, and the growing season. Limitations include: MODIS’s 250 m resolution leading to mixed-pixel effects in fragmented wetlands, limited validation coverage of extreme habitats and single-year verification, and the Geodetector model’s reliance on spatial stratification and factor independence assumptions (deviating from wetland’s continuous factor variation) that preclude causal inference. Full article

Driven by the goal of carbon neutrality, low-carbon development in township spaces is essential for sustainable urban–rural growth. This paper employs a carbon accounting methodology, taking Fuqiushan Town in the Dongting Lake Ecological Economic Zone as a case study to develop a detailed carbon measurement inventory at the township scale. Using spatial analysis techniques, it synthesizes multi-source data—including land use, agricultural inputs, and population—to estimate emissions from key sources such as crop cultivation, livestock and poultry breeding, industrial production, and residential activities. The study also evaluates the carbon sequestration capacity of sinks such as woodlands and water bodies, enabling the spatial visualization of both carbon emissions and carbon sinks. Key findings include: (1) Fuqiushan Town exhibits a carbon emission profile characterized by “industrial activities as the primary source, supplemented by agriculture, with additional contributions from residential and transportation sectors,” while forested areas and water bodies serve as core carbon sink zones. (2) An innovative multidimensional indicator system for low-carbon development efficiency was established, consisting of the Low-Carbon Development Efficiency Index in Production, the Daily Life Carbon Responsibility Efficiency Index, and the Ecological Carbon Sink Efficiency Index, which together form a Comprehensive Efficiency Index for Low-Carbon Development. (3) Analysis reveals significant spatial coupling relationships and efficiency differentiation patterns among carbon emissions, industrial structure, energy dependence, and ecological background. Based on dominant carbon emission types, low-carbon efficiency thresholds, and spatial factor interactions, the 17 villages and one forest farm in the township are classified into five zones: “Industrial High-Carbon Transition Zone,” “Agricultural Pollution Reduction and Carbon Emission Reduction Synergy Zone,” “Ecological Low-Carbon Conservation Zone,” “Human Settlements Balanced Development Zone,” and “Ecological Core Zone.” Tailored low-carbon spatial planning strategies for material resources are proposed for each zone. These results offer quantitative support and spatially targeted insights for low-carbon spatial planning in ecologically sensitive townships, contributing to the achievement of objectives such as “carbon reduction and sink increase” and “rural revitalization.” Full article

Extreme droughts are projected to become more frequent and severe under climate change, posing significant risks to wetland ecosystems and submerged macrophyte communities. We combined field surveys in West Dongting Lake, China, combined with controlled greenhouse experiments to examine how drought intensity (expressed as contrasting soil moisture conditions during drought) and drought timing affect submerged macrophyte species richness, biomass, as well as resilience, mediated through species response in their reproductive strategies. Field observations revealed a sharp decline in clonal species (Hydrilla verticillata, Ceratophyllum demersum, Vallisneria spinulosa) following an extreme drought, while the sexual species Najas marina emerged as dominant. Greenhouse experiments confirmed these patterns and elucidated underlying mechanisms: extreme drought suppressed biomass, leaf area, and seedling re-germination in clonal species, whereas N. marina maintained regeneration via a persistent soil seed bank. Moderate drought enhanced leaf area, consistent with the intermediate disturbance hypothesis, while early drawdowns were most detrimental to growth. Species-specific responses highlight the role of reproductive strategy in drought resilience. These findings underscore the need for climate-adaptive water-level management, including limiting early drawdowns, mitigating extreme drought, and conserving seed banks to sustain biodiversity and ecosystem function under increasing hydroclimatic variability. Full article

Methane (CH₄) emissions are regulated by the balance between CH₄ production and oxidation, which are mediated by methanogens and methanotrophs. Little is known about the key drivers of potential methane production (PMP) under different land use types in the Dongting Lake area. This study investigated four land use types (natural wetland, poplar plantation, rice cropland, and vegetable field) in the Dongting Lake area. The effects of land use types on (a) the abundances and community compositions of soil methanogens and methanotrophs and (b) soil potential methane production were investigated. The results showed that the soil potential methane production of the rice cropland (0.26 ± 0.02 µg g⁻¹ h⁻¹) and vegetable field (0.26 ± 0.01 µg g⁻¹ h⁻¹) was higher than that of the poplar plantation (0.16 ± 0.01 µg g⁻¹ h⁻¹). The compositions of methanogenic and methanotrophic communities varied in response to different land uses. The mcrA gene abundance in the rice cropland (0.84 ± 0.05 × 10⁸ copies/g) and vegetable fields (1.23 ± 0.15 × 10⁸ copies g⁻¹) was higher than that in the natural wetland (0.09 ± 0.01 × 10⁸ copies g⁻¹) and poplar plantation (0.08 ± 0.03 × 10⁸ copies g⁻¹). The pmoA gene abundances in the rice cropland (1.65 ± 0.08 × 10⁸ copies g⁻¹) and vegetable fields (1.88 ± 0.32 × 10⁸ copies g⁻¹) were higher than those in the natural wetland (0.16 ± 0.02 × 10⁸ copies g⁻¹) and poplar plantation (0.11 ± 0.03 × 10⁸ copies g⁻¹). In addition, both pmoA and mcrA gene abundances were positively correlated with potential methane production. However, the regression line between pmoA gene abundance and potential methane production showed a shallower slope than that between mcrA gene abundance and potential methane production. These results suggest that soil potential methane production was primarily driven by increased methanogenesis rather than reduced methane oxidation. In addition, soil organic carbon, total nitrogen, water content, and pH were key abiotic factors regulating potential methane production and the abundance and community compositions of methanogens and methanotrophs in the Dongting Lake area. Full article

Guaranteeing the ecological security of the Dongting Lake Basin is of paramount importance for national-scale programs, such as the Yangtze River Economic Belt and aquatic conservation projects. Within this framework, carbon storage and its determining drivers act as essential indicators of regional ecological stability. However, the historical trajectory of carbon pools and their response to future multi-scenario land-use transitions remain insufficiently understood. Therefore, this study aims to quantify the spatiotemporal evolution of carbon storage in the Dongting Lake Basin from 2000 to 2020 and project its future dynamics under diverse development pathways. This study, utilizing land use data from 2000 to 2020 and the carbon density database of the Dongting Lake Basin, assessed land use changes over two decades and determined the spatiotemporal distribution of carbon storage. Additionally, using 17 driving factors and various spatial policies, the study projected the land use and land cover changes (LUCC) for 2030 under four scenarios: natural development, ecological protection, economic development, and planned development. The spatiotemporal distribution of carbon storage and its response mechanisms were analyzed for each scenario. The results showed that carbon storage was directly impacted by LUCC, with an overall “decrease-increase-decrease” trend from 2000 to 2020, resulting in a net increase of 3.685 × 10⁶ t. By 2030, the changes in carbon storage under the natural development, ecological protection scenario, economic development, and planned development scenarios were projected to be −1.008 × 10⁷ t, 1.276 × 10⁷ t, 3.292 × 10⁸ t, and −1.200 × 10⁵ t, respectively. Notably, the ecological protection scenario showed a significant positive growth in carbon storage, primarily driven by an increase in forest and wetland areas. Additionally, the spatial distribution of carbon storage exhibited a pattern of “high in the west and low in the east”. These results imply that to achieve the “Dual Carbon Strategy”, future land use planning in the Dongting Lake Basin should prioritize ecological protection and planned development models, including strict control of construction land expansion, increasing ecological land area, and enhancing carbon storage. Full article

In recent years, infrared detection technology for embankment leakages has become a popular research topic. The seepage and heat transfer characteristics of embankments under seepage failure conditions form the theoretical basis of infrared detection technology for leakage hazards. Nevertheless, a majority of prior research has relied on predetermined seepage pathways, which fail to accurately simulate the actual scenarios encountered in engineering practice. Accordingly, taking a typical soil embankment in the Dongting Lake area as the research object, a seepage damage test of the embankment body and surface soil of the embankment foundation was conducted. The mechanical and seepage damage of the embankment soil was established. FLAC3D6.0 software was used to develop a coupled numerical model of the unsaturated seepage, temperature, and stress of the embankment based on the damage model. The distribution laws of the seepage and temperature fields in the embankment body and foundation were calculated and analyzed. The results of this study show that there is a strong correlation between seepage, temperature, and structure during local seepage failure and even the overall structural failure of the embankment. Moreover, the evolution of the downstream embankment toe and surface temperature shows a phased change. By capturing this feature, it is possible to quickly screen the seepage location of an embankment to thereby provide a basis for determining the infrared detection indicators of the embankment. 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

Hunan Province from 2000 to 2023 is the study area. Based on NOAA precipitation data and county-level rice yield statistics in Hunan Province, the Mann–Kendall test, extreme precipitation indices, and wavelet analysis examine the spatial and temporal evolution characteristics of extreme precipitation and its multi-scale impact on rice yield. The results show that the extreme precipitation in Hunan Province showed a stable pattern of fluctuation, and the main extreme precipitation indexes had no significant change trend. The spatial distribution showed a pattern of “high value in central-northern Hunan and stable in southern Hunan”, and the precipitation was concentrated in June–August. The rice yield showed the characteristics of “stable increase in the core area, intensified fluctuation in the transition area, and continuous shrinkage in the marginal area”, and the Dongting Lake Plain was a high-yield and stable area. Multi-scale analysis shows significant coupling between extreme precipitation and yield: in the 4–8-year cycle, the peak value of precipitation lags behind the response of 1–2 years, and changes synchronously in a short period. The response of rice to extreme precipitation showed a threshold-type nonlinear characteristic. Moderate wetting was beneficial to stable yield, while the yield decreased significantly when the intensity or continuous precipitation exceeded the threshold. Hunan’s rice system has strong climate resilience but requires a multi-scale climate-adaptive agricultural system via engineering, technology, and policy for long-term stability and sustainable grain production. Full article

Change detection (CD) with synthetic aperture radar (SAR) images remains pivotal for environmental monitoring and disaster management. Deep learning has powerful feature extraction capabilities for CD, but suffers from complex architectures and limited interpretability. While BLSs demonstrate advantages in structural simplicity and interpretability, their feature representation capacity remains constrained. In high-precision CD with SAR images, strong feature representation capability is required, along with an uncomplicated framework and high interpretability. Therefore, a novel paradigm named PC-BiBL is proposed which achieves seamless integration of deep learning and broad learning. On the one hand, it employs a hierarchical cross-convolutional encoding (HCCE) module that uses pseudo-random cross-convolution (PCConv) for hierarchical cross-feature representation, aggregating contextual information. PCConv is an untrained convolution layer, which can utilize specialized pseudo-random kernels to extract features from bitemporal SAR images. On the other hand, since back-propagation algorithms are not required, the features can be directly fed into the bifurcated broad learning (BiBL) module for node expansion and direct parameter computation. BiBL constructs dual-branch nodes and computes their difference nodes, explicitly fusing bitemporal features while highlighting change information—an advancement over traditional BLS. Experiments on five SAR datasets demonstrate the state-of-the-art performance of PC-BiBL, surpassing existing methods in accuracy and robustness. Quantitative metrics and visual analyses confirm its superiority in handling speckle noise and preserving boundary information. Full article