Search Results (4,143)

As a novel approach to address the lack of systematic studies on spatial Gross Ecosystem Product (GEP) accounting and Ecological Security Pattern construction, this study integrates GEP thresholds with Morphological Spatial Pattern Analysis (MSPA) to identify ecological sources. A resistance surface is constructed using five representative influencing factors, and the Minimum Cumulative Resistance (MCR) model is applied to extract ecological corridors, thereby establishing the Ecological Security Pattern for the Yellow River-Fronting Region of Henan in 2020. The results indicate the following: (1) GEP in the study area exhibits a spatial distribution of “high in the northwest, low in the southeast,” with regulating services accounting for more than 90% of the GEP. (2) A total of 11 ecological sources, 13 ecological corridors, and 7 ecological nodes were identified, primarily distributed in mountainous regions. (3) The Ecological Security Pattern exhibits spatial imbalance, with dense corridors in the western mountains and sparse distribution in the eastern plains. These findings provide scientific support for formulating ecological conservation measures and optimizing ecosystem management in the Yellow River Basin. Full article

Karst ecosystems play an important hydrological role in regulating regional water availability and supporting biodiversity, yet they face increasing threats from deforestation, land-use conversion, and limited scientific data to inform sustainable conservation efforts. This study aims to assess karst geodiversity, aquatic habitat diversity, and freshwater endemism in the Maybrat Karst, and to explain the linkages among these three aspects as a scientific basis for regional karst conservation. The research employed geospatial analysis and descriptive ecological analysis. Data were collected through satellite image interpretation, participatory mapping, field observations, and a comprehensive literature review. Results show that the Maybrat Karst has very high geodiversity, with ±2322.91 km² (41.49%) of the region classified as karst. All seven karst elements were identified, including 40–56 hills/km², 110 water-filled dolines, 334 springs, 178 subterranean rivers, 90 caves, and three major karst lakes. Aquatic habitat diversity is likewise very high, comprising seven habitat types across the full 100–500 m elevational range, accompanied by 17 Cherax morphotypes, indicating strong environmental differentiation. The literature review identified 18 endemic freshwater species, consisting of five Cherax species, ten rainbowfish species of the genus Melanotaenia, and three additional taxa: Pseudomugil reticulatus, Glossogobius hoesei, and Zenarchopterus ornithocephala. These findings confirm that high karst geodiversity and habitat heterogeneity make the Maybrat Karst a key aquatic endemism center, highlighting the urgent national and global imperative for comprehensive karst protection to safeguard long-term biodiversity and ecosystem sustainability. Full article

Forest biomass reflects the carbon storage capacity of forest ecosystems. Although remote sensing-based biomass estimation techniques have become increasingly mature, the issue of signal saturation in optical remote sensing still requires further investigation. This study was conducted in the Picea schrenkiana forest of the Ili River Valley in the western Tianshan Mountains. By integrating multimodal data from ICESat-2 LiDAR and GF-6 optical imagery, we developed machine learning and deep learning models to achieve high-precision biomass estimation. Based on forest management inventory data, we extracted spectral and textural features from GF-6, along with canopy structure attributes derived from the four acquisition modes (day/night, strong/weak beams) of ICESat-2. After correlation-based feature selection, LightGBM, CatBoost, and TabNet models were trained and compared. The results showed that models integrating multi-source data significantly outperformed those based on a single data source. The TabNet model not only achieved high estimation accuracy but also provided clear feature importance rankings, with ICESat-2-derived canopy height percentiles and GF-6 red-edge vegetation indices contributing most significantly to the biomass estimation of Picea schrenkiana. These findings demonstrate the feasibility of synergistically utilizing domestic high-resolution satellites and multi-mode spaceborne LiDAR for forest biomass estimation in arid regions, providing an effective technical reference for accurate carbon sink monitoring of specific tree species in forest areas. Full article

Electricity production is one of the most significant sources of environmental pollution. Traditional energy sources involve environmental devastation associated with the extraction of fossil fuels, greenhouse gas emissions, dust, and the byproducts of ash and other harmful substances. Therefore, the choice of energy source directly impacts the environmental impact of technological processes. Obtaining energy from sources that do not generate such a significant negative impact on the environment, such as hydroelectric power plants or wind farms, is not always possible, as it depends on the location of a given enterprise near rivers or areas with regularly strong winds. Therefore, the aim of our study was to assess the environmental impact of switching the power source for the technological process of mass bottle packaging from grid-connected to photovoltaic power. To this end, a 1 MW photovoltaic PV installation was designed to replace traditional grid-connected power. The design was carried out using PVsyst 7.4 software. An analysis of the monthly yields from the PV installation showed that it could power the analyzed technological process independently for ten months of the year, excluding January and December. Using Simapro 9.6 software and the Ecoinvent database, an environmental impact analysis of the change in electricity source was conducted. The study showed that powering the process with energy from the proposed photovoltaic farm reduces the potential environmental impact by approximately 75% in terms of human health, approximately 65% in terms of ecosystems, and approximately 50% in terms of resources. Full article

With the intensification of human activities and climate variability, balancing ecosystem service (ES) supply and demand is critical for regional sustainable development. Existing studies predominantly focus on linear driving effects and lack integrated quantitative frameworks that link the spatiotemporal dynamics of ES supply–demand relationships (ESSDRs) with their nonlinear driving mechanisms, and few have systematically quantified the critical thresholds of driving factors and their interactive effects. To address these research gaps, this study quantified the supply, demand, and supply–demand ratios of four key ESs (food production [FP], carbon sequestration [CS], water yield [WY], and soil retention [SR]) in the Yellow River Basin of Henan Province (2000–2020) using the InVEST model and multi-source data. An analytical framework integrating the Extreme Gradient Boosting (XGBoost) model and Shapley Additive Explanations (SHAP) was established to identify dominant drivers, reveal nonlinear response patterns, and quantify critical thresholds. The results showed that FP and CS supply increased continuously, while WY and SR supply slightly declined; CS and WY demand grew faster than supply, leading to expanding deficits, whereas FP and SR maintained relative balance. Spatially, FP/CS surpluses concentrated in eastern plains and southwestern forests, WY deficits occurred in the northwest, and SR balance prevailed in most regions. Dominant drivers differed by ES type—arable land proportion (FP), population density (CS), precipitation (WY), and slope (SR)—all exhibiting distinct threshold effects (e.g., arable land proportion >0.6, slope >3°). These findings provide novel insights into ESSDR spatial heterogeneity and threshold-based regulation, offering a scientific basis for differentiated ecological management and sustainable spatial planning in the Yellow River Basin and similar ecologically vulnerable regions. Full article

Frequent flash floods threaten human well-being, hydropower infrastructure, and ecosystems. However, the long-term evolution of flash flood patterns over recent decades remains insufficiently understood, particularly in data-scarce high-altitude regions. Using multi-source remote sensing data integrated with historical disaster records and field investigations, this study examined the spatiotemporal evolution and driving factors of flash floods across the Qinghai–Tibet Plateau (QTP). The results indicate that flash floods have increased exponentially, which may be influenced by disaster management policies, with peaks in July–August and frequent occurrences from April to September. The seasonal trajectory of the center of gravity of flash floods from April to September exhibited a clear directional pattern. Regions with the highest disaster density were concentrated in the headwaters of five major rivers, including the Yarlung Zangbo, Jinsha, Nu, Lancang, and Yellow Rivers. Shapley Additive Explanation (SHAP) and Random Forest analyses reveal that soil moisture, anthropogenic intensity, and seasonal runoff variability are the dominant driving factors. With ongoing socioeconomic development, intensified human activities have become a key contributor to the increasing frequency of flash floods. These findings highlight the value of remote sensing-based assessments for flash flood monitoring and early warning and provide scientific support for risk mitigation, loss reduction, and the advancement of water-related targets under the United Nations’ Sustainable Development Goals. Full article

The Vietnam Mekong River Delta (VMRD) is a climate-sensitive region characterized by diverse ecosystems, including extensive mangrove forests that protect against sea-level rise and contribute to global carbon sequestration. Accurate land cover classification in the VMRD is essential but remains challenging due to complex landscapes and dynamic environmental conditions. The primary objective of this study is to propose a semi-supervised deep learning framework that integrates satellite indices with multi-temporal remote sensing data to address key classification challenges, particularly in situations where ground truth data is limited, as compared to unsupervised and supervised machine learning methods. Our comparative analysis across different sample sizes (500 to 6000 ground-truth data points) reveals critical insights into model performance and scalability. Supervised models, including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), demonstrated strong performance when sufficient labeled data were available, with CNN achieving the highest accuracy (0.97 at 6000 samples). However, at minimal sample sizes (500 sample points), these supervised approaches exhibited substantial limitations, with accuracies dropping dramatically (RF: 0.75, SVM: 0.80, CNN: 0.81). Supervised models also showed overfitting tendencies compared to official land cover statistics. In contrast, the semi-supervised approach (SoC4SS-FGVC) achieves remarkably high performance at small sample sizes (0.92 accuracy with 500 sample points), demonstrating strength under minimal data availability. The framework also showed improved capability in distinguishing spectrally similar land-cover classes and detecting environmentally sensitive types such as mangrove forests. Cross-validation with official statistics confirmed the semi-supervised model’s superior effectiveness in delineating paddy rice fields and its resistance to overfitting. The performance analysis demonstrates that SoC4SS-FGVC provides a practical and cost-effective solution for land cover mapping, particularly in regions where extensive ground-truth data collection is prohibitively expensive or logistically challenging. Full article

Climate change is altering the frequency, duration, and seasonality of low flows, which are critical for water availability, ecosystem functioning, and river management. Low-flow characteristics, defining the minimum, often seasonal, flow levels in rivers or streams primarily fed by groundwater, snow or glacier melt, or lake drainage, are essential for assessing hydrological droughts and water resource vulnerability. In the Upper Vistula River Basin, variable precipitation and rising air temperatures increase the risk of droughts, impacting both natural systems and human water use. This study analyzed long-term trends in annual low flows and associated parameters, including drought frequency, duration, and deficit volume, across 41 small- and medium-sized catchments. Two datasets were considered: 25 stations with 58-year daily discharge records (1961–2019) and 41 stations with 38-year records (1981–2019). Low flows were identified using the threshold level method (TLM) at 70% and 90% exceedance (FDC70 and FDC90). Trends were assessed with the Mann–Kendall test, and spatial drought patterns were mapped to evaluate regional variability. Deep and shallow low flows occurred at all analyzed cross-sections. For the period 1961–2019, deep low flows (FDC90) occurred almost annually in 18 of the 25 cross-sections since 2012. Statistically significant increasing trends in deep low-flow parameters were detected in five cross-sections for 1961–2019 and in seven cross-sections for 1981–2019. Shallow low flows (FDC70) occurred in all sections; four rivers exhibited annual shallow droughts during 1961–2019, whereas 12 rivers showed annual events in 1981–2019. Summer droughts predominated over winter events, reflecting enhanced evapotranspiration and higher seasonal water demand. These findings highlight the relevance of analyzing low-flow parameters for understanding hydrological droughts. Such information can support water resource management, planning, and ecosystem protection under variable climatic conditions. Full article

The deep integration of green finance and the digital economy serves as a critical lever for achieving the “dual carbon” goals and the “Digital China” strategy. This study constructs a “Technology–Capital–Environment” (TCE) analytical framework and integrates a coupling coordination degree model with a dynamic Qualitative Comparative Analysis (QCA) approach. Based on panel data of the Pearl River Delta urban agglomeration from 2014 to 2023, we investigate the synergistic development level, multiple pathways, and dynamic evolution between the two systems. Key findings include: (1) The coupling coordination degree of the two systems has steadily increased, yet significant spatial heterogeneity persists. The average annual growth rate of potential catch-up cities (3.37%) surpasses that of core leading cities (1.77%). (2) Four equifinal driving pathways are identified, which can be summarized into three patterns: technology-dominated institutional synergy, human capital–policy dual-core guidance, and technology–infrastructure synergistic driven. (3) Dynamic analysis reveals that pathways embedded with digital human capital and new infrastructure exhibit stronger resilience to shocks, whereas pathways reliant on institutional synergy demonstrate higher vulnerability. (4) Guangzhou and Shenzhen have already exhibited “ecosystem-level” synergistic characteristics, rendering existing configurational models limited in explanatory power. This study provides a theoretical foundation for promoting regionally differentiated deep integration of green finance and the digital economy and for building a resilience-oriented synergistic development system. Full article

Evaluating the aquatic ecological and environmental consequences of dredging projects with precision is essential for reconciling engineering objectives with the long-term health of aquatic ecosystems. This study establishes an evaluation system for the aquatic ecological and environmental impacts of dredging engineering based on the Pressure–State–Response (PSR) analytical framework, and constructs a comprehensive assessment system through Velocity Pausing Particle Swarm Optimization–Projection Pursuit (VPPSO-PP) coupled with fuzzy pattern recognition. Taking the Pinglu Canal project as a case study, the objective weights of indicators are obtained via the VPPSO-PP method, and the impact levels are determined by combining the fuzzy pattern recognition model. Case studies show that the quality of discharged residual water is the most critical factor affecting the aquatic ecological environment, ranking highest with a weight of 0.0839, followed by the proportion of aquatic ecological restoration investment at 0.0685. Among the five typical dredging sections of the Pinglu Canal, the Shaping River section and the Offshore Estuary Section were rated as having a “mild impact.” In contrast, the Main Stream of Qinjiang River section, the Watershed section, and the Qinzhou urban section were rated as having a “moderate impact.” These evaluation results are consistent with the actual engineering conditions. The model developed in this study enables a quantitative and objective assessment of the aquatic ecological impacts of dredging projects. It provides a scientific basis and a practical tool for ecological management and decision-making in dredging operations. Full article

Arid and semi-arid regions are critical to terrestrial ecosystems and regional carbon cycle regulation, directly contributing to peak carbon and carbon neutrality goals. However, the fragmented landscapes in these regions pose significant challenges to conventional pixel-based classification, which often struggles with mixed pixel issues and lacks biophysical interpretability. To address these limitations, this study develops an Ecologically Constrained Deep Learning Autoencoder (ECO-DEAU) framework for sub-pixel land cover mapping by integrating biophysical constraints. Specifically, ECO-DEAU employs spectral indices to extract standard spectral signatures for five primary land cover types, which serve as initial weights to guide the autoencoder in estimating fractional abundances. The model was trained across ten representative landscape zones in the Inner Mongolia section of the Yellow River Basin and validated against high-resolution Gaofen-2 data. Results demonstrated that ECO-DEAU yielded an average $R^2$ of 0.687, reaching a maximum $R^2$ of 0.749 in spatially heterogeneous transition zones, representing a substantial improvement over the baseline unconstrained Deep Autoencoder (DEAU). By effectively resolving the blind source separation problem and improving decomposition accuracy, ECO-DEAU serves as a robust tool for addressing mixed pixel challenges in heterogeneous environments, thereby facilitating large-scale, high-resolution carbon sink monitoring. Full article

Land use change profoundly impacts the trade-offs and synergies among ecosystem services in ecologically fragile watersheds. Optimizing land use patterns based on ecological function zoning is an important approach to coordinate multiple ecosystem services and promote sustainable watershed management. This study focuses on the Wuding River Basin within the Chinese Loess Plateau, using Self-Organizing Map, multi-objective genetic algorithms, and the Future Land-Use Simulation model to explore land use optimization schemes. The results show that the windbreak and sand fixation service in the Wuding River Basin presents a spatial pattern of higher values in the northwest and lower values in the southeast, while the other six services exhibit a pattern of higher values in the east and lower values in the west. Based on the ecosystem service cluster characteristics, the basin can be divided into soil and water conservation zones, habitat conservation zones, and ecologically fragile zones. The trade-offs and synergies between ecosystem services within different zones differ significantly, with the trade-off between food supply, soil conservation, and habitat quality being particularly prominent. After optimization, the food supply and soil conservation in the soil and water conservation zones increased by an average of 0.63 × 10⁴ t and 1.94 × 10⁵ t, respectively. The food supply in the habitat conservation zones increased by 0.11 × 10⁴ t, while habitat quality remained stable. In the ecologically fragile area, water production and carbon sequestration services increased by an average of 0.26 × 10⁴ t and 0.58 × 10⁵ t, respectively. During the optimization process, the reasonable allocation of grassland and unused land played a key role in balancing service conflicts. This study provides a scientific basis for coordinating trade-offs in watershed ecosystem services and achieving land use optimization management through the framework of service clusters, functional zones, and multi-objective optimization. Full article

Under global climate change, frequent droughts threaten ecosystem functions, but how drought characteristics affect ecosystem resilience remains unclear. Focusing on the Dongjiang River Basin, China, we identified drought events at an 8-day scale from 2000–2024 using multi-source remote sensing and reanalysis data. The water use efficiency-based resilience index (R_de) was calculated, and a random forest model quantified the contributions of 21 potential driving factors. The model explained 68% of R_de variance (R² = 0.68, RMSE = 0.12). Downward shortwave radiation was the primary factor, followed by antecedent water use efficiency and soil moisture anomaly, with drought intensity and air temperature ranking fourth and fifth. All dominant factors exhibited nonlinear threshold effects: R_de decreased significantly after radiation exceeded ~110 W·m⁻²·(8d)⁻¹; R_de declined when standardized soil moisture anomaly fell below −2.0; and R_de increased sharply when drought intensity exceeded 12%. Drought intensity far outweighed duration and severity, establishing it as the key drought attribute. This study reveals the dominant drivers and their thresholds governing ecosystem resilience in the Dongjiang River Basin, providing quantifiable indicators for ecological drought early warning. Full article

Mining is an economic activity with significant socio-environmental implications, particularly in regions where communities depend directly on natural resources. This study aimed to analyze the perceptions of residents of the Pungala parish regarding the impacts of mining in the Alao River basin. A questionnaire was administered, considering sociodemographic, social, and environmental variables. The surveyed population was predominantly older adults and had a balanced gender distribution. The majority identified as indigenous or mestizo, with primarily secondary school educational levels and with a labor structure characterized by independent work. At the social level, mining is perceived as a source of economic benefits through job creation and increased income. However, negative impacts are also recognized, including conflicts over water use, displacement of families, and increased costs of goods and services. From an environmental perspective, the majority perceived negative changes, particularly water pollution, deforestation, erosion, and biodiversity loss. Regarding the ecosystem services, provisioning services were perceived as having the greatest importance and frequency of use, especially water for human consumption, irrigation, and productive activities. These results demonstrate the coexistence of benefits and risks and highlight the need for sustainable management strategies that integrate ecosystem conservation and community well-being. Full article

The urban ecosystem constitutes a complex adaptive system comprising interdependent subsystems—environment, population, infrastructure, public services, environmental governance, and socio-economic factors. Conventional system dynamics (SD) modeling relies on expert-derived causal assumptions, which have limitations in objectivity, transferability, and adaptability. To solve these, this study develops a data-driven SD modeling framework that infers causal structures from time-series data of 38 sustainability indicators. The framework integrates multiple causal inference techniques to identify causal relationships among variables, then systematically identifies stock variables and constructs an SD simulation model. Applying it to panel data from 41 cities in China’s Yangtze River Delta (2013–2022), the study characterizes the causal network topology, interaction patterns between subsystems, dominant feedback loops, and temporal evolution trajectories of key stock variables. Results show: (1) There is significant cross-city variation in causal network structure due to differences in urban development and institutional configurations; (2) Environmental conditions are the most frequently affected terminal node with an average normalized causal strength of 0.277, higher than other subsystems; (3) Several cross-subsystem positive and negative feedback loops are identified, highlighting potential path dependencies and intervention-sensitive nodes for sustainable urban transitions. This study provides a replicable, comparable, and scalable framework for urban sustainable development analysis, offering data-driven support for smart city management and policy formulation. Full article