Search Results (2,730)

Rocket launches constitute a major anthropogenic source of disturbance in the near-Earth space environment, inducing significant ionospheric perturbations through both chemical and dynamic mechanisms. This study presents a systematic analysis of ionospheric disturbances—specifically, electron density depletion and traveling ionospheric disturbances (TIDs)—triggered by four rocket launches from China’s Jiuquan Satellite Launch Center between 2023 and 2025. Using high-rate, multi-constellation GNSS data from 370 ground stations and BeiDou GEO satellites, we extracted total electron content (TEC) signals and applied advanced detection methods, including the Multi-Rolling-Multi-Image-Tracking (MRMIT) algorithm for depletion identification and a parametric integration framework for quantitative comparison. Our results reveal that all launches produced rapid TEC depletions, evolving along the rocket trajectory and peaking within approximately 30 min. Launch mass was the dominant factor controlling depletion intensity, while propellant chemistry (UDMH-based vs. liquid oxygen/methane) and local time/background TEC levels modulated the recovery rate and spatial extent. Additionally, distinct TIDs exhibiting wave-like and V-shaped structures were observed, propagating outward from the trajectory with latitudinal variations in amplitude and waveform. These findings highlight the critical roles of rocket attributes and ambient ionospheric conditions in shaping disturbance characteristics. The study underscores the value of multi-source GNSS networks and novel methodologies in monitoring anthropogenic space weather effects, with implications for GNSS performance and sustainable space operations. Full article

Under the combined influence of global climate change and intensified human activities, ecosystem services (ESs) are undergoing substantial transformations. Identifying their nonlinear driving mechanisms is crucial for promoting regional sustainable development. Taking Anhui Province as a case study, this research evaluates the spatial patterns and temporal dynamics of six key ecosystem services from 2000 to 2020—namely, biodiversity maintenance (BM), carbon fixation (CF), crop production (CP), net primary productivity (NPP), soil retention (SR), and water yield (WY). The InVEST and CASA models were employed to quantify service values, and the XGBoost–SHAP framework was used to reveal the nonlinear response paths and threshold effects of dominant drivers. Results show a distinct “high in the south, low in the north” spatial gradient of ES across Anhui. Regulatory services such as BM, NPP, and WY are concentrated in the southern mountainous areas (high-value zones > 0.7), while CP is prominent in the northern and central agricultural zones (>0.8), indicating a clear spatial complementarity of service types. Over the two-decade period, areas with significant increases in NPP and CP accounted for 50% and 64%, respectively, suggesting notable achievements in ecological restoration and agricultural modernization. CF remained stable across 98.3% of the region, while SR and WY exhibited strong sensitivity to topography and precipitation. Temporal trend analysis indicated that NPP rose from 395.83 in 2000 to 537.59 in 2020; SR increased from 150.02 to 243.28; and CP rose from 203.18 to 283.78, reflecting an overall enhancement in ecosystem productivity and regulatory functions. Driver analysis identified precipitation (PRE) as the most influential factor for most services, while elevation (DEM) was particularly important for CF and NPP. Temperature (TEM) and potential evapotranspiration (PET) affected biomass formation and hydrothermal balance. SHAP analysis revealed key threshold effects, such as the peak positive contribution of PRE to NPP occurring near 1247 mm, and the optimal temperature for BM at approximately 15.5 °C. The human footprint index (HFI) exerted negative impacts on both BM and NPP, highlighting the suppressive effect of intensive anthropogenic disturbances on ecosystem functioning. Anhui’s ES exhibit a trend of multifunctional synergy, governed by the nonlinear coupling of climatic, hydrological, topographic, and anthropogenic drivers. This study provides both a modeling toolkit and quantitative evidence to support ecosystem restoration and service optimization in similar transitional regions. Full article

Environmental degradation and soil desertification are among the most severe environmental issues of recent decades worldwide. Over time, these processes have led to increasingly extreme and highly dynamic climatic conditions. In Brazil, the Northeast Region is characterized by semi-arid and arid areas that exhibit high climatic variability and are extremely vulnerable to environmental changes and pressures from human activities. The application of geotechnologies and geographic information system (GIS) modeling is essential to mitigate the impacts and pressures on the various ecosystems of Northeastern Brazil (NEB), where the Caatinga biome is predominant and critically threatened by these factors. In this context, the objective was to map and assess the spatiotemporal patterns of land use and land cover (LULC), detecting significant trends of loss and gain, based on surface reflectance data and precipitation data over two decades (2000–2019). Remote sensing datasets were utilized, including Landsat satellite data (LULC data), MODIS sensor data (surface reflectance product) and TRMM data (precipitation data). The Google Earth Engine (GEE) software was used to process orbital images and determine surface albedo and acquisition of the LULC dataset. Satellite data were subjected to multivariate analysis, descriptive statistics, dispersion and variability assessments. The results indicated a significant loss trend over the time series (2000–2019) for forest areas (Z_MK = −5.872; Tau = −0.958; p < 0.01) with an annual loss of −3705.853 km² and a total loss of −74,117.06 km². Conversely, farming areas (agriculture and pasture) exhibited a significant gain trend (Z_MK = 5.807; Tau = 0.947; p < 0.01), with an annual gain of +3978.898 km² and a total gain of +79,577.96 km², indicating a substantial expansion of these areas over time. However, it is important to emphasize that deforestation of the region’s native vegetation contributes to reduced water production and availability. The trend analysis identified an increase in environmental degradation due to the rapid expansion of land use. LULC and albedo data confirmed the intensification of deforestation in the Northern, Northwestern, Southern and Southeastern regions of NEB. The Northwestern region was the most directly impacted by this increase due to anthropogenic pressures. Over two decades (2000–2019), forested areas in the NEB lost approximately 80.000 km². Principal component analysis (PCA) identified a significant cumulative variance of 87.15%. It is concluded, then, that the spatiotemporal relationship between biophysical conditions and regional climate helps us to understand and evaluate the impacts and environmental dynamics, especially of the vegetation cover of the NEB. Full article

Landslides are a persistent and destructive hazard in Angra dos Reis, located in the highlands of Rio de Janeiro State, southeastern Brazil, where steep slopes, intense orographic rainfall, and unregulated urban expansion converge to trigger recurrent mass movements. In this study, we applied Multiscale Geographically Weighted Regression (MGWR) to examine the spatially varying relationships between landslide occurrence and topographic, hydrological, geological, and anthropogenic factors. A detailed inventory of 319 landslides was compiled using high-resolution PlanetScope imagery after the December 2023 rainfall event. Following multicollinearity testing and variable selection, thirteen predictors were retained, including slope, rainfall, lithology, NDVI, forest loss, and distance to roads. The MGWR achieved strong performance (R² = 0.94; AICc = 134.99; AUC = 0.99) and demonstrated that each factor operates at a distinct spatial scale. Slope, rainfall, and lithology exerted broad-scale controls, while road proximity had a consistent global effect. In contrast, forest loss and land use showed localized significance. These findings indicate that landslide susceptibility in Angra dos Reis is primarily driven by the interaction of orographic rainfall, steep terrain, and geological substrate, intensified by human disturbances such as road infrastructure and vegetation removal. The study underscores the need for targeted adaptation strategies, including slope stabilization, restrictions on road expansion, and vegetation conservation in steep, rainfall-prone sectors. Full article

Groundwater serves as a vital water resource for agricultural irrigation and domestic use in farmland areas. Its chemical composition is jointly influenced by agricultural fertilization, land use practices, and natural geological processes. However, research on the controlling factors and spatial distribution characteristics of groundwater hydrochemistry in agricultural regions remains insufficient. In this study, 56 groundwater samples were collected from the central-eastern plain of Henan Province, China. A combination of hierarchical cluster analysis, ionic ratio methods, principal component analysis, and kriging interpolation was employed to investigate the hydrochemical characteristics, spatial patterns, and primary controlling factors of regional groundwater. The results indicate that the first group of samples is characterized by high total dissolved solids (TDS), elevated Na⁺ and Cl⁻ concentrations, predominantly controlled by evaporation and concentration processes. The second group exhibits high pH and low Ca²⁺ concentrations, mainly influenced by silicate weathering, with reverse cation exchange acting as a secondary controlling process. The third group is characterized by elevated concentrations of Ca²⁺ and NO₃⁻, primarily controlled by carbonate weathering and agricultural activities. The western part of the study area serves as the main groundwater recharge zone and has the highest NO₃⁻ and Ca²⁺ concentrations. In the central area, most ion concentrations are relatively high, forming a distinct gradient with surrounding regions. Meanwhile, the eastern area displays elevated concentrations of HCO₃⁻, TDS, Na⁺, and Cl⁻, highlighting pronounced spatial heterogeneity. Overall, the hydrochemical composition of groundwater in the study area is shaped by both natural processes and anthropogenic activities, exhibiting significant spatial heterogeneity. Notably, the spatial variation of NO₃⁻ concentrations is substantial, indicating that certain localities have already been affected by agricultural non-point source pollution. Full article

To elucidate the hydrochemical characteristics and evolution mechanisms of shallow groundwater in the alluvial–coastal transitional zone of the Tangshan Plain, 76 groundwater samples were collected in July 2022. An integrated approach combining Piper and Gibbs diagrams, ionic ratio analysis, multivariate statistical methods (including Pearson correlation, hierarchical cluster analysis, and principal component analysis), and PHREEQC inverse modeling was employed to identify hydrochemical facies, dominant controlling factors, and geochemical reaction pathways. Results show that groundwater in the upstream alluvial plain is predominantly of the HCO₃–Ca type with low mineralization, primarily controlled by carbonate weathering, water–rock interaction, and natural recharge. In contrast, groundwater in the downstream coastal plain is characterized by high-mineralized Cl–Na type water, mainly influenced by seawater intrusion, evaporation concentration, and dissolution of evaporite minerals. The spatial distribution of groundwater follows a pattern of “freshwater in the north and inland, saline water in the south and coastal,” reflecting the transitional nature from freshwater to saline water. Ionic ratio analysis reveals a concurrent increase in Na⁺, Cl⁻, and SO₄²⁻ in the coastal zone, indicating coupled processes of saline water mixing and cation exchange. Statistical analysis identifies mineralization processes, carbonate weathering, redox conditions, and anthropogenic inputs as the main controlling factors. PHREEQC simulations demonstrate that groundwater in the alluvial zone evolves along the flow path through CO₂ degassing, dolomite precipitation, and sulfate mineral dissolution, whereas in the coastal zone, continuous dissolution of halite and gypsum leads to the formation of high-mineralized Na–Cl water. This study establishes a geochemical evolution framework from recharge to discharge zones in a typical alluvial–coastal transitional setting, providing theoretical guidance for salinization boundary identification and groundwater management. Full article

Forest fire risk assessment methodologies vary considerably, presenting challenges for adaptation to specific local contexts. This study provides a systematic analysis of forest fire assessment approaches across the Mediterranean basin, American, African, and Asian regions through a comprehensive review of 112 peer-reviewed studies published from 2015 to 2025. Statistical significance testing (Chi-square tests, p < 0.05) confirmed significant regional variation in methodological preferences and indicator usage patterns. Key findings revealed that Multi-Criteria Decision Analysis dominates the field (44% of studies, n = 49), with Analytical Hierarchical Process being the most utilized method (39 studies). Machine learning approaches represent 25% (n = 28), with Random Forest leading significantly (22 applications). The analysis identified 67 indicators across seven major categories, with topographic factors (slope: 105 studies) and anthropogenic indicators (road networks: 92 studies) showing statistically significantly highest usage rates (p < 0.001), representing a statistically significant critical gap in vulnerability assessment (p < 0.01). Organizational factors remain severely underrepresented (a maximum of 14 studies for any factor), representing a statistically significant critical gap in risk assessments (p < 0.01). Statistical analysis revealed that while Mediterranean approaches excel in integrating historical and cultural factors, American methods emphasize advanced technology integration, while Asian approaches focus on socio-economic dynamics and land-use interactions. This study serves as a foundation for developing tailored assessment frameworks that combine remote sensing analysis, ground-based surveys, and community input while accounting for local constraints in data availability and technical capacity. The study concludes that effective forest fire risk assessment requires a balanced integration of global best practices with local environmental, social, and technical considerations, offering a roadmap for future forest fire risk assessment approaches in different regions worldwide. Full article

Xiamen, a rapidly developing coastal metropolis and tourist hub in southeastern China, faces air quality challenges due to its dense population and tourism reliance. This study investigates PM_2.5 sources and temporal variations during autumn 2013–2017 via chemical characterization, mass reconstruction, and receptor modeling. The Positive Matrix Factorization (PMF) model identified five sources: secondary sulfate (31%), coal/vehicle emissions (28%), industrial emissions with secondary organic aerosols (SOA, 20%), ship emissions (14%), and fugitive dust (7%). Interannual variations in source contributions highlighted impacts of anthropogenic activities, meteorology, power plant upgrades, and stricter vehicle standards. PM_2.5 declined 19% (2013–2017), driven by emission controls, while SOA surged 42% (2015–2017) due to VOC oxidation and lower temperatures. Backward trajectory and Potential Source Contribution Function (PSCF) analyses revealed significant regional transport from northern industrial zones (32% contribution) and maritime activities. Ship emissions, which have remained relatively stable over the years, underscore the need for stricter marine regulations. Fugitive dust peaked in 2015 (25.8% of PM_2.5), linked to urban construction. The findings emphasize the interplay of local emissions and regional transport in shaping PM_2.5 pollution, providing a scientific basis for targeted control strategies in coastal cities with similar socioeconomic and geographic contexts. Full article

Fish secondary production integrates multiple demographic parameters, including population density, growth, mortality, and recruitment, and thereby provides a comprehensive measure of ecological performance. It is also a valuable tool for assessing the ecological integrity of stream ecosystems and the responses of fish populations to habitat alteration, climatic variability, and anthropogenic pressures. Despite its relevance, empirical estimates of fish production remain limited due to methodological constraints. In this study, we quantified secondary production and production-to-biomass (P/B) ratios for Mediterranean brown trout (Salmo trutta complex) across six headwater stream reaches in the northeastern Iberian Peninsula, characterized by contrasting hydrological regimes, channel morphology, and water chemistry. Field sampling was conducted over two consecutive annual cycles (2008/2009 and 2009/2010) at all sites, with extended monitoring at two reaches until 2017 to assess long-term variability. Annual trout production, over the two consecutive annual cycles, ranged from 30.9 to 167.8 kg ha⁻¹ year⁻¹ (mean = 82.2 kg ha⁻¹ year⁻¹), and mean P/B ratios ranged from 0.61 to 1.13 (mean = 0.80). These values fall within the intermediate range reported for brown trout globally and reflect the constrained energy dynamics of Mediterranean streams. Higher production was generally associated with strong age-1 recruitment, elevated standing biomass, and greater water alkalinity. Long-term analyses revealed that interannual variation in trout production was significantly correlated with discharge variability, with higher production occurring under more stable flow conditions. However, in addition to flow variability other factors, such as habitat complexity, may modulate local productivity. Consequently, interannual fluctuations at the long-term sites revealed substantial demographic variability influenced by site-specific environmental conditions. These findings offer reference baselines for Mediterranean trout populations and contribute to the ecological basis for their conservation and management. Full article

Landslides are complex phenomena controlled by natural and anthropogenic factors. In recent years, the need to understand their dynamics has driven the development of methodologies for improving risk monitoring and mitigation. In this context, landslide occurrence dating helps identify triggering causes and critical thresholds. This study introduces a fully automated and objective methodology, implemented on the Google Earth Engine platform, which allows access to and processing of large volumes of satellite data online, speeding up analyses and facilitating method sharing. The procedure exploits the complementarity between changes in vegetation cover detected through vegetation indices and changes in radar backscattering, intending to narrow the time window in which the landslide occurred. In 45 out of 46 cases analyzed, the time interval of landslide occurrence could be correctly identified, with a mean temporal window of approximately 8 days (range—3–12 days), confirming the robustness of the approach across different geomorphological settings and landslide types. The complete automation of the workflow is among the most innovative aspects of the methodology, as it allows the script to be directly and consistently applied to a wide range of recent and vegetated landslides with sizes larger than about 10 Sentinel-2 pixels without requiring additional manual procedures. Full article

Using Landsat series imagery and the deep learning model CITNet, this study conducted high-accuracy classification and spatiotemporal change analysis of wetlands on Chongming Island from 2000–2020 and explored the driving mechanisms by integrating climatic and anthropogenic factors. The results demonstrate that the total wetland area decreased by approximately 125.5 km² over the two decades. Among natural wetlands, tidal mudflats and shallow seawater zones continuously shrank, while herbaceous marshes exhibited a “decline recovery” trajectory. Artificial wetlands expanded before 2005 but contracted significantly thereafter, mainly due to aquaculture pond reduction. Wetland transformation was dominated by wetland-to-non-wetland conversions, peaking during 2005–2010. Driving factor analysis revealed a “human pressure dominated, climate modulated” pattern: nighttime light index (NTL) and GDP demonstrated strong negative correlations with wetland extent, while minimum temperature and the Palmer Drought Severity Index (PDSI) promoted herbaceous marsh expansion and accelerated artificial wetland contraction, respectively. The findings indicate that wetland changes on Chongming Island result from the combined effects of policy, economic growth, and ecological processes. Sustainable management should focus on restricting urban expansion in ecologically sensitive zones, optimizing water resource allocation under drought conditions, and incorporating climate adaptation and invasive species control into restoration programs to maintain both the extent and ecological quality of wetlands. Full article

Optimizing Ecological security patterns (ESPs) is critical for advancing territorial spatial restoration and fostering sustainable regional development. While research on ESPs’ construction has grown significantly, key challenges persist, particularly in the accurate identification of priority conservation areas and the integration of socioeconomic development with ecological conservation. To address these challenges, this study selects Jiangsu Province as a representative case. We move beyond single-factor assessments by combining ecosystem service importance evaluation with a multi-factor ecological sensitivity analysis (including water pollution, soil erosion, air pollution, and anthropogenic pressure). A comprehensive ecological resistance surface is then developed, incorporating both natural and anthropogenic disturbance factors, to evaluate spatial patterns of ecological security. Utilizing the Minimum Cumulative Resistance (MCR) model, we delineate ecological corridors and ultimately construct the ESPs by synthesizing ecological sources and corridors. Key results include: Jiangsu’s ESPs comprises 33 ecological patches (total area: 14,622.46 km², 13.71% of the study region), predominantly composed of water bodies, wetlands, and cultivated land. Thirteen ecological corridors (total length: 1920.38 km) primarily traverse cultivated land, construction land, and water bodies. The optimized ESPs strategy termed “Two Cores, Two Barriers, Three Belts, Multiple Corridors” offers a concrete spatial blueprint. The findings provide effective scientific reference for assessing and managing regional ecological security trends. Full article

Soil organic matter (SOM) plays a critical role in maintaining soil fertility, sustaining ecosystem stability, and mitigating climate change impacts, making its conservation essential for agricultural sustainability. However, systematic county-level assessments of SOM spatial heterogeneity and its drivers across Northeast China remain limited, constraining region-specific soil management strategies. Understanding the spatial distribution and drivers of SOM is therefore vital for effective black soil protection in Northeast China. This study investigated the spatial heterogeneity and driving mechanisms of SOM in Northeast China, covering 289 counties across Heilongjiang, Jilin, and Liaoning Provinces. High-resolution (10 m) SOM data combined with 15 natural, climatic, soil, vegetation, and socioeconomic variables were analyzed using spatial autocorrelation (global and local Moran’s I) and the Geodetector model. Results showed that SOM exhibited a clear spatial pattern of “higher in the north and east, lower in the south and west,” with significant spatial clustering (Moran’s I = 0.730, p < 0.001). At the regional scale, climate factors were the dominant drivers, with potential evapotranspiration (q = 0.810) and mean annual temperature (q = 0.794) exerting the strongest explanatory power. At the provincial scale, dominant factors varied: topographic controls in Liaoning, climate–topography interactions in Jilin, and climate dominance in Heilongjiang. Anthropogenic footprint had limited overall influence but showed amplifying effects in certain local areas. These findings highlight the multi-scale, multi-factor nature of SOM heterogeneity and underscore the need for region-specific management strategies. Full article

Bare sand patches are extensively distributed in dryland ecosystems, and their spatiotemporal evolution provides critical insights into regional eco-environmental changes. The Mu Us Sandy Land, a typical dryland region, exemplifies a distinctive mosaic distribution of bare sand and vegetation patches. Based on the Google Earth Engine (GEE) platform and Landsat time-series imagery (1986–2023), this study extracted multi-temporal bare sand patches using the random forest algorithm. We quantified their spatiotemporal dynamics and identified driving mechanisms through integration with natural/socioeconomic datasets. Key findings include the following: (1) The total area of bare sand patches decreased significantly after 2000, with an average annual reduction of 530.08 km² (p < 0.01), a rate markedly exceeding pre-2000 rates. (2) Before 2000, bare sand patches were widespread across the entire region; however, by 2023, only residual patches persisted in the northwestern regions. (3) The most significant reduction in bare sand patch area is attributable to the shrinkage of giant patches (>10 km²). (4) The spatial distribution of bare sand patches is primarily controlled by a combination of natural factors, including stream, precipitation, topography, and wind regime. (5) The principal drivers of the reduction in bare sand patch area are anthropogenic activities, such as the implementation of ecological restoration projects, advancements in agricultural technology, and transformations in breeding patterns. These findings provide a scientific foundation for desertification control and ecosystem management strategies in drylands. Full article

As a critical ecological security barrier in the Indo-China Peninsula, the Lao People’s Democratic Republic (Lao PDR) is increasingly threatened by forest degradation, frequent geological hazards, and intensified anthropogenic disturbances. To address the urgent need for a scientific evaluation of eco-geological environmental quality, this study develops a comprehensive assessment framework integrating multi-source remote sensing imagery, geological maps, and socio-economic datasets. A total of ten indicators were selected across four dimensions—geology, topography, ecology, and human activity. A stacking ensemble learning model was constructed by combining seven heterogeneous base classifiers—AdaBoost, KNN, Gradient Boosting, Random Forest, SVC, MLP, and XGBoost—with a logistic regression meta-learner. Model interpretability was enhanced using SHAP values to quantify the contribution of each input variable. The stacking model outperformed all individual models, achieving an accuracy of 91.14%, an F1 score of 93.62%, and an AUC of 95.05%. NDVI, GDP, and slope were identified as the most influential factors: vegetation coverage showed a strong positive relationship with environmental quality, while economic development intensity and steep terrain were associated with degradation. Spatial zoning results indicate that high-quality eco-geological zones are concentrated in the low-disturbance plains of the northeast and southeast, whereas vulnerable areas are primarily distributed around the Vientiane metropolitan region and tectonically active mountainous zones. This study offers a robust and interpretable methodological approach to support ecological diagnosis, zonal management, and sustainable development in tropical mountainous regions. Full article