Search Results (13,323)

To address the limitations of traditional forest above-ground biomass (AGB) retrieval methods—namely, the restricted accuracy of physical models and the limited generalization ability of purely data-driven models—this study proposes a differentiable physical modeling (DPM) approach for forest AGB estimation. The method adopts the water cloud model (WCM) as a physics-based framework, grounded in radiative transfer theory, and integrates C-band synthetic aperture radar (SAR) data with multispectral imagery. Within the PyTorch tensor computation framework, automatic differentiation (AD) is employed to seamlessly couple the WCM with the deep fully connected neural network (DFCNN), enabling a differentiable implementation of the WCM. Using mean squared error (MSE) as the loss function, the neural network parameters are optimized through backpropagation and gradient descent, thereby constructing an end-to-end trainable DPM model that effectively retrieves forest AGB while preserving physical interpretability and generalization capability. To validate the proposed method, two representative test sites were selected: Simao in Pu’er, Yunnan Province, and Genhe in Inner Mongolia. GF-3 PolSAR and RADARSAT-2 data were used to extract backscattering coefficients and compute the radar vegetation index (RVI), while Landsat 8 OLI imagery was employed to calculate the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and soil-adjusted vegetation index (SAVI). These datasets, together with ASTER GDEM, field-measured biomass, and other relevant datasets, were integrated to construct a multisource dataset combining remote sensing and ground observations. The performance of the DPM model was then compared with the traditional WCM and several data-driven models, including the fully connected neural network (FNN), generalized regression neural network (GRNN), RF, and Adaptive Boosting (AdaBoost). The results indicate that the DPM model achieved R² = 0.60, RMSE = 24.23 Mg/ha, Bias = 0.4 Mg/ha, and ubRMSE = 22.43 Mg/ha in Simao, and R² = 0.48, RMSE = 33.29 Mg/ha, Bias = 0.87 Mg/ha, and ubRMSE = 33.28 Mg/ha in Genhe, demonstrating consistently better performance than both the WCM and all tested data-driven models. The DPM model demonstrated consistent performance across ecologically contrasting forest regions. It alleviated the systematic overestimation bias of purely data-driven models and overcame the limitations in predictive accuracy resulting from the simplified structure of the WCM. The differentiability of the WCM enables the loss function errors to be backpropagated through the neural network, thereby allowing the optimization of the physical model parameters. Overall, the DPM framework integrates the advantages of both physical models and data-driven approaches, providing an estimation method with acceptable accuracy for forest AGB retrieval. It also offers theoretical and practical insights for the integration of deep learning and physical knowledge in other research fields. Full article

Volatile organic compounds (VOCs) from historical industrial activities can persist for decades, contaminating groundwater and the unsaturated zone, yet their transport through thick, heterogeneous vadose zones is poorly understood. This study reconstructs long-term migration of tetrachloroethylene (PCE) from a former industrial site in the Jerusalem Mountains, where leakage likely began ten years after plant commissioning and systematic monitoring started decades later. A three-dimensional numerical model of flow and transport was applied, incorporating calibrated hydraulic parameters, karstic conduits, and multiphase VOC processes including advection, dispersion, phase partitioning, volatilization, and first-order degradation kinetics. Multiple model runs explored plausible leakage scenarios under sparse historical data. Simulated PCE concentrations reproduce measurements in the vadose zone (R² = 0.89) and deep regional aquifer (~20% normalized relative error). Results reveal pronounced preferential flows horizontally through perched aquifers and vertically along discrete faults, amplified by karstic networks. The upper vadose zone remains a persistent source, sustaining gas-phase emissions toward nearby residential areas unless targeted remediation is applied. Integrated modeling, even with limited monitoring, quantitatively reconstructs complex contaminant dynamics across saturated and unsaturated compartments, providing critical guidance for remediation. Protecting groundwater and human health requires addressing both vadose and saturated zones to prevent prolonged environmental and exposure risks. Full article

Under China’s rural revitalization and agricultural modernization strategies, digital village construction overcomes resource limits to drive transformation. Using 2013–2022 provincial panel data and a case study of Lin’an, Hangzhou, this study reveals how digital villages boost high-quality agriculture. The empirical results show they significantly enhance agricultural total factor productivity via three paths: IoT-driven precision production, blockchain-enabled green value addition, and e-commerce direct sales demonstrate more pronounced effectiveness in major grain-producing regions and those characterized by balanced production and sales. Simultaneously, this study employs the instrumental variable (TI) approach to address endogeneity from reverse causality and omitted variables. Mechanism testing reveals agricultural technological innovation exerts a significant 77.5% mediating effect. Finally, digital rural construction exhibits a non-linear threshold (0.3082); surpassing it triggers a structural leap with increasing marginal returns. The Lin’an case validates the empirical results while revealing structural barriers, including industrial chain penetration gaps, data silos, and factor supply constraints, leading to the formulation of targeted optimization strategies. The practical contribution of this study is the proposal of a “data-value-technology” closed loop: public brands like “Tianmu Mountain Treasures” channel premiums into R&D funds, creating a self-sustaining mechanism. The findings indicate that digital villages drive high-quality agriculture primarily through direct effects, powered by full-chain tech coordination, institutional reform, and inclusive factor supply. Finally, this study proposes a coordinated governance framework encompassing “technical synergy, institutional innovation, and factor optimization,” providing theoretical support and strategic references for optimizing the pathways of regional agricultural digital transformation. Full article

Protected areas play a critical role in conserving biodiversity and ecosystem services, yet their effectiveness in mitigating anthropogenic pressures, particularly in fragile alpine ecosystems like the Three-River-Source region of the Qinghai Plateau—a vital water tower for Asia—requires long-term and rigorous assessment. This study evaluates the effectiveness of the Three-River-Source Nature Reserve by analyzing spatiotemporal changes in the human footprint from 2000 to 2024. Utilizing a globally consistent human footprint dataset refined with high-resolution grazing intensity data for the Qinghai Plateau, we compared human footprint dynamics inside and outside the reserve and across its three functional zones (core, buffer, experimental). To isolate the policy effect, we employed a propensity score matching (PSM) approach to control for confounding geographical and socio-economic factors. Results indicate that while human pressure increased overall, the nature reserve was partially effective. The PSM-based comparison revealed that the increase in human footprint inside the nature reserve was lower than in matched external control areas. This effect was spatially heterogeneous and positively correlated with management intensity: it was most pronounced in the core zone, moderate in the buffer zone, and negligible in the experimental zone. The conservation outcomes showed notable improvement following policy enhancements, particularly after the national park’s formal establishment. The findings confirm the value of strict internal protection and functional zoning but highlight the challenge of intensifying peripheral pressures, underscoring the need for integrated landscape-level management strategies beyond the reserve’s boundaries to ensure long-term ecological integrity. Full article

The European Union’s transition to climate neutrality by 2050 requires measurable reductions in energy consumption and greenhouse gas emissions, especially in territories characterized by geographical constraints, such as mountainous regions. The study analyzes how European funding guidelines are translated into concrete technical interventions for public buildings in mountainous areas of Romania, using a representative case study from Rodna, Bistrița-Năsăud County. The methodology is based on the national energy performance calculation framework (Mc 001/2022), harmonized with Directive 2010/31/EU and aligned with the EN ISO 52016-1 framework, while maintaining compatibility with the quasi-steady-state methodology implemented in MC 001/2022, and includes the assessment of compliance with the “Do No Significant Harm” (DNSH) principle also. The integrated energy rehabilitation of the analyzed building led to reductions in final energy consumption of 30–45%, primary energy consumption of 40–45%, and operational CO₂ emissions of 45–50%. The integration of renewable energy sources increased their share to approximately 35% of the building’s energy mix. The estimated annual reduction of 40–45 tons of CO₂ highlights the direct climate impact of investments financed from European funds. The results confirm that European funding instruments function not only as financial mechanisms, but also as governance instruments capable of steering the transition towards a low-emission construction sector in vulnerable mountain regions. Full article

To address the limitations regarding poor adaptability to complex forest environments as well as high installation and operational costs in existing mountain transportation equipment, a modular cable-type equipment for moso bamboo transportation was designed based on the terrain characteristics of steep bamboo forests and specific transportation requirements. This study first presents the overall structure and working principle of the transportation equipment. Next, a theoretical analysis and component selection were conducted for critical parts such as the wire rope, supporting components, wire-rope-driven devices, and hydraulic systems. Then, the static characteristics of the supporting components and the vibration characteristics of the wire rope were simulated and analyzed. Finally, performance testing of the equipment was conducted, focusing on transportation productivity and machine utilization. The results showed that the maximum deformation of the supporting components was 1.75 mm, occurring at the lower roller–rail contact region. During unloading, the first-order principal vibration amplitude of the wire rope had the greatest impact at the mid-span position, with a value of 0.27 m. The vibration frequency of the wire rope during operation is influenced by the its initial tension, load mass, and attachment distance, with the first-order frequency range approximately between 0.85 and 3.90 Hz. Within this frequency range, the bouncing excitation caused by moso bamboo does not induce resonance in the wire rope. The transportation productivity of the equipment was 2.61 tons per hour, with the machine utilization rate exceeding 95%. This study indicates that the designed cable-type equipment effectively meets the requirements for moso bamboo transportation in complex forest environments. Full article

Rapid urbanization has seriously interfered with the carbon sink function of forests, and has even led to an increased risk of forest carbon imbalance. It is important to explore the regional carbon compensation mechanism and zoning optimization path based on forest carbon accounting to achieve the “dual carbon” goal and sustainable forest management in Fujian Province. Based on remote sensing and GIS technologies, this study measured forest carbon emissions and carbon sequestration of each county in Fujian Province, revealed spatial and temporal evolution of forest carbon budget during the period from 2000 to 2020, and calculated carbon compensation value of each county, so as to realize scientific accounting of forest carbon compensation, and then explored zoning optimization pathways of forest carbon compensation in Fujian Province. The results show the following: (1) From 2000 to 2020, the forest carbon budget in Fujian Province as a whole showed a spatial pattern of “coastal deficit, northwest surplus”, with obvious spatial imbalance characteristics, and showed a high growth trend of net carbon sequestration. (2) From 2000 to 2020, the average carbon compensation rate in Fujian Province was 7.92, and compensation zones were mainly concentrated in the economically developed southeast coastal regins such as Fuzhou, Quanzhou, Xiamen, Zhangzhou, and Putian, while compensation-receiving zones were mainly concentrated in northwestern mountainous areas such as Nanping, Ningde, and Longyan, which had a high forest coverage rate. (3) From 2000 to 2020, there was a significant difference in growth rates of compensation amounts and compensation-receiving amounts in Fujian Province. The cumulative increase in compensation amounts was 322.82%, while the cumulative increase in compensation-receiving amounts was only 17.5%. (4) Based on priority levels, the counties in Fujian Province are classified into six types of forest carbon compensation zones—potential compensation zones, secondary compensation zones, priority compensation zones, potential compensation-receiving zones, secondary compensation-receiving zones and priority compensation-receiving zones—and optimization paths of differentiated zones are explored. Full article

Drought is a highly destructive natural disaster that inflicts severe economic losses. Its formation mechanisms are complex, yet existing studies have often focused on single driving factors, leaving the synergistic effects of multiple factors insufficiently explored. Based on multi-source data from Xinjiang spanning 1981–2020, this study systematically examined the combined impacts of atmospheric circulation, underlying surface conditions, and human activities on drought, using the multi-temporal-scale Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSI), along with partial correlation analysis, spatial autocorrelation, and principal component analysis. The results show that Xinjiang experienced a pronounced drying trend over the past 40 years, with the seasonal SPEI and SSI both exhibiting significant declines. Drought intensity was higher in northern Xinjiang than in the south. Correlations between drought indices and circulation indices, such as Atlantic Multidecadal Oscillation (AMO), were relatively weak, indicating a limited regulatory influence of large-scale circulation on regional drought under the dual constraints of topography and an inland setting. Among underlying surface factors, slope significantly influenced drought spatial patterns. Mountainous areas and basin interiors showed positive spatial correlations, characterized respectively by high–high clustering (high slope and high drought index) and low–low clustering (low slope and low drought index). In contrast, basin margins exhibited low–high clustering (low slope surrounded by high drought index), reflecting negative spatial correlation. Aspect showed no significant effect. Vegetation cover displayed clear seasonal coupling with drought, with strong negative correlations in spring due to intensified water stress. Human activities also played a prominent role. Since the mid-1990s, the expansion of built-up land and increased agricultural water use have shifted drought–land use relationships toward low–high clustering (low drought index surrounded by high land-use intensity) in southern Xinjiang oases, and toward low–low clustering (low drought index and low land-use intensity) in eastern Xinjiang. Meanwhile, ecological restoration projects promoted a transition from low–high to high–high clustering (high drought index and high land-use intensity) in some areas, alleviating local drying trends. Principal component analysis further revealed a shift in the dominant driver: land-use change was the primary factor before 2005, whereas vegetation cover became the key driver thereafter. By clarifying the mechanisms underlying multi-factor interactions in drought in Xinjiang, this study provides scientific support for integrated water resource management, ecological conservation, and climate adaptation strategies in arid regions. Full article

In high-density expressway environments, lane-changing (LC) maneuvers act as stochastic perturbations that compromise the hydrodynamic stability of traffic flow, leading to safety hazards and operational delays. While existing literature has extensively modeled crash severity in static complex environments (e.g., tunnels and mountainous terrains), there remains a critical deficiency in quantifying the dynamic, systemic risks induced by LC maneuvers under saturation conditions. To address this gap, this study proposes a novel Systemic Risk Assessment Framework. First, a Hidden Markov Model (HMM) is employed to decode the latent state transitions of following vehicles, quantifying the systemic consequence of LC maneuvers as “operational delay” based on traffic wave theory. Second, a Bayesian Network (BN) is constructed to infer the causal probability of risk, integrating geometric proxies such as insertion angle with kinematic variables. Validated with real-world trajectory data, the model achieves high accuracy in identifying risk accumulation precursors. This research contributes to the field of transportation systems by shifting the risk paradigm from static collision prediction to dynamic system reliability analysis, offering theoretical support for Connected and Autonomous Vehicle (CAV) decision logic. Full article

Assessing and optimizing regional ecological networks is critical for mitigating fragmentation-driven ecological risks and informing evidence-based territorial spatial planning in China. In this study, we developed a comprehensive evaluation framework integrating ecosystem services, ecological sensitivity, and landscape connectivity to identify ecological sources in Anyang City, China. We then extracted ecological corridors and nodes using circuit theory and constructed the city’s ecological network. Notably, we applied complex network theory combined with topological robustness analysis for optimization to enhance network stability. The analysis identified 43 ecological sources (820.72 km²; 11.16% of the region), predominantly distributed in western Anyang. A total of 82 corridors (460.35 km), 62 pinch points, and 120 barrier points were mapped—primarily in the west, revealing critical connectivity deficits. Network optimization through the addition of 10 strategic corridors significantly enhanced structural balance and functionality, with average degree, closeness centrality, clustering coefficient, eigenvector centrality, and graph density increasing by 5.55–12.19%, and their standard deviations decreasing by an average of 19.32%. Global efficiency (+8.74%), the largest connected component ratio (+0.73%), and node/edge recovery robustness (+17.44%/+18.08%) also improved markedly, confirming greater connectivity and resilience. Our methodology comprehensively integrates ecosystem functional services, disturbance resistance, and spatial structural stability, providing a practical reference for the construction and optimization of regional ecological networks in mountainous–plain transition zones of China. Full article

Global high-mountain ecosystems are increasingly subjected to intensified anthropogenic disturbances, which facilitate the spread of invasive alien plants and threaten agricultural sustainability and ecological security. Using Laojun Mountain in Yunnan as the study area, this research investigates the relationship between the distribution patterns of invasive plants and land-use changes, based on data from 38 transect surveys conducted in 2023 and 30-m-resolution land-use data spanning 2003–2023. The analysis incorporates a random forest model and a land-use transition matrix. The key findings are as follows: (1) Variable importance analysis revealed elevation as the most critical factor influencing invasion occurrence (mean decrease in Gini index: 8.0), followed by slope, aspect, and land-use type. (2) Cultivated land exhibited the highest probability of invasion, with high-risk areas (>0.8) concentrated in agricultural zones in the central-southern and northeastern regions. (3) From 2003 to 2023, cultivated land increased by a net area of 20.85 km², primarily due to conversion from forests (19.57 km²) and grasslands, while grassland area decreased by 24.70 km². This study concludes that agricultural expansion has intensified habitat fragmentation and anthropogenic disturbances, creating favorable conditions for invasive plant establishment. It is recommended that invasive species monitoring and ecological restoration efforts be strengthened in agroforestry transition zones to enhance landscape resilience against biological invasions. Full article

Mountain ecosystems are susceptible to climate change, and alpine treeline ecotones (ATEs) represent one of the significant responsive indicators of climate-driven environmental change. This study examines long-term spatiotemporal dynamics of the ATE across the Eastern Slopes of the Canadian Rocky Mountains (ESCR) from 1984 to 2023, with the objective of assessing whether regional climate warming has influenced ATE extent and elevation across different aspects and watersheds. Multi-decadal Landsat imagery, ERA5-Land temperature data, and topographic variables were integrated within a Google Earth Engine (GEE) framework to map ATEs using the Alpine Treeline Ecotone Index (ATEI), a probabilistic approach designed to capture transitional vegetation zones. Temporal trends were evaluated using non-parametric statistics, correlation analyses, and watershed- and aspect-based comparisons. Results indicate that the total alpine treeline ecotone (ATE) area in the ESCR was approximately 13.3% larger in 2023 than in 1984. However, the temporal evolution of ATE extent and elevation was non-monotonic, and linear trend analyses did not detect statistically significant increasing or decreasing trends over the full study period. ATE elevation and expansion exhibited pronounced spatial heterogeneity, with greater changes occurring on north- and northwest-facing slopes and within selected watersheds. In contrast, summer (July–September) temperatures increased significantly (+2.84 °C), exceeding global land-only warming rates, and vegetation greenness (NDVI) showed a strong, statistically significant positive relationship with temperature. These findings show that while climate warming has clearly increased vegetation productivity, elevational ATE dynamics remain spatially heterogeneous and temporally non-synchronous with summer temperature trends. Full article

Tea buds are the key raw material for high-quality tea production, and their accurate perception is essential for intelligent harvesting and quality-oriented management. However, tea bud detection in mountainous large-leaf tea plantations remains challenging because small, densely distributed targets are embedded in complex field environments, significantly limiting the stability and accuracy of existing detection methods. To address these challenges, this study proposes an improved tea bud detection model, termed YOLO-LAR, for mountainous large-leaf tea plantations in Yunnan Province, China, which is developed as an enhanced framework based on the YOLOv11 baseline. YOLO-LAR improves feature representation through multi-scale feature fusion, enabling more effective detection of densely distributed small tea buds. In addition, an optimized downsampling strategy is employed to preserve critical spatial information, and a context-enhanced feature aggregation mechanism is introduced to strengthen robustness under complex backgrounds and illumination variations. The results demonstrate that YOLO-LAR achieves precision, recall, mAP@0.50, and mAP@0.50:0.95 of 0.959, 0.908, 0.961, and 0.814, respectively, outperforming mainstream YOLO-based models, including YOLOv11n, YOLOv10n, and YOLOv8n. These results indicate that YOLO-LAR provides an effective and practical solution for accurate tea bud detection, offering strong technical support for intelligent harvesting and precision management in mountainous tea plantation environments. Full article

Eclogites exhumed from subduction channels are pivotal for deciphering the thermal structure of continental subduction zones. However, heterogeneities in bulk-rock composition and evolutionary history within the subduction channel can lead to variations in petrographic textures and elemental characteristics among eclogites. Therefore, investigating the pressure–temperature (P-T) evolution of eclogites from different outcrops is crucial for refining dynamic models of convergent plate boundaries. The Western Dabie Mountain represents an ideal locality for studying the petro-thermodynamics of continental subduction channels. This study focuses on samples collected from the Dongyuemiao area, situated at the boundary between the high-pressure and ultrahigh-pressure metamorphic belts in the Western Dabie. We integrate petrographic observations, mineral chemistry, phase equilibrium modeling, Zr-in-rutile thermometry and hornblende-plagioclase thermobarometry to constrain the P-T evolution of the eclogite. The samples exhibit a consistent mineral assemblage: garnet + omphacite + amphibole + quartz + phengite, with accessory minerals including rutile and titanite. Garnet grains display characteristic “cloudy-core” and “atoll” textures. Major and trace element analyses of large garnet porphyroblasts reveal pronounced growth zoning in divalent cations, with cores showing enrichment in light rare earth elements (LREEs). Based on phase equilibrium modeling and calculated isopleths for garnet (Ca, Mg) and phengite (Si content), we interpret that the garnet core mineral assemblage (glaucophane + rutile + sphene) records a blueschist-facies metamorphic stage, situated near the rutile-titanite transition. A prograde P-T path is reconstructed, comprising an initial stage of isobaric heating (from ~480 °C at 20 kbar to ~550 °C at 21 kbar), followed by an isothermal compression to the Pmax stage (from ~550 °C at 21 kbar to ~575 °C at 26 kbar). Subsequent retrograde evolution is characterized by decompression and cooling, with symplectite formation recording conditions of ~570 °C and 13 kbar. This study demonstrates that the reconstructed P-T path for the Dongyuemiao eclogites shows stepped geothermal gradient for the prograde stage, and that fluid activity during exhumation resulted from a combination of internal and external factors. Full article

Soil texture exerts fundamental control over nutrient retention in arid ecosystems; however, its mechanistic coupling with nutrient stoichiometry in wind-eroded desert steppes remains poorly resolved. We investigated soil particle-size distribution and nutrient characteristics across contrasting vegetation types in a desert steppe on the northern slope of the Yinshan Mountains. The interactions between soil texture and nutrient distribution were quantified through field sampling and laboratory analyses. The Caragana grassland was dominated by fine-textured soils, with a silt-to-sand ratio of 21.58% and a fractal dimension ranging from 2.1 to 3.95, indicating a complex soil structure with strong nutrient-retention capacity. In contrast, the Leymus grassland and desert sites were characterized by higher sand content, with a median particle size of 1.67 mm and sorting coefficients ranging from 0.06 to 4.2, reflecting a simpler structure and comparatively lower nutrient levels. Overall, soils in the region were nutrient-deficient, with widespread phosphorus and potassium limitations, whereas nitrogen was relatively more abundant. Total nitrogen (<0.75 mg kg⁻¹), total phosphorus (0.2–0.4 mg kg⁻¹), total potassium and available nutrients were predominantly classified as ‘deficient’ to ‘extremely deficient’, exhibiting a clear surface accumulation pattern. The Poaceae meadow surface layer showed the highest total nitrogen and phosphorus contents. The sorting coefficient and fractal dimension were identified as key particle-size parameters regulating soil nutrient stoichiometric ratios. The silt-to-sand ratio exerted negative path effects (−0.11 to −0.18) on SOC/TN and AK/AN, whereas fractal dimension showed positive path effects (0.17–0.23) on AK/AN. These findings provide a scientific basis for ecological restoration and soil management in the region. Full article