Search Results (2,681)

Understanding the dynamics of soil water and root systems is essential for managing and restoring ecosystems impacted by coal mining subsidence. However, existing research treats soil and plant responses separately, also with limited comparisons across different soil types, which hampers our understanding of their coupled effects. We examined the distribution of plant roots, soil water content and stable isotopes within the root zone in the subsidence and non-subsidence plots located in mining areas with sand-capped loess and sandy soil. Our results show that coal mining subsidence induces cracks and fissures in both sand-capped loess and sandy soil, enhancing soil infiltration and increasing deep soil water (>1 m). The increase in deep soil water was more pronounced in sand-capped loess, where subsidence exhibited near-precipitation lc-excess values (−5.9‰ to −0.2‰) and also shifted the soil water infiltration mechanism from piston flow to preferential flow. Moreover, land subsidence provides a more suitable soil physical environment that supports the growth of deeper and more extensive plant roots. The coupling degree (D) between the soil water system and root system was significantly higher in subsidence areas (D > 0.4), indicating enhanced root water absorption. These changes benefit plant physiological activities and stress response, providing an adaptive mechanism for plants in subsidence regions. This study provides new insights into the effects of coal mining subsidence on the root-soil interface in Earth’s Critical Zones and serves as a foundation for ecological restoration and management in subsidence-impacted areas. Full article

Agricultural activities are major contributors to global greenhouse gas (GHG) emissions, with methane (CH₄) and nitrous oxide (N₂O) emissions accounting for 40% and 60% of total agricultural emissions, respectively. Therefore, developing effective emission reduction pathways in agriculture is crucial for achieving carbon budget balance. This article synthesizes the impact of farmland management practices on GHG emissions, evaluates prevalent accounting methods and their applicable scenarios, and proposes mitigation strategies based on systematic analysis. The present review (2000-2025) indicates that fertilizer management dominates research focus (accounting for over 50%), followed by water management (approximately 18%) and tillage practices (approximately 14%). Critically, the effects of these practices extend beyond GHG emissions, necessitating concurrent consideration of crop yields, soil health, and ecosystem resilience. Therefore, it is necessary to conduct joint research by integrating multiple approaches such as water-saving irrigation, conservation tillage and intercropping of leguminous crops, so as to enhance productivity and soil quality while reducing emissions. The GHG accounting framework and three primary accounting methods (In situ measurement, Satellite remote sensing, and Model simulation) each exhibit distinct advantages and limitations, requiring scenario-specific selection. Further refinement of these methodologies is imperative to optimize agricultural practices and achieve meaningful GHG reductions. Full article

Soil erosion in the hilly and gully region of the middle reaches of the Yellow River is severe, threatening regional ecological security and the water–sediment balance of the Yellow River. The area features fragmented topography and significant spatial heterogeneity in soil thickness, forming a unique binary “soil–rock” structural system. The soil in the study area is characterized by silt-based loess, and the underlying bedrock is an interbedded Jurassic-Cretaceous sandstone and sandy shale. It has strong weathering, well-developed fissures, and good permeability, rather than dense impermeable rock layers. However, the spatiotemporal differentiation mechanism of soil moisture in this system remains unclear. This study focuses on the typical hilly and gully region—the Geqiugou watershed. Through field investigations, soil thickness sampling, multi-scale soil moisture monitoring, and analysis of meteorological data, it systematically examines the cascade relationships among microtopography, soil–rock combinations, soil moisture, and meteorological drivers. The results show that: (1) Based on the field survey of 323 sampling points in the study area, it was found that soil samples with a thickness of less than 50 cm accounted for 85%, which constituted the main structure of soil thickness in the region. Macrotopographic units control the spatial differentiation of soil thickness, forming a complete thickness gradient from erosional units (e.g., Gully and Furrow) to depositional units (e.g., Gently sloped terrace). Based on this, five typical soil–rock combination types with soil thicknesses of 10 cm, 30 cm, 50 cm, 70 cm, and 90 cm were identified. (2) Soil–rock combination structures regulate the vertical distribution and seasonal dynamics of soil moisture. In thin-layer combinations, soil moisture is primarily retained within the shallow soil profile with higher dynamics, whereas in thick-layer combinations, under conditions of substantial rainfall, moisture can percolate deeply and become notably stored within the fractured bedrock, sometimes exceeding the moisture content in the overlying soil. (3) The response of soil moisture to precipitation is hierarchical: light rain events only affect the surface layer, whereas heavy rainfall can infiltrate to depths below 70 cm. Under intense rainfall, the soil–rock interface acts as a rapid infiltration pathway. (4) The influence of meteorological drivers on soil moisture exhibits vertical differentiation and is significantly modulated by soil–rock combination types. This study reveals the critical role of microtopography-controlled soil–rock combination structures in the spatiotemporal differentiation of soil moisture, providing a scientific basis for the precise implementation of soil and water conservation measures and ecological restoration in the region. Full article

Loess subgrades are prone to significant strength reduction and deformation under cyclic traffic loads and moisture ingress. Permeable polyurethane polymer grouting has emerged as a promising non-excavation technique for rapid subgrade reinforcement. This study systematically investigated the fatigue behavior of polymer-grouted loess using laboratory fatigue tests and numerical simulations. A series of stress-controlled cyclic tests were conducted on grouted loess specimens under varying moisture contents and stress levels, revealing that fatigue life decreased with increasing moisture and stress levels, with a maximum life of 200,000 cycles achieved under optimal conditions. The failure process was categorized into three distinct stages, culminating in a “multiple-crack” mode, indicating improved stress distribution and ductility. Statistical analysis confirmed that fatigue life followed a two-parameter Weibull distribution, enabling the development of a probabilistic fatigue life prediction model. Furthermore, a 3D finite element model of the road structure was established in Abaqus and integrated with Fe-safe for fatigue life assessment. The results demonstrated that polymer grouting reduced subgrade stress by nearly one order of magnitude and increased fatigue life by approximately tenfold. The consistency between the simulation outcomes and experimentally derived fatigue equations underscores the reliability of the proposed numerical approach. This research provides a theoretical and practical foundation for the fatigue-resistant design and maintenance of loess subgrades reinforced with permeable polyurethane polymer grouting, contributing to the development of sustainable infrastructure in loess-rich regions. Full article

In the loess region, the hydraulic properties of the loess, used as either surrounding rock, backfilling or geoplomer material, are significant for engineering construction and agriculture development projects. This work investigated the soil-water characteristic curves (SWCC) of the undisturbed and remolded loess during the drying process using the tensiometer and psychrometer method. Based on the test results, SWCC was fitted using the Van Genuchten, and Fredlund and Xing models. Moreover, the permeability was comparatively calculated by the Childs and Collis-George, Van Genuchten, and Fredlund models, respectively. Results revealed that the SWCC of both the undisturbed and remolded loess exhibited three-stage characteristics in the relationship between the logarithmic matric suction and moisture, including the boundary effect zone, transition zone, and residual zone. The corrected Fredlund and Xing model provided an optimal calculation for the SWCC of the loess, while the Van Genuchten model showed suction deviations of about 103 kPa. Meanwhile, the undisturbed loess had a low water retention at the low (<103 kPa) suction range, which was attributed to the large pore structure of the undisturbed loess that reduces the air-entry value. This research clarified the differences in the water retention and permeability properties of the loess, providing a theoretical foundation for evaluating the hydraulic properties of the loess. Full article

Rapid and accurate acquisition of landslide inventories is essential for effective disaster relief. Deep learning-based pixel-wise semantic segmentation of remote sensing imagery has greatly advanced in landslide mapping. However, the heavy dependance on extensive annotated labels and sensitivity to domain shifts severely constrain the model performance in unseen domains, leading to poor generalization. To address these limitations, we propose LandsDANet, an innovative unsupervised domain adaptation framework for cross-domain landslide identification. Firstly, adversarial learning is employed to reduce the data distribution discrepancies between the source and target domains, thereby achieving output space alignment. The improved SegFormer serves as the segmentation network, incorporating hierarchical Transformer blocks and an attention mechanism to enhance feature representation capabilities. Secondly, to alleviate inter-domain radiometric discrepancies and attain image-level alignment, a Wallis filter is utilized to perform image style transformation. Considering the class imbalance present in the landslide dataset, a Rare Class Sampling strategy is introduced to mitigate bias towards common classes and strengthen the learning of the rare landslide class. Finally, a contrastive loss is adopted to further optimize and enhance the model’s ability to delineate fine-grained class boundaries. The proposed model is validated on the Potsdam and Vaihingen benchmark datasets, followed by validation in two landslide scenarios induced by earthquakes and rainfall to evaluate its adaptability across different disaster domains. Compared to the source-only model, LandsDANet achieved improvements in IoU of 27.04% and 35.73% in two cross-domain landslide disaster recognition tasks, respectively. This performance not only showcases its outstanding capabilities but also underscores its robust potential to meet the demands for rapid response. Full article

Ecosystem health assessment is essential for informing ecological protection and sustainable management, yet current evaluation frameworks often overlook the foundational role of natural background conditions and struggle with methodological uncertainties in indicator weighting, particularly in ecologically fragile regions. To address these dual challenges, this study proposes a novel Base–Pressure–State–Response (BPSR) framework that systematically integrates key natural background factors as a fundamental “Base” layer. Focusing on the Qinling Mountains—a critical ecological barrier in China—we implemented this framework at the county scale using multi-source data (2000–2023) and introduced a Monte Carlo simulation with triangular probability distributions to quantify and synthesize weight uncertainties from multiple methods, thereby enhancing assessment robustness. Furthermore, the Geodetector method was employed to quantitatively identify the driving forces behind the spatiotemporal heterogeneity of ecosystem health. Supported by 3S technology, our analysis demonstrates a sustained improvement in ecosystem health: the composite index rose from 0.723 to 0.916, healthy areas expanded from 60.17% to 68.48%, and nearly half of the region achieved a higher health grade. Spatially, a persistent “low–south, high–north” pattern was observed, shaped by human disturbance gradients, while temporally, the region evolved from localized improvement (2000–2010) to broad-scale recovery (2010–2023), despite lingering degradation in human-dominated zones. Driving force analysis revealed a shift from early dominance by natural and land use factors to a later complex interplay where urbanization pressure and climatic conditions jointly shaped the health pattern. The BPSR framework, combined with probabilistic weight optimization and driving force quantification, offers a methodologically robust and spatially explicit tool that advances ecosystem health evaluation and supports targeted ecological governance, policy formulation, and sustainable management in fragile mountain ecosystems, with transferable insights for similar regions globally. Full article

Wildfires are increasing globally and pose major challenges for assessing post-fire vegetation recovery and ecosystem resilience. We analyzed long-term Landsat time series in two contrasting fire-prone ecosystems in the United States and Australia. Vegetation area was extracted using the Enhanced Vegetation Index (EVI) with Otsu thresholding. Recovery to pre-fire baseline levels was modeled using linear, logistic, locally estimated scatterplot smoothing (LOESS), and generalized additive models (GAM), and their performance was compared using multiple metrics. The results indicated rapid recovery of Australian forests to baseline levels, whereas this was not the case for forests in the United States. Among climatic factors, temperature was the dominant parameter in Australia (Spearman ρ = 0.513, p < 10⁻⁸), while no climatic variable significantly influenced recovery in California. Methodologically, GAM consistently performed best in both regions due to its success in capturing multiphase and heterogeneous recovery patterns, yielding the lowest values of AIC (United States: 142.89; Australia: 46.70) and RMSE_cv (United States: 112.86; Australia: 2.26). Linear and logistic models failed to capture complex recovery dynamics, whereas LOESS was highly sensitive to noise and unstable for long-term prediction. These findings indicate that post-fire recovery is inherently nonlinear and ecosystem-specific and that simple models are insufficient for accurate estimation, with GAM emerging as an appropriate method for assessing vegetation recovery using remote sensing data. This study provides a transferable approach using remote sensing and GAM to monitor forest resilience under accelerating global fire regimes. Full article

Vegetation restoration is an effective strategy to improve the ecosystem function of the Loess Plateau. Soil microbiomes play a critical role in maintaining soil multifunctionality (SMF). However, the role of aggregate-scale microbial communities and interactions in regulating SMF during vegetation restoration remains poorly understood. Here, we selected six types of vegetation restoration measures in the Loess Plateau, including natural grassland (NL), Medicago sativa (MS), Hippophae rhamnoides (HR), Caragana korshinskii (CK), Armeniaca vulgaris (AV), and Populus alba (PA), and used abandoned land (AL) as a control to identify key microbial mechanisms driving SMF at the aggregate scale. The results show that vegetation restoration increased bacterial diversity, fungal network complexity, and SMF, especially in AV. In contrast, fungal diversity and bacterial network complexity exhibited asynchronous dynamics across different-sized aggregates. Soil microbial diversity peaked at micro-aggregates (0.053–0.25 mm), while fungal network complexity increased with decreasing aggregate size. The structural equation model confirmed that fungal community composition in large macro-aggregates (>2 mm) and fungal network complexity in <2 mm aggregates were the key drivers of SMF. Our results emphasize the divergent mechanisms by which microbial properties influence SMF across aggregate sizes, highlighting the importance of fungal communities in maintaining soil ecosystem functions. Full article

The prevailing challenges of fading characteristics and identity crises in historical and cultural districts of small and medium-sized cities have been identified. Traditional analytical methods have been found to be deficient in systematically capturing the unique forms and urban memory of these districts. The present study thus adopts the Shangcheng Historical and Cultural District of Huangling County as a case study, proposing a comprehensive analytical framework that integrates urban memory and multi-dimensional methods such as space syntax, grounded-theory-inspired coding, and urban image analysis. The district is subject to a systematic assessment of its spatial form, structural design, and the mechanisms by which urban memory is conveyed. The proposal sets out targeted renewal strategies for four aspects: paths, edges, nodes and landmarks, and districts. The research findings are as follows: (1) Paths with high integration and connection degrees simultaneously serve as both sacrificial axes and carriers of folk narratives. (2) Edges are composed of the city wall ruins, Loess Plateau landform, and street spaces. The fishbone-like street structure leads to significant differences in the connection degrees of main and secondary roads. (3) Nodes such as Guanyv Temple-Confucian Temple, the South Gate, and the North City Wall Ruins Square have high visual control, while the visual integration and visual control of the Qiaoshan Middle School and Gongsun Road historical nodes are relatively low, and their spatial accessibility is insufficient. (4) Based on the “memory–space” coupling relationship, the district is divided into the Academy Life Area, the Historical and Cultural Core Experience Area, and the Comprehensive Service Area, providing an effective path to alleviate the problem of functional homogenization. The present study proffers a novel perspective on the revitalization mechanisms of historical districts in small and medium-sized cities, encompassing both theoretical integration and practical strategy levels. It further contributes methodological inspirations and localized planning experiences for addressing the cultural disconnection and spatial inactivity problems of historical urban areas on a global scale. Full article

Accurate estimation of hourly near-surface air temperature (NSAT) is critical for climate analysis, environmental monitoring, and urban thermal studies. However, existing temperature datasets remain constrained by coarse spatial resolution and limited hourly accuracy. This study systematically evaluates four widely used land surface temperature (LST) datasets—MODIS, ERA5-Land, FY-2F, and CGLS—and five machine learning models (RF, MDN, DNN, XGBoost, and GTNNWR) for NSAT estimation across two contrasting regions in Shaanxi, China: a complex-terrain region in southwestern Shaanxi and the urban area of Xi’an. Results demonstrate that single-source LST inputs outperform multi-source LST stacking, largely due to compounded systematic biases across heterogeneous datasets. MODIS provides the best performance in the mountainous region, while CGLS excels in the urban environment. Among all models, GTNNWR—which explicitly captures spatiotemporal non-stationarity—consistently achieves the highest accuracy, reducing RMSE by 44.8% and 44.2% relative to the second-best model in the two study areas, respectively, whereas the remaining four models exhibit broadly comparable performance. This work identifies effective data–model configurations for generating high-resolution hourly NSAT products and provides methodological insights for climate and environmental applications in regions with complex terrain or strong urban heterogeneity. Full article

Significant wave height (SWH) is a key descriptor of sea state, yet providing accurate, site-specific forecasts at low computational cost remains challenging. This study proposes a transfer-learning-based framework for SWH forecasting that combines Seasonal and Trend decomposition using Loess (STL), a stacked long short-term memory (LSTM) network, and an efficient sliding-window updating scheme. First, STL is applied to decompose the SWH time series into trend, seasonal, and remainder components; the resulting sub-series are then fed into a transfer-learning architecture in which the parameters of the stacked LSTM backbone are kept fixed, and only a fully connected output layer is updated in each window. Using multi-year observations from five National Data Buoy Center (NDBC) buoys, the proposed STL-LSTM-T model is compared with a STL-LSTM configuration that is fully retrained after each STL decomposition. For example, the transfer-learning setup reduces MAE, MSE, and RMSE by up to 11.2%, 19.2%, and 14.5% at buoy 46244, respectively, while reducing the average training time per update to about one-fifth of the baseline. Parameter analyses indicate that a two-layer LSTM backbone and moderate continuous forecast step (6–12 steps) provide a good balance between predictive accuracy, error accumulation, and computational cost, making STL-LSTM-T suitable for SWH forecasting on resource-constrained platforms. Full article

The repeated extraction of coal seams in the Loess Plateau mining region has greatly increased the severity of surface deformation and associated damage. Accurately characterizing the spatio-temporal evolution of subsidence and the underlying mechanisms is a critical engineering challenge for mining safety. Taking the Dafosi Coal Mine located in the loess gully region as a case study, this paper thoroughly examines the variations in surface deformation and damage characteristics caused by single and repeated seam mining. The analysis integrates surface movement monitoring data, global navigation satellite system (GNSS) dynamic observations, field surveys, unmanned aerial vehicle (UAV) photogrammetry, and numerical simulation methods. Notably, to ensure the accuracy of prediction parameters, a refined Particle Swarm Optimization (PSO) algorithm incorporating a neighborhood-based mechanism was employed specifically for the inversion of probability integral parameters. The results indicate that the subsidence factor and horizontal movement factor increase markedly following repeated mining. The maximum surface subsidence velocity also increases substantially, and this acceleration remains evident after normalizing by mining thickness and face-advance rate. The fore effective angle is smaller in repeated mining than in single-seam mining, and the duration of surface movement is substantially extended. Repeated mining fractured key strata and caused a functional transition from the classic three-zone response to a two-zone connectivity pattern, while the thick loess cover responds as a disturbed discontinuous-deformation layer, which together aggravates step-like and slope-related damage. The severity of surface damage is strongly influenced by topographic features and geotechnical properties. These findings demonstrate that the proposed integrated approach is highly effective for geological hazard assessment and provides a practical reference for engineering applications in similar complex terrains. Full article

Rubber particles have been proven to have the advantages of improving the energy absorption effect and enhancing the friction between soil particles when used to modify the soil. The rubber-modified soil technology also provides a new solution for the pollution-free disposal of waste rubber. However, when rubber particles are used to modify collapsible loess, they cannot significantly enhance its strength. Previous studies have not systematically clarified whether combining rubber particles with different cementation mechanisms can overcome this limitation, nor compared their shear mechanical effectiveness under identical conditions. In view of this, a dual synergistic strategy is implemented by combining rubber with lime and rubber with enzyme-induced calcium carbonate precipitation (EICP). Direct shear tests and scanning electron microscopy are used to evaluate four modification approaches: rubber alone, lime alone, rubber with EICP, and rubber with lime. Accordingly, shear strength, cohesion, and internal friction angle are quantified. At a vertical normal stress of 100 kPa and above, samples modified with rubber and lime (7–9% lime and 6–8% rubber) achieve peak shear strength values of 200–203 kPa, representing an 86.4% increase compared to rubber alone. Microscopic analysis reveals that calcium silicate hydrate gel effectively anchored rubber particles, forming a composite structure with a rigid skeleton and elastic buffer. In comparison, the rubber and EICP group (10% rubber) shows a substantial increase in internal friction angle (24.25°) but only a modest improvement in cohesion (16.5%), which is due to limited continuity in the calcium carbonate bonding network. It should be noted that the performance of EICP-based modification is constrained by curing efficiency and reaction continuity, which may affect its scalability in conventional engineering applications. Overall, the combination of rubber and lime provided an optimal balance of strength, ductility, and construction efficiency. Meanwhile, the rubber and EICP method demonstrates notable advantages in environmental compatibility and long-term durability, making it suitable for ecologically sensitive applications. The results offer a framework for loess stabilization based on performance adaptation and resource recycling, supporting sustainable use of waste rubber in geotechnical engineering. Full article

Urban tree canopy is increasingly recognized as a health-protective form of green infrastructure, yet its distribution remains uneven across socioeconomically stratified neighborhoods. This study quantifies fine-scale tree-canopy inequity across Census Block Groups (CBGs) in Baltimore and examines associations with socioeconomic deprivation and modeled pollution-related cancer risk. We integrated (i) 2023 US Forest Service canopy estimates aggregated to CBGs, (ii) Area Deprivation Index (ADI) national and state ranks, (iii) American Community Survey 5-year population counts, and (iv) EPA NATA/HAPs cancer-risk estimates aggregated to CBGs using population-weighted means. Associations were assessed using Spearman correlations and visualized with LOESS smoothers. Canopy was negatively associated with ADI national and state ranks (ρ = −0.509 and −0.503), explaining 29–31% of canopy variation. Population-weighted canopy declined from 47–51% in the least deprived decile to 13–15% in the most deprived (3.4–4.1× disparity). Beyond socioeconomic gradients, overall distributional inequity was quantified using a population-weighted Tree Canopy Inequality Index (TCI; weighted Gini), yielding TCI = 0.312, indicating substantial inequality. The population-weighted Atkinson index rose sharply under increasing inequality aversion (A_0.5 = 0.084; A₂ = 0.402), revealing extreme canopy deficits concentrated among the most disadvantaged neighborhoods. Canopy was also negatively associated with modeled cancer risk (ρ = −0.363). We constructed a Triple Burden Index integrating canopy deficit, deprivation, and cancer risk, identifying spatially clustered high-burden neighborhoods that collectively house over 86,000 residents. These findings demonstrate that canopy inequity in Baltimore is structurally concentrated and support equity-targeted greening and sustained maintenance strategies guided by distributional justice metrics. Full article