Search Results (1,438)

Photovoltaic (PV) power generation is essential for achieving carbon neutrality and advancing renewable energy development. In Northwest China, the rapid expansion of PV installations requires accurate and timely spatial data to support effective monitoring and planning. Addressing the limitations of existing datasets in spatiotemporal resolution and driver analysis, this study develops a scalable solar facility inventory framework on the Google Earth Engine (GEE) platform. The framework integrates Sentinel-1 SAR, Sentinel-2 multispectral imagery, and interpretable machine learning. Feature redundancy is first assessed using correlation-based metrics, after which a Random Forest classifier is applied to generate a 10 m resolution distribution map of utility-scale photovoltaic power plants as of December 2023. To elucidate model behavior, SHAP (SHapley Additive exPlanations) is used to identify key predictors, and MaxEnt is incorporated to provide a preliminary quantitative assessment of spatial drivers of PV deployment. The RFECV-optimized model, retaining 44 key features, achieves an overall accuracy of 98.4% and a Kappa coefficient of 0.96. The study region contains approximately 2560 km² of PV installations, with pronounced clusters in northern Ningxia, central Shaanxi, and parts of Xinjiang and Gansu. SHAP analysis highlights the Enhanced Photovoltaic Index (EPVI), the Normalized Difference Built-up Index (NDBI), Sentinel-2 Band 8A, and related texture metrics as primary contributors to model predictions. High EPVI, NDBI, and Sentinel-2 Band 8A values contribute positively to PV classification, whereas vegetation-related indices (e.g., NDVI) exhibit predominantly negative contributions; these results indicate that PV mapping relies on the integrated discrimination of multiple spectral and texture features rather than on a single dominant variable. MaxEnt results indicate that grid accessibility and land-use constraints (e.g., nighttime light intensity reflecting human activity) are dominant drivers of PV clustering, often exerting more influence than solar irradiance alone. This framework provides robust technical support for PV monitoring and offers high-resolution spatial distribution data and driver insights to inform sustainable energy management and regional renewable-energy planning. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

Remote sensing provides an efficient approach for monitoring ecosystem dynamics in the arid and semi-arid regions of Xinjiang, yet traditional forest-land extraction methods (e.g., spectral indices, threshold segmentation) show limited adaptability in complex environments affected by terrain shadows, cloud contamination, and spectral confusion with grassland or cropland. To overcome these limitations, this study used three convolutional neural network-based models (FCN, DeepLabV3+, and PSPNet) for accurate forest-land extraction. Four tri-band training datasets were constructed from Sentinel-2 imagery using combinations of visible, red-edge, near-infrared, and shortwave infrared bands. Results show that the FCN model trained with B4–B8–B12 achieves the best performance, with an mIoU of 89.45% and an mFscore of 94.23%. To further assess generalisation in arid landscapes, ESA WorldCover and Dynamic World products were introduced as benchmarks. Comparative analyses of spatial patterns and quantitative metrics demonstrate that the FCN model exhibits robustness and scalability across large areas, confirming its effectiveness for forest-land extraction in arid regions. This study innovatively combines band combination optimization strategies with multiple deep learning models, offering a novel approach to resolving spectral confusion between forest areas and similar vegetation types in heterogeneous arid ecosystems. Its practical significance lies in providing a robust data foundation and methodological support for forest monitoring, ecological restoration, and sustainable land management in Xinjiang and similar regions. Full article

Soil fertility plays a crucial role in crop productivity, particularly in cocoa cultivation, which is highly dependent on soil quality that directly influences both productivity and sustainability. Understanding how to achieve and maintain soil fertility on cocoa farms is fundamental to sustaining higher yields. Cocoa production in Ghana and Togo remains low, at 350–600 kg/ha, compared to the potential yield of over 1–3 tons per hectare. Given the growing demand for cocoa and limited arable land, adequate soil nutrients are essential to optimise productivity. Soil fertility indices (SFIs) have been widely used as soil metrics by integrating multiple physical, chemical, and biological soil properties. In this study, standard analytical methods were employed to evaluate the SFI through laboratory analyses of 49 surface soil samples collected at a depth of 0–30 cm with an auger. Eleven soil chemical indicators were analysed: pH (water), organic matter (OM), potassium (K), calcium (Ca), magnesium (Mg), available phosphorus (P), total nitrogen (N), cation exchange capacity (CEC), electrical conductivity (EC), and carbon-to-nitrogen ratio (C/N). Principal component analysis, followed by normalisation, was used to select a minimum dataset, which was then integrated into an additive SFI. Results indicated that N, Ca, Mg, CEC, and pH were within the optimal range for most surveyed locations (96%, 94%, 92%, 73%, and 63%, respectively), while OM and C/N were within the optimal range in approximately half of the study area. Available P, K, and C/N were highly deficient in 100%, 67%, and 96% of surveyed locations, respectively. Soil fertility varied significantly among locations (p = 0.007) and was generally low, ranging from 0.15 to 0.66. Only 20% of the soils in the study area were classified as adequately fertile for cocoa cultivation. Therefore, it is necessary to restore soil nutrient balance, especially the critically low levels of K and P, through appropriate management practices that improve fertility over time and help close the yield gap. Full article

The Dead Sea (DS) region has experienced a sharp increase in sinkhole formation in recent years, posing environmental and infrastructure risks. The Geological Survey of Israel (GSI) employs Interferometric Synthetic Aperture Radar (InSAR) to monitor sinkhole activity and manually map land subsidence along the western shore of the DS. This process is both time-consuming and prone to human error. Automating detection with Deep Learning (DL) offers a transformative opportunity to enhance monitoring precision, scalability, and real-time decision-making. DL segmentation architectures such as UNet, Attention UNet, SAM, TransUNet, and SegFormer have shown effectiveness in learning geospatial deformation patterns in InSAR and related remote sensing data. This study provides a first comprehensive evaluation of a DL segmentation model applied to InSAR data for detecting land subsidence areas that occur as part of the sinkhole-formation process along the western shores of the DS. Unlike image-based tasks, our new model learns interferometric phase patterns that capture subtle ground deformations rather than direct visual features. As the ground truth in the supervised learning process, we use subsidence areas delineated on the phase maps by the GSI team over the years as part of the operational subsidence surveillance and monitoring activities. This unique data poses challenges for annotation, learning, and interpretability, making the dataset both non-trivial and valuable for advancing research in applied remote sensing and its application in the DS. We train the model across three partition schemes, each representing a different type and level of generalization, and introduce object-level metrics to assess its detection ability. Our results show that the model effectively identifies and generalizes subsidence areas in InSAR data across different setups and temporal conditions and shows promising potential for geographical generalization in previously unseen areas. Finally, large-scale subsidence trends are inferred by reconstructing smaller-scale patches and evaluated for different confidence thresholds. Full article

Background: The Identification of Functional Ankle Instability (IdFAI) questionnaire is widely used to screen for functional ankle instability (FAI), but its link to objective landing kinetics in multidirectional sports like netball is not well-understood. This study aimed to (i) compare landing kinetics between idFAI stratified netball players, and (ii) examine associations between IdFAI scores with dynamic postural stability (DPS) indices and peak vertical ground reaction forces (PvGRF) during vertical drop landings. Methods: A cross-sectional exploratory study using a repeated-measures landing protocol was conducted on female university netball players (n = 24), stratified into FAI (n = 12) and non-FAI (n = 12) groups using the IdFAI (≥11 indicating possible FAI). Participants completed 18 unilateral drop jump landings in forward (FW), diagonal (DI), and lateral (LA) directions. Ground reaction forces (GRFs) were recorded to obtain DPS and PvGRF metrics (1000 Hz). Mann–Whitney U tests compared FAI groups, and Spearman correlations assessed associations (p < 0.05). Results: Players with FAI showed greater anteroposterior instability during LA landings (U = 33.5, p = 0.020, ES = 0.65). IdFAI scores correlated moderately with lateral anteroposterior deficits (rs = 0.473, p = 0.020, CI = 0.062–0.746). Conclusions: These findings suggest that players with greater FAI display increased anteroposterior instability during LA landings, with higher IdFAI scores moderately associated with these deficits. Despite the small exploratory, hypothesis-generating sample, the results emphasize the practical relevance of direction-targeted landing-stability training to improve DPS in vertical landings. This may provide insight into ankle-injury risk among FAI netball players, given that LA landings represent a documented ankle sprain mechanism. Full article

The floodplain landscape of the lower Yellow River is jointly shaped by natural water-sediment processes and human activities. With intensified regulation by large reservoirs and increasing human development intensity, the landscape pattern of the floodplain has undergone significant changes. Clarifying the relative contributions of natural and anthropogenic factors, as well as their interactive mechanisms, is crucial for ecological management of the floodplain. Based on 40-year long-term land-use data and hydrological and meteorological observations, this study integrates landscape metrics, the human interference index (HI), grey relational analysis, and partial least squares regression to quantify the spatiotemporal dynamics of landscape pattern in the floodplain of the lower Yellow River and to elucidate the driving mechanisms underlying landscape-pattern evolution. The results indicate that (1) during the study period, the areas of cultivated land and built-up land in the floodplain continuously increased, whereas natural wetlands and grassland decreased accordingly. Taking 2000 as a breakpoint, the rate and direction of landscape change exhibited stage-dependent differences. (2) Landscape pattern metrics changed nonlinearly: the number of patches decreased first and then increased; the patch cohesion index increased first and then declined; and Shannon’s diversity index showed an overall downward trend. These changes suggest a process of landscape consolidation induced by agricultural cultivation, followed by re-fragmentation driven by the expansion of built-up land. (3) Driving-mechanism analysis shows that the HI is the primary driver of the current changes in floodplain landscape pattern. After the operation of the Xiaolangdi Dam, water-sediment conditions tended to stabilize and flood risk in the floodplain decreased, thereby creating favourable conditions for human activities. This study highlights the stage-dependent influences of natural and anthropogenic factors on floodplain landscape evolution under dam regulation and suggests that management strategies should be adapted to the current re-fragmentation phase, prioritizing the strict control of agricultural expansion and the restoration of ecological corridors to mitigate anthropogenic interference under stable dam regulation. Full article

Amidst the surge of high-density urban development and the growing demand for high-quality spaces, the Usable Area Coefficient (UAC) has emerged as a pivotal metric in the architectural planning. The rational calibration of the UAC for primary school buildings is key to balancing intensive land use, educational demands, and the well-being of children. Taking primary schools in a district of Qingdao as the research subject, this research rationally optimizes the range of UAC by constructing an evolutionary game model, based on quantitatively analyzing the divergent perspectives and requirements of three stakeholders: the government, school administrators, and students. After further identifying the key factors that influence the ultimate decision, the study yields the following insights: (1) The incremental comprehensive benefit emerges as the linchpin influencing the UAC. (2) The government’s risk compensation to schools and the benefit-sharing coefficient between schools and students exert significant impacts on system evolution. (3) Effective control of construction and land costs, coupled with enhanced availability of open activity spaces, paves the way for consensus on low UAC. This research not only furnishes a theoretical framework and practical guidance for harmonizing land use efficiency with educational excellence but also steers the design of salubrious primary school environments and informs pertinent policy-making. Full article

Forest cover dynamics strongly influence ecological integrity and resource sustainability, particularly in ecotonal landscapes, where vegetation is highly sensitive to climate variability, long-term climate change, and anthropogenic disturbances. This study examined Forest Land (FL), representing all areas of dense, canopy-forming woody vegetation with forest-like structure, aggregated from SANLC classes, in relation to eight other land cover classes across three periods: 1990–2014, 2014–2022, and 1990–2022. The study used South African National Land Cover datasets and the TerrSet–LiberaGIS Land Change Modeller to quantify changes in magnitude, direction, and source–sink relationships. Analyses included post-classification comparison to determine spatial changes, transition matrices to identify land-cover conversions, and asymmetric gain–loss metrics to reveal sources and sinks of forest change. The result shows that between 1990 and 2014, forests remained marginal and fragmented in the eastern central part of the study area, while shrubland increased from 40.4% to 60.2% at the expense of grasslands, cultivated land, bare land, wetlands, and forest land. From 2014 to 2022, FL regeneration was pronouncedly increased from 2% to 6%, especially along riparian corridors and reservoir margins, coinciding with shrubland decline (99.3%) and grassland recovery (261.2%). Over the entire 1990–2022 period, FL increased from 2.4% to 6% expanding into bare land, cultivated land, grassland, shrubland, and wetlands. Asymmetric analysis indicated that forests acted as a sink during the first period but as a source of ecological resilience in the second and final. These findings demonstrate strong vegetation feedback to hydrological and anthropogenic drivers. Overall, the findings underscore the potential for forest recovery to enhance biodiversity, ecosystem services, carbon storage, and hydrological regulation, while identifying priority areas for riparian conservation and integrated catchment management. Full article

Karst regions cover about 12% of Earth’s land surface and exhibit high uncertainty in soil organic carbon (SOC) pools due to strong spatial heterogeneity. This study quantifies the association between rock fissure network morphology and SOC pools across three karst rocky habitat types in the Maolan National Nature Reserve (Guizhou, China): Type I (predominantly sub-horizontal and weakly connected fissures), Type II (oblique and moderately connected fissures), and Type III (predominantly subvertical and highly connected fissures). Fissure network morphology was characterized using quantitative network morphology metrics, and SOC pools (content, density, and stock) were measured from field samples (with long-term sequestration estimated). Type I habitats showed the highest SOC content, density, stock, and sequestration estimates, whereas Type III habitats consistently showed the lowest values. Across habitats, SOC density and stock were negatively associated with metrics reflecting steeper fissure orientation, greater spatial heterogeneity, and higher network connectivity, while SOC content was positively associated with fissure network complexity. These findings highlight fissure network morphology as an important structural dimension for explaining SOC variability in karst rocky habitats and suggest incorporating fissure information into SOC assessment and habitat-specific soil and vegetation management in karst landscapes. Full article

In recent decades, rapid socioeconomic development and population growth have led to the degradation of river and lake health worldwide, posing severe challenges to watershed ecological management. The growing intensity of land-use has significantly contributed to the accelerated deterioration of aquatic ecosystems. River and lake health assessment has evolved from single-parameter metrics (e.g., water quality) to multidimensional frameworks integrating hydrological, biological, and anthropogenic factors. This research conducted a bibliometric analysis of 1302 publications from 1996 to 2023 in the Web of Science database to identify research trends and hotspots. Results showed that publications exhibited a three-phase growth incubation (1996–2000), expansion (2001–2012), and acceleration (2013–2023), with the U.S., China, and Australia as leading contributors characterized by regionally clustered international collaborations. Research themes have shifted from single water quality parameters to integrated assessments. “Land-use”, “water quality”, and “biotic integrity” have emerged as core hotspots, forming a synergistic assessment framework that combines physicochemical, biological, and socioeconomic factors. The research scale underwent a spatial refinement process from the whole watershed to the buffer zone of rivers and lakes, and land-use effects on aquatic ecosystems vary significantly across spatial scales (entire watershed and riparian zones). Fine-scale studies better capture localized pollution pathways, supporting targeted conservation strategies. This review systematically outlines research status, hotspots, and development directions for river and lake health studies, highlighting the need for integrated watershed management, emphasizing conservation through fine-scale land-use monitoring, and providing scientific support for integrated refined governance of watershed ecology. Full article

Understanding cultivated land use transition (CLUT) requires analytical frameworks capable of capturing the interconnected changes in production inputs, land use structure, and multifunctional outcomes. However, existing CLUT studies often rely on fragmented metrics that separately examine dominant or recessive transitions, limiting their ability to reveal the internal mechanisms of land use transition. Therefore, this study developed an integrated “factor-structure-function” analytical framework based on the theory of induced technological innovation. An evaluation system was constructed to operationalize the proposed framework, and Zhejiang Province—a rapidly urbanizing region in southeastern China, was selected as an empirical validation case to demonstrate its analytical value. The results showed that the integrated framework not only identified temporal and spatial patterns of CLUT, but also revealed internal trade-offs and synergies among factor substitution, structural reconfiguration, and functional transition that were not detectable using conventional CLUT metrics. In particular, the framework highlighted unique regional transition pathways driven by different modes of factor substitution. By connecting factor inputs, output structures, and land functions within the integrated framework, this study offers a practical tool for diagnosing CLUT and serves as a methodological guide for future CLUT research in rapidly urbanizing regions. Full article

Urban helicopter activity is intermittent and route-focused, yet most strategic mapping tools were developed for fixed-wing traffic and long-term averages, leaving urban rotorcraft noise under-represented. In the EU, the Environmental Noise Directive (2002/49/EC) and its CNOSSOS-EU methods require Member States to measure, map, and report aviation noise at major airports (using indicators such as Lden and Lnight), covering helicopter operations as part of overall aviation noise; yet current practice and tooling remain largely fixed-wing oriented. To the authors’ knowledge, no peer-reviewed real-case applications of NORAH2 to urban helicopter operations have yet been published. Therefore, this study demonstrates an end-to-end NORAH2 workflow using Cannes, France, as an urban case study, modelling 556 helicopter operations recorded between 12 and 25 May 2025 over an 8.3 km × 2.5 km analysis grid, and utilising openly available ADS-B/Mode-S trajectories to generate noise-related maps that can be used to support policy-making. Radar trajectories were conditioned to retain sampling while ensuring kinematic plausibility; environmental layers (terrain, land cover, basic meteorology) and rotorcraft representations were configured in NORAH2. Standard indicators were produced on a uniform grid, Lden (day–evening–night) and LAeq, 16 h, alongside event-count metrics (N60/N65/N70) and single-event LAmax footprints. Over a two-week window, outputs exhibited coherent corridor-level structure and event footprints consistent with observed operations, indicating that ADS-B-derived trajectories, after light conditioning, are suitable inputs for urban NORAH2 mapping. The period analysed is short; results are demonstrative for that window and not intended as statutory exposure assessments. The contribution is twofold: (i) the first published demonstration that connects open radar-like data to NORAH2 outputs in a dense urban setting, and (ii) evidence that NORAH2 can provide both energy-average and frequency-of-occurrence views useful for city noise management. Full article

Fine-grained building function identification (BFI) is essential for sustainable urban development, land-use analysis, and data-driven spatial planning. Recent progress in fully supervised semantic segmentation has advanced multimodal BFI; however, most approaches still rely on static fusion and lack explicit multi-scale alignment. As a result, they struggle to adaptively integrate heterogeneous inputs and suppress cross-modal interference, which constrains representation learning. To overcome these limitations, we propose BuildFunc-MoE, an adaptive multimodal Mixture-of-Experts (MoE) network built on an effective end-to-end Swin-UNet backbone. The model treats high-resolution remote sensing imagery as the primary input and integrates auxiliary geospatial data such as nighttime light imagery, DEM, and point-of-interest information. An Adaptive Multimodal Fusion Gate (AMMFG) first refines auxiliary features into informative fused representations, which are then combined with the primary modality and passed through multi-scale Swin-MoE blocks that extend standard Swin Transformer blocks with MoE routing. This enables fine-grained, dynamic fusion and alignment between primary and auxiliary modalities across feature scales. BuildFunc-MoE further introduces a Shared Task-Expert Module (STEM), which extends the MoE framework to share experts between the main BFI task and auxiliary tasks (road extraction, green space segmentation, and water body detection), enabling parameter-level transfer. This design enables complementary feature learning, where structural and contextual information jointly enhance the discrimination of building functions, thereby improving identification accuracy while maintaining model compactness. Experiments on the proposed Wuhan-BF multimodal dataset show that, under identical supervision, BuildFunc-MoE outperforms the strongest multimodal baseline by over 2% on average across metrics. Both PyTorch and LuoJiaNET implementations validate its effectiveness, while the latter achieves higher accuracy and faster inference through optimized computation. Overall, BuildFunc-MoE offers a scalable solution for fine-grained BFI with strong potential for urban planning and sustainable governance. Full article

Background: Passing in lacrosse is a fundamental skill essential for both offense and defense, directly influencing game flow. Although the speed–accuracy trade-off is well recognized in motor control, its features in lacrosse passing—particularly regarding directional aspects and skill differences—remain unclear. This study quantified the relationship between pass speed, accuracy, bias, and consistency and examined directional effects and skill-level differences. Methods: Twenty-two female university players (skilled: n = 9; unskilled: n = 13) executed overhand passes to a 5 cm × 5 cm target from 11 m under three effort conditions: warm-up, game intensity, and full effort. Ball speed was derived from lateral video, and landing coordinates from posterior footage. Accuracy, bias, and consistency were assessed using radial error (RE), centroid error (CE), absolute CE (|CE|), and bivariate variable error (BVE). Directional patterns were analyzed through lateral and vertical components and the 95% confidence intervals of the major and minor axes of an error ellipse. A two-way analysis of variance was performed with condition as the within-subject factor and skill level as the between-subject factor. Results: Ball speed increased significantly across conditions. RE, |CE|, and BVE increased with speed, showing directional dependence: variability expanded mainly along the major axis, while the minor axis remained stable. Skilled players showed smaller RE and BVE, with differences most evident vertically and along the major axis. CE direction stayed consistent, indicating that reduced accuracy stemmed from greater bias magnitude and lower consistency rather than shifts in the mean landing point. Conclusions: Findings confirm a speed–accuracy trade-off in lacrosse passing, characterized by directional specificity and skill-related effects. Combining RE, CE, BVE, and ellipse-axis analyses clarified error structure, showing variability concentrated along the movement axis. These results support training focused on vertical control and timing and highlight the value of directional metrics for assessing lacrosse performance. Future research should include male athletes, advanced levels, and in-game scenarios to extend generalizability. Full article