Search Results (4,698)

The dynamics of aboveground biomass (AGB) in rubber plantations (RPs) provide an important basis for evaluating carbon stocks and environmental adaptability in tropical plantations. However, continuous monitoring of AGB of RPs at the regional scale is lacking, and its nonlinear responses to hydrothermal conditions remain insufficiently understood. This study focused on RPs in western Hainan Island (WHI), including Danzhou, Baisha, Lingao, and Chengmai, and integrated field plot data with multi-source remote sensing datasets. A framework for mapping RPs combining rule-based constraints and phenology-based random forest (RF) classification was developed. After key variable screening, extreme gradient boosting (XGBoost), Shapley additive explanations (SHAP), and generalized additive model (GAM) were used for AGB estimation and identification of climatic responses. The results showed that mapping of RPs achieved an overall accuracy of 92.89% and a Kappa coefficient of 0.854. The XGBoost-derived estimates showed that AGB of RPs in the study area increased by approximately 1.43 × 10⁶ Mg from 2017 to 2025, with growth areas mainly concentrated in the Danzhou–Baisha and western Chengmai. AGB exhibited significant nonlinear responses to climatic factors. Specifically, the effect of precipitation (PRE) shifted to negative after approximately 1945 mm yr⁻¹, whereas annual mean maximum temperature (TMAX) shifted to a positive effect after about 29.72 °C, although this effect gradually weakened as temperature continued to rise. Combinations such as PRE × annual mean temperature (PRE × TMP), PRE × TMAX, and PRE × potential evapotranspiration (PRE × PET) exhibited significant nonlinear interactions, indicating that the direction and magnitude of the effect of PRE shifted with changes in temperature and PET levels. These findings link the spatiotemporal changes in AGB of RPs in WHI with hydrothermal thresholds and their interacting effects, deepening our understanding of the climatic response characteristics of AGB in RPs in this region. They also provide a scientific basis for RP monitoring, carbon stock assessment, and climate-adaptive management in WHI. Full article

Long-term exploitation of ion-adsorption rare earth deposits has played a vital role in ensuring the supply of strategic mineral resources. However, intensive mining activities have also resulted in severe ecological degradation, including vegetation loss, land degradation, and soil erosion. Although the Remote Sensing Ecological Index has been widely used for ecological environment assessment, it inadequately characterizes land degradation in ion-adsorption rare earth mining areas, while its fixed-weight framework is unable to effectively capture the influence of localized ecological limiting factors. To address these limitations, this study selected a typical ion-adsorption rare earth mining area in southern Jiangxi, China, as the study area. A Desertification Difference Index was incorporated into the conventional RSEI framework to establish a five-dimensional evaluation system consisting of greenness, wetness, dryness, heat, and desertification. Furthermore, a Dynamic Variable-Weight Remote Sensing Ecological Index (DV-RSEI) was developed by integrating variable-weight theory, enabling adaptive adjustment of indicator weights according to local ecological conditions. Using Landsat imagery from 2000, 2005, 2010, 2016, 2020, and 2023, the spatiotemporal evolution and spatial heterogeneity of ecological environmental quality were systematically investigated. The results indicate that: (1) ecological environmental quality exhibited a characteristic evolution process of mining disturbance–ecological degradation–comprehensive restoration–ecological recovery during 2000–2023, with an overall trend dominated by stability and improvement; (2) ecological environmental quality showed significant spatial clustering, with High–High clusters mainly distributed in areas with favorable ecological conditions, whereas Low–Low clusters were concentrated in regions strongly affected by mining activities; and (3) compared with the conventional RSEI, the DV-RSEI better characterized mining-related ecological degradation patterns and the spatial heterogeneity of ecological environmental quality. The proposed approach provides a scientific basis for dynamic ecological monitoring, evaluation of ecological restoration effectiveness, and the construction of green mines in ion-adsorption rare earth mining areas. Full article

The Younger Dryas (YD) terminated the last glaciation and initiated the Holocene, which was accompanied by widespread glacial advances. Traditional scaling models, production rates, and outlier screening approaches for ¹⁰Be exposure dating often yield inconsistent formation ages for the same moraine, hindering a comprehensive understanding of spatiotemporal patterns and climatic controls of YD glaciation across the Tibetan Plateau. In this study, we reprocessed ¹⁰Be exposure ages using the Probabilistic Cosmogenic Age Analysis Tool 2.2 (P-CAAT) to constrain YD moraine chronologies. Three glacial events were dated to 12.5 ka, 11.9 ka, and 11.5 ka. Glacier-climate simulations showcased cold dry conditions in the monsoon domain and cold-wet conditions in the westerly domain during the YD. The modelled temperature reductions matched global climatic records. Glacial advances were primarily forced by cooling in monsoon areas, whereas joint cooling and increased precipitation dominated in westerly regions. This study clarifies the temporal characteristics and driving mechanisms of YD glaciation on the plateau, though additional dating information is required for further verification. Full article

The Longmenshan Fault Zone, as the steep boundary on the eastern edge of the Qinghai–Xizang Plateau, is the seismogenic structure that includes strong earthquakes such as the 7.9 magnitude Wenchuan earthquake in 2008 and the 6.6 magnitude Lushan earthquake in 2013. Based on the U.S. Geological Survey (M ≥ 2.5) earthquake catalogue from 2000 to 2025, this study systematically analyzed the spatio-temporal evolution of seismic activities in this area. We determined the completeness of the seismic magnitude by time periods and drew a spatial B-value distribution map using the maximum likelihood estimation method to reveal its variation characteristics. The analysis is divided into three intervals: 2000–2007 (pre-Wenchuan), 2008–2012 (co- and post-Wenchuan), and 2013–2025 (long-term postseismic stage; the 2008–2025 interval includes an observational and forecast assessment window). Low b values persist in the central and southern parts of the LMSF, indicating that the degree of stress concentration in these two regions is relatively high. After 2008, the b value of the Wenchuan Fault Zone rose briefly. After 2013, the b value gradually declined. This fluctuation confirmed the re-accumulation process of regional stress. The analysis results of the Z-value rate change show that there is obvious stillness in the central fault zone (Z > 2), while there is slight activation in some southern areas of the LMSF (Z ≈ −0.5 to 0). These patterns are roughly consistent with the spatial B-value structure, and our research results also provide diagnostic conclusions for interpreting the long-term seismic activity evolution and stress heterogeneity of the LMSF. Full article

To address the mismatch between agricultural development and water resource conditions, this study examined 22 provinces and four municipalities in China over the period 2014–2023 and incorporated terrain factors into the analytical framework. An integrated evaluation system was developed for the Agricultural Development Index (ADI) and Water Resource Condition Index (WCI). The entropy weight method, coupling coordination degree model, standard deviation ellipse model, spatial difference coefficient, and two-way fixed-effects model were employed to characterize the spatiotemporal dynamics and coordination between agricultural development and water resource conditions. The results indicate that the ADI increased steadily throughout the study period, whereas the WCI remained relatively stable, reflecting distinct evolutionary trajectories of the two systems. Although the coordination level of the Agriculture–Water Resources System improved overall, pronounced regional disparities persisted. Plain regions exhibited stronger coordination, whereas mountainous and plateau regions showed lower coordination levels because of terrain constraints and limited resource endowments. Spatial mismatches between agricultural and water resource advantage regions were evident, although the overall matching relationship between the two systems gradually strengthened. The proposed evaluation framework provides a scientific basis for optimizing regional agricultural layouts, improving water resource allocation, and promoting coordinated agricultural and water resource development. Full article

Sugarcane (Saccharum spp.) is a globally vital high-biomass sugar crop with a massive demand for potassium (K). Low-K⁺ stress severely restricts its yield and stress resistance. Plasma membrane intrinsic proteins (PIPs) play pivotal roles in transmembrane water transport and ion homeostasis; however, their evolutionary characteristics and molecular mechanisms underlying nutritional stress responses in the complex polyploid sugarcane remain poorly understood. In this study, genome-wide identification in the sugarcane cultivar XTT22 yielded 149 PIP gene family members (comprising 54 PIP1s and 95 PIP2s). Phylogenetic and chromosomal localization analyses demonstrated that the sugarcane PIP family underwent drastic paralogous expansion during evolution, with tandem duplication acting as the core driving force for the dramatic expansion of the PIP2 subfamily. Spatiotemporal expression profiling unveiled significant modular functional division among PIP genes, identifying a core co-expression group driving rapid early seedling elongation and a PIP2-specific expression cluster dedicated to the physiological homeostasis of mature stems. Notably, the core member ScPIP2-70 exhibited significant early-induced responses at both transcriptional and protein levels in roots under low-K⁺ stress. Functional complementation assays in the K⁺-uptake deficient yeast strain R5421 further confirmed that the heterologous expression of ScPIP2-70 effectively rescued the growth defects of yeast under low-K⁺ conditions, demonstrating its potential transmembrane K⁺ transport activity. This study not only comprehensively elucidates the evolutionary dynamics and spatiotemporal expression profiles of the sugarcane PIP gene family but also uncovers the novel pleiotropic function of ScPIP2-70 in mediating low-K⁺ stress tolerance, providing critical theoretical support and candidate gene resources for breeding “potassium-efficient” sugarcane cultivars via modern biotechnology. Full article

To understand the epidemiological characteristics and patterns of mushroom poisoning in Zhejiang Province from 2012 to 2023, and to overcome the limitations of previous descriptive studies in precise early warning and spatial identification, this study explored the feasibility of identifying spatial distribution characteristics and high-risk spatiotemporal clusters. First, descriptive epidemiological analysis was conducted on 2276 cases from the Foodborne Disease Case Surveillance System and 408 outbreaks from the Foodborne Disease Outbreak Surveillance System reported over the 12-year period to clarify the basic characteristics and trends of poisoning. Subsequently, spatial autocorrelation analysis (Moran’s I) was employed to reveal spatial dependence and clustering patterns. Finally, spatiotemporal scan statistics (SatScan) were used to precisely identify high-risk spatiotemporal clusters, systematically analyzing the spatiotemporal distribution and clustering patterns of mushroom poisoning cases. The results showed a distinct summer–autumn seasonal peak (June–October), attributed to the subtropical monsoon climate with high temperatures and abundant rainfall, which is conducive to mushroom growth. Farmers were the most affected population (47.93%), and homes were the primary poisoning locations (71.7%), reflecting widespread foraging habits and insufficient risk awareness in rural areas. Chlorophyllum molybdites (36.27%) and Russula japonica (10.05%) were the dominant poisoning mushroom species, with gastrointestinal symptoms being the predominant clinical manifestation (84.07%). Spatial analysis revealed significant spatiotemporal clustering of mushroom poisoning in Zhejiang Province. The global Moran’s I index showed significant positive autocorrelation in some years (p < 0.05), with local hotspots mainly distributed in western Zhejiang counties. This pattern is driven by a dual model of environmental suitability and behavioral risk, resulting from the high forest coverage and humid climate of the western Zhejiang mountainous areas providing suitable habitats, combined with long-standing foraging habits among local residents. Retrospective spatiotemporal scanning identified high-risk clusters for each year from 2018 to 2023, with the Lishui area in 2023 being the most significant cluster (Relative Risk (RR) = 15.44, Log-Likelihood Ratio (LLR) = 114.49). The results confirm that mushroom poisoning in Zhejiang Province exhibits a stable and identifiable spatiotemporal clustering pattern, providing a quantitative basis for precise health education and targeted prevention and control in high-risk counties of western Zhejiang during June–October, thereby shifting the approach from passive reporting to targeted intervention. Full article

Accurate wind speed forecasting plays a crucial role in the safe and stable operation of wind farms and the efficient integration of renewable energy into modern power systems. However, wind speed exhibits complex spatiotemporal variations affected by diverse meteorological conditions, making high-precision prediction a long-standing bottleneck in wind power scheduling. This paper develops a U-shaped spatiotemporal feature fusion network named U-STNet, which realizes joint modeling of inter-turbine spatial correlations and multi-period long-range temporal dependencies. The model maps raw wind speed series to high-dimensional embeddings and adopts an encoder–decoder U-shaped architecture to complete feature encoding, reconstruction and multi-scale feature extraction, which effectively captures the inherent periodic and seasonal patterns of wind speed. Experiments on the SDWPF wind farm dataset show that U-STNet obtains competitive prediction accuracy across all multi-step forecasting horizons. Compared with traditional statistical models, recurrent neural networks and state-of-the-art Transformer baselines, the proposed method exhibits more stable error accumulation characteristics and superior long-step prediction performance. This verifies the effectiveness of jointly modeling turbine spatial topology and multi-scale temporal dependencies for wind speed forecasting. Full article

As a major beef cattle producing country, China’s beef industry is expanding and undergoing structural transformation. A systematic assessment of the spatiotemporal evolution and driving factors of its water footprint is of great significance for the green and sustainable development of the beef cattle industry, and also provides a reference for understanding the current status of beef cattle water footprint, formulating environmental policies, and supporting the sustainable development of other livestock and poultry species. Based on the life cycle assessment (LCA) method, quantified the green, blue, and grey water footprints of China’s beef cattle industry “from cradle to farm gate” across 31 provinces from 2002 to 2022, covering three farming scales (small-scale, medium-scale, and large-scale) classified according to the proportion of beef cattle slaughter numbers (1–49 head, 50–500 head, and >500 head), and encompassing four stages: feed crop cultivation, beef cattle farming, manure leaching, and transportation and processing, covering three farming scales (small-scale, medium-scale, and large-scale) classified according to the proportion of beef cattle slaughter numbers (1–49 head, 50–500 head, and >500 head), and encompassing four stages: feed crop cultivation, beef cattle farming, manure leaching, and transportation and processing. Furthermore, kernel density estimation and standard deviational ellipse methods were employed to reveal the spatiotemporal evolution characteristics, and a Spatial Durbin Model (SDM) was constructed to identify the driving factors. The findings indicate that the total water footprint of China’s beef cattle industry first decreased and then increased, reaching 918.70 km³ in 2022. The grey water footprint accounted for an average of 90.85% annually, and the manure leaching stage contributed the largest share (averaging 62.24% annually), suggesting that this stage warrants priority attention from the perspective of this indicator. Large-scale farming exhibited the lowest water footprint per unit of beef (averaging 29.39 m³/kg), while small-scale farming had the highest (54.65 m³/kg). The spatial pattern showed a trend of “high in the west and low in the east, rising in the west and declining in the east.” The spatial model results revealed that the water footprint per unit of beef exhibited significant spatial agglomeration and spatial spillover effects. Full article

Urban mobility systems generate large volumes of heterogeneous geospatial data that differ in temporal resolution, spatial coverage, update frequency, and semantic structure, making integrated near-real-time analysis difficult. This paper presents a geospatial big-data framework for integrating and analyzing multimodal urban mobility data from the Norwegian transport ecosystem, including public transport, micromobility, road infrastructure, weather sensing, and civil aviation. The framework is implemented as a modular pipeline for data ingestion, source-specific normalization, temporal alignment, and analytical processing, enabling minute-level comparison across heterogeneous operational feeds. The proposed approach preserves source-level semantics while supporting unified spatiotemporal analysis across transport modes with different operational characteristics. The framework is evaluated through analytical scenarios focused on peak and off-peak mobility dynamics, weather-related multimodal variability, and spatial autocorrelation of public transport activity and delay across four analysis windows and six Norwegian cities. The results show that mobility–weather relationships vary across transport modes and temporal windows, particularly in public transport activity, cycling behavior, and delay patterns, and that spatial clustering of public transport activity and delay is itself city- and window-dependent, with some cities showing strong, stable clustering and others showing none. The findings indicate that multimodal urban mobility should be interpreted as a context-dependent and interconnected spatiotemporal system rather than through isolated modal indicators. The study demonstrates how geospatial big-data integration can support near-real-time urban mobility monitoring and operational analytics in smart-city environments. Full article

Against the dual strategic backdrop of carbon peaking and carbon neutrality goals and high-quality urbanization development, extant literature exhibits four prominent research gaps: oversimplified evaluation indicator systems, exclusive exploration of the unidirectional carbon impacts exerted by urbanization, a scarcity of long-time-series coupling analyses targeting the Yangtze River Economic Belt (YEB), and functional fragmentation between coupling coordination assessment and predictive simulation tools. Drawing on panel data covering 11 provinces and municipalities within the YEB spanning 2000 to 2021, this study constructs a comprehensive urbanization evaluation framework encompassing four dimensions: population, economy, society, and spatial layout. Meanwhile, an integrated carbon emission assessment system is established from the perspectives of population, economy, energy consumption, and carbon sinks. The entropy-weight method is adopted to assign indicator weights, and a combination of the coupling coordination degree model and system dynamics (SD) model is employed to analyze spatiotemporal evolutionary characteristics and simulate development trends from 2022 to 2032. By organically integrating the coupling coordination model and the SD model, this study establishes an integrated analytical framework that unifies static comprehensive evaluation and driving-mechanism decomposition, thereby compensating for the limitations of time-series forecasting models such as the grey prediction model and ARIMA, which only fit trends from historical data. Empirical results reveal that regional urbanization levels witnessed sustained growth across 2000–2021, with spatial urbanization acting as the core driving pillar. The overall coupling coordination degree maintained a steady upward trajectory, while the east–west regional disparity gradually narrowed. The simulation projections for 2022–2032 demonstrate continuous improvements in coordinated development across the entire basin: the coupling coordination degree ranges from 0.788 to 0.954 for the eastern region, 0.810 to 0.859 for the central region, and 0.752 to 0.865 for the western region. Such spatial differentiation corresponds to distinct practical development pathways: low-carbon stock optimization in the east, low-carbon industrial undertaking in the central zone, and clean energy transition acceleration in the west. All provincial-level administrative regions are projected to achieve an upgrade in their coupling coordination grades by 2032. This study acknowledges several limitations: missing raw data are supplemented via interpolation, only a single baseline scenario is simulated, predictive uncertainty is not quantitatively measured, and subjectivity persists in the weight assignment of coupling subsystems. Ultimately, differentiated low-carbon urbanization governance strategies are proposed for the three sub-regions, offering empirical references for the coordinated realization of dual carbon targets throughout the Yangtze River basin. Full article

Accurate fault location in low-observability active distribution networks is hindered by uncertain inter-node relationships, underutilized early transients, and insufficient global context. To address these challenges, this paper proposes an adaptive Mamba-driven spatiotemporal graph neural network (AM-STGNN). It provides a unified task-driven spatiotemporal representation framework that progressively integrates fault-propagation modeling, global dependency modeling, transient-state learning, and topology-aware discriminative enhancement. Specifically, a prior-guided adaptive implicit topology is first learned to characterize task-dependent electrical coupling relationships among sparse observation nodes. Based on the resulting topology, topology-conditioned multi-order feature propagation and a dual-axis linear-attention module based on the spatiotemporal graph transformer (STGformer) are employed to capture local and global spatiotemporal dependencies. The resulting global spatiotemporal representation is subsequently processed by a Mamba selective state-space encoder to model input-dependent temporal evolution and emphasize informative fault transients. Finally, element-wise gated fusion, topology-aware differential output, and a margin constraint are employed to integrate the STGformer and Mamba representations and enhance the separability of adjacent faulted line sections with similar response characteristics. Extensive experiments demonstrate the effectiveness of AM-STGNN, while additional evaluations confirm its applicability to larger-scale networks, strongly phase-unbalanced conditions, and field-measured operating backgrounds. Robustness tests under individual and multi-level joint disturbances further demonstrate the practical relevance of the proposed architecture. Compared with the baseline models, AM-STGNN achieves consistent improvements in the macro-averaged F1 score (Macro-F1), exact accuracy, and one-hop accuracy under the clean IEEE 123-node condition. More importantly, it maintains clear performance advantages under identical mild, moderate, and severe joint disturbances, demonstrating improved robustness and practical relevance under simulated non-ideal operating conditions. Full article

Urban park green spaces are key infrastructure for improving the quality of the living environment, enhancing residents’ well-being, and strengthening ecological resilience. Targeting 1760 county seats in China, this study combines the stock and incremental synergy analysis matrix, exploratory spatial data analysis, and explainable machine learning (EML) methods (SHAP) to systematically reveal the spatiotemporal patterns, spatial association characteristics, and nonlinear paths of influencing factors regarding the spatial configuration of park green spaces in county seats from 2015 to 2024. The low-stock expansion zone is dominant and concentrated in the western region and non-core urban agglomerations. The high-stock expansion zone is of the dominant type, concentrated in the eastern coastal areas and core urban agglomerations. The low-stock contraction zone and high-stock contraction zone are, respectively, marginalized lock-in and degradation risk types, requiring priority intervention. The dynamics of the park green space configuration, including the stock, increment, and their synergy, all exhibit significant positive spatial autocorrelation. The influences of the nine factors exhibit four major characteristics: directional mixing, intensity gradation, path nonlinearity, and regional heterogeneity. The threshold effect is widespread, and the inflection point value varies depending on the factor and region type. This study constructs a nonlinear and interpretable analytical paradigm and, based on empirical results, proposes a new governance framework of “zoning–grading–staging–synergy”. It provides large-sample empirical evidence and theoretical support for the transformation of small-town park green spaces from “sectoral management” to “spatial governance”, offering significant policy value toward achieving the precise distribution and equitable sharing of regional green space resources. Full article

Advances in artificial intelligence models offer promising approaches for intelligent convection initiation (CI) identification—a critical step in severe weather nowcasting—thereby driving demand for high-quality, long-term labeled datasets. Accordingly, this study develops a CI identification technique using quality-controlled, gridded composite reflectivity data from the weather radar and CMA global atmospheric reanalysis wind data over central-eastern China (2018–2023) to construct a labeled dataset. The proposed CI identification method integrates a “forward-time search and backward-time verification” strategy, which involves three key steps: screening grid points with absent or weak convection; monitoring these points for convective development within 30 min; and finally, confirming the first occurrence of convection. Additionally, quality control is applied to eliminate the influence of outliers and anomalous radar data. The resulting dataset constructed from 829 severe convective processes comprises ~25.6 million grid points labeled for CI occurrences at one or more lead times of 10, 20, or 30 min to resolve spatiotemporal evolution. Of these, 71.90% are accompanied by surface weather phenomena. This study provides a reliable dataset to support the learning of intelligent identification and nowcasting models for CI events. Furthermore, based on this dataset, the spatiotemporal distribution characteristics of warm-season CI over central-eastern China are delineated. Full article

Against the backdrop of global climate change, drought–flood abrupt alternation (DFAA) has become a major compound climate extreme threatening ecosystem stability and sustainable watershed management. This study investigated the spatiotemporal characteristics and ecological responses of DFAA in the Jialing River Basin, China, using meteorological and hydrological observations from 1971 to 2020. DFAA events were identified using the Standardized Weighted Average Precipitation Index (SWAP) and run theory, and their spatiotemporal heterogeneity was characterized using spatial autocorrelation analysis. The Long-duration DFAA Index (LDFAI) was derived using the WEP-L distributed hydrological model. Ecological responses during 2000–2020 were evaluated by integrating the Remote Sensing Ecological Index (RSEI), grey relational analysis, and a Copula-based conditional probability model. The results showed that drought-to-flood events exhibited stronger spatial clustering than flood-to-drought events. Ecosystem responses showed significant lag effects, averaging 6.9 months for spring–summer events and 5 months for summer–autumn events, with greater sensitivity during the summer–autumn period. Under DTF events, the probability of maintaining relatively high ecological quality was significantly higher than under FTD events, whereas FTD events were associated with a higher probability of ecological degradation. Under compound scenarios, consecutive same-type events were more conducive to ecosystem stability, while alternating sequences of different event types significantly amplified negative ecological stress and represented high-risk scenarios for ecological degradation. These findings provide scientific support for adaptive watershed management, ecological restoration, and climate change adaptation in drought–flood-prone regions. Full article