Search Results (3,440)

Air moving-target detection is crucial for national defense, civil aviation, and airspace supervision. Spaceborne synthetic aperture radar (SAR) provides high-resolution, continuous observations for this task, but faces challenges including target attitude variation-induced weak signals and Doppler defocusing from targets’ high-speed motion, which hinder target-background separation. To address this, we propose a novel method combining sub-aperture segmentation with GoDec+ low-rank decomposition to enhance signal-to-noise ratio and suppress defocusing. Critically, ADS-B flight data is integrated as ground truth for spatio-temporal validation. Experiments using Sentinel-1 SM mode SLC imagery across farmland, forest, and mountainous regions confirm the method’s effectiveness and robustness in real airspace scenarios. Full article

Accurate prediction of water quality involves early identification of future pollutant concentrations and water quality indicators, which is an important prerequisite for optimizing water environment management. Although deep learning algorithms have demonstrated considerable potential in predicting water quality parameters, their broader adoption remains hindered by limited interpretability. This study proposes an interpretable deep learning framework integrating an artificial neural network (ANN) model with Shapley additive explanations (SHAP) analysis to predict spatiotemporal variations in water quality and identify key influencing factors. A case study was conducted in the Poyang Lake Basin, utilizing multi-dimensional datasets encompassing topographic, meteorological, socioeconomic, and land use variables. Results indicated that the ANN model exhibited strong predictive performance for dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), ammonia nitrogen (NH₃N), and turbidity (Turb), achieving R² values ranging from 0.47 to 0.77. Incorporating land use and socioeconomic factors enhanced prediction accuracy by 37.8–246.7% compared to models using only meteorological data. SHAP analysis revealed differences in the dominant factors influencing various water quality parameters. Specifically, cropland area, forest cover, air temperature, and slope in each sub-basin were identified as the most important variables affecting water quality parameters in the case area. These findings provide scientific support for the intelligent management of the regional water environment. Full article

To characterize the spatiotemporal distribution of temperature and airflow in single-span plastic-film greenhouses, we coupled field experiments with three-dimensional computational fluid dynamics (CFD) simulations in a warm–temperate region of China. Model reliability and validity were evaluated against field measurements. The average and maximum relative errors between simulated and measured values were 6% and 9%, respectively. Significant spatial heterogeneity in both temperature and airflow was observed. Vertically, temperature rose with height; horizontally, it declined from the center toward the sidewalls. Under prevailing meteorological conditions, the daily maxima occurred at distinct elevations above the fan-vent outlets. Airflow was most vigorous near the vents, whereas extensive stagnant zones aloft reduced overall ventilation efficiency. These findings provide a quantitative basis for designing single-span plastic film greenhouses in China’s hot–humid regions, informing ventilation improvements, and guiding future optimization efforts. Full article

Soil fertility is the essential attribute of soil quality. Large-scale coal mining has led to the continuous deterioration of the fragile ecosystems in arid and semi-arid mining areas. As one of the key indicators for land ecological restoration in these coal mining regions, rapidly and accurately monitoring topsoil fertility and its spatial variation information holds significant importance for ecological restoration evaluation. This study takes Wuhai City in the Inner Mongolia Autonomous Region of China as a case study. It establishes and evaluates various soil indicator inversion models using multi-temporal Landsat8 OLI multispectral imagery and measured soil sample nutrient content data. The research constructs a comprehensive evaluation method for surface soil fertility based on multispectral remote sensing monitoring and achieves spatiotemporal variation analysis of soil fertility characteristics. The results show that: (1) The 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum Vector version)-SVM (Support Vector Machine) prediction model for surface soil indicators based on Landsat8 OLI imagery achieved prediction accuracy with R² values above 0.85 for all six soil nutrient contents in the study area, thereby establishing for the first time a rapid assessment method for comprehensive topsoil fertility using multispectral remote sensing monitoring. (2) Long-term spatiotemporal evaluation of soil indicators was achieved: From 2015 to 2025, the spatial distribution of soil indicators showed certain variability, with soil organic matter, total phosphorus, available phosphorus, and available potassium contents demonstrating varying degrees of increase within different ranges, though the increases were generally modest. (3) Long-term spatiotemporal evaluation of comprehensive soil fertility was accomplished: Over the 10 years, Grade IV remained the dominant soil fertility level in the study area, accounting for about 32% of the total area. While the overall soil fertility level showed an increasing trend, the differences in soil fertility levels decreased, indicating a trend toward homogenization. Full article

Global warming impacts agricultural production and food security, particularly in high-latitude regions with high temperature sensitivity. As a major grain-producing area in China and one of the fastest-warming regions globally, Northeast China (NEC) has received considerable research attention. However, the existing literature lacks sufficient exploration of the spatiotemporal heterogeneity in climate change impacts. Based on data on rice, corn, and soybean yields, as well as temperature, rainfall, and sunshine duration in NEC from 1993 to 2022, this study employs Sen’s slope estimation, the Mann–Kendall (MK) test, spatial autocorrelation analysis, and the Geographically and Temporally Weighted Regression (GTWR) model to analyze the spatiotemporal evolution of grain yields and their responses to climate change. The results show that ① 1993–2022 witnessed an overall rise in grain yields per unit area in NEC, with Liaoning growing fastest. Rice yields increased regionally; corn yields rose in Liaoning and Jilin, while soybean yields increased only in Liaoning. During the growing season, rainfall trended upward with fluctuations, temperatures rose steadily, and sunshine duration declined in Heilongjiang. ② Except for corn and soybeans in the early period, other crops exhibited significant yield spatial agglomeration. High–high agglomeration areas first expanded, then shrank, eventually shifting northward to the region of Jilin Province. ③ Climatic factors show marked spatiotemporal heterogeneity in impacts: positive effect areas of rainfall and temperature expanded northward; sunshine duration’s influence weakened, but its negative effect areas spread. ④ Differences in crop responses are closely linked to their physiological characteristics, regional climate evolution, and agricultural adaptation measures. This study provides a scientific basis for formulating region-specific agricultural adaptation strategies to address climate change in NEC. Full article

Red industrial heritage is a crucial component of global socialist industrial civilization, embodying both industrial memory and revolutionary spirit. However, its preservation faces significant challenges, including insufficient policy attention, homogenized revitalization models, and a lack of systematic research. This study uses Hebei Province, a key region where modern industry and revolutionary history intersect, as a case study. By employing Geographic Information System (GIS) spatial analysis and historical geography, the research explores the spatiotemporal patterns and underlying factors that influence the distribution of red industrial heritage. The findings reveal: (1) the spatial distribution is irregular, exhibiting concentration, with high density in the central and southern parts of Hebei, while the northern and eastern areas are more dispersed; (2) The spatiotemporal evolution aligns with significant historical events; (3) The distribution pattern is shaped by multiple factors, with the dynamics of modern Chinese warfare and historical policies serving as the primary driving forces, interacting with natural geographical factors. This study enhances our comprehension of the significance of red industrial heritage and, based on its spatiotemporal variations, proposes a tiered, sustainable preservation strategy. It provides valuable insights into the preservation of socialist industrial heritage both in China and globally. Full article

This study investigates the spatio-temporal characteristics and driving mechanisms of ecological quality in the mountain–river–sea regional system using the Remote Sensing Ecological Index (RSEI) model, moderate-resolution imaging spectroradiometer (MODIS) data, and the Google Earth Engine (GEE) platform. The analysis, conducted at both the grid and county scales using spatial autocorrelation and geodetector, showed a notable improvement in ecological quality, with the average RSEI value rising from 0.549 in 2000 to 0.627 in 2022. The distribution pattern reveals superior quality in the northwest and inferior quality in central urban cores and coastal zones. Ecological quality exhibited significant spatial clustering, with high–high clusters in karst mountains and low–low clusters in urban and industrial zones. Geodetector analysis identified GDP and population density as dominant factors at the grid scale, and GDP and elevation at the county scale. By quantifying spatio-temporal variations and driving mechanisms of ecological quality across scales, this study provides a solid scientific foundation for regional ecological conservation and sustainable development. Full article

Coastal ecosystems have faced escalating environmental degradation in recent years, with eutrophication and nutrient imbalances emerging as critical concerns, particularly in estuarine regions. Understanding the spatiotemporal dynamics of key nutrients, including dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and silicate (SiO₃-Si), is essential for effective coastal management. This study examines the spatial and seasonal variations in these nutrients across 36 sampling sites in the Yellow River estuary from 2016 to 2018. Results indicate that DIN was the primary contributor to water quality degradation, with more than 27% of sampling sites exceeding the Class II seawater quality standard in 2018. Nutrient concentrations were notably elevated near the estuary. The eutrophication index (EI) revealed predominantly mild-to-moderate eutrophication levels throughout the study area. The study area exhibited a widespread phosphorus (P) limitation, with 44.4–94.4% of coastal waters experiencing P-restricted eutrophication. The N/P ratio significantly exceeded the Redfield ratio (16), indicating a pronounced nutrient imbalance. Furthermore, SiO₃-Si concentrations displayed a declining trend, highlighting the need for balanced nutrient management alongside eutrophication mitigation. Full article

With the rapid growth of urban logistics demand, carbon emissions and the time-varying nature of vehicle speeds have become critical challenges in sustainable transportation planning. This paper addresses a Time-Dependent Green Vehicle Routing Problem (TDGVRP) that integrates time-varying speeds, carbon emissions, and cold chain logistics under a joint distribution framework involving multiple depots and homogeneous refrigerated vehicles. A Mixed-Integer Linear Programming (MILP) model is developed, explicitly considering carbon pricing, refrigeration energy consumption, and speed variations across different time periods. To efficiently solve large-scale instances, a Three-Phase Heuristic (TPH) algorithm is proposed, combining spatiotemporal path construction, local-improvement strategies, and an Adaptive Large Neighborhood Search (ALNS) mechanism. Computational experiments show that the proposed method outperforms traditional Genetic Algorithms (GAs) in both solution quality and computation time, and in some benchmark cases even achieves better results than the commercial solver Gurobi, demonstrating its robustness and scalability. Using real-world traffic speed data, comparative analysis reveals that the joint distribution strategy reduces total logistics costs by 14.40%, carbon emission costs by 23.12%, and fleet size by approximately 25% compared to single-entity distribution. The findings provide a practical and scalable solution framework for sustainable cold chain logistics routing in time-dependent urban road networks. Full article

Macroinvertebrates are a crucial part of aquatic ecosystems and significantly contribute to the maintenance of their health and stability. Our aims were to explore spatio-temporal patterns in macroinvertebrate communities and evaluate the ecological health of various parts of the Poyang Lake Basin during the early stage of a fishing ban. We collected samples using a Peterson grab sampler and conducted ecological evaluations using the B-IBI index. A total of 107 species of macroinvertebrates were identified, and most species were arthropods. The density and biomass of macroinvertebrates significantly differed among seasons and water bodies. No significant differences in diversity among seasons were observed; however, diversity significantly varied among water bodies. Environmental parameters such as water depth, pH, turbidity, total nitrogen, total phosphorus, and chlorophyll a played a crucial role in shaping the community structure of macroinvertebrates. Most of the sampling sites were classified as healthy or sub-healthy, indicating that the fishing ban policy has started to have a positive effect. The effects of this ban are achieved through a cascading sequence of processes, including the elimination of fishing disturbance, the restoration of habitat structure, and the reallocation of trophic energy, in addition to increases in microhabitat diversity associated with habitat heterogeneity. Together, these processes drive the multidimensional recovery of macroinvertebrate communities, manifested as increased species richness, higher density and biomass, and elevated B-IBI scores. Full article

Automatic Facial Expression Recognition (AFER) is a key component of affective computing, enabling machines to recognize and interpret human emotions across various applications such as human–computer interaction, healthcare, entertainment, and social robotics. Dynamic AFER systems, which exploit image sequences, can capture the temporal evolution of facial expressions but often suffer from high computational costs, limiting their suitability for real-time use. In this paper, we propose an efficient dynamic AFER approach based on a novel spatio-temporal representation. Facial landmarks are extracted, and all possible Euclidean distances are computed to model the spatial structure. To capture temporal variations, three statistical metrics are applied to each distance sequence. A feature selection stage based on the Extremely Randomized Trees (ExtRa-Trees) algorithm is then performed to reduce dimensionality and enhance classification performance. Finally, the emotions are classified using a linear multi-class Support Vector Machine (SVM) and compared against the k-Nearest Neighbors (k-NN) method. The proposed approach is evaluated on three benchmark datasets: CK+, MUG, and MMI, achieving recognition rates of 94.65%, 93.98%, and 75.59%, respectively. Our results demonstrate that the proposed method achieves a strong balance between accuracy and computational efficiency, making it well-suited for real-time facial expression recognition applications. Full article

The vulnerability of regional agricultural systems continues to intensify under the influence of global climate change. Understanding the spatiotemporal variation in meteorological elements and their agricultural response mechanisms has become a critical scientific challenge for ensuring food security. This study focuses on the 852 Farm in the typical area of the albic soil region on the Sanjiang Plain in China. This research integrates multi-source meteorological observations and crop yield data from 2001 to 2024. Using methods such as wavelet analysis, grey relational analysis, and cross-wavelet analysis, this study systematically investigates the dynamic changes and cyclical evolution patterns of key meteorological factors and their impact on the yields of different staple crops. The results indicate that, in terms of trend evolution, air temperature, relative humidity, and surface temperature show no significant upward trend (Z > 0; p > 0.05), while precipitation significantly increases (Z > 0; p < 0.05). Evaporation and sunlight show a nonsignificant downward trend (Z < 0; p > 0.05). The yields of rice, soybean, and corn generally exhibit fluctuating upward trends (Z > 0; p > 0.05). In terms of periodic coupling characteristics, meteorological factors exhibit multi-time-scale oscillations at 22a, 12a, and 8a. The yields of the three staple crops form significant time–frequency couplings with meteorological factors in the 22a and 8a periods. Regarding the correlation, air temperature demonstrates the highest grey correlation degree (γ ≥ 0.8) and strong coherence with crop yields, followed by precipitation and sunlight. These findings provide a theoretical and quantitative basis for understanding the multi-scale interactive mechanisms of climate adaptation in agricultural systems of the albic soil region, as well as for managing and optimizing climate-resilient farming practices. Full article

Extreme sea surface temperature (SST) events, such as marine heatwaves (MHWs) and marine cold spells (MCSs), severely affect warm water coral reefs. However, further study is required on their historical and future spatiotemporal patterns, driving mechanisms, and impacts in coral reef regions. This study analyzed the spatiotemporal patterns in MHWs/MCSs for the periods 1982–2022 and 2023–2070 using ten indices based on OISSTv2.1 and CMIP6 data, respectively, identified key MHW drivers via four machine learning methods (Random Forest, Extreme Gradient Boosting, Light Gradient Boosting Machine, and categorical boosting) and SHAP values (Shapley Additive Explanations), and then examined their relationship with coral coverage across ten global marine regions. Our results revealed that (1) MHWs are not only increasing in their average intensity but also becoming more extreme, while MCSs have declined. More MHW days are observed in regions like the Red Sea, the Persian Gulf, and the South Pacific Islands, with increases of up to 28 days per decade. (2) Higher-latitude coral reefs are experiencing more severe MHWs than equatorial regions, with up to 1.24 times more MHW days, emphasizing the urgent need to protect coral refuges. (3) MHWs are projected to occur nearly year-round by 2070 under scenario SSP5–8.5. The area ratio of MHWs to MCSs is expected to rise sharply from 2040 onward, reaching approximately 100-fold under the SSP2–4.5 scenario and 196-fold under the SSP5–8.5 scenario, particularly in the Marshall Islands and Caribbean Sea regions. (4) The coefficient of variation (CV) of annual temperature, annual ocean heat content, and monthly temperature were the top three factors driving MHW intensity. We emphasize that future MHW predictions should focus more on the CV of forecasting indicators rather than just the climate means. (5) Coral coverage exhibited post-mortality processes following MHWs, showing a strong negative correlation (r = −0.54, p < 0.01) with MHWs while demonstrating a significant positive correlation (r = 0.6, p < 0.01) with MCSs. Our research underscores the sustained efforts to protect and restore coral reefs amid escalating climate-induced stressors. Full article

Background: The Dirigent (DIR) gene family is pivotal for lignin polymerization and stress adaptation in plants, yet its systematic characterization in Sorghum bicolor (S. bicolor), a critical bioenergy crop, remains underexplored. Methods: Leveraging the S. bicolor genome database, we conducted a genome-wide identification, phylogenetic classification, and expression profiling of the DIR gene family. Evolutionary dynamics, gene structure variations, promoter cis-regulatory elements, and spatiotemporal transcriptome patterns were analyzed using bioinformatics and experimental validation (RT-qPCR). Results: A total of 53 SbDIR genes were systematically identified, exhibiting uneven chromosomal distribution. Phylogenetic analysis clustered them into five clades (DIR-a, DIR-b/d, DIR-c, DIR-e, DIR-f), with subfamily-specific exon number variations suggesting functional divergence. Evolutionary studies revealed tandem duplication (TD) as the primary driver of family expansion, accompanied by strong purifying selection. Promoter analysis highlighted abundant hormone- and stress-responsive cis-elements. Tissue-specific RNA-seq data revealed root-enriched expression of SbDIR2/4/18/39/44/53, implicating their roles in root development. Notably, SbDIR39 and SbDIR53 were significantly upregulated (2.8- and 5-fold, respectively) under 150 mM NaCl stress, underscoring their stress-responsive functions. Conclusions: This study provides the first comprehensive atlas of the DIR gene family in S. bicolor, elucidating its evolutionary mechanisms and tissue-specific/stress-induced expression profiles. Key candidates (SbDIR39/53) were identified as promising targets for molecular breeding or CRISPR-based editing to enhance stress resilience in S. bicolor. These findings lay a foundation for translating genomic insights into agronomic improvements. Full article

Atmospheric pollution constitutes one of the key environmental challenges hindering Atmospheric pollution is a key environmental challenge constraining the sustainable development of Gansu Province’s land-based Belt and Road corridor and its regional ecological barrier function. The spatiotemporal heterogeneity of aerosol optical depth (AOD) profoundly impacts regional environmental quality. Based on MODIS AOD, NCEP reanalysis, and emission data, this study employed trend analysis (Mann–Kendall test) and attribution analysis (multiple linear regression combined with LMG and Spearman correlation) to investigate the spatiotemporal evolution of AOD over Gansu Province during 2009–2019 and its meteorological and emission drivers. Key findings include the following: (1) AOD exhibited significant spatial heterogeneity, with high values concentrated in the Hexi Corridor and central regions; monthly variation showed a unimodal pattern (peak value of 0.293 in April); and AOD generally declined slowly province-wide during 2009–2019 (52.8% of the area showed significant decreases). (2) Following the implementation of the Air Pollution Prevention and Control Action Plan in 2013 (2014–2019), AOD trends stabilized or declined in 99.8% of the area, indicating significant improvement. (3) Meteorological influences displayed distinct regional-seasonal specificity—the Hexi Corridor (arid zone) was characterized by strong negative correlations with relative humidity (RH2) and wind speed (WS) year-round, and positive correlations with temperature (T2) in spring but negative in summer in the north; the Hedong region (industrial zone) featured strong positive correlations with planetary boundary layer height (PBLH) in summer (r > 0.6) and with T2 in spring/summer; and the Gannan Plateau (alpine zone) showed positive WS correlations in spring and weak positive RH2 correlations in spring/autumn, highlighting the decisive regulatory role of underlying surface properties. (4) Emission factors (PM_2.5, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{NO}}_3^{-}$, ${\mathrm{NH}}_4^{+}$, OM, and BC) dominated (>50% relative contribution) in 80% of seasonal scenarios, prevailing in most regions (Hexi: 71–95% year-round; Hedong: 68–80% year-round; and Gannan: 69–72% in spring/summer). Key components included BC (contributing > 30% in 11 seasons, e.g., 52.5% in Hedong summer), ${\mathrm{NO}}_3^{-}$ + ${\mathrm{NH}}_4^{+}$ (>57% in Hexi summer/autumn), and OM (20.3% in Gannan summer, 19.0% province-wide spring). Meteorological factors were the primary driver exclusively in Gannan winter (82%, T2-dominated) and province-wide summer (67%, RH2 + WS-dominated). In conclusion, Gansu’s AOD evolution is co-driven by emission factors (dominant province-wide) and meteorological factors (regionally and seasonally specific). Post-2013 environmental policies effectively promoted regional air quality improvement, providing a scientific basis for differentiated aerosol pollution control in arid, industrial, and alpine zones. Full article