Search Results (1,381)

The Gonghe–Yushu Expressway (GYE) traverses the degrading permafrost region of the Qinghai–Xizang Plateau, where climate warming has resulted in widespread water ponding, posing significant engineering challenges. However, the spatiotemporal dynamics of this water accumulation and its impacts on permafrost embankment stability remain inadequately understood. This study integrates high-resolution unmanned aerial vehicle (UAV) remote sensing with ground-penetrating radar (GPR) to characterize the spatial patterns of water ponding and to quantify the spatial distribution, seasonal dynamics, and hydrothermal effects of roadside water on permafrost sections of the GYE. UAV-derived point cloud models, optical 3D models, and thermal infrared imagery reveal that approximately one-third of the 228 km study section of GYE exhibits water accumulation, predominantly occurring near the embankment toe in flat terrain or poorly drained areas. Seasonal monitoring showed a nearly 90% reduction in waterlogged areas from summer to winter, closely corresponding to climatic variations. Statistical analysis demonstrated significantly higher embankment distress rates in waterlogged areas (14.3%) compared to non-waterlogged areas (5.7%), indicating a strong correlation between surface water and accelerated permafrost degradation. Thermal analysis confirmed that waterlogged zones act as persistent heat sources, intensifying permafrost thaw and consequent embankment instability. GPR surveys identified notable subsurface disturbances beneath waterlogged sections, including a significant lowering of the permafrost table under the embankment and evidence of soil loosening due to hydrothermal erosion. These findings provide valuable insights into the spatiotemporal evolution of water accumulation along transportation corridors and inform the development of climate-adaptive strategies to mitigate water-induced risks in degrading permafrost regions. Full article

Land use and land cover change (LUCC) is central to regulating human–land relationships and crucial for urban planning and sustainable development in arid oasis cities. As a typical oasis city in Xinjiang, Shihezi City faces the triple challenges of agricultural protection, urban expansion, and ecological conservation. Taking Shihezi City as the research object, this study used the 30 m resolution China Land Cover Dataset and applied the land use dynamic degree, comprehensive index of land use degree, transfer matrix, Geodetector, and PLUS model to analyse the spatiotemporal dynamics of LUCC from 2002 to 2022, identify driving mechanisms, and predict the land use pattern from 2027 to 2032. The results showed that (1) from 2002 to 2022, farmland decreased by 86.1075 km², man-made surfaces increased by 63.7389 km² (annual expansion rate of 2.86%), grassland slightly increased by 24.5592 km², and other land types remained stable; (2) the dynamics of land use showed a phased characteristic of “growth–equilibrium–acceleration”, and the land use degree index rose to 2.8639; natural factors (elevation, soil, temperature) dominated LUCC, and most interactions among factors showed enhancement effects; (3) the PLUS model predicted that by 2032, farmland would decrease to 224.347 km² and man-made surfaces would increase to 111.941 km². This study clarifies the laws of LUCC in Shihezi, demonstrates driving analysis and simulation prediction, and provides scientific support for balancing urban development, agricultural protection, and ecological security in arid oasis regions. Full article

This study leverages multi-source remote sensing data—Nighttime Light (NTL) imagery and POI (Point of Interest) datasets—to quantify the spatiotemporal interaction between urban spatial restructuring and logistics industry evolution in Zhengzhou, China. Using calibrated NPP/VIIRS NTL data (2012–2022) and fine-grained POI data, we (1) identified urban functional spaces through kernel density-based spatial grids weighted by public awareness parameters; (2) extracted built-up areas via the dynamic adaptive threshold segmentation of NTL gradients; (3) analyzed logistics agglomeration dynamics using emerging spatiotemporal hotspot analysis (ESTH) and space–time cube models. The results show that Zhengzhou’s urban form transitioned from a monocentric to a polycentric structure, with NTL trajectories revealing logistics hotspots expanding along air–rail multimodal corridors. POI-derived functional spaces shifted from single-dominant to composite patterns, while ESTH detected policy-driven clusters in Airport Economic Zones and market-driven suburban cold chain hubs. Bivariate LISA confirmed the spatial synergy between logistics growth and urban expansion, validating the “policy–space–industry” interaction framework. This research demonstrates how integrated NTL-POI remote sensing techniques can monitor policy impacts on urban systems, providing a replicable methodology for sustainable logistics planning. Full article

Against the backdrop of urban–rural integrated development, special ecological function zones, as spatial carriers with significant regional ecological value and rural development functions, are confronted with a striking conflict between ecological conservation and regional advancement. This contradiction is comprehensively reflected in the interactions among land use functions (LUFs) that differ in nature and intensity. Therefore, exploring the trade-off and synergy (TOS) among regional LUFs is not only of great significance for optimizing territorial spatial patterns and advancing rural revitalization but also provides scientific evidence for the differentiated administration of regional land use. Taking 185 townships in the Funiu Mountain area of China as research units, this study constructs a land use assessment system based on the ‘Production–Living–Ecological’ (PLE) framework, utilizing multi-source datasets from 2000 to 2020. Spearman correlation analysis, geographically weighted regression (GWR), and bivariate local spatial autocorrelation methods are employed to examine the spatio-temporal dynamics of LUFs and the spatial non-stationarity of their TOSs. The findings indicate that, throughout the research period, the production function (PF) displayed a fluctuating declining trend, whereas the living function (LF) and ecological function (EF) demonstrated a fluctuating increasing trend. Notably, EF held an absolute dominant position in the overall structure of LUFs. This is highly consistent with the region’s positioning as a special ecological function zone and also a direct reflection of the effectiveness of continuous ecological construction over the past two decades. Spatially, PF is stronger in southern, eastern, and northern low-altitude townships, correlating with higher levels of economic development; LF is concentrated around townships near county centers; and high EF values are clustered in the central and western areas, showing an opposite spatial pattern to PF and LF. A synergistic relationship is observed between PF and LF, while both PF and LF exhibit trade-offs with EF. The TOSs between different function changes demonstrate significant spatial non-stationarity: linear synergy was the primary type for PF-LF, PF-EF, and LF-EF combinations, but each combination exhibited unique spatial characteristics in terms of non-stationarity. Notably, towns identified as having different types of trade-off relationships in the study of spatial non-stationarity are key areas for township spatial governance and optimization. Through the allocation of regional resources and targeted policy tools, the functional relationships can be adjusted and optimized to attain sustainable land use. Full article

Fuel Moisture Content (FMC) is a critical indicator for assessing forest fire risk and formulating early warning strategies, as its spatiotemporal dynamics directly influence the accuracy of fire danger rating. To improve the accuracy of forest FMC estimation, this study proposes an innovative deep learning method integrating multi-source remote sensing data. By combining the global feature extraction capability of the Transformer architecture with the local temporal modeling advantages of Gated Recurrent Units (GRU) (referred to as the Transformer-GRU model), a high-precision FMC estimation framework is established. The study focuses on forested areas in California, USA, utilizing ground-measured FMC data alongside multi-source remote sensing datasets from MODIS, Sentinel-1, and Sentinel-2. A systematic comparison was conducted among Transformer-GRU model, standalone Transformer models, single GRU models, and two classical machine learning models (Random Forest, RF, and Support Vector Regression, SVR). Additionally, forward feature selection was employed to evaluate the performance of different models and feature combinations. The results demonstrate that (1) All models effectively utilize the derived features from multi-source remote sensing data, confirming the significant enhancement of multi-source data fusion for forest FMC estimation; (2) The Transformer-GRU model outperforms other models in capturing the nonlinear relationship between FMC and remote sensing data, achieving superior estimation accuracy (R² = 0.79, MAE = 8.70%, RMSE = 11.44%, rRMSE = 12.60%); (3) The spatiotemporal distribution patterns of forest FMC in California generated by the Transformer-GRU model align well with regional geographic characteristics and climatic variability, while exhibiting a strong relationship with historical wildfire occurrences. The proposed Transformer-GRU model provides a novel approach for high-precision FMC estimation, offering reliable technical support for dynamic forest fire risk early warning and resource management. Full article

The 400 mm isohyet in China serves as a critical geographical demarcation of dry and wet regions. Amidst intensifying global warming, this climatic boundary has undergone notable shifts, with significant implications for China’s agriculture, water resources, and ecosystems. This study integrates meteorological station data, the China Gridded Daily Precipitation dataset (CN05.1), and Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM IMERG) satellite observations to assess the spatiotemporal distribution of precipitation across mainland China and analyze the migration trend of the 400 mm isohyet. Furthermore, utilizing outputs from five models of the Coupled Model Intercomparison Project Phase 6 (CMIP6), we projected future trends of China’s annual mean precipitation and the 400 mm isohyet’s migration under three Shared Socioeconomic Pathways (SSPs: low, medium, and high radiative forcing scenarios) until the end of this century (2100). Results reveal that from 2001 to 2017, the 400 mm isohyet exhibited a prominent northwestward migration trend. This trend is projected to continue in the future. These findings provide a crucial reference for understanding the spatial distribution and changing dynamics of precipitation patterns in China, offering vital decision support for land resource planning and water resource management. Full article

Lung development is governed by tightly regulated signaling mechanisms, including endocytosis-mediated pathways critical for epithelial–mesenchymal communication and tissue remodeling. This study investigated the effects of Dab1 deficiency on the expression of endocytic and signaling-related proteins, Megalin, Cubilin, Caveolin-1, GIPC1, and Dab2IP, during embryonic lung development in yotari mice. Using immunofluorescence and quantitative image analysis, protein expressions were compared between yotari and wild-type embryos at gestational days E13.5 and E15.5. Results showed significantly reduced expression of Caveolin-1 in the yotari epithelium across both stages, along with diminished mesenchymal levels of Megalin and GIPC1 at E13.5. Cubilin and Dab2IP expression patterns showed no statistically significant differences, although developmental and compartmental shifts were observed. These findings suggest that Dab1 deficiency selectively disrupts endocytic and signaling scaffolds crucial for branching morphogenesis and alveolar maturation. The altered spatiotemporal expression of these proteins underscores the essential role of Dab1 in regulating lung epithelial–mesenchymal dynamics and maintaining developmental homeostasis during critical stages of organogenesis. Full article

To explore the spatiotemporal evolution of extreme temperature events in Guangxi (1940–2023), reveal regional response mechanisms, and assess future trends of persistence under climate warming, a multi-scale analysis was conducted using ERA5 reanalysis data. Methodologies included RH tests for homogeneity correction, collaborative kriging for data optimisation, Mann–Kendall tests for trend and abrupt change detection, Morlet wavelet analysis for cyclic pattern identification, Exploratory Spatio-Temporal Data Analysis (ESTDA) for spatial heterogeneity quantification, and Rescaled Range (R/S) analysis to calculate Hurst indices for future persistence assessment. Results showed the following: (1) The ERA5 dataset exhibited high applicability in Guangxi (R = 0.9989, RMSE = 1.9492 °C), supporting robust evidence of continuous warming—warm indices (e.g., SU25, TX90p) increased significantly (SU25 at 0.2044 d/10a), while cold indices (e.g., TN10p, FD0) declined (TN10p at −0.0519 d/10a); abrupt changes of cold indices were concentrated in 1942–1950, with warm indices accelerating post-2000 and TXx exhibited the highest warming rate (0.23 °C/decade). (2) Extreme temperature indices displayed a primary 19–21-year oscillation cycle (dominant in warm indices) and a secondary 13-year cycle (prominent in cold indices). (3) Spatial heterogeneity featured northwest–southeast cold–heat inversion, coastal–inland intensity gradients, and latitudinal zonation of extreme indices; ESTDA revealed intensified polarisation, with warm indices clustering in low-latitude regions (e.g., Baise) and cold indices declining homogeneously in mountainous areas (e.g., Guilin), indicating an irreversible transition to a warming steady state. (4) R/S analysis indicated all indices had Hurst indices of 0.65–0.92, reflecting persistent future trends consistent with historical evolution, with warm indices (e.g., TNn, SU25) showing stronger persistence (H > 0.85). This work clarifies the spatial polarisation mechanism and future persistence of extreme temperature dynamics in Guangxi, providing a multi-scale scientific basis for disaster early warning and adaptation planning in climate-sensitive karst-monsoon regions. Full article

Excessive nitrogen export from lowland polders is a key contributor to cultural eutrophication in downstream aquatic ecosystems. This study investigated the spatiotemporal characteristics, migration pathways, and sources of nitrogen pollution in a typical polder system. Eight surface water sampling campaigns were conducted at 13 sites in Quyuan Polder, Dongting Lake, from 2022 to 2023, combining ArcGIS spatial analysis, multivariate statistics, and dual-isotope (δ¹⁵N-NO⁻), δ¹⁸O-NO₃⁻) techniques. Nitrate and ammonium nitrogen dominated the nitrogen pool, accounting for ~76% of total nitrogen. Concentrations were higher in the dry season (2.48 mg/L) than in the wet season (1.89 mg/L) and differed significantly among hydrological periods (p < 0.05). Within the polder, total nitrogen and ammonium nitrogen were elevated, whereas nitrate nitrogen was higher at the outlet, reflecting distinct nitrogen profiles along the hydrological gradient. Nitrogen transport patterns were largely consistent with flow direction, driven by both upstream inputs and in situ generation. Isotopic signatures indicated that nitrate originated mainly from ammonium fertilizer and soil nitrogen, with contributions from manure and sewage. These findings enhance understanding of nitrogen dynamics in lowland catchments and provide a scientific basis for targeted pollution control in polder waters. Full article

Yan Mountain, located in the northern part of the Beijing–Tianjin–Hebei economic zone in northern China, serves as a crucial ecological barrier for this densely populated and industrialized region. However, vegetation phenology and its responses to climate change in this region remain unclear. This study utilized MODIS EVI data to extract key phenological metrics and identify dominant vegetation types in Yan Mountain from 2000 to 2022. We analyzed spatiotemporal dynamics and driving mechanisms of vegetation phenology by examining relationships among phenological parameters and their responses to climatic variables. Results revealed significant spatial heterogeneity and interannual variation in vegetation phenology. The start of the growing season (SOS) and peak of the growing season (POS) advanced significantly at rates of 0.54 and 0.19 days/year, respectively. Meanwhile, the end of the growing season (EOS) was delayed by 0.38 days/year, and the growing season length (GSL) was extended by 0.97 days/year. These shifts were more pronounced in grasslands and shrublands than in forests. Significant physiological correlations existed among phenological parameters. Earlier green-up probably advanced POS and significantly delayed EOS. SOS advancement exerted a greater influence on GSL extension than EOS delay, with variations across vegetation types. Mean annual temperature and precipitation jointly regulated phenological shifts; precipitation primarily drove SOS advancement and GSL extension, while temperature mainly advanced POS and delayed EOS. This study elucidates spatiotemporal changes in vegetation phenology and its climate change response patterns in Yan Mountain, providing a crucial reference for ecological management and climate change adaptation strategies. Full article

Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly intensified. The spatio-temporal correlation risks faced in cross-border investment activities have become highly complex, posing a severe challenge to traditional investment risk prediction methods. Existing research has three limitations: first, traditional analytical tools struggle to capture the dynamic spatio-temporal correlations among financial markets; second, mainstream deep learning models lack the ability to directly output interpretable economic parameters; third, the uncertainty of model prediction results has not been systematically quantified for a long time, leading to a lack of credibility assessment in practical applications. To address these issues, this study constructs a spatio-temporal graph convolutional neural network panel regression model (STGCN-PDR) that incorporates uncertainty quantification. This model innovatively designs a hybrid architecture of “one layer of spatial graph convolution + two layers of temporal convolution”, modeling the spatial dependencies among global stock markets through graph networks and capturing the dynamic evolution patterns of market fluctuations with temporal convolutional networks. It particularly embeds an interpretable regression layer, enabling the model to directly output regression coefficients with economic significance, significantly enhancing the decision-making reference value of risk prediction. By designing multi-round random initialization perturbation experiments and introducing the coefficient of variation index to quantify the stability of model parameters, it achieves a systematic assessment of prediction uncertainty. Empirical results based on stock index data from 20 countries show that compared with the benchmark models, STGCN-PDR demonstrates significant advantages in both spatio-temporal feature extraction efficiency and risk prediction accuracy, providing a more interpretable and reliable quantitative analysis tool for cross-border investment decisions in complex market environments. Full article

Since 2007, the Yellow Sea has experienced the world’s largest green tides, with Ulva prolifera O.F. Müller as the dominant species. Those blooms severely impacted the local tourism and aquaculture, resulting in significant economic losses, as well as negative social and ecological consequences. Unlike other global green tides, those in the Yellow Sea are characterized by long-distance drifting and an astonishing scale. These destructive events display significant temporal and spatial variability, which is largely driven by dynamic environmental conditions and human activities. In this review, we summarize recent advancements in understanding the spatiotemporal patterns of long-distance transport, the interannual variability in bloom size, and the underlying mechanisms driving these fluctuations. Additionally, we highlight important knowledge gaps that need further investigation to support the development of effective management strategies for mitigating the impacts of green tides in the Yellow Sea. Full article

The process of social globalization and urbanization has developed rapidly in China, and the tension between economic development and the eco-environment is becoming increasingly tense, posing a major challenge to the sustainable development strategy of the Shandong Peninsula Urban Agglomeration (SPUA). Coordination development between economic development and habitat quality has become essential for preserving ecological stability and advancing long-term regional sustainability. This study constructed the optimal regression model to measure GDP density using night-time lighting data and economic statistical data and calculated habitat quality at the grid scale with the InVEST model. The spatiotemporal dynamics and driving factors of the coupling coordination between economy and habitat quality (EHCCD) were revealed using the coupling coordination degree model and the Geo-detector model. The results show that (1) between 2000 and 2020, the spatial pattern of GDP density has evolved from a single-core to a multi-core networked development. (2) The habitat quality of the SPUA exhibited a spatial pattern high in the east and low in the west, showing a downward trend. (3) The synergistic effect between GDP density and habitat quality was strengthened continuously, showing an overall strengthening tendency. (4) Driving factors’ influence on the EHCCD showed evident differences; socio-economic factors such as built-up area especially had greater explanatory power for the EHCCD; the interaction factors had shifted from socio-economic dominance to synergistic dominance of natural and human factors. This study not only overcomes the limitations imposed by administrative boundaries on assessing inter-regional coupling coordination but also provides fundamental data support for cross-regional cooperation, thereby advancing the sustainable development goal of the SPUA. Full article

Understanding the spatio-temporal evolution of land consolidation is essential for optimizing regional land resource allocation and mitigating human–land conflicts during socio-economic development. This study introduced a novel framework for analyzing such patterns in China. Utilizing a two-decade (2002–2022) prefecture-level city dataset of land consolidation projects in Lianyungang, Jiangsu Province, we developed a “land consolidation intensity” metric and applied quantitative techniques—including land use transfer matrices, landscape pattern indices, Sankey diagrams, and standard deviation ellipses—to assess spatio-temporal dynamics and centroid shifts. Key findings included: (1) Land consolidation intensity exhibited distinct stages, evolving from initial development to rapid growth and eventual stabilization, closely aligning with national policy shifts. (2) The primary sources for supplemented cultivated land were ponds, rivers, and tidal flats, followed by grassland, construction land, and forest land, with cultivated land consistently dominating the consolidated landscape. (3) Land consolidation projects distribution concentrated in economic and political centers, with a spatial shift from inland western region towards the eastern coastal region. (4) Gray relational analysis identified economic development as the predominant driver, with policy and social factors providing secondary guidance. This research elucidates the spatio-temporal evolution characteristics of land consolidation at the prefecture-level city and demonstrates the utility of the proposed framework for similar analyses, offering insights relevant to national land use planning and policy formulation. Full article

This study employs a systems theory approach to investigate the coupling coordination and driving mechanisms within the economic–social–environmental (ESE) system in China’s ethnic regions. It analyzes 67 ethnic counties in Sichuan Province, using an integrated framework that combines dynamic Shannon entropy, coupling coordination modeling, and GeoDetector. Based on data from 2005 to 2024, the study reveals the spatiotemporal patterns of ESE coupling coordination. The key findings are as follows: (1) The coupling coordination degree has gone through four stages: moderate imbalance → mild imbalance → primary coordination → moderate coordination. By 2024, 81.8% of counties had achieved coordinated development, and “highly coordinated” counties emerged for the first time. (2) The Western Sichuan Plateau has formed a high–high agglomeration zone by monetizing ecological assets and utilizing ethnic cultural resources. In contrast, the hilly and parallel ridge–valley regions in central and eastern Sichuan remain in low–low agglomerations due to their dependency on traditional industrialization paths. The decrease in high–low and low–high outliers indicates the recent policy polarization effects. (3) The interaction between habitat quality and per capita GDP has the strongest explanatory power. The rising marginal contributions of energy and carbon emission intensity suggest that green industrialization is crucial to breaking the “poverty–pollution” trap. Full article