Search Results (3,513)

Ground deformation monitoring is pivotal for enhancing urban resilience and mitigating geohazards. This study presents a synergistic monitoring framework integrating 26 Sentinel-1A (C-band) and 16 LuTan-1 (L-band) SAR scenes acquired between December 2023 and August 2025 to characterize the deformation dynamics in Beijing. Utilizing SBAS-InSAR, we first established a regional deformation baseline using Sentinel-1A observations, identifying critical subsidence and uplift zones in the eastern plains. Subsequently, high-resolution (3 m) LT-1 data were leveraged to achieve refined spatiotemporal characterization of these deformation hotspots. Validation against ground leveling benchmarks confirmed that both satellites yield high accuracy. LuTan-1 (RMSE = 3.810 mm/a) shows slightly better agreement with the ground leveling data than Sentinel-1A (RMSE = 4.853 mm/a). Analysis of the spatiotemporal patterns derived from InSAR revealed that the study area is characterized by widespread gene uplift (averaging ~10 mm/a), interspersed with acute localized subsidence exceeding 40 mm/a. Correlation analysis demonstrates a high spatiotemporal coupling between the extent and rate of surface uplift and groundwater level recovery. To further investigate these dynamics, Terzaghi’s effective stress principle is employed to quantify the contribution of groundwater level fluctuations to the observed surface deformation. A Parametric Harmonic Model was implemented to decouple elastic and trend components, and attribution analysis confirms that the continuous recovery of groundwater levels is the fundamental driver of the regional surface uplift. The inverted elastic skeletal storativity (S_ke), ranging from 1.587 × 10⁻³ to 9.184 × 10⁻³, reveals that regional surface uplift is predominantly driven by the elastic rebound of aquifer systems following groundwater recovery. In contrast, localized subsidence anomalies observed at large-scale engineering construction sites, landfill facilities, major expressway corridors, and high-density residential areas are independent of groundwater fluctuations, instead they are primarily attributed to anthropogenic stressors. This study elucidates a dual-drive mechanism, which comprising macroscopic hydrogeological rebound and localized anthropogenic disturbance, providing a robust scientific basis for differentiated urban hazard management. Full article

Identifying the heterogeneous characteristics of habitat quality (HQ) trajectories is a key prerequisite for refined ecological spatial management. We used kernel Normalized Difference Vegetation Index (kNDVI) to correct the highly sensitive parameters, validated the correction results based on their consistency with the prior study findings, developed a framework for the evolution of HQ using Sen+MK and Pettitt’s tests, and utilized XGBoost and partial correlation analysis to identify the primary drivers of dynamic changes in HQ from both spatiotemporal perspectives. Our findings include the following: (1) between 2000 and 2023, the average annual rate of change in the HQ index was 0.0037 per year, indicating a continuous improvement in HQ. Compared with the period from 2011 to 2023 (0.0026 per year), the rate of improvement in HQ was faster during 2000–2011 (0.0047 per year). (2) Mutational improvement and progressive improvement were the main evolutionary trajectories, accounting for over 50.33% of the total. (3) Precipitation, land-use intensity (LUI), temperature, and elevation show a strong correlation with HQ distribution. The magnitude of HQ variation is related to HQ status, LUI, precipitation, and elevation. This study establishes a scientific foundation for developing differentiated regulatory strategies for YRB. Full article

The accurate and explainable prediction of carbon emissions is crucial for the efficient operation of hybrid and electrified transportation systems and their integration with energy grids. An Explainable Spatio-Temporal Graph Neural Network (EST-GNN) is proposed for highly precise CO₂ emission forecasting using Lévy Flight-guided Optuna optimization. By modelling vehicles and their operational characteristics as nodes in a dynamic graph, the proposed framework can jointly learn timing and spatial correlations while sustaining interpretability. The accuracy of the EST-GNN model is compared with models based on one-hot encoded features, SMOTE-enhanced datasets, and ensemble regressors. Using a real-world dataset of 7385 vehicle registrations with 12 predictive features experiments are conducted. When applied the EST-GNN model outperformed all baseline and traditional models achieving the highest reliability (R² = 0.98754) while solving competitive error metrics (RMSE = 6.55, MAE = 2.556). There is strong indication that reasonable machine learning (ML) models can be used accurately to confirm their suitability for resource-prevented and real-time applications, while predictable ML techniques have relatively low reliability. The optimal solution ensures scalability, robustness, and independence of the deployment environment. The distribution analysis of best performing models develops the ability of EST-GNN, which accounts for the largest proportion of best results across evaluation metrics. To achieve superior predictive accuracy, graph-based learning, explainability, and advanced hyperparameter optimization are combined. EST-GNN provides a powerful tool for analyzing fleet emission levels, making energy-aware decisions, and planning sustainable transportation, while ML models continue to be a useful complement for deployment states with high computation costs and quick responses. Full article

This study analyzed the spatiotemporal dynamics of the vegetation ecological quality under climate change. Focusing on the vegetation conditions, a vegetation ecological quality index was constructed, expressing as the product of vegetation fraction cover (VFC), net primary productivity (NPP), and geographic coverage area. The results of trend and significance analysis showed that from 2000 to 2023, the VEQI in the Qinling Mountains exhibited a significant improvement, with an average slope of 4.91 gC·a⁻¹ and 96.2% of the area showing high stable improvement. Partial correlation analysis revealed that precipitation had a stronger positive influence on VEQI than temperature, with over 98% of the area showing a positive correlation with precipitation, while temperature was positively correlated in 95.0% of the area but negatively correlated in high-altitude mountain zones. Therefore, four climate-driven patterns were identified: precipitation-driven (31.2%), temperature-driven (2.3%), co-driven (54.2%), and climate-stable (12.3%), suggesting that vegetation ecological quality in most regions is co-driven by both temperature and precipitation. Based on the results of trend and significance analysis and climate-driven patterns, the Qinling Mountains were divided into three ecological risk zones: low-risk (36.1%), middle-risk (56.9%), and high-risk (7.0%), with corresponding differentiated control measures proposed. Full article

Accurate measurement of ocean wave parameters is paramount for offshore engineering design and marine environmental monitoring. Distributed pressure sensing technology provides a robust data foundation for analyzing the spatio-temporal characteristics of wave fields through synchronized observations at multiple stations. However, multi-sensor data exhibit high-dimensional spatio-temporal coupling, posing significant challenges for traditional single-point signal processing methods in capturing the topological associations between measurement sites. To address these limitations, this study develops a framework for spatio-temporal feature modeling and wave height reconstruction based on Graph Neural Networks (GNNs). The proposed framework integrates the spatial configuration of sensor arrays with graph-theoretic topological representations. By fusing geometric distances and signal correlations, an adaptive adjacency matrix is constructed to establish a dynamically adjustable graph structure. On the feature extraction level, a spatio-temporal fusion method combining multi-scale graph convolutions and gated temporal modeling is proposed. The experimental results obtained on the Blancs Sablons Bay multi-sensor dataset demonstrate that the proposed method significantly outperforms traditional approaches, achieving lower prediction errors and validating the effectiveness of graph-structured modeling in distributed wave sensing. Full article

Environmental loading is a major driver of nonlinear GNSS vertical displacements, yet its spatiotemporal heterogeneity remains insufficiently understood in regions with complex topography. In this study, we investigate the environmental loading effects on GNSS vertical motions across Southwest China using observations from a network of 66 stations. Singular Spectrum Analysis (SSA) and Empirical Orthogonal Function (EOF) analysis were applied to extract annual signals, while component-wise RMS reduction quantified hydrological and atmospheric loading contributions. Spatial statistical analysis, cross-wavelet transform, and k-means clustering examined correlation patterns and phase hysteresis between GNSS observations and modeled loads. Results show that hydrological loading dominates seasonal vertical oscillations, but crustal responses exhibit pronounced spatial heterogeneity controlled by regional topography and hydro-climatic gradients. EOF analysis reveals a dipole pattern induced by the Hengduan Mountains’moisture-blocking effect. Atmospheric loading anomalously dominates the eastern Sichuan Basin, whereas Yunnan displays strong amplitudes with high heterogeneity due to karst hydrogeology. Phase analysis identifies three distinct regimes: a rapid elastic response on the Tibetan Plateau, (with the lag of ~20 ± 5 days, correlation coefficient R ≈ 0.65), intermediate delays in Yunnan (~60 ± 5 days, R ≈ 0.58), and pronounced hysteresis in the Sichuan Basin (~105 ± 5 days, R ≈ 0.38) linked to slow groundwater diffusion and poroelastic processes. These findings highlight the critical role of local hydrogeological dynamics in modulating GNSS vertical deformation and provide new insights for improving environmental loading corrections in complex mountainous regions. Full article

Rapid urbanization in the Yellow River Basin intensifies the conflict between urban expansion and the thermal environment, threatening ecological security and sustainable development. Utilizing multi-source data (2000–2023) including nighttime light (NTL) and land surface temperature (LST), this study applies spatial analysis and Geographically Weighted Regression (GWR) to explore the spatial associations between urban development and LST and its drivers across core cities. The results indicate significant spatiotemporal differentiation: mid-downstream cities exhibited contiguous urban expansion, whereas upstream growth remained constrained by local topography, with heat islands consistently concentrating in built-up areas. The warming rate decreased gradually from downstream (0.29–0.40 °C/year) to upstream (0.20–0.30 °C/year). The LST-NTL correlation strengthened notably in mid-downstream regions but remained moderate upstream. GWR analysis revealed that urban development intensity, represented by NTL, is the primary driver of LST increase downstream, while natural factors predominantly mitigate warming upstream. This long-term, multi-city comparison provides a scientific basis for precise urban heat island management and sustainable planning in the basin. Full article

Infrared focal plane arrays (IRFPAs) often suffer from spatiotemporal nonuniformity that persists after conventional two-point nonuniformity correction (NUC), especially under temperature drift and time-varying readout conditions. These residuals are typically structured, including column-group striping caused by shared column-end circuits and row-wise baseline/common-mode drift induced by row-scanning paths. We propose a structured, digital-twin-inspired detector-side refinement of two-point NUC that augments the bias term with interpretable low-dimensional components: a static column bias vector capturing group-correlated residuals and a row-related structured term consisting of a static row baseline and a frame-synchronous common-mode component with row-dependent sensitivity, while keeping the two-point gain/offset backbone unchanged. Rather than representing a full system-level digital twin of the infrared payload, the proposed framework serves as a detector-side virtual representation of dominant readout-induced structured residual states that can be estimated and updated from calibration data. Experiments on blackbody calibration data across multiple temperature points demonstrate that the column-related structured component significantly reduces group-wise column residuals, the row-related structured component suppresses time-varying row striping, and the combined method improves both column- and row-direction metrics consistently across temperatures. Full article

Understanding the spatiotemporal formation mechanisms of built cultural heritage is essential to interpreting regional cultural landscapes and informing differentiated conservation strategies. Using Fujian Province, China, as a representative mountain–sea transitional region, this study constructs a province-scale, multi-category, and dynamically oriented analytical framework to investigate the temporal evolution, spatial structure, and driving mechanisms of immovable cultural relics. Based on a georeferenced dataset of 940 immovable cultural relics, textual historical records were standardized into continuous temporal variables and integrated with GIS-based kernel density estimation, spatial autocorrelation analysis, distance-to-coast modeling, and category co-occurrence analysis. The results reveal a pronounced temporal concentration in the Ming–Qing and modern periods, with a primary formation peak during the Qing Dynasty and a secondary peak in the early 20th century driven by modern heritage. Spatially, relics exhibit significant positive spatial autocorrelation (Global Moran’s I = 0.375, p < 0.001) and form a structured dual-core pattern, consisting of a persistent coastal heritage belt and a distinct inland modern core centered in western Fujian. More than 75% of relics are located within 110 km of the coastline, confirming strong maritime orientation, while regression analysis reveals that this inland shift is primarily driven by the Modern Era rather than representing a continuous long-term trend. Category-level correlation analysis further demonstrates a clear spatial decoupling between traditional heritage and modern sites, indicating fundamentally different locational logics. Synthesizing these findings, this study proposes a dual-core driven model under a mountain–sea geographical framework, in which a path-dependent, economically reinforced coastal core coexists with a historically contingent, politically driven inland core. The results advance quantitative understanding of how multiple cultural logics, operating across different temporal scales, jointly shape complex regional heritage systems and provide a transferable framework for heritage analysis and spatially differentiated conservation planning. Full article

Crop planting structure adjustments in irrigated agricultural regions alter irrigation and drainage regimes, with potential consequences for regional surface water dynamics. However, the nature and scale dependence of these linkages remain insufficiently understood. This study investigates the spatiotemporal dynamics of crop planting structure and surface water bodies in the Ningxia Plain from 2004 to 2023, and systematically quantifies their scale-dependent coupling mechanisms. Annual crop maps were generated using a Random Forest classifier (Sentinel-2, 2019–2023) and a Transformer-based model applied to multi-source satellite imagery (2004–2018). Surface water bodies were derived from long-term remote sensing datasets covering the full study period. Results show that the agricultural system underwent a pronounced transition toward maize dominance. Maize area expanded by 50.8%, whereas wheat and rice declined by 74.3% and 44.6%, respectively. Crop diversity also decreased, with the Shannon Diversity Index declining from 1.41 to 1.06 in 2023, indicating progressive system simplification. Meanwhile, surface water bodies exhibited a sustained downward trend, decreasing at an average rate of −5.32 km² per year after 2013 and reaching a minimum in 2022. The Yellow River water surface area also contracted by 14.41% (p = 0.001), indicating a basin-scale reduction in surface water extent. Lake classification results reveal strong scale-dependent hydrological responses. Small lakes (≤18 ha), accounting for 73.2% of lake numbers, are primarily controlled by local irrigation–drainage processes. Medium lakes (18–80 ha) are influenced by both anthropogenic regulation and natural variability. Large lakes (>80 ha), although representing only 4.9% of lake numbers but 62.9% of total water area, are mainly sustained by climatic variability and ecological water supplementation. Principal component analysis explains 84.44% of total variance, highlighting agricultural structural change and irrigation–drainage dynamics as key system drivers. Correlation analysis further reveals strong climate sensitivity of large lakes and the Yellow River (ρ = 0.50, p = 0.031), while small lakes are predominantly influenced by agricultural drainage processes. Overall, crop planting structure affects regional water dynamics through scale-dependent processes, with maize expansion altering irrigation and diversion patterns and local irrigation–drainage processes controlling small water bodies. Full article

In this study, global spatial autocorrelation, local spatial autocorrelation, Spearman correlation analysis, gray correlation analysis, entropy weight method, and the gravity model were used to analyze the spatiotemporal variation and environment-urban-economy-associated factors of air quality of 31 provinces in China during 2015~2022. From 2015 to 2022, the Air Quality Index (AQI) exhibited a downward trend in 30 out of 31 Chinese provinces, with the exception of Shaanxi Province. Concurrently, the annual average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO declined across the study period. High-high clusters and low-high outliers were observed in northern China, whereas low-low clusters and high-low outliers were distributed in southern China. Twelve provinces (38.7%) showed positive correlation (0.095~0.95), 18 provinces (58.1%) showed negative correlation (−0.76~0.095), and only Anhui showed no correlation between AQI and O₃. The comprehensive AQI quality presented a dual-core model in Sichuan (in the southwest) and Henan (in the central part) of China, while the comprehensive AQI improvement rate presented a single-core model in Jiangsu in the east of China. The gravity models incorporating AQI and GDP revealed that both air quality and economic performance improved over the study period. The spatial pattern of pollution evolved from a multi-core structure to a non-core structure, whereas the pattern of economic growth transitioned from a non-core structure to a dual-core structure, with the Beijing-Tianjin-Hebei region and the Yangtze River Delta emerging as the primary urban agglomerations. Full article

Fog is a near-surface weather phenomenon with low visibility that significantly threatens transportation safety. Understanding the spatiotemporal evolution and formation mechanisms of fog is essential for improving fog forecasting and warning services to reduce related casualties and economic losses. This study examines consecutive dense fog events with long duration and high intensity that occurred along the Ma-Zhao Expressway in northeastern Yunnan from 24 to 30 October 2022. Yunnan is a typical low-latitude plateau region in southwestern China with complex terrain and diverse climates, leading to particularly complicated fog formation processes. Correlation analysis indicates that thermal and vapor factors show stronger correlations with visibility, with correlation coefficients reaching 0.68 for vertical temperature difference and −0.63 for surface relative humidity, both significant at the 99% confidence level. These values are notably higher than those of dynamic factors such as near-surface wind speed, which yields a correlation coefficient of 0.47. The results confirm the dominant role of thermal and vapor conditions in the formation and maintenance of these dense fog events, together with favorable conditions including near-surface air saturation, weak dynamic processes, and a temperature inversion in the lower troposphere. Standardized anomaly analysis reveals obvious atmospheric anomalies during the fog episodes. A strong southerly wind anomaly appears in the lower troposphere, driven by a cyclone over the Philippines and an anomalous anticyclone east of Yunnan. This southerly transport delivers warm and moist air toward the Ma-Zhao Expressway, accompanied by a positive temperature anomaly of 1.7, standard deviations near 700 hPa and a positive specific humidity anomaly of more than 2 standard deviations in the lower troposphere. These conditions favor the development of temperature inversions and atmospheric saturation, further promoting the occurrence and persistence of consecutive dense fog events. This study clarifies the key effects of thermal and vapor conditions as well as low-level southerly wind anomalies on dense fog over the Yunnan low-latitude plateau. These conclusions deepen the understanding of fog formation mechanisms in complex plateau terrain and provide a scientific reference for fog forecasting and early warning along mountain expressways in similar low-latitude plateau regions. Full article

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article

Gas concentration series in coal mining faces are jointly affected by multiple coupled factors, including geological conditions, mining disturbances, ventilation organization, and gas drainage intensity, and therefore exhibit pronounced nonstationarity, strong fluctuations, spatiotemporal correlations across multiple monitoring points, and occasional abrupt spikes. To address these challenges, this study proposes a gas concentration prediction and early-warning method that integrates CEEMDAN–SST with GraphPINN-TimesFM (Graph Physics-Informed Neural Network–Time Series Foundation Model). First, based on multi-source monitoring data such as wind speed, gas concentrations at multiple monitoring points, and equipment operating status, anomaly removal, operating-condition segmentation, and change-point detection are performed to construct stable operating-state labels. Feature selection is then conducted by combining optimal time-lag correlation, Shapley value contribution, and dynamic time warping. Second, WGAN-GP is employed to augment samples from minority operating conditions, while CEEMDAN–SST is used to decompose and reconstruct the target series so as to reduce the interference of nonstationary noise and enhance sequence predictability. On this basis, TimesFM is adopted as the backbone for long-sequence forecasting to capture long-term dependency features in gas concentration evolution. Furthermore, GraphPINN is introduced to embed the topological associations among monitoring points, airflow transmission delays, and convection–diffusion mechanisms into the training process, thereby enabling collaborative modeling that integrates data-driven learning with physical constraints. Finally, the predictive performance, early-warning capability, and interpretability of the proposed model are systematically evaluated through regression forecasting, warning discrimination, and Shapley-based interpretability analysis. The results demonstrate that the proposed method can effectively improve the accuracy, robustness, and physical consistency of gas concentration prediction under complex operating conditions, thereby providing a new technical pathway for gas over-limit early warning and safety regulation in coal mining faces. Full article

With urbanization transforming from scale expansion to high-quality development and the increasing prominence of the ecological environment constraints of drainage basins, systematically identifying the mechanism of action of non-point source pollution from a high-quality development perspective is significant for coordinating urban development and environmental protection. Based on remote sensing data on atmospheric pollution and multi-source spatial big data such as nighttime light (NTL), LandScan population, point of interest (POI), and land use data from 2013 to 2025, this study applies methods including deposition flux analysis, deep learning fusion, bivariate spatial autocorrelation, and geographically weighted regression (GWR) to empirically analyze the spatiotemporal evolution characteristics, spatial correlation, and local impacts of high-quality urban development on non-point source pollution in the Chenghai drainage basin. We find that, firstly, non-point source pollution and high-quality urban development in the Chenghai drainage basin both present significant stage-specific and spatial heterogeneity. In other words, the two are not mutually independent spatial elements in space; instead, they are closely and significantly correlated, with their correlation types showing obvious spatial agglomeration characteristics. Secondly, the impact of high-quality urban development on non-point source pollution evolves in stages. It gradually shifts from a whole-region, homogeneous, strongly positive driving force to spatial differentiation. Specifically, from 2013 to 2017, the whole-region regression coefficients are generally greater than 0.5, meaning that urban development represents a strong, whole-region driving force promoting pollution. However, after 2017, this impact evolves into a stable spatial differentiation pattern. It mainly shows that the northern urban core area, where coefficients are greater than 0.5, maintains a continuous strong positive driving force. Meanwhile, the peripheral area, where coefficients are generally lower than 0, creates a negative inhibition effect. Based on the above rules, further analysis shows that the impact of high-quality urban development on non-point source pollution is absolutely not a simple linear relationship. Instead, it is a result of the coupling effect of multiple factors, including development stage, spatial location, and governance level. Therefore, to positively affect the ecological environment through high-quality development, model transformation and precise governance are essential. The findings of this study deepen our understanding of the transformation of urban development models and the response mechanism of non-point source pollution. They also provide a scientific basis and decision support for promoting the coordinated governance of high-quality urban development and non-point source pollution by region and stage in plateau lake drainage basins, as well as for improving the sustainable development of drainage basins. Full article