Search Results (5,704)

The spatiotemporal forecasting of temperature is a critical issue in meteorological prediction, with significant implications for fields such as agriculture and energy. With the rapid advancement of data-driven deep learning methods, deep learning-based spatiotemporal sequence forecasting models have seen widespread application in temperature spatiotemporal forecasting. However, statistical analysis reveals that temperature evolution varies across temporal and spatial scales due to factors like terrain, leading to a lack of existing temperature prediction models that can simultaneously learn both large-scale global features and small to medium-scale local features over time. To uniformly model temperature variations across different temporal and spatial scales, we propose the Multi-Scale Large Kernel Spatiotemporal Attention Neural Network (MSLKSTNet). This model consists of three main modules: a feature encoder, a multi-scale spatiotemporal translator, and a feature decoder. The core module of this network, Multi-scale Spatiotemporal Attention (MSSTA), decomposes large kernel convolutions from multi-scale perspectives, capturing spatial feature information at different scales, and focuses on the evolution of multi-scale spatial features over time, encompassing both global smooth changes and local abrupt changes. The results demonstrate that MSLKSTNet achieves superior performance, with a 35% improvement in the MSE metric compared to SimVP. Ablation studies confirmed the significance of the MSSTA unit for spatiotemporal forecasting tasks. We apply the model to the regional ERA5-Land reanalysis temperature dataset, and the experimental results indicate that the proposed method delivers the best forecasting performance, achieving a 42% improvement in the MSE metric over the widely used ConvLSTM model for temperature prediction. This validates the effectiveness and superiority of MSLKSTNet in temperature forecasting tasks. Full article

Analyzing the trajectory and mechanism of rural industrial change is important for understanding urban–rural integration and facilitating rural revitalization. Based on the data of industrial enterprises in 1112 administrative villages of Guangzhou, China, from 1978 to 2020, we identify the evolution trend of rural industry by investigating the spatial–temporal dynamics of industrial changes in rural areas. An extended triple-process framework incorporating urbanization and greenization was employed to unravel the underlying mechanism of the trajectory of rural industrialization. The results highlight the transformation from rural to suburban industrialization. In the past twenty years, rapid urbanization has contributed to the establishment of development zones. The agglomeration economy has led to a higher concentration of manufacturing industries in these development zones rather than rural areas. The eco-friendly development has resulted in a green transition in rural areas, further restricting the growth of traditional rural industries. Our analysis provides a nuanced picture of Guangzhou’s spatial–temporal dynamics of rural and suburban industrialization. Meanwhile, it emphasizes the importance of urbanization and greenization in explaining the recent transformation of industrialization in China. Full article

The ecological security of the water environment is a key element in evaluating the dynamic balance and ecological service functions in the construction of urban ecological civilizations. Through the regional study of water resources in Huizhou, we selected 24 indicators in five dimensions of the DPSIR theory, such as “driving force-pressure-state-impact-response”, and constructed an ecological evaluation index system of the water environment. Combined with the entropy weight TOPSIS model, the analysis was carried out for spatial differentiation features and spatio-temporal deduction features, and the results showed that the weight coefficients of the spatial differentiation features for the guideline layer exhibited significant stratification characteristics. The overall spatial and temporal interpretation characteristics of the water’s environmental ecology in the Huizhou region from 2016 to 2021 showed a pull-up enhancement effect. The relative proximity value showed a 63.43% increase from 0.361 in 2016 to 0.590 in 2021 over the six-year period. The region is characterized by regional differences in the ecological carrying capacity of the water environment, which is high in the south-east and low in the north-west. The top three areas in the quantitative calculation of the ecological carrying capacity of the water environment are Shexian County, Jixi County, and Qimen County, in that order. Full article

Vegetation quality is crucial for maintaining ecological health, and remote sensing techniques offer precise assessments of vegetation’s environmental quality. Although existing indicators and remote sensing approaches provide extensive spatial coverage, challenges remain in effectively integrating diverse indicators for a comprehensive evaluation. This study introduces a comprehensive ecological quality index (EQI) to assess vegetation quality on the Mongolian Plateau from 2001 to 2020 and to identify the determinants of EQI variations over space and time. We developed the EQI using remotely sensed normalized difference vegetation index (NDVI) data and the net primary productivity (NPP). Our analysis revealed distinct spatial patterns, with high ecological quality concentrated in northern Mongolia and eastern Inner Mongolia. Temporal fluctuations, indicative of ecological shifts, were primarily observed in eastern Mongolia and specific zones of Inner Mongolia. We employed a Geographically Optimal Zones-based Heterogeneity (GOZH) model to analyze the spatial scales and interactions influencing EQI patterns. This study found that precipitation, with an Omega value of 0.770, was the dominant factor affecting the EQI, particularly at spatial scales of 40–50 km. The GOZH model provided deeper insights into the spatial determinants of the EQI compared with previous models, highlighting the importance of climatic variables and their interactions in driving ecological quality. This research enhanced our understanding of vegetation quality dynamics and established a foundation for ecosystem conservation and informed management strategies, emphasizing the critical role of climate, especially precipitation, in shaping ecological landscapes. Full article

A thorough exploration of Ecosystem Services (ESs) and their intricate interactions across time and space is a prerequisite for the sustainable management of multiple ESs. This study aimed to systematically evaluate the ESs of the middle reaches of the Yangtze River Urban Agglomeration (MRYRUA) across multiple spatial and temporal scales, thereby enhancing ecosystem management and informed scientific decision-making. Specifically, this study employed the InVEST model, hot spot analysis, a geographically weighted regression model, and self-organizing feature mapping combined with K-means clustering to systematically quantify the spatiotemporal characteristics, trade-offs, synergies, and ecosystem service clusters of habitat quality (HQ), water yield (WY), carbon storage (CS), soil conservation (SC), and landscape aesthetics (LA) at grid and county scales from 2000 to 2020. The results revealed the following: (1) There was significant spatial heterogeneity among various ESs, with an overall spatial pattern exhibiting layered and interwoven variations. (2) Trade-offs predominantly characterized the relationships among ESs in the MRYRUA, with the absolute values of correlation coefficients mostly reaching their nadir in 2010. The interaction strengths between HQ and CS, and between CS and SC, increased with scale, while the relationships and strengths between LA and other ESs were less affected by scale changes. (3) At the grid scale, five types of ecosystem service bundles (ESBs) were identified, whereas at the district scale, four types of ESBs were delineated, including three common types: the WY–LA synergy bundle, Ecological transition bundle, and Key synergetic bundle, and three distinct types: the HQ–CS synergy bundle, Integrated ecological bundle, and Key synergetic bundle. The transitions of these ESBs over the 20 year period generally exhibited fluctuating evolutionary characteristics, with more pronounced fluctuations as the scale expanded. The results improve our comprehension of how ESs are related across various scales and provide theoretical and scientific references for multi-scale sustainable ecosystem zoning management and ecological environment governance. Full article

The atmosphere is a complex nonlinear system, with the information of its temperature, water vapor, pressure, and cloud being crucial aspects of remote-sensing data analysis. There exist intricate interactions among these internal components, such as convection, radiation, and humidity exchange. Atmospheric phenomena span multiple spatial and temporal scales, from small-scale thunderstorms to large-scale events like El Niño. The dynamic interactions across different scales, along with external disturbances to the atmospheric system, such as variations in solar radiation and Earth surface conditions, contribute to the chaotic nature of the atmosphere, making long-term predictions challenging. Grasping the intrinsic chaotic dynamics is essential for advancing atmospheric analysis, which holds profound implications for enhancing meteorological forecasts, mitigating disaster risks, and safeguarding ecological systems. To validate the chaotic nature of the atmosphere, this paper reviewed the definitions and main features of chaotic systems, elucidated the method of phase space reconstruction centered on Takens’ theorem, and categorized the qualitative and quantitative methods for determining the chaotic nature of time series data. Among quantitative methods, the Wolf method is used to calculate the Largest Lyapunov Exponents, while the G–P method is used to calculate the correlation dimensions. A new method named Improved Saturated Correlation Dimension method was proposed to address the subjectivity and noise sensitivity inherent in the traditional G–P method. Subsequently, the Largest Lyapunov Exponents and saturated correlation dimensions were utilized to conduct a quantitative analysis of FY-4A and Himawari-8 remote-sensing infrared observation data, and ERA5 reanalysis data. For both short-term remote-sensing data and long-term reanalysis data, the results showed that more than 99.91% of the regional points have corresponding sequences with positive Largest Lyapunov exponents and all the regional points have correlation dimensions that tended to saturate at values greater than 1 with increasing embedding dimensions, thereby proving that the atmospheric system exhibits chaotic properties on both short and long temporal scales, with extreme sensitivity to initial conditions. This conclusion provided a theoretical foundation for the short-term prediction of atmospheric infrared radiation field variables and the detection of weak, time-sensitive signals in complex atmospheric environments. Full article

County’s level economic disparities remain as a key policy issue for sustainable and healthy regional development, particularly for their spatiotemporal dynamics. This research adopted Geographic Information Systems software and spatial econometric analysis methods to analyze the temporal and spatial disparities, spatial structures, and dynamic evolution processes of the Xizang Autonomous Region’s county-level economy. With the application of the coefficient of variation and spatial autocorrelation methods, the research identified a significant trend of narrowing economic differences among the 74 counties. The study also observes a growing spatial autocorrelation, pointing towards a more clustered economic growth pattern, particularly influenced by the Lhasa economic circle’s expanding regional radiation capacity. The findings underscore the importance of strategic development planning, including the integrated development of Lhasa and Shannan. This study contributes to the literature on regional economic development and offers insights for policy formulation aimed at sustainable and equitable growth in Xizang, which could also benefit future development of counties in developing countries with comparable economic environments. Full article

Backfill mining is an important means of ensuring the high efficiency and safety of the coal mining under thin bedrock and loose aquifers. Based on the case study of Taiping Coalmine, the theoretical analysis of entropy and numerical modeling methods are adopted to establish the visualization model of temporal–spatial cube of stress and displacement induced by the multiple layers backfill mining. Moreover, the quantitative characterization and measurement framework of symmetric KL-divergence is established based on information entropy and mutual information. The results show that: (1) The non-uniformity of stress and displacement is enhanced due to the multiple layers backfill mining, showing certain fluctuation characteristics. (2) The KL-divergence of stress to displacement is slightly greater than that of displacement to stress, and the hotspot distribution law of stress–displacement related efficiency is consistent with KL-divergence. (3) The hotspots of stress entropy and the gap between stress entropy and displacement entropy in multiple layers backfill mining decrease obviously. (4) Stress plays a main role in displacement, and displacement is a linkage response to stress due to the coordinated deformation. Multiple layers backfill mining results in an enhanced correlation degree and more chaotic state between stress and displacement. The results will provide engineering geological basis for optimal design and safe production of backfill mining under loose aquifers. Full article

Stock index fluctuations are characterized by high noise and their accurate prediction is extremely challenging. To address this challenge, this study proposes a spatial–temporal–bidirectional long short-term memory (STBL) model, incorporating spatiotemporal attention mechanisms. The model enhances the analysis of temporal dependencies between data by introducing graph attention networks with multi-hop neighbor nodes while incorporating the temporal attention mechanism of long short-term memory (LSTM) to effectively address the potential interdependencies in the data structure. In addition, by assigning different learning weights to different neighbor nodes, the model can better integrate the correlation between node features. To verify the accuracy of the proposed model, this study utilized the closing prices of the Hong Kong Hang Seng Index (HSI) from 31 December 1986 to 31 December 2023 for analysis. By comparing it with nine other forecasting models, the experimental results show that the STBL model achieves more accurate predictions of the closing prices for short-term, medium-term, and long-term forecasts of the stock index. Full article

In the current context of urbanization, urban agglomerations face complex challenges in maintaining an ecological balance. This study uses remote sensing images of the Central Yunnan urban agglomeration from 2000 to 2020, along with socioeconomic data, to analyze the spatiotemporal characteristics of the green space evolution. Utilizing dynamic geographically weighted regression analysis based on principal components (PCA-GWR), we identify the key socioeconomic factors influencing these changes and quantitatively analyze the driving forces in each stage. Our findings reveal a continuing trend of decreasing total green space alongside increasing individual forest types and pronounced regional disparities in green space dynamics. The results indicate that socioeconomic factors exert both positive facilitative effects and negative pressures, with evident spatial and temporal variability. Urbanization and economic development promote forest expansion in certain areas, while contributing to the reduction in farmland and shrub–grass lands. Significant variations are influenced by factors such as the urbanization rate, the agricultural population, the industrial composition, and fiscal revenue. This study enhances the in-depth understanding of the relationship between the spatiotemporal dynamics of green spaces and socially driven mechanisms, offering significant insights for sustainable urban planning and landscape management and harmonizing urban agglomeration development. Full article

In the ecological context of global climate change, ensuring the stable carbon sequestration capacity of forest ecosystems, which is among the most important components of terrestrial ecosystems, is crucial. Forest fires are disasters that often burn vegetation and damage forest ecosystems. Accurate recognition of firegrounds is essential to analyze global carbon emissions and carbon flux, as well as to discover the contribution of climate change to the succession of forest ecosystems. The common recognition of firegrounds relies on remote sensing data, such as optical data, which have difficulty describing the characteristics of vertical structural damage to post-fire vegetation, whereas airborne LiDAR is incapable of large-scale observations and has high costs. The new generation of satellite-based photon counting radar ICESat-2/ATLAS (Advanced Topographic Laser Altimeter System, ATLAS) data has the advantages of large-scale observations and low cost. The ATLAS data were used in this study to extract three significant parameters, namely general, canopy, and topographical parameters, to construct a recognition index system for firegrounds based on vertical structure parameters, such as the essential canopy, based on machine learning of the random forest (RF) and extreme gradient boosting (XGBoost) classifiers. Furthermore, the spatio-temporal parameters are more accurate, and widespread use scalability was explored. The results show that the canopy type contributed 79% and 69% of the RF and XGBoost classifiers, respectively, which indicates the feasibility of using ICESat-2/ATLAS vertical structure parameters to identify firegrounds. The overall accuracy of the XGBoost classifier was slightly greater than that of the RF classifier according to 10-fold cross-validation, and all the evaluation metrics were greater than 0.8 after the independent sample test under different spatial and temporal conditions, implying the potential of ICESat-2/ATLAS for accurate fireground recognition. This study demonstrates the feasibility of ATLAS vertical structure parameters in identifying firegrounds and provides a novel and effective way to recognize firegrounds based on different spatial–temporal vertical structure information. This research reveals the feasibility of accurately identifying fireground based on parameters of ATLAS vertical structure by systematic analysis and comparison. It is also of practical significance for economical and effective precise recognition of large-scale firegrounds and contributes guidance for forest ecological restoration. Full article

By utilizing top-of-atmosphere radiation (TOAR) data from China’s new generation of geostationary satellites (FY-4A and FY-4B) along with interpretable machine learning models, near-surface particulate matter concentrations in China were estimated, achieving hourly temporal resolution, 4 km spatial resolution, and 100% spatial coverage. First, the cloudless TOAR data were matched and modeled with the solar radiation products from the ERA5 dataset to construct and estimate a fully covered TOAR dataset under assumed clear-sky conditions, which increased coverage from 20–30% to 100%. Subsequently, this dataset was applied to estimate particulate matter. The analysis demonstrated that the fully covered TOAR dataset (R² = 0.83) performed better than the original cloudless dataset (R² = 0.76). Additionally, using feature importance scores and SHAP values, the impact of meteorological factors and air mass trajectories on the increase in PM₁₀ and PM_2.5 during dust events were investigated. The analysis of haze events indicated that the main meteorological factors driving changes in particulate matter included air pressure, temperature, and boundary layer height. The particulate matter concentration products obtained using fully covered TOAR data exhibit high coverage and high spatiotemporal resolution. Combined with data-driven interpretable machine learning, they can effectively reveal the influencing factors of particulate matter in China. Full article

Garlic constitutes a significant small-scale agricultural commodity in China. A key factor influencing garlic prices is the planted area, which can be accurately and efficiently determined using remote sensing technology. However, the spectral characteristics of garlic and winter wheat are easily confused, and the widespread intercropping of these crops in the study area exacerbates this issue, leading to significant challenges in remote sensing image analysis. Additionally, remote sensing data are often affected by weather conditions, spatial resolution, and revisit frequency, which can result in delayed and inaccurate area extraction. In this study, historical data were utilized to restore Sentinel-2 remote sensing images, aimed at mitigating cloud and rain interference. Feature combinations were devised, incorporating two vegetation indices into a comprehensive time series, along with Sentinel-1 synthetic aperture radar (SAR) time series and other temporal datasets. Multiple classification combinations were employed to extract garlic within the study area, and the accuracy of the classification results was systematically analyzed. First, we used passive satellite imagery to extract winter crops (garlic, winter wheat, and others) with high accuracy. Second, we identified garlic by applying various combinations of time series features derived from both active and passive remote sensing data. Third, we evaluated the classification outcomes of various feature combinations to generate an optimal garlic cultivation distribution map for each region. Fourth, we developed a garlic fragmentation index to assess the impact of landscape fragmentation on garlic extraction accuracy. The findings reveal that: (1) Better results in garlic extraction can be achieved using active–passive time series remote sensing. The performance of the classification model can be further enhanced by incorporating short-wave infrared bands or spliced time series data into the classification features. (2) Examination of garlic cultivation fragmentation using the garlic fragmentation index aids in elucidating variations in accuracy across the study area’s six counties. (3) Comparative analysis with validation samples demonstrated superior garlic extraction outcomes from the six primary garlic-producing counties of the North China Plain in 2021, achieving an overall precision exceeding 90%. This study offers a practical exploration of target crop identification using multi-source remote sensing data in mixed cropping areas. The methodology presented here demonstrates the potential for efficient, cost-effective, and accurate garlic classification, which is crucial for improving garlic production management and optimizing agricultural practices. Moreover, this approach holds promise for broader applications, such as nationwide garlic mapping. Full article

The wake behind an aircraft carrier under heavy wind condition is a key concern in ship design. The Chinese Liaoning ship’s upturned bow and the island on the deck could cause serious flow separation in the landing and take-off area. The flow separation induces strong velocity gradients and intense pulsations in the flow field. In addition, the sway of the aircraft carrier caused by waves could also intensify the flow separation. The complex flow field poses a significant risk to the shipboard aircraft take-off and landing operation. Therefore, accurately predicting the wake of an aircraft carrier during wave action motion is of great interest for design optimization and recovery aircraft control. In this research, the aerodynamic around an aircraft carrier (i.e., Liaoning) was analyzed using the computational fluid dynamics technique. The validity of two turbulence models was verified through comparison with the existing data from the literature. The upturned bow take-off deck and the right-hand island were the main areas where flow separation occurred. Delayed detached eddy simulation (DDES), which combines the advantages of LES and RANS, was adopted to capture the full-scale spatial and temporal flow information. The DDES was also coupled with the overset grid to calculate the flow field characteristics under the effect of hull sway. The downwash area at 15° starboard wind became shorter when the hull was stationary, while the upwash area and turbulence intensity increased. The respective characteristics of the wake flow field in the stationary and swaying state of the ship were investigated, and the flow separation showed a clear periodic when the ship was swaying. Comprehensive analysis of the time-dependent flow characteristic of the approach line for fixed-wing naval aircraft is also presented. Full article

The electrification of mobility has introduced considerable challenges to distribution networks due to varying demand patterns in both time and location. This underscores the need for adaptable tools to support strategic investments, grid reinforcement, and infrastructure deployment. In this context, the present study employs real-world datasets to propose a comprehensive spatial–temporal energy model that integrates a traffic model and geo-referenced data to realistically evaluate the flexibility potential embedded in the light-duty transportation sector for a given study region. The methodology involves assessing traffic patterns, evaluating the grid impact of EV charging processes, and extending the analysis to flexibility services, particularly in providing primary and tertiary reserves. The analysis is geographically confined to the Lombardy region in Italy, relying on a national survey of 8.2 million trips on a typical day. Given a target EV penetration equal to 2.5%, corresponding to approximately 200,000 EVs in the region, flexibility bands for both services are calculated and economically evaluated. Within the modeled framework, power-intensive services demonstrated significant economic value, constituting over 80% of the entire potential revenues. Considering European markets, the average marginal benefit for each EV owner is in the order of 10 € per year, but revenues could be higher for sub-classes of users better fitting the network needs. Full article