Search Results (2,836)

The rapid urbanization observed in major Chinese cities has resulted in the degradation of both urban and rural environments. In response to this challenge, the concept of regional green spaces has emerged as an innovative approach to coordinate and manage green space resources across urban and rural areas. This study focuses on conducting a comprehensive analysis of the evolution, driving factors, and future scenarios of regional green spaces in Changzhou, which serves as a representative Type I large city in China. To accomplish this analysis, Landsat satellite images from 1992, 2002, 2012, and 2022 were utilized. Various methodologies, including landscape pattern indices for quantitative evaluation, the CLUE-S model, logistic regression for qualitative evaluation, and the Markov–FLUS model, were employed. The findings indicate a continuous decline in the area of regional green spaces in Changzhou, decreasing from 248.23 km² in 1992 to 204.46 km² in 2022. Landscape pattern analysis reveals an increase in fragmentation, complexity, irregularity, and human interference within these green spaces. Logistic regression analysis identifies key driving factors influencing regional green spaces, including elevation, urban population, and proximity to water bodies and transportation. The scenario simulations provide valuable insights into potential future trends of regional green spaces. According to the economic priority scenario, a modest increase in regional green spaces is anticipated, while the ecological priority scenario indicates substantial growth. Conversely, the inertial development scenario predicts a continued decline in regional green spaces. This research emphasizes the significance of achieving a harmonious coexistence between economic progress and environmental preservation. It emphasizes the necessity of optimizing the arrangement of green areas within a region while fostering public engagement in the conservation of these spaces. The findings contribute to the protection and sustainable development of the urban environment in the Yangtze River Delta region. Full article

As global climate change intensifies and human activities escalate, changes in vegetation cover, an important ecological indicator, hold significant implications for ecosystem protection and management. Shandong Province, a critical agricultural and economic zone in China, experiences vegetation changes that crucially affect regional climate regulation and biodiversity conservation. This study employed normalized difference vegetation index (NDVI) data, combined with climatic, topographic, and anthropogenic activity data, utilizing trend analysis methods, partial correlation analysis, and Geodetector to comprehensively analyze the spatiotemporal variations and primary driving factors of vegetation cover in Shandong Province from 2001 to 2020.The findings indicate an overall upward trend in vegetation cover, particularly in areas with concentrated human activities. Climatic factors, such as precipitation and temperature, exhibit a positive correlation with vegetation growth, while land use changes emerge as one of the key drivers influencing vegetation dynamics. Additionally, topography also impacts the spatial distribution of vegetation to a certain extent. This research provides a scientific basis for ecological protection and land management in Shandong Province and similar regions, supporting the formulation of effective vegetation restoration and ecological conservation strategies. Full article

The high-accuracy detection of multiple distinct human emotions is crucial for advancing affective computing, mental health diagnostics, and human–computer interaction. The integration of deep learning networks with entropy measures holds significant potential in neuroscience and medicine, especially for analyzing EEG-based emotion states. This study proposes a method combining ResNet18 with differential entropy to identify five types of human emotions (happiness, sadness, fear, disgust, and neutral) from EEG signals. Our approach first calculates the differential entropy of EEG signals to capture the complexity and variability of the emotional states. Then, the ResNet18 network is employed to learn feature representations from the differential entropy measures, which effectively captures the intricate spatiotemporal dynamics inherent in emotional EEG patterns using residual connections. To validate the efficacy of our method, we conducted experiments on the SEED-V dataset, achieving an average accuracy of 95.61%. Our findings demonstrate that the combination of ResNet18 with differential entropy is highly effective in classifying multiple distinct human emotions from EEG signals. This method shows robust generalization and broad applicability, indicating its potential for extension to various pattern recognition tasks across different domains. Full article

The Gannan Water Conservation area is an indispensable part of the ecological barrier on the Tibetan Plateau and is a key ecological area for the water supply. Exploring the coupled coordination relationship between vegetation and soil contributes to the conservation and planning of the natural environment. In this study, soil water conservation function (SWCF) was investigated with Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Gannan Water Conservation Area at the source of the Yellow River, and the spatiotemporal coupling relationship between the SWCF and vegetation was explored. Meanwhile, their responses to topographic and climatic factors were investigated with structural equation models. The main results indicated that the coupling coordination degree (D_VS) in the soil depth was in a barely coordinated state, with 0–10 cm > 20–30 cm > 10–20 cm, showing that the area proportion of ‘Basic balanced–Synchronous development of VEG and SWCF’ was the highest, and the spatial aggregation feature was obvious. As the gradient of topographic factors varied, the coupling coordination also varied at various soil depths. Meanwhile, the absolute values of the correlation coefficients of the temperature and precipitation with the coupling coordination were the highest at 20–30 cm compared to the other soil depths, demonstrating that the effect was more significant in deeper soils than in shallower ones. Furthermore, the path coefficients of the topographic factors were larger than those of the climatic factors in the 10–20 cm and 20–30 cm layers, while the opposite was true in the 0–10 cm layer. In general, the vegetation conditions and water conservation function of soil in the source area of the Yellow River are basically developing synchronously, and the topographic factor is the key factor for the geographical difference in the coupling relationship between the two factors. Full article

Research on wildfires and social vulnerability has gained significant importance due to the increasing frequency and severity of wildfires around the world. This study investigates the dynamic changes in social vulnerability to wildfires over a decade in Idaho, USA, utilizing GIS-based tools and a quasi-experimental design. We assess the evolving nature of social vulnerability at a local scale, emphasizing both spatial and temporal dynamics. Initially, we identified social vulnerability trends in relation to varying levels of wildfire risk. The research then employs propensity score matching to contrast areas affected by wildfires in 2012 with similar non-affected regions, thereby quantifying the short-term shifts in social vulnerability post-wildfires. The results indicate that regions with a high wildfire risk may display elevated vulnerability, characterized by an increase in unemployment rates and a reduction in high-income households. These findings tentatively demonstrate the compounded effect of wildfires on already vulnerable populations, highlighting the critical need for targeted interventions. Ultimately, this study underscores the importance of integrating dynamic social vulnerability assessments into wildfire management and planning, aiming to enhance community resilience and equitable resource distribution in the face of escalating wildfire threats. Full article

The traditional settlements are of paramount significance as indispensable elements of China’s cultural heritage, simultaneously functioning as prime assets for the enhancement of rural economic and social dynamics. Nestled within the comprehensive framework of China’s rural revitalization endeavor and Shandong Province’s proactive initiatives toward the amalgamation of cultural and tourism sectors, a meticulous exploration of the spatiotemporal evolution and connectivity of traditional villages in Shandong Province is indispensable for their preservation and forward-thinking evolution. For this study, 557 traditional villages across Shandong Province are identified as pivotal points, with the application of geographic information system (GIS) techniques to scrutinize their spatiotemporal transformation patterns and spatial characteristics. Additionally, a suite of analytical instruments, encompassing metrics for accessibility assessment, ordinary least squares (OLS) linear regression, and geographically weighted regression (GWR) models, are deployed to evaluate the accessibility levels and influential factors shaping traditional villages within the region. The analytical outcomes reveal the following: (1) Chronologically, approximately 80% of the traditional villages in the province of Shandong were established during the Ming and Qing epochs, and they demonstrate a migratory pattern that is spatially and temporally oriented from “southwest to northeast”; geographically, these traditional villages are characterized by pronounced clustering, predominantly situated at the confluence of Jinan and Zibo Cities, the Shantou District of Zaozhuang City, Zhaoyuan City of Yantai City, and Rongcheng City of Weihai City, forming a coherent “four-core” spatial distribution configuration. (2) Considering the criteria for village location, traditional villages in Shandong are predominantly found in areas with a predominantly flat landscape and a certain proximity to water bodies. (3) On the whole, the accessibility of traditional villages in Shandong is relatively high, with the average accessibility assessed at 199.92 min, a range spanning from 175 min, and approximately 57.99% of the villages falling within the 100 to 200 min accessibility bracket, indicating a systematic decline in accessibility from the central areas to the periphery. (4) The pivotal factors influencing the accessibility of traditional villages in Shandong are primarily altitude, slope, and road network density, with altitude and slope showing a negative correlation with accessibility, whereas road network density exhibits a positive correlation, and the proximity to water bodies has a relatively minor impact on accessibility. Full article

In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a⁻¹ over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones. Full article

Investigation of cities’ spatiotemporal dynamics, including vegetation and urban areas, is of utmost importance for understanding ecological balance, urban planning, and sustainable development. This study investigated the dynamic interactions between vegetation, settlement patterns, and surface urban heat islands (SUHIs) in Istanbul using spatiotemporal hotspot analysis. Utilizing Landsat satellite imagery, we applied the Getis-Ord Gi* statistic to analyze Land Surface Temperature (LST), Urban Index (UI), and Normalized Difference Vegetation Index (NDVI) across the city. Using satellite images and the Getis-Ord Gi* statistic, this research investigated how vegetation and urbanization impact SUHIs. Based on the main results, mean NDVI, UI, and LST values for 2009 and 2017 were analyzed, revealing significant vegetation loss in 37 of Istanbul’s 39 districts, with substantial urbanization, especially in the north, due to new infrastructure development. On the other hand, hotspot analysis was conducted on normalized NDVI, UI, and LST images by analyzing 977 neighborhoods. Results showed a significant transformation of green areas to non-significant classes in NDVI, high urbanization in UI, and the formation of new hot areas in LST. SUHIs were found to cluster in areas with increasing residential and industrial activities, highlighting the role of urban development on SUHI formation. This research can be applied to any region since it offers crucial perspectives for decision-makers and urban planners aiming to mitigate SUHI effects through targeted greening strategies and sustainable urban development. By integrating environmental metrics into urban planning, this study underscores the need for comprehensive and sustainable approaches to enhance urban resilience, reduce environmental impact, and improve livability in Istanbul. Full article

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes; the interplay between fire weather index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks; and geographical variations in the relationship between the FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. The findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r > 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ −0.7, p ≤ 0.05). Full article

Elucidating the response mechanism of vegetation change trends is of great value for environmental resource management, especially in coal mining areas where climate fluctuations and human activities are intense. Taking the Shendong mining area as an example, based on the Google Earth Engine cloud platform, this study used the kernel Normalized Vegetation Index (kNDVI) to study the spatiotemporal change characteristics of vegetation cover during 1994–2022. Then, it carried out an attribution analysis through the partial derivative analysis method to explore the driving mechanism behind vegetation greening. The results showed that (1) the growth rate of vegetation cover change from 1994 to 2022 was 0.0052/a. The area with an upward trend of kNDVI accounted for 94.11% of the total area of the study area. The greening effect was obvious, and the kNDVI change would continue to rise. (2) Under the scenario of regional climate warming and humidifying, kNDVI responds slightly differently to different climatic factors, and kNDVI is positively correlated with temperature and precipitation in 85.20% of the mining area. The average contribution of precipitation, temperature, and human activities to kNDVI change in the Shendong mining area were 0.00094/a, 0.00066/a, and 0.0036/a, respectively. The relative contribution rates of human activities and climate change were 69.23% and 30.77%, respectively. Thus, human activities are the main driving factor for the changing of vegetation cover in this mining area, and climate change is the secondary driving factor. (3) The dynamic change in land use presents an increase in forest area under the ecological restoration project. The results of this study can provide a scientific basis for the future ecological construction of the Shendong mining area and help in the realization of regional green sustainable development goals. Full article

As an important ecosystem, the wild fruit forest in the Tianshan Mountains is one of the origins of many fruit trees in the world. The wild fruit forest in Emin County, Xinjiang, China, was taken as the research area, the spatial and temporal distribution of the wild fruit forest was inverted using random forest and PLUS models, and the 2027 distribution pattern of the wild fruit forest was simulated and predicted. From 2007 to 2013, damage to the wild fruit forest from tourism and overgrazing was very serious, and the area occupied by the wild fruit forest decreased rapidly from 9.59 km² to 7.66 km². From 2013 to 2020, suitable temperatures and reasonable tourism management provided strong conditions for the rejuvenation of wild fruit forests. The distance of the center of gravity of the wild fruit forest increased, and the density of distribution of the wild fruit forest in the northwest direction of the study area also increased. It is predicted that the wild fruit forest in the study area will show a steady and slowly increasing trend in places far away from tourist areas and with more complex terrain. It is suggested that non-permanent fences be set up as buffer zones between wild fruit forests, ensuring basic maintenance of wild fruit forests, limiting human disturbance such as overgrazing, and reducing the risk of soil erosion. Full article

In the past, during development processes, major ecological and environmental problems have occurred in the agro-pastoral ecotone of China, which have had a strong impact on regional sustainable development. As such, analyzing the evolution of the regional ecosystem service value (ESV) and predicting the futural spatio-temporal evolution under different development scenarios will provide a scientific basis for further sustainable development. This research analyzed the regional land use and land cover change (LUCC) from 2000 to 2020, adopted the Mark-PLUS model to construct different scenarios (prioritizing grassland development, PDG; prioritizing cropland development, PCD; business as usual, BAU), and simulated the future LUCC. The driving factors influencing each land use type were revealed using the PLUS model. Based on the LUCC data, the spatio-temporal distribution of the regional ESV was calculated via the ESV equivalent factor method, including four primary services (supply service, adjustment service, support service, and cultural service) and eleven secondary services (water resource supply, maintaining nutrient circulation, raw material production, aesthetic landscape, food production, environmental purification, soil conservation, maintaining biodiversity, gas regulation, climate regulation, and hydrologic regulation). The results showed that the total ESV increased first and then declined from 2000 to 2020, reaching the highest value of CNY 8207.99 million in 2005. In the different future scenarios, the ESV shows a trend of PGD (CNY 8338.79 million) > BAU (CNY 8194.82 million) > PCD (CNY 8131.10 million). The global Moran index also follows this distribution. Additionally, precipitation (18%), NDVI (16%), and DEM (16%) are the most important factors in the regional LUCC. The spatial agglomeration characteristics of ESV were revealed using the global Moran’s index and local indicators of spatial auto-correlation, which show a high coordination degree between the high–high cluster areas and water areas. These results point out the key points in the next step of ecological restoration projects and help with achieving the sustainable development goals more effectively. Full article

Baiyangdian Lake, the largest freshwater shallow lake on the North China Plain, plays a pivotal role in maintaining the regional ecological balance and biodiversity. Meiofauna are integral components of Baiyangdian Lake; however, their community characteristics and relationship with environmental factors have not yet been studied. The aim of the following study was to evaluate the density, spatiotemporal patterns, and habitat response dynamics of meiofauna in Baiyangdian Lake. A field investigation was conducted at 33 sites spanning various habitats, including aquatic plant-dominant, trench, and pelagic areas, across the spring, summer, and autumn seasons of 2021. The results revealed that the meiofauna in Baiyangdian Lake primarily comprise freshwater nematodes (91.78%), ostracods, and copepods, with a mean abundance of 69.40 ± 35.20 ind. 10 cm⁻², peaking in the spring, followed by summer and autumn. The mean biomass was 164.95 ± 99.39 dwt. 10 cm⁻², with that of ostracods being the most substantial and that of copepods being the least, with both of them exhibiting seasonal fluctuations. Notably, in the summer, the abundance of meiofauna was positively correlated with the water depth and negatively correlated with ammonia nitrogen levels (R² = 0.13 and R² = 0.24, respectively; p < 0.05 and p < 0.01; n = 33). The results of our study indicate that the distribution and abundance of meiofauna are significantly affected by environmental factors, with the water depth and ammonia nitrogen levels being potential key determinants. The results of the present study are conducive to evaluating the health status of the Baiyangdian ecosystem, protecting biodiversity, and studying the impacts of anthropogenic activities and environmental changes on the lake, and can also provide scientific support for its ecological restoration and governance as well as the assessment of ecological service functions. Full article

With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural prediction network based on perceptual multi-scale spatiotemporal dynamic changes (PMSTD-Net). By designing Multi-Scale Space Motion Change Attention Unit (MCAU) to perceive the local situation and spatial displacement dynamic features of prediction targets at different scales, attention is ensured on capturing the dynamic information in their spatial dimensions adequately. On this basis, this paper proposes Multi-Scale Spatiotemporal Evolution Attention (MSEA) unit, which further integrates the spatial change features perceived by MCAU units in higher channel dimensions, and learns the spatiotemporal evolution characteristics at different scales, effectively predicting the dynamic characteristics and regularities of targets in sensor information.Through experiments on spatiotemporal prediction standard datasets such as Moving MNIST, video prediction dataset KTH, and Human3.6m, PMSTD-Net demonstrates prediction performance surpassing previous methods. We construct the GPM satellite remote sensing precipitation dataset, demonstrating the network’s advantages in perceiving multi-scale spatiotemporal dynamic changes in remote sensing meteorological data. Finally, through extensive ablation experiments, the performance of each module in PMSTD-Net is thoroughly validated. Full article

Terrestrial gross primary productivity (GPP) is a critical part of land carbon fluxes. Accurately quantifying GPP in terrestrial ecosystems and understanding its spatiotemporal dynamics are essential for assessing the capability of vegetation to absorb carbon from the atmosphere. Nevertheless, traditional remote sensing estimation models often require complex parameters and data inputs, and they do not account for spatial effects resulting from the distribution of monitoring sites. This can lead to biased parameter estimation and unstable results. To address these challenges, we have raised a spatial autocorrelation light gradient boosting machine model (SA-LGBM) to enhance GPP estimation. SA-LGBM combines reflectance information from remote sensing observations with eigenvector spatial filtering (ESF) methods to create a set of variables that capture continuous spatiotemporal variations in plant functional types and GPP. SA-LGBM demonstrates promising results when compared to existing GPP products. With the inclusion of eigenvectors, we observed an 8.5% increase in R² and a 20.8% decrease in RMSE. Furthermore, the residuals of the model became more random, reducing the inherent spatial effects within them. In summary, SA-LGBM represents the first attempt to quantify the impact of spatial autocorrelation and addresses the limitations of underestimation present in existing GPP products. Moreover, SA-LGBM exhibits favorable applicability across various vegetation types. Full article