Search Results (3,693)

The Tibetan Plateau, known as the “Third Pole” and the “Water Tower of Asia”, has experienced significant changes in its surface water due to global warming. Accurately understanding and monitoring the spatiotemporal distribution of surface water is crucial for ecological conservation and the sustainable use of water resources. Among existing satellite data, the MODIS sensor stands out for its long time series and high temporal resolution, which make it advantageous for large-scale water body monitoring. However, its spatial resolution limitations hinder detailed monitoring. To address this, the present study proposes a dynamic endmember selection method based on phenological features, combined with mixed pixel decomposition techniques, to generate monthly water abundance maps of the Tibetan Plateau from 2000 to 2023. These maps precisely depict the interannual and seasonal variations in surface water, with an average accuracy of 95.3%. Compared to existing data products, the water abundance maps developed in this study provide better detail of surface water, while also benefiting from higher temporal resolution, enabling effective capture of dynamic water information. The dynamic monitoring of surface water on the Tibetan Plateau shows a year-on-year increase in water area, with an increasing fluctuation range. The surface water abundance products presented in this study not only provide more detailed information for the fine characterization of surface water but also offer a new technical approach and scientific basis for timely and accurate monitoring of surface water changes on the Tibetan Plateau. Full article

The increase in yearly particulate matter concentrations has been a constant issue since 2017 in the Aburrá Valley, located in Antioquia, Colombia. Although local certified air quality monitors provide high accuracy, they are limited in spatial coverage, limiting chemical transport and pollution dynamic studies in this mountainous environment. In this work, a local, Low-Cost Sensor network is proposed as an alternative and has been installed around the valley in representative locations and heights. To calibrate PM_2.5 and O₃ sensors used by the network, temporal delays were analyzed with Dynamic Time Warping and the linear scale was corrected with a Single Linear Regression model. As a result, the correlation coefficient R² of the sensor reached values of 0.8 and 0.9 after calibration. For all network stations, rescaled data agrees with official historical reports on the behavior of pollutant concentrations and meteorological variables. The ability to compare the network results with certified data confirms the success of the calibration/validation method employed and contributes to the growing field of low-cost air quality sensors in Latin America. Full article

Recent advances in Arctic observational capabilities have revealed that the Arctic Ocean is highly turbulent in all seasons and have improved temporal and spatial sampling of sea level retrievals from remote sensing, even above 80°N. Such data are expected to be increasingly valuable in the future when the extent of sea ice in the Arctic Ocean is reduced. Assimilating this new data into ocean models, together with in situ observations, provides an enriched representation of the mesoscale population that induces new eddy-driven contributions to local dynamics and thermodynamics. To quantify the content of the new information, we compare three-year-long assimilative experiments at ¼° resolution incorporating in situ-only data, in situ and standard altimetry, and in situ and high-latitude-enhanced altimetry, respectively. The enhanced altimetry data lead to an increase in three-dimensional eddy kinetic energy, generated by coherent vortexes, of up to 20% in several areas. Robust ocean warming is generated in the Arctic sector down to 800 m. Via heat budget analysis, this warming can be ascribed to a local enhancement of vertical mixing, as well as an increase in meridional heat transport. The assimilation of enhanced altimetry amplifies the transport, compared to standard altimetry, especially north of 70°N. Full article

Quantifying the dynamic changes and relationships between ecosystem services (ESs) and land use change is critical for sustainable ecosystem management and land use optimization. However, comprehensive discussions on the spatiotemporal variations of ESs and their relationships with land use intensity (LUI) are lacking, particularly in the context of significant climate warming. Systematic analyses of the forestry management unit scale are limited, leading to considerable uncertainty in sustainable ecosystem management, especially in alpine ecosystems of the Northern Hemisphere, where ESs have significantly degraded. The study focuses on the Daxing’anling forest area, Inner Mongolia (DFIAM), a representative sensitive alpine ecosystem and crucial ecological security barrier in Northern China. Utilizing the InVEST model, we analyzed the spatiotemporal variations in land use and four essential ESs, water yield (WY), carbon storage (CS), soil conservation (SC), and habitat quality (HQ), from 2013 to 2018. We also assessed the dynamic relationships between LUI and these ESs using a four-quadrant model. Our findings indicate the following: (1) Land use types in DFIAM remained relatively stable between 2013 and 2018, with forest being the dominant type (approximately 93%). During this period, areas of forest, cropland, impervious surfaces, and bare land increased, while areas of grassland, water, and wetland decreased. Although the overall change of LUI was gentle, a spatial pattern of “high in the southeast and low in the northwest” emerged, with low LUI areas showing slight expansion. (2) WY, SC, and HQ decreased, while CS increased from 2013 to 2018. The spatial distributions of these ESs showed higher values in the center and lower values at the edges, with forests demonstrating a strong capacity to provide multiple ESs. (3) The relationship between LUI and the four ESs from 2013 to 2018 was predominantly negative, primarily situated in Quadrant II, indicating that increased LUI inhibited ES supply capacity. Within Quadrant II, the distribution range of LUI, WY, and HQ decreased, while CS remained stable and SC increased. Furthermore, Quadrant III (positive correlation) accounted for a large proportion (19.23%~42.31%), highlighting the important role of non-anthropogenic factors in ES changes. Overall, most ESs in the DFAIM showed a decline while LUI remained relatively stable, with predominantly negative correlations between LUI and ESs. The increased LUI driven by human activities, and other non-human factors, may have contributed significantly to ES degradation. To improve ESs, we proposed implementing differentiated land use planning and management, systematic ecological protection and restoration strategies, a multi-level ecological early-warning monitoring and evaluation network, ecological corridors and buffer zones, and a collaborative management system with multiple participation. These results provide scientific guidance for the sustainable management of alpine ecosystems, enhancement of ESs, and formulation of land resource protection policies. Full article

Tidal creeks are vital geomorphological features of tidal flats, and their spatial and temporal variations contribute significantly to the preservation of ecological diversity and the spatial evolution of coastal wetlands. Traditional methods, such as manual annotation and machine learning, remain common for tidal creek extraction, but they are slow and inefficient. With increasing data volumes, accurately analyzing tidal creeks over large spatial and temporal scales has become a significant challenge. This study proposes a residual U-Net model that utilizes full-dimensional dynamic convolution to segment tidal creeks in the Yellow River Delta, employing Gaofen-2 satellite images with a resolution of 4 m. The model replaces the traditional convolutions in the residual blocks of the encoder with Omni-dimensional Dynamic Convolution (ODConv), mitigating the loss of fine details and improving segmentation for small targets. Adding coordinate attention (CA) to the Atrous Spatial Pyramid Pooling (ASPP) module improves target classification and localization in remote sensing images. Including dice coefficients in the focal loss function improves the model’s gradient and tackles class imbalance within the dataset. Furthermore, the inclusion of dice coefficients in the focal loss function improves the gradient of the model and tackles the dataset’s class inequality. The study results indicate that the model attains an F1 score and kappa coefficient exceeding 80% for both mud and salt marsh regions. Comparisons with several semantic segmentation models on the mud marsh tidal creek dataset show that ODU-Net significantly enhances tidal creek segmentation, resolves class imbalance issues, and delivers superior extraction accuracy and stability. Full article

Tool wear monitoring is crucial for optimizing cutting performance, reducing costs, and improving production efficiency. Existing tool wear prediction models usually design integrated models based on a convolutional neural network (CNN) and recurrent neural network (RNN) to extract spatial and temporal features separately. However, the topological structures between multi-sensor networks are ignored, and the ability to extract spatial features is limited. To overcome these limitations, a novel spatial–temporal adaptive graph convolutional network (STAGCN) is proposed to capture spatial–temporal dependencies with multi-sensor signals. First, a simple linear model is used to capture temporal patterns in individual time-series data. Second, a spatial–temporal layer composed of a bidirectional Mamba and an adaptive graph convolution is established to extract degradation features and reflect the dynamic degradation trend using an adaptive graph. Third, multi-scale triple linear attention (MTLA) is used to fuse the extracted multi-scale features across spatial, temporal, and channel dimensions, which can assign different weights adaptively to retain important information and weaken the influence of redundant features. Finally, the fused features are fed into a linear regression layer to estimate the tool wear. Experimental results conducted on the PHM2010 dataset demonstrate the effectiveness of the proposed STAGCN model, achieving a mean absolute error (MAE) of 3.40 μm and a root mean square error (RMSE) of 4.32 μm in the average results across three datasets. Full article

This work concerns a study of the thermomechanical behaviour of a commercial thruster for aerospace use. The thruster, operated using a bipropellant liquid mixture, is used for the motion and in-orbit altitude control of small telecommunications satellites. The mixture used in the combustion process is composed of propylene and nitrous oxide, while the wall of the thruster is made of PH15-5 stainless steel. A computational fluid dynamics analysis of conjugate heat transfer determines the spatial–temporal distribution of temperature within the thruster wall. This information is passed to a finite element mechanical model that simulates the stress and the equivalent plastic strain distribution within the thruster wall. Full article

The carbon stock in terrestrial ecosystems is closely linked to changes in land use. Understanding how land use alterations affect regional carbon stocks is essential for maintaining the carbon balance of ecosystems. This research leverages land use and driving factor data spanning from 2000 to 2020, utilizing the Patch-generating Land Use Simulation (PLUS) model alongside the InVEST ecosystem services model to examine the temporal and spatial changes in carbon storage across Beijing. Additionally, four future scenes for 2030—urban development, natural development, cropland protection, as well as eco-protection—are explored, with the PLUS and InVEST models employed to emulate dynamic land use changes and the corresponding carbon stock variations. The results show that the following: (1) Between 2000 and 2020, changes in land use resulted in a significant decline in carbon storage, with a total reduction of 1.04 × 10⁷ tons. (2) From 2000 to 2020, agricultural, forest, and grassland areas in Beijing all declined to varying extents, while built-up land expanded by 1292.04 km² (7.88%), with minimal changes observed in water bodies or barren lands. (3) Compared to the carbon storage distribution in 2020, carbon storage in the 2030 urban development scenario decreased by 6.99 × 10⁶ tons, highlighting the impact of rapid urbanization and the expansion of built-up areas on the decline in carbon storage. (4) In the ecological protection scenario, the optimization of land use structure resulted in an increase of 6.01 × 10⁵ tons in carbon storage, indicating that the land use allocation in this scenario contributes to the restoration of carbon storage and enhances the carbon sink capacity of the urban ecosystem. This study provides valuable insights for policymakers in optimizing ecosystem carbon storage from a land use perspective and offers essential guidance for the achievement of the “dual carbon” strategic objectives. Full article

To investigate the influence of factors such as tourism, agriculture, and population density on the presence of microplastic (MP) content in aquatic environments and their associated ecological risks, Jingpo Lake, a remote high-mountain lake situated away from urban areas, was selected as the research subject. This study examined the abundance, types, sizes, colors, and polymer compositions of MPs within the water body, fish, and sediments. By considering variables, including fishing practices, agricultural activities, population dynamics, and vegetation cover, an analysis was conducted to unravel the spatial and temporal distribution of MPs concerning human activities, ultimately leading to an assessment of the ecological risks posed by MP pollution. The findings revealed that the average abundance of MPs in the lake’s surface water was recorded as (304.8 ± 170.5) n/m³, while in the sediments, it averaged (162.0 ± 57.45) n/kg. Inside the digestive tracts of fish, the MP abundance was measured at 11.4 ± 5.4 n/ind. The contamination of MPs within the aquatic environment of Jingpo Lake was found to be relatively minimal. Variations in MP loads across time and space were observed, with MPs predominantly falling within the size range of small planktonic organisms (50–1000 μm). Additionally, the prevalent colors of MPs in the water samples were white or transparent, constituting approximately 55.65% of the entire MP composition. Subsequently, they were black, red, and blue. This colors distribution were consistent across MPs extracted from fish and sediment samples. The chemical compositions of the MPs predominantly comprised PE (31.83%) and PS (25.48%), followed by PP (17.56%), PA (11.84%), PET (6.71%), EVA (4.56%), and PC (2.03%). Regarding the seasonal aspect, MP concentrations were highest during summer (46.68%), followed by spring (36.75%) and autumn (16.56%). The spatial distribution of MPs within Jingpo Lake’s water body, fish, and sediments was notably influenced by human activities, as confirmed by Pearson correlation coefficients. A strong association was observed between MP levels and water quality indicators such as ammonium nitrogen (NH₄-N), total phosphorus (TP), and chlorophyll-a (Chla), suggesting that human-related pollution contributed significantly to MP contamination. The diversity assessment of MP pollutants exhibited the highest variability in chemical composition (1.23 to 1.79) using the Shannon–Wiener Index. Subsequently, the diversity of colors ranged from 0.59 to 1.54, shape diversity from 0.78 to 1.30, seasonal diversity from 0.83 to 1.10, and size diversity from 0.44 to 1.01. The assessment results of ecological risk highlighted that the risk categories for MPs within the surface water, fish, and sediments of Jingpo Lake were categorized as I for the PHI and PLI and as “Minor” for the PERI. These relatively low-risk values were attributed to the predominantly low toxicity of the distributed MPs within the Jingpo Lake basin. Moreover, the results of the risk assessment were found to be interconnected with the distribution of the local population and agricultural activities around the sampling sections. Usage patterns of coastal land and population density were recognized as influential factors affecting MP loads within the water body, sediments, fish, and other components of the lake ecosystem. Full article

Cardiovascular diseases have become the leading cause of death in developed countries. Among these, some are related to disruptions in the electrical synchronization of cardiac tissue leading to arrhythmias such as atrial flutter, ventricular tachycardia, or ventricular fibrillation. Their origin is diverse and involves several spatial and temporal scales, ranging from nanoscale ion channel dysfunctions to tissue-level fibrosis and ischemia. Mathematical models play a crucial role in elucidating the mechanisms underlying cardiac arrhythmias by simulating the electrical and physiological properties of cardiac tissue across different spatial scales. These models investigate the effects of genetic mutations, pathological conditions, and anti-arrhythmic interventions on heart dynamics. Despite their varying levels of complexity, they have proven to be important in understanding the triggers of arrhythmia, optimizing defibrillation protocols, and exploring the nonlinear dynamics of cardiac electrophysiology. In this work, we present diverse modeling approaches to the electrophysiology of cardiac cells and share examples from our own research where these approaches have significantly contributed to understanding cardiac arrhythmias. Although computational modeling of the electrical properties of cardiac tissue faces challenges in integrating data across multiple spatial and temporal scales, it remains an indispensable tool for advancing knowledge in cardiac biophysics and improving therapeutic strategies. Full article

Change detection, as a popular research direction for dynamic monitoring of land cover change, usually uses hyperspectral remote-sensing images as data sources. Hyperspectral images have rich spatial–spectral information, but traditional change detection methods have limited ability to express the features of hyperspectral images, and it is difficult to identify the complex detailed features, semantic features, and spatial–temporal correlation features in two-phase hyperspectral images. Effectively using the abundant spatial and spectral information in hyperspectral images to complete change detection is a challenging task. This paper proposes a hyperspectral image change detection method based on the balanced metric, which uses the spatiotemporal attention module to translate bi-temporal hyperspectral images to the same eigenspace, uses the deep Siamese network structure to extract deep semantic features and shallow spatial features, and measures sample features according to the Euclidean distance. In the training phase, the model is optimized by minimizing the loss of distance maps and label maps. In the testing phase, the prediction map is generated by simple thresholding of distance maps. Experiments show that on the four datasets, the proposed method can achieve a good change detection effect. Full article

Desertification is one of the most severe environmental issues facing the world today, and effective desertification monitoring is critical for understanding its dynamics and developing prevention and control strategies. Although numerous studies on desertification monitoring using remote sensing have been conducted, there remain differences in indicator selection, and a unified monitoring system has yet to be established. In this study, we constructed the Desertification Composite Index (DCI) using Landsat satellite images, integrating six remote sensing indicators reflecting the natural and ecological characteristics of desertified areas. We also incorporated 383 UAV imagery datasets to accurately identify and analyze the spatial and temporal distributions of desertification in the Ring-Tarim Basin from 1990 to 2020 and subsequently assess its spatiotemporal trends. The results show the following: (1) The constructed DCI was used to identify desertification in 2020, achieving an overall accuracy of 0.86 and a Kappa coefficient of 0.8, indicating that the DCI is suitable for extracting regional desertification information. (2) From 1990 to 2020, the area of desertification decreased significantly, with an average annual reduction rate of −0.0022 ha/a, indicating continuous ecological improvement. Despite localized deterioration, the overall trend was one of “general improvement and local containment.” (3) GeoDetector-based analysis showed that cultivated land area and land use type were the primary single-factor drivers of desertification. The interaction between cultivated land and vegetation type exhibited a synergistic effect as a two-factor driver. (4) Desertification in the Ring-Tarim Basin is primarily influenced by human activities. Appropriate management and intervention measures, efficient and intensive cropland management, and rational land use planning can help develop effective strategies to combat desertification. Full article

New quality productive forces serve as a catalyst for high-quality development and act as a critical driver of Chinese-style modernization. This study evaluated the degree of new quality productive force in China’s five major urban agglomerations between 2013 and 2022 using the entropy approach. Additionally, it utilized kernel density estimation, the Dagum Gini coefficient, and Markov chain analysis to explore the spatial and temporal dynamics of these forces and their evolutionary trends. The findings revealed the following: (1) Overall, the new quality productive forces in China’s five major urban agglomerations have exhibited a steady upward trend, although the overall level remains relatively low. Among these regions, the Pearl River Delta ranks the highest, followed by the Yangtze River Delta, Beijing–Tianjin–Hebei, Chengdu–Chongqing, and the Urban Cluster in the Middle Reaches of the Yangtze River. Nevertheless, significant potential for improvement persists. (2) The traditional Markov probability transfer matrix suggests that the new quality productive forces in these urban agglomerations are relatively stable, with evidence of “club convergence”. Meanwhile, the spatial Markov transfer probability matrix indicates that transfer probabilities are influenced by neighborhood contexts. (3) Over time, the new quality productive forces in Chinese urban agglomerations show a tendency to concentrate at higher levels, reflecting gradual improvement. The developmental state and evolutionary patterns of new quality productive forces in Chinese urban agglomerations are thoroughly evaluated in this paper, along with advice for accelerating their growth to promote Chinese-style modernization. Full article

Wet rainfall pulses control vegetation growth through evapotranspiration in most dryland areas. This topic has not been extensively analyzed with respect to the vast semi-arid ecosystems of Central Australia. In this study, we investigated vegetation water responses to in situ root zone soil moisture (SM) variations in savanna woodlands (Mulga) in Central Australia using satellite-based optical and thermal data. Specifically, we used the Land Surface Water Index (LSWI) derived from the Advanced Himawari Imager on board the Himawari 8 (AHI) satellite, alongside Land Surface Temperature (LST) from MODIS Terra and Aqua (MOD/MYD11A1), as indicators of vegetation water status and surface energy balance, respectively. The analysis covered the period from 2016 to 2021. The LSWI increased with the magnitude of wet pulses and showed significant lags in the temporal response to SM, with behavior similar to that of the Enhanced Vegetation Index (EVI). By contrast, LST temporal responses were quicker and correlated with daily in situ SM at different depths. These results were consistent with in situ relationships between LST and SM, with the decreases in LST being coherent with wet pulse magnitude. Daily LSWI and EVI scores were best related to subsurface SM through quadratic relationships that accounted for the lag in vegetation response. Tower flux measures of gross primary production (GPP) were also related to the magnitude of wet pulses, being more correlated with the LSWI and EVI than LST. The results indicated that the vegetation response varied with SM depths. We propose a conceptual model for the relationship between LST and SM in the soil profile, which is useful for the monitoring/forecasting of wet pulse impacts on vegetation. Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands. Full article

As the only large sulfate-type salt lake in the global warm temperate deciduous forest zone, Yuncheng Salt Lake plays a crucial role in maintaining ecosystem stability and establishing a regional ecological barrier due to its unique ecological characteristics. Currently, there is a lack of research on the spatial and temporal differentiation of ecological risks in inland lakes, particularly salt lake wetland ecosystems, under current and future scenarios. Moreover, studies using optimal parameter-based geographical detectors to identify the influencing factors of landscape ecological risks—while avoiding subjective bias—remain limited. This study utilizes land use/land cover data of Yuncheng Salt Lake from 1990 to 2022 to construct a landscape ecological risk assessment model. By employing spatial autocorrelation analysis, the optimal geographical detector, and the Patch-level Land Use Simulation (PLUS) model, the study explores the dynamic evolution of ecological risks in Yuncheng Salt Lake wetlands under different current and future scenarios. Furthermore, it analyzes the influence of various natural and socio-economic factors on ecological risk, aiming to provide valuable insights for targeted ecological risk warning and management measures in inland salt lake regions. The results indicate that: (1) Between 1990 and 2022, the area of built-up land in Yuncheng Salt Lake wetlands increased significantly, primarily due to the continuous decline in farmland area, while the water area initially decreased and then increased. (2) The landscape ecological risk index declined over the study period, indicating an improvement in the ecological risk status of Yuncheng Salt Lake wetlands in recent years, with the overall ecosystem security trending positively. (3) Topographical conditions are the primary factors influencing landscape ecological risk in Yuncheng Salt Lake wetlands, followed by mean annual temperature and population density. The synergistic effect of elevation with annual precipitation and NDVI (Normalized Difference Vegetation Index) exhibits the strongest explanatory power for the landscape ecological risk in the region. (4) Under different future scenarios, the proportion of high ecological risk areas in Yuncheng Salt Lake wetlands is projected to decrease to varying extents, with the ecological protection scenario contributing more effectively to the sustainable development of the salt lake wetland ecosystem. Full article