Search Results (4,238)

Urbanization is an important process in China’s urban development, significantly contributing to resource allocation and the cooperative development of neighboring cities. In recent years, remote-sensing technology has emerged as a powerful tool in urbanization research. However, the disparity in development between urban and rural areas poses challenges in evaluating the degree of urbanization within a region. This paper addresses this issue by using LCZ (Local Climate Zone) data to provide a unified framework for analyzing a human settlement environmental geographic dataset. This study focuses on the spatial development and transformation of the Guangzhou–Foshan urbanization from 2000 to 2020. The LCZ data offer a suitable framework for examining urban–rural gradients, facilitating the analysis of spatial characteristics under varying development conditions. This unified framework enables a comprehensive analysis of the spatiotemporal characteristics of urban spatial integration. The results show that the analysis of the Guangzhou–Foshan metropolitan area reveals that the region has maintained a “core–edge” spatial structure over the past 20 years. The development rate has decelerated following policy changes in 2010, with the adjacent area experiencing significantly slower development compared to the overall study area. LCZ data are effective for comparative analysis of internal spatial development within urban areas, offering a novel approach to studying spatial integration amid urban development. Full article

In the context of climate change, it is essential for sustainable development to assess the risks associated with climate change and human-induced vegetation degradation. The Hunshandake Sandy Land provides a variety of ecosystem services and is a substantial ecological security barrier in the Beijing–Tianjin–Hebei area of China. This study used the Normalized Difference Vegetation Index (NDVI) to analyze the spatiotemporal variation trend in vegetation in the Dabusennur Watershed using linear trend analysis and the GeoDetector model to identify the main drivers of vegetation change in the watershed. Finally, the study assessed the risk of ecological degradation in the vegetation of the watershed. The results show that the NDVI in the study area has had a fluctuating trend in the last 22 years, and the change has been small. Precipitation and groundwater depth are the key factors affecting vegetation change. The NDVI reaches its maximum value when the groundwater depth is at 2.75 m. The vegetation ecology of the basin is relatively fragile, mainly with medium risk and large risk. To cope with the ecological risk of vegetation degradation caused by climate change, appropriate water use strategies should be formulated to ensure ecological water use. The present study’s outcomes provide the basis for developing ecological engineering solutions in the arid and semi-arid parts of northern China. Full article

A comprehensive understanding of the heterogeneity of urbanization development at different levels and its influencing factors is crucial for promoting global urbanization and advancing China’s new urbanization. Using indicators related to urbanization development, a multidimensional index system was constructed based on five dimensions: population, economy, space, society, and ecology. Employing methods such as the Mann–Kendall test, Sen’s trend analysis, multiple linear regression, and spatial autocorrelation analysis, the spatiotemporal evolution characteristics of urbanization from 2000 to 2019 were analyzed comprehensively at national, economic zone, provincial, and prefectural city scales. The results indicate the following. (1) From 2000 to 2019, urbanization levels at all levels showed an overall upward trend, with the national urbanization rate increasing most rapidly at 5.39%. (2) Trend analysis reveals rapid and significant growth trends in urbanization at the national and economic zone scales, while urban-level changes exhibit greater diversity and spatiotemporal heterogeneity. (3) Spatial distribution patterns show that urbanization levels in the eastern coastal economic zones are significantly higher than those in the northeastern economic zones, highlighting pronounced regional disparities in development and agglomeration effects in economically advanced regions and provinces. (4) Regression analysis demonstrates that spatial urbanization significantly influences urbanization development in China, with urban infrastructure playing a crucial role across different levels. Full article

The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and lacks an analysis of spatiotemporal evolution characteristics. This study utilized monthly sea ice concentration (SIC) data from the National Snow and Ice Data Center (NSIDC) for the period from 1979 to 2022, utilizing classical spatiotemporal clustering algorithms to analyze the clustering patterns and evolutionary characteristics of SIC anomalies in key Arctic regions. The results revealed that the central-western region of the Barents Sea was a critical area where SIC anomaly evolutionary behaviors were concentrated and persisted for longer durations. The relationship between the intensity and duration of SIC anomaly events was nonlinear. A positive correlation was observed for shorter durations, while a negative correlation was noted for longer durations. Anomalies predominantly occurred in December, with complex evolution happening in April and May of the following year, and concluded in July. Evolutionary state transitions mainly occurred in the Barents Sea. These transitions included shifts from the origin state in the northwestern margin to the dissipation state in the central-north Barents Sea, from the origin state in the central-north to the dissipation state in the central-south, and from the origin state in the northeastern to the dissipation state in the central-south Barents Sea and southeastern Kara Sea. Various evolutionary states were observed in the same area on the southwest edge of the Barents Sea. These findings provide insights into the evolutionary mechanism of sea ice anomalies. Full article

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks. Full article

Coastal ecosystems are facing critical water quality deterioration, while the most convenient passage to the South China Sea, Beibu Gulf, has been under considerable pressure to its ecological environment due to rapid development and urbanization. In this study, we characterized the spatiotemporal change in the water quality in Beibu Gulf and proposed a machine learning approach to predict the water pollution level in Beibu Gulf on the basis of 5-year (2018–2022) observation data of ten water quality parameters from ten selected sites. Random forest (rf) and linear algorithms were utilized. Results show that a high frequency of exceedance of water quality parameters was observed particularly in summer and autumn, e.g., the exceeding rate of Dissolved Inorganic Nitrogen (DIN) at GX01, GX03, GX06, and GX07 station were 28.2~78.1% (average is 52.0%), 6.0~21.7% (average is 52.0%), 23.0~44.7% (average is 31.9%), and 5.2~33.4% (average is 21.2%), respectively. With regard to the spatial distribution, the pH, Water Salinity (WS), and Dissolved Oxygen (DO) values of stations inside the bay were overall lower than those of corresponding stations at the mouth of the bay and stations outside the bay. The concentrations of Chlorophyll-a concentration (except QZB) and nutrient salts showed a clearly opposite trend compared with the above concerned three parameters. For instance, the average Chl-a value of station GX09 was 22.5% higher than that of GX08 and GX10 between 2018 and 2022. Correlation analysis among water quality factors shows a significant positive correlation (r > 0.85) between Dissolved Inorganic Nitrogen (DIN) and NO₃-N, followed by NO₂-N and NH₄-N, indicating that the main component of DIN is NO₃-N. The forecasting results with machine learning also demonstrate the possibility to estimate the water quality parameters, such as chl-a concentration, DIN, and NH₄-N in a cost-effective manner with prediction accuracy of approximately 60%, and thereby could provide near-real-time information to monitor the water quality of the Beibu Gulf. Predicting models initiated in this study could be of great interest for local authorities and the tourism and fishing industries. Full article

Phenology and photosynthetic capacity both regulate carbon uptake by vegetation. Previous research investigating the impact of phenology on vegetation productivity has focused predominantly on the start and end of growing seasons (SOS and EOS), leaving the influence of peak phenology metrics—particularly in typical climatic transition zones—relatively unexplored. Using a 24-year (2000–2023) enhanced vegetation index (EVI) dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS), we extracted and examined the spatiotemporal variation for peak of season (POS) and peak growth (defined as EVI_max) of forest vegetation in the Funiu Mountain region, China. In addition to quantifying the factors influencing the peak phenology metrics, the relationship between vegetation productivity and peak phenological metrics (POS and EVI_max) was investigated. Our findings reveal that POS and EVI_max showed advancement and increase, respectively, negatively and positively correlated with vegetation productivity. This suggested that variations in EVI_max and peak phenology both increase vegetation productivity. Our analysis also showed that EVI_max was heavily impacted by precipitation, whereas SOS had the greatest effect on POS variation. Our findings highlighted the significance of considering climate variables as well as biological rhythms when examining the global carbon cycle and phenological shifts in response to climate change. Full article

Traditional frame-based cameras, despite their effectiveness and usage in computer vision, exhibit limitations such as high latency, low dynamic range, high power consumption, and motion blur. For two decades, researchers have explored neuromorphic cameras, which operate differently from traditional frame-based types, mimicking biological vision systems for enhanced data acquisition and spatio-temporal resolution. Each pixel asynchronously captures intensity changes in the scene above certain user-defined thresholds, and streams of events are captured. However, the distinct characteristics of these sensors mean that traditional computer vision methods are not directly applicable, necessitating the investigation of new approaches before being applied in real applications. This work aims to fill existing gaps in the literature by providing a survey and a discussion centered on the different application domains, differentiating between computer vision problems and whether solutions are better suited for or have been applied to a specific field. Moreover, an extensive discussion highlights the major achievements and challenges, in addition to the unique characteristics, of each application field. Full article

Land use change drives the ecosystem service value (ESV) to some extent. Investigating the impact of land use distribution patterns under different scenarios on ESV is crucial for optimizing land spatial utilization in tropical island regions. This study employs a combination of multi-objective programming (MOP) and the patch-generating land use simulation (PLUS) model to simulate and predict the spatial distribution of land use in Hainan Island for the year 2030 under three scenarios: natural development, ecological protection priority, and tourism development priority. The ESV for these scenarios is then assessed to provide insights into the sustainable economic, social, and ecological development of tropical island regions. The results indicate the following: (1) Between 2010 and 2020, forest land was the dominant land use type in Hainan Island, accounting for 63% of the total area, followed by arable land. Land use changes were characterized mainly by increases in built-up land and grass land, which increased by 497.13 km² and 18.87 km², respectively, with decreases in other types. The largest area of land conversion was from forest land, which was predominantly converted to built-up land and arable land, measuring 259.97 km² and 174.49 km², respectively. (2) The PLUS model was used to simulate land use changes in Hainan Island, achieving a Kappa coefficient of 0.88 and an overall accuracy of 0.94, indicating a high consistency between the simulation results and actual data. (3) The ecological protection priority scenario yielded the highest ecosystem service values (CNY 72.052 billion), while the values under other scenarios decreased compared to 2020. The natural development scenario saw a decrease of CNY 1.821 billion, and the tourism development priority scenario saw a decrease of CNY 0.595 billion. Spatially, the ecological protection priority scenario also showed the greatest increase in areas with high ecosystem service values, particularly due to an increase in forest land area, which contributed to an overall increase in the ecosystem service values of the study area. This study offers a scientific foundation and a decision-making reference for selecting priority scenarios for tourism development on Hainan Island, aimed at supporting its future sustainable development. It emphasizes the protection of forest resources, the promotion of greening initiatives, and the achievement of a balance between ecological preservation and tourism activities. Full article

Accurate predictions are crucial for addressing the challenges posed by climate change. Given South Korea’s location within the East Asian summer monsoon domain, characterized by high spatiotemporal variability, enhancing prediction accuracy for regions experiencing heavy rainfall during the summer monsoon is essential. This study aims to derive temporal weighting functions using hybrid surface rainfall radar-observation data as the target, with input from two forecast datasets: the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) and the KLAPS Forecast System. The results indicated that the variability in the optimized parameters closely mirrored the variability in the rainfall events, demonstrating a consistent pattern. Comparison with previous blending results, which employed event-type-based weighting functions, showed significant deviation in the average AUC (0.076) and the least deviation (0.029). The optimized temporal weighting function effectively mitigated the limitations associated with varying forecast lead times in individual datasets, with RMSE values of 0.884 for the 1 h lead time of KLFS and 2.295 for the 4–6 h lead time of MAPLE. This blending methodology, incorporating temporal weighting functions, considers the temporal patterns in various forecast datasets, markedly reducing computational cost while addressing the temporal challenges of existing forecast data. Full article

Exploring NDVI variation and what drives it on the Qinghai–Tibet Plateau can strategically inform environmental protection efforts in light of global climate change. For this analysis, we obtained MODIS NDVI data collected during the vegetative growing season, vegetation types for the region, and meteorological data for the same period from 2001 to 2020. We performed Theil–Sen trend analysis, Mann–Kendall significance testing, spatial autocorrelation analysis, and Hurst index calculation to review the spatiotemporal changes in NDVI characteristics on the plateau for various vegetation types. We used the correlation coefficients from these analyses to investigate how the NDVI responds to temperature and precipitation. We found the following: (1) Overall, the Qinghai–Tibet Plateau NDVI increased throughout the multi-year growing season, with a much larger area of improvement (65.68%) than of degradation (8.83%). (2) The four main vegetation types were all characterized by improvement, with meadows (72.13%) comprising the largest portion of the improved area and shrubs (18.17%) comprising the largest portion of the degraded area. (3) The spatial distribution of the NDVI had a strong positive correlation and clustering effect and was stable overall. The local clustering patterns were primarily low–low and high–high clustering. (4) The Hurst index had an average value of 0.46, indicating that the sustainability of vegetation is poor; that is, the trend of vegetation change in the growing season in a large part of the Qinghai–Tibet Plateau in the future is opposite to that in the past. (5) The plateau NDVI correlated positively with air temperature and precipitation. However, the correlations varied geographically: air temperature had a wide influence, whereas precipitation mainly influenced meadows and grassland in the northern arid zone. The overall temperature-driven effect was stronger than that of precipitation. This finding is consistent with the current research conclusion that global warming and humidification promote vegetation growth in high-altitude areas and further emphasizes the uniqueness of the Qinghai–Tibet Plateau as a climate-change-sensitive area. This study also offers a technical foundation for understanding how climate change impacts high-altitude ecosystems, as well as for formulating ecological protection strategies for the plateau. Full article

Along with climate warming, extreme heat events have become more frequent, severe, and seriously threaten rice production. Precisely evaluating rice heat levels based on heat duration and a cumulative intensity index dominated by temperature and humidity is of great merit to effectively assess regional heat risk and minimize the deleterious impact of rice heat along the middle and lower reaches of the Yangtze River (MLRYR). This study quantified the response mechanism of daytime heat accumulation, night-time temperature, and relative humidity to disaster-causing intensity in three categories of single-season rice heat (dry, medium, and wet conditions) using Fisher discriminant analysis to obtain the Heat Comprehensive Intensity Index daily (HCIId). It is indicated that relative humidity exhibited a negative contribution under dry heat, i.e., heat disaster-causing intensity increased with decreasing relative humidity, with the opposite being true for medium and wet heat. The Kappa coefficient, combined with heat duration and cumulative HCIId, was implemented to determine classification thresholds for different disaster levels (mild, moderate, and severe) to construct heat evaluation levels. Afterwards, spatiotemporal changes in heat risk for single-season rice through the periods of 1986–2005, 2046–2065 and 2080–2099 under SSP2-4.5 and SSP5-8.5 were evaluated using climate scenario datasets and heat evaluation levels carefully constructed. Regional risk projection explicitly revealed that future risk would reach its maximum at booting and flowering, followed by the tillering stage, and its minimum at filling. The future heat risk for single-season rice significantly increased under SSP5-8.5 than SSP2-4.5 in MLRYR. The higher risk would be highlighted in eastern Hubei, eastern Hunan, most of Jiangxi, and northern Anhui. As time goes on, the heat risk for single-season rice in eastern Jiangsu and southern Zhejiang will progressively shift from low to mid-high by the end of the twenty-first century. Understanding the potential risk of heat exposure at different growth stages can help decision-makers guide the implementation of targeted measures to address climate change. The proposed methodology also provides the possibility of assessing other crops exposure to heat stress or other extreme events. Full article

Ecosystem resilience, the ability of an ecosystem to recover from disturbances, is a critical indicator of environmental health and stability, particularly under the impacts of climate change and anthropogenic pressures. This study focuses on the Three-River Headwater Region (TRHR), a critical ecological area for East and Southeast Asia, often referred to as the “Water Tower of China”. We used the Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation growth and productivity and calculated Critical Slowing Down (CSD) indicators to assess the spatiotemporal dynamics of grassland ecosystem resilience in the TRHR from 1984 to 2021. Our research revealed a sustained improvement in ecosystem resilience in the TRHR starting in the late 1990s, with a reversal in this trend observed after 2011. Spatially, ecosystem resilience was higher in areas with greater precipitation and higher vegetation productivity. Temporally, changes in grazing intensity were most strongly correlated with resilience dynamics, with explanatory power far exceeding that of NDVI, temperature, and precipitation. Our study underscores the importance of incorporating ecosystem resilience into assessments of ecosystem function changes and the effectiveness of ecological conservation measures, providing valuable insights for similar research in other regions of the world. Full article

Within rowing, lower back disorders (LBDs) are common, but the mechanisms underpinning LBDs are poorly understood. Considering this, it is essential to understand how coordination and motor control change under different constraints such as ergometer rowing and fatigue. This can help better inform movement features linked to LBDs. Measurement of the continuous relative phase (CRP) is a method used to quantify body segment and joint coordination, as CRP measures the spatiotemporal control of multi-joint movement. The purpose of this study was twofold: to examine the general spatiotemporal coordination aspects of ergometer rowing in an unfatigued state, and to quantify how the spatiotemporal coordination of a rowing movement changes in response to a fatigue-inducing rowing trial. Wearable IMUs monitored 20 participants’ movement during a 2000 m ergometer row. The Borg-10 Rating of Perceived Exertion (RPE) scale was used to quantify perceived fatigue. Despite significant RPE increases across all athletes, the spatiotemporal coordination of rowing revealed prevailing strategies for the lumbar spine and lower extremity but no significant effects (α = 0.05) of fatigue on CRP outcomes (MARP, DP), cross-correlation lag (R_XY), or range of motion. These findings provide further insight into rowing movements and support the idea that heterogeneous responses to fatigue may exist, requiring further study. Full article

The rational allocation of land resources is crucial to ensuring human well-being, livelihood, and survival. The study of Production-Living-Ecological Space (PLES) provides new perspectives on land resource allocation. However, few studies have assessed the feasibility of PLES optimization in ecological transition zones. For this study, using the composite functional space classification method, a classification and functional utility scoring system were constructed. Various methods, including dynamic attitude, transfer matrix, and spatial autocorrelation, were employed to characterize the evolution of the quantity and quality of PLES in the Hexi Corridor. Moreover, the mechanisms driving these changes were explored using a geodetector. Our findings revealed that: (1) The distribution of Production-Ecological Space (PES) is higher in the west and south and lower in the east and north. Production-Living Space (PLS) is scattered. Ecological-Production Space (EPS) is mostly distributed in the south or west, whereas Ecological Space (ES) is mainly located in the north and west of the Hexi Corridor. (2) From 1980 to 2020, the area of PES and PLS increased by 2037.84 km² and 673 km², respectively; the area of EPS was relatively stable, and the area of ES decreased by 2523.06 km². (3) The evolution of PLES quality indicated that the high functional utility area of PES and PLS was roughly the same as the expanded functional utility area, whereas the expanded functional utility area of EPS and ES is similar to the median functional utility area. (4) The spatiotemporal evolution of PLES is closely linked to natural, economic, and social factors. Full article