Spatial-temporal Characteristics Research Articles

Spatial-temporal characteristics analysis and ecological environment quality evaluation of forest health care bases in Yunnan, Guizhou and Sichuan provinces

As the forest health care in China is still in the early stage of development, the construction standards of forest health care base are not unified yet, resulting in large differences in the evaluation criterion for the ecological environment of forest health care bases. So, it is urgent to develop a new forest health care ecological environment quality assessment method. Yunnan, Guizhou and Sichuan provinces of China were selected as the study area, the previous 6 batches of 165 national forest health care pilot construction bases were selected as the main data source. This study explored the spatial and temporal distribution characteristics of forest health care bases in the study area using standard deviation ellipses, kernel density estimation method and cold and hot spot analysis. Furthermore, this study evaluated the ecological environment quality of the forest health care bases with a new ecological environment quality evaluation index model, which assembled Fraction Vegetation Coverage (FVC), Wetness (WET), Evapotranspiration (ET), Land Surface Temperature (LST) and Normalized Difference Bare Soil Index (NDBSI). The results are as follows: (1) the forest health bases in the study area are mainly located by the east of the Hu Line with a northeast-southwest distribution direction characteristics, and gradually expanded into a shape of “high in the east and low in the west, multi-point development”. (2) the area with ecological environment quality in excellent, good and medium grade accounts for about 87.73 % in the study area, indicating that most of the study area is suitable for the construction of forest health care base. These results can provide a practical guidance for the further rational layout and balanced development of forest health care bases in the study area.

Construction and Spatial-Temporal Characteristic Analysis of the Carbon Atlas of Low-Carbon Villages in the Yangtze River Delta

Countryside is an integral part of China's social and economic systems. For a long time, the continuous growth of rural carbon emissions has led to a series of social, economic and ecological consequences. Carbon emission reduction has become a critical task for rural revitalization and sustainable development in China. Existing studies on low-carbon assessment mainly focus on urban areas, resulting in limited studies on rural areas. What's more, most of the planning strategies for low-carbon villages are also based on subjective evaluation and qualitative analysis, lacking support from quantitative simulation. Combining relevant theories, the concept of a rural "carbon atlas" was proposed in this study. The current rural carbon emissions and mathematics, graphics, and rationales of the temporal-spatial evolutionary characteristics were investigated using the GIS system as the technical platform for information storage and processing. The spatial domain of carbon emission units based on microscopic residential sites, factories and markets was defined and investigated. Carbon emissions of each unit were estimated according to energy consumptions for life, production and transportation. Later, carbon emissions were expressed and presented visually within the "atlas". Meanwhile, temporal and spatial distribution patterns of carbon emissions in various types of rural areas dominated by different industries were analyzed to provide resources and guidelines for building and planning low-carbon countryside and villages.An empirical study was undertaken on four different village types dominated by different industries in the Yangtze River Delta. Results show that the rural carbon atlas has apparent characteristics of tendentious distribution, periodical changes, and typified structures:(1) Different village types have different regional tendencies of high carbon emission. This phenomenon is most evident in industrial villages, where most of the carbon emissions happen in large factories or family workshops with large homestead areas. Traditional fishing and agricultural villages are the least representative in this regard because rural industries are mainly small fishing and agricultural families, and the industrial link among families is weak. (2) Due to the industry's cyclical nature, the fluctuation of carbon emissions in different types of villages is significantly different. Since the leisure tourism industry is greatly affected by festivals and seasons, the carbon emissions in leisure tourism villages fluctuate the most. Carbon emission of professional markets is the most stable, which is attributed to their immunity to seasonal and climatic changes. (3) The rural carbon emission map has prominent typified structural characteristics, including the scattered homogeneous pattern (traditional fishing and agricultural village), the group infiltration pattern (industrial production village), the dissipative fragmentation pattern (leisure tourism village), and the kernel domain recursive pattern (professional market village). Different industrial types lead to noticeable regional differences in the temporal-spatial characteristics and trends of carbon atlas. Hence, there is an urgent need to develop an overall optimization strategy and mechanism model. The outcomes from the current study explore the method and practical application of a rural carbon emission atlas, yet more extensive research exploring various facets of the atlas are still required.

Temporal-spatial characteristics and sources of heavy metals in bulk deposition across China

With the rapid development of industries, agriculture, and urbanization (including transportation and population growth), there has been a significant alteration in the emission and atmospheric deposition of heavy metal pollutants. This has consequently given rise to a range of ecological and environmental health issues. In this study, we conducted a comprehensive two-year investigation on the temporal and spatial distribution characteristics of heavy metals in atmospheric deposition across China based on the Nationwide Nitrogen Deposition Monitoring Network (NNDMN). The atmospheric bulk deposition of Lead (Pb), Arsenic (As), Nickel (Ni), Selenium (Se), Chromium (Cr) and Cadmium (Cd) were 6.32 ± 1.59, 4.49 ± 0.57, 1.31 ± 0.21, 1.05 ± 0.16, 0.60 ± 0.06 and 0.21 ± 0.03 mg m−2 yr−1, respectively, with a large variation among the different regions of China. The order for atmospheric deposition flux was Southwest China > Southeast China > North China > Northeast China > Qinghai-Tibet Plateau and rural area > urban area > background area. The concentrations of heavy metals in bulk deposition exhibit seasonal variation with higher levels observed during winter compared to summer and spring, which are closely associated with anthropogenic activities. The Positive Matrix Factorization (PMF) results indicated that combustion, industrial emissions and traffic are the primary contributors to atmospheric deposition of heavy metals. The single factor pollution index (Pi) of heavy metals is consistently below 1, and the composite pollution index (Ni) is 0.16 across China, indicating that atmospheric heavy metal deposition is at a pollution-free level. The comprehensive potential ecological risk index of heavy metals is 11.8, with Cd exhibiting the highest single factor potential ecological risk index at 7.09, suggesting that more attention should be paid to Cd deposition in China. The present study reveals the spatial-temporal distribution pattern of atmospheric heavy metals deposition in China, identifying regional source characteristics and providing a theoretical foundation and strategies for reducing emissions of atmospheric pollutants.

Traffic Flow Prediction Research Based on an Interactive Dynamic Spatial–Temporal Graph Convolutional Probabilistic Sparse Attention Mechanism (IDG-PSAtt)

Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden dynamic correlations among road nodes, and the dynamic spatial–temporal characteristics of traffic flows, a traffic flow prediction model based on an interactive dynamic spatial–temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed. Specifically, the IDG-PSAtt model consists of an interactive dynamic graph convolutional network (IL-DGCN) with a spatial–temporal convolution (ST-Conv) block and a probabilistic sparse self-attention (ProbSSAtt) mechanism. The IL-DGCN divides the time series of a traffic flow into intervals and synchronously and interactively shares the captured dynamic spatiotemporal features. The ST-Conv block is utilized to capture the complex dynamic spatial–temporal characteristics of the traffic flow, and the ProbSSAtt block is utilized for medium-to-long-term forecasting. In addition, a dynamic GCN is generated by fusing adaptive and learnable adjacency matrices to learn the hidden dynamic associations among road network nodes. Experimental results demonstrate that the IDG-PSAtt model outperforms the baseline methods in terms of prediction accuracy. Specifically, on METR-LA, the mean absolute error (MAE) and root mean square error (RMSE) induced by IDG-PSAtt for a 60 min forecasting scenario are reduced by 0.75 and 1.31, respectively, compared to those of the state-of-the-art models. This traffic flow prediction improvement will lead to more precise estimates of the emissions produced by mobile sources, resulting in more accurate air quality forecasts. Consequently, this research will greatly support local environmental management efforts.

Spatial-temporal Characteristics of Eco-efficiency of Cultivated Land Use in Shannxi Plain, North-west China

Cultivated land plays a crucial role in guaranteeing food security, but the production factor inputs in the cultivated land use can also cause environmental pollution. Assessing the eco-efficiency of cultivated land use (ECLU) has an essential effect on achieving sustainable agricultural development. However, studies of the ECLU have focused on the assessment of large regions and differences due to environmental factors but have lacked the assessment of single production areas. This study utilizes the SBM to measure the ECLU in Shannxi Plain, based on 2011 to 2021 statistics. The results of the study provide a factual basis for the realization of sustainable agricultural development and recommendations for the formulation of policies for sustainable agricultural development.

Enhanced Two-Stream Bayesian Hyper Parameter Optimized 3D-CNN Inception-v3 Based Drop-ConvLSTM2D Deep Learning Model for Human Action Recognition

Human Action Recognition (HAR) has grown to be the toughest and most attractive concern in the domains of computer vision, communication between a person and the surroundings, and video surveillance. In variation to the conventional methods that usually make use of the Long Short Term Memory model (LSTM) for training, this work designed dropout variant Drop-ConvLSTM2D, to provide more effectiveness in regularization for deep Convolution Neural Networks (CNNs).In addition, to speed up the runtime performance of the Deep Learning model, Bayesian Hyper Parameter Optimization (BHPO) is also introduced to autonomously optimize, the hyperparameters of the trained architecture. In this study, a two-stream Bayesian Hyper Parameter optimized Drop-ConvLSTM2D model is designed for HAR to overcome the current research deficiencies. In one stream, an Inception-v3 model extracts the temporal characteristics from the optical frames which are generated through the dense flow process. In another stream, a 3D-CNN involves the mining of the spatial-temporal characteristics from the RGB frames. Finally, the features of Inception-v3 and 3D-CNN are fused using which the Drop-ConvLSTM2D model is trained to recognize human behavior. On perceptive public video datasets UCF-101, and HMDB51, the quantitative assessments are conducted on the Drop-ConvLSTM2D BHPO model. For all hyperparameters, the built model explicitly obtains optimized values in this process, which can save time and improve performance. The experimental outcome shows that with a precision of at least 3%, the designed model beats the traditional two-stream model.

Analysis of regional differences and spatial and temporal evolution of tourism carbon emissions in China: considering carbon sink effect

ABSTRACT The purpose of this study is to analyze the regional differences and spatial-temporal evolutionary characteristics of carbon dioxide emissions of tourism in China during 2001–2022 from the perspective of carbon sink effect by using an econometric modeling approach. The findings show that CO2 emissions from China's tourism industry exhibit non-equilibrium spatial characteristics or non-linear relationships. Secondly, the results of Theil's index show that the intra-regional differences in tourism CO2 emissions are greater than the inter-regional differences and exhibit a gradient of ‘West > Central > East’. Finally, under the condition of considering the existence of spatial regional heterogeneity, it is found that tourism CO2 emissions have significant spatial agglomeration, with high CO2 emission regions primarily clustered within the eastern region and the economically developed regions, and low CO2 emission regions distributed centrally within the central region and less developed areas of the tourism economy. It is worth noting that the intra-regional differences mainly come from the eastern belt, and that there are large regional differences in tourism CO2 emissions within the eastern region, while the western region is the next largest and the central region the smallest.

Spatial-temporal differentiation of urban eco-efficiency and its driving factors: A comparison of five major urban agglomerations in China.

This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006-2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations.

Типологические схождения в лирических произведениях В. А. Жуковского «Узник к мотыльку, влетевшему в его темницу» и Дж. Г. Байрона «Шильонский узник»

The aim of the research is to detect typological similarities in the works of V. A. Zhukovsky and G. G. Byron at the level of the images of the main characters, as well as the spatial-temporal organization of the text, with particular attention to the category of “liberation”. The research material includes lyrical works “The Prisoner to the Butterfly That Flew into His Dungeon” (1813) and “The Prisoner of Chillon” (1816). Comparative studies have already noted the contact links and typological similarities between the creative works of V. A. Zhukovsky and G. G. Byron, but a comparative analysis to identify and study parallels on different levels of the text has not been conducted yet, and this in turn determines the research novelty of the present study. For the first time, an analysis of the texts is presented at the level of characters, themes (prison, prisoner, impossibility of liberation), and spatial-temporal characteristics. The article thoroughly examines the interaction of the two lyrical works at the plot and imagery levels. The basis for examination comprises common elements of plots and motifs, similarities and differences in the images of central characters. The result of the research is that, despite the authorship of the authors belonging to the same literary movement (Romanticism), not only similarities in the realization of such images and situations are observed, but also significant differences. The comparative categories will include: the image of the hero, space and time, the image of freedom, liberation, prison. The identified connections and similarities indicate a literary contact between the authors and literatures, revealing the specifics of contact relationships and typological similarities in the works of V. A. Zhukovsky and G. G. Byron.

Spatial and Temporal Changes in Soil Freeze-Thaw State and Freezing Depth of Northeast China and Their Driving Factors

It is necessary to further investigate the spatial considerations, temporal characteristics, and drivers of change affecting the beginning and end of soil freezing and thawing, including the maximum depth of the seasonal freezing (MDSF) and the active layer thickness (ALT) in Northeast China. Hourly soil temperature, among other data, from 1983–2022 were investigated, showing a delay of about 6 days in freezing. In contrast, thawing and complete thawing advanced by about 26 and 20 d, respectively. The freezing period and total freeze-thaw days decreased by about 29 and 23 days, respectively. The number of complete thawing period days increased by about 22 days, while the MDSF decreased by about 25 cm. The ALT increased by about 22 cm. Land Surface Temperature (LST) is the main factor influencing the beginning and end of soil freezing and thawing, MDSF and ALT changes in Northeast China; air temperature, surface net solar radiation, and volumetric soil water content followed. The influence of the interacting factors was greater than the single factors, and the interactive explanatory power of the LST and surface net solar radiation was highest when the soil started to freeze (0.858). The effect of the LST and the air temperature was highest when the soil was completely thawed (0.795). LST and the volumetric soil water content interacted to have the first explanatory power for MDSF (0.866) and ALT (0.85). The results of this study can provide scientific reference for fields such as permafrost degradation, cold zone ecological environments, and agricultural production in Northeast China.

Spatial-temporal characteristics and influencing factors of carbon emission in Chengdu-Chongqing area: an urban transportation perspective.

The Chengdu-Chongqing twin-city economic circle is a vital growth pole and a new power source for Chinese high-quality development. Studying the spatial-temporal characteristics of carbon emissions and the role of factors affecting them under the transportation perspective is of great significance for this region to realize the carbon peak and carbon neutrality and to formulate carbon emission reduction policies. We use the exploring spatial data analysis (ESDA) and spatial regression model combined with the STIRPAT model, and research finding: (1) The total carbon emissions in the research area gradually increased from 2014 to 2020, but the growth rate showed a significant decline in 2019. (2) There is significant spatial heterogeneity of carbon emissions in the study area; the hotspot areas of total carbon emissions are in Chongqing and Chengdu, forming a high-low aggregation of carbon emissions. Per capita carbon emissions show a high trend in the southwest and a low in the northeast. (3) From the factors of transportation perspective, highway density and private vehicles have a positive impact on carbon emissions, and urban road areas and public transportation have a very significant inhibition of carbon emissions and a spatial spillover effect. (4) Other factors, such as population size, national economic development, urbanization level, and industrial structure, all have a positive effect on carbon emissions, and disposable income has a negative effect on carbon emissions.

The Influence of Vegetation on Climate Elements in Northwestern China

Vegetation plays a crucial role in maintaining the balance between nature, water and soil resources. However, understanding its impact mechanisms in arid and semi-arid areas remains limited. This study aims to analyze the spatial–temporal characteristics of the vegetation leaf area index (LAI) and climate elements in typical regions of northwest China and the correlations between LAI and climate elements; it also aims to explore the influence of regional vegetation growth on climate change. The results reveal significant correlations between LAI and various climate elements. Specifically, within the same region, surface temperature, precipitation, vegetation transpiration, and total evaporation show positive correlations with the LAI, whereas surface albedo shows a negative correlation. Vegetation may affect climate through both heat and water exchange between the land and atmosphere. Increased vegetation leads to the enhanced absorption of solar radiation by the land surface, elevating surface temperature. Increased levels of vegetation also increase vegetation transpiration and total evaporation, increasing the water vapor content in the atmosphere and thus leading to increased surface precipitation. Therefore, vegetation distribution plays a role in climate change, and ecological restoration projects in the northwest region hold significant potential for addressing ecological challenges in its arid and semi-arid areas.

Measurement, Spatial-Temporal Evolution, and Optimization Path of the Level of Coordinated Development of Ecological Civilisation: The Case of China

Based on the energy–economy–environment–society (3E1S) system theory, this paper constructed a comprehensive evaluation system for the coordinated development of China’s ecological civilisation construction using panel data from 31 provinces and cities from 2005 to 2020, which is specifically divided into four sub-systems, namely, energy, economy, environment, and society, and covers 10 ordinal covariates and 40 ordinal variable indicators. In terms of measurement and evaluation methods, this paper adopted the composite system synergy model to measure the level of coordinated development of ecological civilisation construction of the country as a whole and 31 provinces and cities and adopted Arcgis maps, standard deviation ellipses, kernel density curves, and Theil index methods to explore the spatial-temporal evolution characteristics and regional differences in the level of coordinated development of ecological civilisation construction. It was found that the coordinated development level of China’s ecological civilisation construction increased year by year from 2005 to 2020, with the most obvious upward trend from 2011 to 2015, with an average increase of 26.5%. Secondly, the spatial distribution pattern of the coordinated development level of China’s ecological civilisation construction was relatively stable, basically showing a dominant southwest–northeast direction, but the spatial centre of gravity shows a trajectory shifting towards the southwest. Finally, there were large regional differences in the level of coordinated development of China’s ecological civilisation, with an overall decreasing regional pattern of “Central-Northeast-East-West”. However, the overall differences among the four regions showed a decreasing trend. The overall differences among the four regions were mainly intra-regional, with the western region having the greatest intra-regional differences and contributing to the overall differences. Although this paper takes China as a case study, its research methodology and conclusions can provide references for the construction of ecological civilisation and green sustainable development in other countries.

Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes

Abstract The seismically active Ka-Ping Block within Xinjiang, China, represents a zone of potential earthquake hazards, and existing surface measurements cannot fully reflect the area’s sub-surface slip rates. To determine these slip rates and characterize the level of hazard that the study area may face in future years, using a cross-correlation of waveforms, we identified 432 repeat sequences along large faults (F1–F5) in the Ka-Ping Block between September 2009 and April 2022 and computed the annual slip rate for every sequence with the empirical relationship of the moment and the seismic magnitude. Spatial distribution images and temporal evolution characteristics of deep deformation in fault zones were constructed. We obtained five asperities in the Ka-Ping Block, the western portion of the PiQiang fault (F4) had a larger yearly slide rate than its eastern portion, and the southwest and eastern areas of the KEP station show an elevated risk of seismic activity within the next two years.

Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs.

Diagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial-temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial-temporal synchronous graph convolution block to capture the spatial, temporal, and spatial-temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients' future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.

Analysis of the Spatial-Temporal Distribution Characteristics of Climate and Its Impact on Winter Wheat Production in Shanxi Province, China, 1964-2018.

The possible influence of global climate changes on agricultural production is becoming increasingly significant, necessitating greater attention to improving agricultural production in response to temperature rises and precipitation variability. As one of the main winter wheat-producing areas in China, the temporal and spatial distribution characteristics of precipitation, accumulated temperature, and actual yield and climatic yield of winter wheat during the growing period in Shanxi Province were analysed in detail. With the utilisation of daily meteorological data collected from 12 meteorological stations in Shanxi Province in 1964-2018, our study analysed the change in winter wheat yield with climate change using GIS combined with wavelet analysis. The results show the following: (1) Accumulated temperature and precipitation are the two most important limiting factors among the main physical factors that impact yield. Based on the analysis of the ArcGIS geographical detector, the correlation between the actual yield of winter wheat and the precipitation during the growth period was the highest, reaching 0.469, and the meteorological yield and accumulated temperature during this period also reached its peak value of 0.376. (2) The regions with more suitable precipitation and accumulated temperature during the growth period of winter wheat in the study area had relatively high actual winter wheat yields. Overall, the average actual yield of the entire region showed a significant increasing trend over time, with an upward trend of 47.827 kg ha-1 yr-1. (3) The variation coefficient of winter wheat climatic yield was relatively stable in 2008-2018. In particular, there were many years of continuous reduction in winter wheat yields prior to 2006. Thereafter, the impact of climate change on winter wheat yields became smaller. This study expands our understanding of the complex interactions between climate variables and crop yield but also provides practical recommendations for enhancing agricultural practices in this region.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience, this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects. A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020. The spatial-temporal evolution characteristics were analyzed using Kernel density estimation, standard deviation ellipse, and spatial Markov chain analysis, and the spatial Tobit model was introduced to discover the influencing factors. The results indicate the following: ① Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend, with the center of gravity moving to the southwest, and the polarization phenomenon intensifying. ② The urban resilience level in a region has certain spatial and geographical dependence, while the probability of urban resilience transfer differs in adjacent cities with different resilience levels. ③ Urban centrality, economic scale, openness level, and financial development promote urban resilience, whereas government scale significantly inhibits it. Finally, this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

Spatial-temporal characteristics of temperature in Indonesian Sea based on a high-resolution reanalysis data

Influenced by both the Pacific and Indian Oceans, the temperature field in the Indonesian Sea are complicated. In order to reveal the spatial-temporal variability, the surface, thermocline and intermediate layers of the temperature field are studied based on the global ocean reanalysis data of Copernicus Marine Environment Monitoring Service (CMEMS) from 1993 to 2019 and the Empirical Orthogonal Function (EOF) analysis method. The results show that there is a warming trend of 1.36×10−2 °C /year in the surface layer. The first two modes both show reverse changes close to the Pacific and Indian Oceans. The correlation coefficients between the first and second modal time coefficients and the Niño 3.4 index are 0.62 and 0.48, respectively. And the correlation coefficient between the second modal time coefficient and the ITF inflow is -0.45. In the thermocline, there is a warmer trend, which is 4.78×10−2 °C /year. The correlation index of the first and second modal time coefficient with the Niño 3.4 and DMI indices are 0.87 and 0.43, respectively. The correlation index between the first and second modal time coefficient and the ITF inflow are -0.60 and 0.38, respectively. In the intermediate layer, the warming trend is 2.18×10−2 °C /year. From 1993 to 1999, from 2000 to 2016 and from 2017 to 2019, the Sulu Sea and northern Halmahera Sea experienced three periods of warming, cooling and warming, respectively. The study is helpful for further understanding the variation of the temperature field in the Indonesian Sea.

Assessing the effect of urban digital infrastructure on green innovation: mechanism identification and spatial-temporal characteristics

Under the background of digitization and greening in China, digital infrastructure offers new opportunities for developing green innovation. This paper investigates the effect of digital infrastructure on urban green innovation using panel data from 285 Chinese prefecture-level cities between 2011 and 2020, and explains the mechanism and its spatial-temporal dynamic effect. The results demonstrate that digital infrastructure promotes urban green innovation. Talent agglomeration, R&D investment increase, and industrial structure upgrading are crucial channels. Furthermore, depending on a city’s size, human capital, environmental regulations, and financial subsidies, digital infrastructure contributes to urban green innovation differently. Also, there is not only a positive spatial spillover effect of digital infrastructure but a threshold effect that presents a nonlinear trend of rising marginal effect. This study provides a new perspective for promoting digital infrastructure and urban green innovation, which makes a difference in facilitating its high-level development collaboratively.

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.