Construction of BDS Spatiotemporal Information Agricultural Product Digital Credit System

Spatiotemporal information,positioning and navigation services have become important elements of new type infrastructure.The rapid development of global digital trade provides a large-scale application scenario for the use of Beidou Navigation Satellite System(BDS)spatiotemporal information to support the certification of origin of agricultural products.The BDS spatiotemporal information agricultural product digital credit system uses such modules as BDS,spatiotemporal information collection,spatiotemporal coding,and spatiotemporal blockchain.It incorporates multi-level joint supervision mechanisms such as government,associations,and users.Starting from the initial production link of agricultural products,it realizes the correspondence and locking of online and offline products,effectively improves the integrity and credibility of information in the production process,finished product quality and circulation process of products,effectively manages the green production and anti-channel conflicts of producers,and provides credible information for consumers,thus realizing the digital credit certification of products.The successful practice of characteristic agricultural products in Yunnan Province has verified the application ability of the BDS spatiotemporal information agricultural product digital credit system.This system has played a significant role in promoting the online and offline locking,credible information,effective supervision and high quality and high price of characteristic agricultural products from the field.The BDS provides services for global digital trade and contributes to the further enhancement of the global application scale of GNSS.

Which Neighborhoods Have Easier Access to Online Home Delivery Services?A Spatiotemporal Accessibility Analysis in Nanjing,China

The rise in online home delivery services(OHDS)has had a significant impact on how urban services are supplied and used in recent years.Studies on the spatial accessibility of OHDS are emerging,but few is known about the temporal dimension of OHDS accessibility as well as the geographic and socioeconomic differences in the spatiotemporal accessibility of OHDS.This study measures the spatiotemporal accessibility of four types of OHDS,namely leisure,fresh and convenient,medical,and catering services.The geographic and socioeconomic disparities in the spatiotemporal accessibility of these four types of OHDS are then identified using spatial statistical methods and the Kruskal-Wallis test(K-W test).The case study in Nanjing,China,suggests that:1)spatiotemporal accessibility better reflects the temporal variation of OHDS accessibility and avoids overestimation of OHDS accessibility when only considering its spatial dimension.2)The spatiotemporal accessibility of OHDS varies geographically and socioeconomically.Neighborhoods located in the main city or neighborhoods with higher housing prices,higher population density,and higher point of interest(POI)mix have better OHDS spatiotemporal accessibility.Our study contributes to the understanding of OHDS accessibility from a spatiotemporal perspective,and the empirical insights can assist policymakers in creating intervention plans that take into account variations in OHDS spatiotemporal accessibility.

Spatiotemporal changes in sunshine duration and its influential factors in Chongqing,China from 1961 to 2020

The surface solar radiation in most parts of the world has undergone a phenomenon known as global dimming and brightening,characterized by an initial decrease followed by an increase.As a result,the sunshine duration(SD)has decreased in the past 60 years.Against the backdrop of global dimming and brightening,SD has decreased to varying degrees in many regions of China.Using the observed data of SD,cloud amount(total cloud amount and low cloud amount,abbreviated as TCA and LCA),precipitation,and relative humidity(RH)from 34 meteorological stations in Chongqing during the period of 1961-2020,along with a digital elevation model(DEM)with a resolution of 90 m,this study analyzed the spatiotemporal variations and influencing factors of SD.The analysis employed methods such as linear regression,Mann-Kendall test,wavelet transformation,and DEM-based possible SD distributed model.The results showed that the annual SD in Chongqing has significantly decreased over the last 60 years,with a decreasing interannual trend rate(ITR)of 40.4 h/10a.Except for no obvious trend in spring,SD decreased significantly in summer,autumn and winter at the ITR of 21.1 h/10a,8.5 h/10a and 7.5 h/10a,respectively.An abrupt decrease in the annual SD was found in 1979.The difference before and after the abrupt decrease was 177.7 h.The difference before and after the abrupt decrease was 177.7 h.The annual SD possessed the oscillation period of 11a.The spatial heterogeneity of the mean annual SD during the last 60 years was obvious.The distribution of SD in Chongqing is high in the northeast and low in the southeast.In addition,about 73%of the total area in Chongqing showed a significant and very significant decreasing trend.The regions with significant changes are mainly concentrated in the regions with altitudes of 200~1000 m.The increasing LCA was the main cause of the decrease of the annual SD in the regions with 200-400 m altitude decreased the most and changed the most.Increasing LCA is the primary cause of the reduction in annual SD,showing a strong negative correlation coefficient of-0.7292.In Chongqing,PM2.5 concentration showed a significant decrease trend in annual,spring,autumn and winter during 2000-2020,but the significant correlation between PM2.5 concentration and SD was only in autumn and reached an extremely significant level.

Spatiotemporal pattern of climate change in the China-Myanmar Economic Corridor from 1901 to 2018

The China-Myanmar Economic Corridor(CMEC) is an important part of China's Belt and Road Initiative and an important area for global ecology and biodiversity. In this study, the annual and seasonal spatiotemporal patterns of temperature and precipitation in the CMEC over the past century were investigated using linear tendency estimation, the Mann-Kendall mutation test, the T-test, and wavelet analysis based on the monthly mean climatic data from 1901 to 2018 released by the Climatic Research Unit(CRU) of the University of East Anglia, UK. The results show that the CMEC demonstrated a trend of warming and drying over the past 100 years, and the rate of change in Myanmar was stronger than that in Yunnan Province of China. The warming rate was 0.039 ℃/10a. Precipitation decreased at a rate of -6.1 mm/10a. From the perspective of spatial distribution, temperature was high in the central and southern, low in the north of the CMEC, and the high-temperature centers were mainly distributed in the southern plain and river valley. Precipitation decreased from west to east and from south to north of the CMEC. From the perspective of the rate of change, warming was stronger in central and northern CMEC than in southern and northeastern CMEC. The rate of precipitation decline was stronger in the central and western regions than in the eastern region. This study provides a scientific reference for the CMEC to address climate change and ensure sustainable social and economic development and ecological security.

Spatiotemporal patterns and drivers of cultivated land conversion in Inner Mongolia Autonomous Region,northern China

Protection and optimization of cultivated land resources are of great significance to national food security.Cultivated land conversion in northern China has increased in recent years due to the industrialization and urbanization of society.However,the assessment of cultivated land conversion in this area is insufficient,posing a potential risk to cultivated land resources.This study evaluated the evolution and spatiotemporal patterns of cultivated land conversion in Inner Mongolia Autonomous Region,China,and the driving factors to improve rational utilization and to protect cultivated land resources.The spatiotemporal patterns of cultivated land conversion in Inner Mongolia were analyzed using the cultivated land conversion index,kernel density analysis,a standard deviation ellipse model,and a geographic detector.Results showed that from 2000 to 2020,the trends in cultivated land conversion area and rate in Inner Mongolia exhibited fluctuating growth,with the total area of cultivated land conversion reaching 7307.59 km^(2) at a rate of 6.69%.Spatial distribution of cultivated land conversion was primarily concentrated in the Hetao Plain,Nengjiang Plain,Liaohe Plain,and the Hohhot-Baotou-Ordos urban agglomeration.Moreover,the standard deviational ellipse of cultivated land conversion in Inner Mongolia exhibited a directional southwest-northeast-southwest-northeast distribution,with the northeast-southwest direction identified as the main driving force of spatial change in cultivated land conversion.Meanwhile,cultivated land conversion exhibited an increase-decrease-increase change process,indicating that spatial distribution of cultivated land conversion in Inner Mongolia became gradually apparent within the study period.The geographic detector results further revealed that the main driving factors of cultivated land conversion in Inner Mongolia were the share of secondary and tertiary industries and per-unit area yield of grain,with explanatory rates of 57.00%,55.00%,and 51.00%,respectively.Additionally,improved agricultural production efficiency and the coordinated development of population urbanization and industry resulted in cultivated land conversion.Collectively,the findings of this study indicated that,from 2000 to 2020,the cultivated land conversion in Inner Mongolia was significant and fluctuated in time,and had strong spatial heterogeneity.The primary drivers of these events included the effects of agriculture,population,and social economy.

Spatiotemporal Evolution of Urban Heat Islands and Optimization of Spatial Network Construction in the Central Urban Area of Fuzhou City, China

Although accelerated urbanization has led to economic prosperity,it has also resulted in urban heat island effects.Therefore,identifying methods of using limited urban spaces to alleviate heat islands has become an urgent issue.In this study,we assessed the spatiotemporal evolution of urban heat islands within the central urban area of Fuzhou City,China from 2010 to 2019.This assessment was based on a morphological spatial pattern analysis(MSPA)model and an urban thermal environment spatial network constructed us-ing the minimum cumulative resistance(MCR)model.Optimization measures for the spatial network were proposed to provide a theor-etical basis for alleviating urban heat islands.The results show that the heat island area within the study area gradually increased while that of urban cold island area gradually decreased.The core area was the largest of the urban heat island patch landscape elements with a significant impact on other landscape elements,and represented an important factor underlying urban heat island network stability.The thermal environment network revealed a total of 197 thermal environment corridors and 93 heat island sources.These locations were then optimized according to the current land use,which maximized the potential of 1599.83 ha.Optimization based on current land use led to an increase in climate resilience,with effective measures showing reduction in thermal environment spatial network structure and function,contributing to the mitigation of urban heat island.These findings support the use of current land use patterns during urban heat island mitigation measure planning,thus providing an important reference basis for alleviating urban heat island effects.

Ultrahigh spatiotemporal resolution beam signal reconstruction with bunch phase compensation

Various electromagnetic signals are excited by the beam in the acceleration and beam-diagnostic elements of a particle accelerator.It is important to obtain time-domain waveforms of these signals with high temporal resolution for research,such as the study of beam–cavity interactions and bunch-by-bunch parameter measurements.Therefore,a signal reconstruction algorithm with ultrahigh spatiotemporal resolution and bunch phase compensation based on equivalent sampling is proposed in this paper.Compared with traditional equivalent sampling,the use of phase compensation and setting the bunch signal zero-crossing point as the time reference can construct a more accurate reconstructed signal.The basic principles of the method,simulation,and experimental comparison are also introduced.Based on the beam test platform of the Shanghai Synchrotron Radiation Facility(SSRF)and the method of experimental verification,the factors that affect the reconstructed signal quality are analyzed and discussed,including the depth of the sampled data,quantization noise of analog-to-digital converter,beam transverse oscillation,and longitudinal oscillation.The results of the beam experiments show that under the user operation conditions of the SSRF,a beam excitation signal with an amplitude uncertainty of 2%can be reconstructed.

Spatiotemporal variations of parameters of internal solitary waves in the northern South China Sea

The dynamic parameters for internal solitary waves(ISWs)derived from the extended Korteweg-de Vries(eKdV)equation play an important role in the understanding and prediction of ISWs.The spatiotemporal variations of the dynamic parameters of the ISWs in the northern South China Sea(SCS)were studied based on the reanalysis of long-term temperature and salinity datasets.The results for spectrum analysis show that there are definite geographical differences for the periodic variation of the parameters:in shallow water,all parameters vary with a wave period of one year,while in deep water wave components of the parameters at other frequencies exist.Using wavelet analysis,the wavelet power spectral densities in deep water exhibited an inter-annual variation pattern.For example,the wave component of the dispersion coefficient with a wave period of about half a year reached its power peak once every two years.Based on previous work,this inter-annual variation pattern was deduced to be caused by dynamic processes.In further work on the regulatory mechanisms,empirical orthogonal function(EOF)decomposition was performed.It was found that the modes of the dispersion coefficient have different geographical distributions,explaining the reason why the wave components in different frequencies appeared in different locations.The numerical simulation results confirm that the variations in the parameters of the ISWs derived from the eKdV equation could affect the waveforms significantly because of changes in the polarity of the ISWs.Therefore,the periodic variations of the dynamic parameters are related to the geographical location because of dynamic processes operating.

Unveiling the adaptation strategies of woody plants in remnant forest patches to spatiotemporal urban expansion through leaf trait networks

Background:With the expansion of urban areas,the remnants of forested areas play a crucial role in preserving biodiversity in urban environments.This study aimed to explore the impact of spatiotemporal urban expansion on the networks of leaf traits in woody plants within remnant forest patches,thereby enhancing our understanding of plant adaptive strategies and contributing to the conservation of urban biodiversity.Methods:Our study examined woody plants within 120 sample plots across 15 remnant forest patches in Guiyang,China.We constructed leaf trait networks (LTNs) based on 26 anatomical,structural,and compositional leaf traits and assessed the effects of the spatiotemporal dynamics of urban expansion on these LTNs.Results and conclusions:Our results indicate that shrubs within these patches have greater average path lengths and diameters than trees.With increasing urban expansion intensity,we observed a rise in the edge density of the LTN-shrubs.Additionally,modularity within the networks of shrubs decreased as road density and urban expansion intensity increased,and increases in the average path length and average clustering coefficient for shrubs were observed with a rise in the composite terrain complexity index.Notably,patches subjected to‘leapfrog’expansion exhibited greater average patch length and diameter than those experiencing edge growth.Stomatal traits were found to have high degree centrality within these networks,signifying their substantial contribution to multiple functions.In urban remnant forests,shrubs bolster their resilience to variable environmental pressures by augmenting the complexity of their leaf trait networks.

Spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice in multiple dimensions during 1979 to 2020

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change.The rapid changes in Arctic sea ice have been widely concerned.However,the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series.In this study,the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data.The results indicated that Arctic sea ice was declining at a remarkably high rate of–5.4×10^(4) km^(2)/a in sea ice area(SIA)and–2.2 cm/a in sea ice thickness(SIT)during 1979 to 2020,and the reduction of SIA and SIT was the largest in summer and the smallest in winter.Spatially,compared with other sub-regions,SIA showed a sharper declining trend in the Barents Sea,Kara Sea,and East Siberian Sea,while SIT presented a larger downward trend in the northern Canadian Archipelago,northern Greenland,and the East Siberian Sea.Regarding to the seasonal trend of sea ice on sub-region scale,the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons,especially in summer and winter,i.e.,the sub-regions linked to the open ocean exhibited a higher decline rate in winter;however,the other sub-regions blocked by the coastlines presented a greater decline rate in summer.For SIT,the sub-regions such as the Beaufort Sea,East Siberian Sea,Chukchi Sea,Central Arctic,and Canadian Archipelago always showed a higher downward rate in all seasons.Furthermore,a striking freeze-thaw asymmetry of Arctic sea ice was also detected.Comparing sea ice changes in different dimensions,sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension.The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period,and the rate of sea ice melting was 0.3×10^(4) km^(2)/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions,respectively.Moreover,there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004,respectively,in the horizontal/vertical dimension.

Spatiotemporal characteristics of water exchange between the Andaman Sea and the Bay of Bengal

A high-resolution customized numerical model is used to analyze the water transport in the three major water passages between the Andaman Sea(AS)and the Bay of Bengal,i.e.,the Preparis Channel(PC),the Ten Degree Channel(TDC),and the Great Channel(GC),based on the daily averaged simulation results ranging from 2010 to 2019.Spectral analysis and Empirical Orthogonal Function(EOF)methods are employed to investigate the spatiotemporal variability of the water exchange and controlling mechanisms.The results of model simulation indicate that the net average transports of the PC and GC,as well as their linear trend,are opposite to that of the TDC.This indicates that the PC and the GC are the main inflow channels of the AS,while the TDC is the main outflow channel of the AS.The transport variability is most pronounced at surface levels and between 100 m and 200 m depth,likely affected by monsoons and circulation.A 182.4-d semiannual variability is consistently seen in all three channels,which is also evident in their second principal components.Based on sea level anomalies and EOF analysis results,this is primarily due to equatorial winds during the monsoon transition period,causing eastward movement of Kelvin waves along the AS coast,thereby affecting the spatiotemporal characteristics of the flow in the AS.The first EOF of the PC flow field section shows a split at 100 m deep,likely due to topography.The first EOF of the TDC flow field section is steady but has potent seasonal oscillations in its time series.Meanwhile,the first EOF of the GC flow field section indicates a stable surface inflow,probably influenced by the equatorial Indian Ocean’s eastward current.

Spatiotemporal variations,influencing factors,and configurational promotion paths of energy efficiency in China’s cities

The promotion of energy efficiency(EE)helps address energy constraints and promote environmental sustainability.This study comprehensively explores the spatiotemporal variations,influencing factors,and configuration promotion paths of EE in 284 Chinese cities during 2003‒2019 using the global super-efficiency minimum distance to strong efficient frontier(G-S-MinDS),exploratory spatial data analysis(ESDA),multiscale geographically weighted regression(MGWR),and fuzzy set qualitative comparative analysis(fsQCA)methods.The findings are:①China’s cities have an annual average EE of 0.658 with a growth rate of 0.53%,showing considerable promotion potential.②Industrial structure optimization,population agglomeration,economic development,and increased green coverage contribute positively,while government intervention and openness hinder China’s urban EE.③Four configurational promotion paths for enhancing China’s urban EE are identified,where among those paths population density is a core condition,while government intervention is not.This study provides valuable insights into substantially improving urban EE,emphasizing the need for targeted policies to address energy and environmental crises in China.

Spatiotemporal mapping of(ultra‐)mafic magmatic mine areas:Implications of economic and political realities in China

The spatiotemporal extension/expansion of mine areas is affected by multiple factors.So far,very little has been done to examine the interaction between mine areas and political or economic realities.The(ultra‐)mafic magmatic mines in China played a specific role in supporting national development and providing an ideal research subject for monitoring their interrelationship.In this study,remote sensing and mining‐related GIS data were used to identify and analyze 1233(ultra‐)mafic magmatic mine area polygons in China,which covered approximately 322.96 km2 of land and included a V–Ti–Fe mine,a copper–nickel mine,a chromite mine,an asbestos mine,and a diamond mine.It was found that(1)the areal expansion of mines is significantly related to the mine types,perimeter,topography,and population density.(2)The mine area variation also reflects market and policy realities.The temporal expansion of the mine area from 2010 to 2020 followed an S‐shaped pattern(with the turning point occurring in 2014),closely related to iron overcapacity and tightened mining policies.(3)The complexity(D)of the mine area may reflect mine design and excavation practices.To be specific,lower D indicates early‐stage or artisanal/small‐scale mining,whereas higher D represents large‐scale mining.This study demonstrates that the detailed mapping of mine land can serve as an indicator to implement miningrelated market and policy changes.The(ultra‐)mafic mines area data set can be accessed at https://zenodo.org/record/7636616#.Y-p0uXaZOa0.

Spatiotemporal evolution and influencing factors of urban resilience in the Yellow River Basin,China

The Yellow River Basin of China is a key region that contains myriad interactions between human activities and natural environment.Industrialization and urbanization promote social-economic development,but they also have generated a series of environmental and ecological issues in this basin.Previous researches have evaluated urban resilience at the national,regional,urban agglomeration,city,and prefecture levels,but not at the watershed level.To address this research gap and elevate the Yellow River Basin’s urban resilience level,we constructed an urban resilience evaluation index system from five dimensions:industrial resilience,social resilience,environmental resilience,technological resilience,and organizational resilience.The entropy weight method was used to comprehensively evaluate urban resilience in the Yellow River Basin.The exploratory spatial data analysis method was employed to study the spatiotemporal differences in urban resilience in the Yellow River Basin in 2010,2015,and 2020.Furthermore,the grey correlation analysis method was utilized to explore the influencing factors of these differences.The results of this study are as follows:(1)the overall level of urban resilience in the Yellow River Basin was relatively low but showed an increasing trend during 2010–2015,and significant spatial distribution differences were observed,with a higher resilience level in the eastern region and a low-medium resilience level in the western region;(2)the differences in urban resilience were noticeable,with industrial resilience and social resilience being relatively highly developed,whereas organizational resilience and environmental resilience were relatively weak;and(3)the correlation ranking of resilience influencing factors was as follows:science and technology level>administrative power>openness>market forces.This research can provide a basis for improving the resilience level of cities in the Yellow River Basin and contribute to the high-quality development of the region.

Analysis of the Spatiotemporal Variation Characteristics and Driving Factors of Land Vegetation GPP in a Certain Region of Asia

Gross primary productivity (GPP) of vegetation is a critical indicator of ecosystem growth and carbon sequestration. The spatiotemporal variation characteristics of land vegetation GPP trends in a specific region of Asia from 2001 to 2020 were analyzed by Sen and MK trend analysis methods in this study .Moreover , a GPP change attribution model was established to explore the driving influences of factors such as Leaf Area Index (LAI), Land Surface Temperature (LST), Vapor Pressure Deficit (VPD), Soil Moisture, Solar Radiation and Wind Speed on GPP. The results indicate that summer GPP values are significantly higher than those in other months, accounting for 60.8% of the annual total GPP;spring and autumn contribute 18.91% and 13.04%, respectively. In winter, due to vegetation being nearly dormant, the contribution is minimal at 7.19%. Spatially, GPP shows a decreasing trend from southeast to northwest. LAI primarily drives the spatial and seasonal variations of regional GPP, while VPD, surface temperature, solar radiation, and soil moisture have varying impacts on GPP across different dimensions. Additionally, wind speed exhibits a minor contribution to GPP across different dimensions.

Exploring the Relationship between Spatiotemporal Variations in Air Quality and Meteorological Parameters before and during the COVID-19 Pandemic in Xi’an

The COVID-19 pandemic has significantly changed the air pollution of the world. The present study investigated the temporal and spatial variability in air quality in Xi’an, China, and its relationship with meteorological parameters during and before the COVID-19 pandemic. The outcomes of this study indicated that air pollutants, PM2.5, NO2, PM10, CO, and SO2 are likely to decrease during winter (25%, 50%, 30%, 40%, and 35%) to spring (30%, 55%, 38%, 50%, and 40%) and summer (40%, 58%, 60%, 55%, and 47%), respectively. However, the concentration of O3-8h increased by 40%, 55%, and 65% during winter, spring, and summer, respectively. The values of the air quality index decreased during the COVID-19 period. Furthermore, significant positive trends were reported in PM2.5, NO2, PM10, O3, and SO2, and no notable trends in CO during the COVID-19 pandemic. Both during and before the COVID-19 period, PM10, NO2, PM2.5, CO, and SO2 showed a negative correlation with the temperature and a moderately positive significant correlation between O3-8h and temperature. The findings of this study would help understand the air pollution circumstances in Xi’an before and during the COVID-19 period and offer helpful information regarding the implications of different air pollution control strategies.

Less is more: a new machine-learning methodology for spatiotemporal systems

Machine learning provides a way to use only portions of the variables of a spatiotemporal system to predict its subsequent evolution and consequently avoids the curse of dimensionality.The learning machines employed for this purpose,in essence,are time-delayed recurrent neural networks with multiple input neurons and multiple output neurons.We show in this paper that such kinds of learning machines have a poor generalization ability to variables that have not been trained with.We then present a one-dimensional time-delayed recurrent neural network for the same aim of model-free prediction.It can be trained on different spatial variables in the training stage but initiated by the time series of only one spatial variable,and consequently possess an excellent generalization ability to new variables that have not been trained on.This network presents a new methodology to achieve finegrained predictions from a learning machine trained on coarse-grained data,and thus provides a new strategy for certain applications such as weather forecasting.Numerical verifications are performed on the Kuramoto coupled oscillators and the Barrio-Varea-Aragon-Maini model.

Controlling and synchronizing spatiotemporal chaos of the coupled Bragg acousto-optical bistable map system using nonlinear feedback

In this paper we present the control and synchronization of a coupled Bragg acousto-optic bistable map system using nonlinear feedback technology. This nonlinear feedback technology is useful to control a temporally chaotic system as well as a spatiotemporally chaotic system. It can be extended to synchronize the spatiotemporal chaos. It can work in a wide range of the controlled and synchronized signals, so it can decrease the sensitivity down to a noise level. The synchronization can be obtained by the analysis of the largest conditional Lyapunov exponent spectrum, and easily implemented in practical systems just by adjusting the coupled strength without any pre-knowledge of the dynamic system required.

Spatiotemporal Variation of Plant Diversity Under a Unique Estuarine Wetland Gradient System in the Yellow River Delta, China

Multiple natural and human factors in estuarine wetlands result in complicated land surface characteristics with distinct spatial and temporal heterogeneities,thereby contributing to the difficulty in identifying spatiotemporal variations and influencing factors of plant diversity.A unique estuarine wetland gradient system(UEWGS)consisting of soil,vegetation,heat,distance,landscape,and anthropogenic gradients was established based on the ecological features of estuarine wetland through remote sensing and field investigation methods.It resolved the complicated land surface characteristics,covered all aspects of factors influencing plant diversity,and possessed distinct spatiotemporal heterogeneities.The Yellow River Delta,the largest estuarine wetland in the northern China,was selected as the study area to demonstrate UEWGS in four seasons in 2017.A total of 123 species were recorded with considerable seasonal difference.Phragmites australis,Suaeda salsa,and Tamarix chinensis were the dominant species,and crop species also played important roles.In single effect,all aspects of gradients exerted significant influences,yet only vegetation gradient possessed significant influences in all seasons.In comprehensive effect,soil,vegetation,heat,and distance gradients showed significant gross influences.Moisture content in soil gradient and net primary productivity in vegetation gradient possessed significant net influences in all seasons and can be considered as the main driving factor and indicator,respectively,of plant diversity.The results validated the significance of UEWGS in revealing the plant diversity spatiotemporal characteristics and influencing factors,and UEWGS possessed universal applicability in the spatiotemporal analysis of plant diversity in estuarine areas.

GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems

Owing to the expansion of the grid interconnection scale,the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important.These characteristics can provide effective support in coordinated security control.However,traditional model-based frequencyprediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models.Therefore,this study presents a rolling frequency-prediction model based on a graph convolutional network(GCN)and a long short-term memory(LSTM)spatiotemporal network and named as STGCN-LSTM.In the proposed method,the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input.An improved GCN embedded with topology information is used to extract the spatial features,while the LSTM network is used to extract the temporal features.The spatiotemporal-network-regression model is further trained,and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information.The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response.The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems.