Cities hold a critical responsibility for achieving the Sustainable Development Goals(SDGs)due to their high population density,extensive resource consumption,and significant economic contributions.To examine the pres...Cities hold a critical responsibility for achieving the Sustainable Development Goals(SDGs)due to their high population density,extensive resource consumption,and significant economic contributions.To examine the present state of understandings regarding urban sustainability(SDG 11:Sustainable Cities and Communities)within Chinese research communities,this study collected 15950 papers from 1994 to 2022 on the 12 indicators of SDG 11,from the China National Knowledge Infrastructure(CNKI),a hub of Chinese academic papers,that directly relate to policymaking.Significant research topics on SDG 11 were identified for each indicator using bibliometrics analysis approaches.The high-frequency keywords and clusters of keywords over the last three decades reveal that existing studies primarily concentrated on the physical aspects,such as transportation and environment,while there is a lack of consideration of societal aspects.This indicates a limited and biased understanding of the urban sustainability within the Chinese academic community.Hence,it is crucial to prioritize the societal aspects in order to develop a research agenda that further advances urban sustainability.展开更多
Megaregion has emerged as a global urban form,typically based on the polycentric strategy to enhance regional development.How to measure megaregional spatial structure and discriminate different roles of cities has be...Megaregion has emerged as a global urban form,typically based on the polycentric strategy to enhance regional development.How to measure megaregional spatial structure and discriminate different roles of cities has become increasingly important to enrich the knowledge of the formation of a megaregion.Meanwhile,various indices have been used to identify vital nodes in the field of complex network.Which indices,however,are suitable for megaregion analysis remain unsolved.To address this requirement,this study first reviewed the typical indices for identifying vital nodes in the complex network theory,and pointed out that in a weighted city network scenario,weighted degree centrality,hub&authority score,and S-core decomposition(which represent network centrality,connectivity,and structures,respectively)are suitable for analyzing megaregional spatial structures.Then,we explored the city hierarchies and spatial structure in Guangdong Province,China,using the three indices.The hierarchical structure of the weighted city network in Guangdong Province had been identified using S-core decomposition.From the perspective of polycentric structure,Guangzhou and Shenzhen have the strongest node degrees and strength of mobility flows,while the Guangzhou-Dongguan-Shenzhen corridor has been identified via the hub&authority score which is designed to evaluate the connectivity in a weighted network.Moreover,we conducted a comparison analysis of three indices.The findings of this study not only enrich the understanding of city hierarchies and the structure of a megaregion,but also highlight that although various indices are available,they should be applied selectively in accordance with the study context.展开更多
Although pesticides have been widely used worldwide to enhance crop yield and product quality,most pesticides are harmful to the environment and human health.Plants absorb pesticides mainly from air and soil.Therefore...Although pesticides have been widely used worldwide to enhance crop yield and product quality,most pesticides are harmful to the environment and human health.Plants absorb pesticides mainly from air and soil.Therefore,the soil-plant pathway is essential for pesticide absorption.Bioconcentration factor(BCF)has extensively been applied to evaluate potential plant contamination by pesticides from soil.Hence,this study developed a simplified plant transpiration-based plant uptake model(PT-model)to estimate plant pesticides’BCF from soil based on plant transpiration.Remote sensing techniques were employed to generate spatiotemporal continuous plant transpiration via evapotranspiration.Pesticide BCF mapping was achieved by integrating PT-model with Moderate Resolution Imaging Spectroradiometer(MODIS)remotely sensed data.The results were compared with a verified model driven by relative humidity and air temperature(RA-model),which has been confirmed byfindings from previous studies.The estimated BCF was within the boundaries of the RA-model,indicating the simulation’s overall acceptability.In this study,the BCF temporal trend estimated by the proposed method agreed with the RA-model assimilating meteorology datasets,while the spatial distribution was partially inconsistent.Overall,the proposed method generates the spatiotemporal patterns of pesticide BCF with relatively consistent results supported by previous records andfindings.展开更多
This paper proposes a novel data indexing scheme,the distributed access pattern R-tree(DAPR-tree),for spatial data retrieval in a distributed computing environment.As compared to traditional distributed indexing schem...This paper proposes a novel data indexing scheme,the distributed access pattern R-tree(DAPR-tree),for spatial data retrieval in a distributed computing environment.As compared to traditional distributed indexing schemes,the DAPR-tree introduces the data access patterns during the indexing utilization stage so that a more balanced indexing structure can be provided for spatial applications(e.g.Digital Earth data warehouse).In this new indexing scheme,(a)an indexing penalty matrix is proposed by considering the balance of data number,topology and access load between different indexing nodes;(b)an‘access possibility’element is integrated to a classic‘Master-Client’structure for a distributed indexing environment;and(c)indexing algorithm for the DAPR-tree is provided for index implementations.By using a duplication of official GEOSS Clearinghouse system as a case study,the DAPR-tree was evaluated in a number of scenarios.The results show that our indexing schemes generally outperform(around 9%)traditional distributed indices with the utilization of data access patterns.Finally,we discuss the applicability of the DARP-tree and document DARP-tree shortcomings to encourage researchers pursuing related topics in Big Data indexing for Digital Earth and other geospatial initiatives.展开更多
Soil temperature(ST)plays a critical role in ecosystems.Monitoring highresolution ST profiles remains challenging due to the inherent heterogeneity of ST in space,time,and depth.To address this challenge,in this study...Soil temperature(ST)plays a critical role in ecosystems.Monitoring highresolution ST profiles remains challenging due to the inherent heterogeneity of ST in space,time,and depth.To address this challenge,in this study,we integrate remote sensing techniques and deep learning methods to retrieve spatiotemporal continuous ST from 2011 to 2020 at four depths(5,10,20,and 40 cm)over the central Tibetan Plateau(TP).Landsat and MODIS observations were fused to obtain land surface properties at different resolutions.The fused variables were integrated with in-situ ST and soil moisture(SM)measurements to estimate spatiotemporally continuous(0.0005°,0.0025°,and 0.0125°)ST profile through deep learning-based training models.The deep belief network(DBN)was applied to estimate ST:(a)from layer to layer(LW-DBN),and(b)from land surface properties directly(Direct-DBN).The ten-fold cross-validation indicates that both approaches achieve promising results(R2>0.836,MAE<2.152°C),and the Direct-DBN outperformed the LW-DBN at all spatial scales and depths.ST retrieval at deeper depths and coarser resolutions tend to have better monitoring accuracy.Further analysis indicates an increment of ST at all four depths over the study period,which provides valuable insights into global warming.展开更多
基金National Natural Science Foundation of China(No.42171449)。
文摘Cities hold a critical responsibility for achieving the Sustainable Development Goals(SDGs)due to their high population density,extensive resource consumption,and significant economic contributions.To examine the present state of understandings regarding urban sustainability(SDG 11:Sustainable Cities and Communities)within Chinese research communities,this study collected 15950 papers from 1994 to 2022 on the 12 indicators of SDG 11,from the China National Knowledge Infrastructure(CNKI),a hub of Chinese academic papers,that directly relate to policymaking.Significant research topics on SDG 11 were identified for each indicator using bibliometrics analysis approaches.The high-frequency keywords and clusters of keywords over the last three decades reveal that existing studies primarily concentrated on the physical aspects,such as transportation and environment,while there is a lack of consideration of societal aspects.This indicates a limited and biased understanding of the urban sustainability within the Chinese academic community.Hence,it is crucial to prioritize the societal aspects in order to develop a research agenda that further advances urban sustainability.
基金supported by the National Natural Science Foundation of China-Joint Programming Initiative Urban Europe[grant number 71961137003]the National Natural Science Foundation of China[grant numbers 42171449,42101464].
文摘Megaregion has emerged as a global urban form,typically based on the polycentric strategy to enhance regional development.How to measure megaregional spatial structure and discriminate different roles of cities has become increasingly important to enrich the knowledge of the formation of a megaregion.Meanwhile,various indices have been used to identify vital nodes in the field of complex network.Which indices,however,are suitable for megaregion analysis remain unsolved.To address this requirement,this study first reviewed the typical indices for identifying vital nodes in the complex network theory,and pointed out that in a weighted city network scenario,weighted degree centrality,hub&authority score,and S-core decomposition(which represent network centrality,connectivity,and structures,respectively)are suitable for analyzing megaregional spatial structures.Then,we explored the city hierarchies and spatial structure in Guangdong Province,China,using the three indices.The hierarchical structure of the weighted city network in Guangdong Province had been identified using S-core decomposition.From the perspective of polycentric structure,Guangzhou and Shenzhen have the strongest node degrees and strength of mobility flows,while the Guangzhou-Dongguan-Shenzhen corridor has been identified via the hub&authority score which is designed to evaluate the connectivity in a weighted network.Moreover,we conducted a comparison analysis of three indices.The findings of this study not only enrich the understanding of city hierarchies and the structure of a megaregion,but also highlight that although various indices are available,they should be applied selectively in accordance with the study context.
基金supported by the Natural Resources of Guangdong[No.[2023]-25]National Natural Science Foundation of China[No.42171400]+1 种基金Natural Science.Foundation of Guangdong Province[No.2021A1515011324]Henan Institute of Sun Yat-sen University[No.2021-006].
文摘Although pesticides have been widely used worldwide to enhance crop yield and product quality,most pesticides are harmful to the environment and human health.Plants absorb pesticides mainly from air and soil.Therefore,the soil-plant pathway is essential for pesticide absorption.Bioconcentration factor(BCF)has extensively been applied to evaluate potential plant contamination by pesticides from soil.Hence,this study developed a simplified plant transpiration-based plant uptake model(PT-model)to estimate plant pesticides’BCF from soil based on plant transpiration.Remote sensing techniques were employed to generate spatiotemporal continuous plant transpiration via evapotranspiration.Pesticide BCF mapping was achieved by integrating PT-model with Moderate Resolution Imaging Spectroradiometer(MODIS)remotely sensed data.The results were compared with a verified model driven by relative humidity and air temperature(RA-model),which has been confirmed byfindings from previous studies.The estimated BCF was within the boundaries of the RA-model,indicating the simulation’s overall acceptability.In this study,the BCF temporal trend estimated by the proposed method agreed with the RA-model assimilating meteorology datasets,while the spatial distribution was partially inconsistent.Overall,the proposed method generates the spatiotemporal patterns of pesticide BCF with relatively consistent results supported by previous records andfindings.
基金funded by the National Key R&D Program of China[grant number 2018YFB2100704]Science,Technology and Innovation Commission of Shenzhen Municipality[grant numbers JCYJ20170412142239369,JCYJ20170818101704025]the National Natural Science Foundation of China[grant numbers 41701444,71961137003,41971341].
文摘This paper proposes a novel data indexing scheme,the distributed access pattern R-tree(DAPR-tree),for spatial data retrieval in a distributed computing environment.As compared to traditional distributed indexing schemes,the DAPR-tree introduces the data access patterns during the indexing utilization stage so that a more balanced indexing structure can be provided for spatial applications(e.g.Digital Earth data warehouse).In this new indexing scheme,(a)an indexing penalty matrix is proposed by considering the balance of data number,topology and access load between different indexing nodes;(b)an‘access possibility’element is integrated to a classic‘Master-Client’structure for a distributed indexing environment;and(c)indexing algorithm for the DAPR-tree is provided for index implementations.By using a duplication of official GEOSS Clearinghouse system as a case study,the DAPR-tree was evaluated in a number of scenarios.The results show that our indexing schemes generally outperform(around 9%)traditional distributed indices with the utilization of data access patterns.Finally,we discuss the applicability of the DARP-tree and document DARP-tree shortcomings to encourage researchers pursuing related topics in Big Data indexing for Digital Earth and other geospatial initiatives.
基金supported by National Natural Science Foundation of China[Grant Number 42171400]National Natural Science Foundation of Guangdong[Grant Number 2021A1515011324]+1 种基金Henan Institute of Sun Yat-sen University[Grant Number 2021-006]Natural Resources of Guangdong[Grant Number[2023]-25].
文摘Soil temperature(ST)plays a critical role in ecosystems.Monitoring highresolution ST profiles remains challenging due to the inherent heterogeneity of ST in space,time,and depth.To address this challenge,in this study,we integrate remote sensing techniques and deep learning methods to retrieve spatiotemporal continuous ST from 2011 to 2020 at four depths(5,10,20,and 40 cm)over the central Tibetan Plateau(TP).Landsat and MODIS observations were fused to obtain land surface properties at different resolutions.The fused variables were integrated with in-situ ST and soil moisture(SM)measurements to estimate spatiotemporally continuous(0.0005°,0.0025°,and 0.0125°)ST profile through deep learning-based training models.The deep belief network(DBN)was applied to estimate ST:(a)from layer to layer(LW-DBN),and(b)from land surface properties directly(Direct-DBN).The ten-fold cross-validation indicates that both approaches achieve promising results(R2>0.836,MAE<2.152°C),and the Direct-DBN outperformed the LW-DBN at all spatial scales and depths.ST retrieval at deeper depths and coarser resolutions tend to have better monitoring accuracy.Further analysis indicates an increment of ST at all four depths over the study period,which provides valuable insights into global warming.
