Lake is an important part of the natural ecosystem, and its morphological characteristics reflect the capacity of lake regulation and storage, the strength of material migration, and the characteristics of shoreline d...Lake is an important part of the natural ecosystem, and its morphological characteristics reflect the capacity of lake regulation and storage, the strength of material migration, and the characteristics of shoreline development. In most existing studies, remote sensing images are used to quantify the morphological characteristics of lakes. However, the extraction accuracy of lake water is greatly affected by cloud cover and vegetation cover, and the inversion accuracy of lake elevation data is poor, which cannot accurately describe the response relationship of lake landscape morphology with water level change. Therefore, this paper takes Tonle Sap Lake as the research object, which is the largest natural freshwater lake in Southeast Asia. DEM is constructed based on high-resolution measured topographic data, and morphological indicators such as lake area, lake shoreline length, perimeter area ratio, longest axis length, maximum width, shoreline development index, lake shape complexity, compactness ratio and form ratio are adopted to researching the evolution law of high water overflows and low water outbursts quantitatively, and clarifying the variation characteristics of landscape morphology with water level gradient in Tonle Sap Lake. The research results have important theoretical significance for the scientific utilization of Tonle Sap Lake water resources and the protection of the lake ecosystem.展开更多
The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,G...The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global citizens.This perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.展开更多
Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with ...Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic.Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures.To address this short-coming,a multi-scale spatiotemporal data product is proposed as a public repository platform,based on a spatiotemporal cube,and allows the integration of different data sources by adopting various data standards.Within the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city levels.This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step.This rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal analysis.The data is available in Harvard Dataverse platform(https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN)and GitHub open source repository(https://github.com/stccenter/COVID-19-Data).展开更多
文摘Lake is an important part of the natural ecosystem, and its morphological characteristics reflect the capacity of lake regulation and storage, the strength of material migration, and the characteristics of shoreline development. In most existing studies, remote sensing images are used to quantify the morphological characteristics of lakes. However, the extraction accuracy of lake water is greatly affected by cloud cover and vegetation cover, and the inversion accuracy of lake elevation data is poor, which cannot accurately describe the response relationship of lake landscape morphology with water level change. Therefore, this paper takes Tonle Sap Lake as the research object, which is the largest natural freshwater lake in Southeast Asia. DEM is constructed based on high-resolution measured topographic data, and morphological indicators such as lake area, lake shoreline length, perimeter area ratio, longest axis length, maximum width, shoreline development index, lake shape complexity, compactness ratio and form ratio are adopted to researching the evolution law of high water overflows and low water outbursts quantitatively, and clarifying the variation characteristics of landscape morphology with water level gradient in Tonle Sap Lake. The research results have important theoretical significance for the scientific utilization of Tonle Sap Lake water resources and the protection of the lake ecosystem.
基金NSF(1841520,1835507,1832465,2028791 and 2025783)the NSF Spatiotemporal Innovation Center members.
文摘The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global citizens.This perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.
基金The research presented in this paper was funded by the National Science Foundation(1841520 and 1835507).
文摘Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic.Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures.To address this short-coming,a multi-scale spatiotemporal data product is proposed as a public repository platform,based on a spatiotemporal cube,and allows the integration of different data sources by adopting various data standards.Within the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city levels.This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step.This rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal analysis.The data is available in Harvard Dataverse platform(https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN)and GitHub open source repository(https://github.com/stccenter/COVID-19-Data).
