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FluxDataONE: An Integrated Solution for theManagement, Visualization, and Analysis of Flux

Data for Agricultural and Ecological StudiesAn Yan, Baolei Lv, Fang Liu, Qing Li, Guanghui Lin, and Yuqi Bai, Senior Member, IEEE

Abstract—Determining water flux as well as emission and uptakerates of various greenhouse gases, including , , and ,is one of the key research issues when performing agricultural andecological research in relation to global change. There are fourimportant and desired functions for the community to effectivelycollaborate with each other when performing studies in this area:metadata description, data interchange speciation, relational data-base, and online analysis. Since these four are still not fully availableamong the flux data networks, the design goal of FluxDataONE is todemonstrate an integrated solution for the management, visualiza-tion, and analysis of flux data when fulfilling large-scale collabora-tive agricultural and ecological studies. The major components ofFluxDataONEaremetadata description standarddedicated forfluxdata, flux data interchange specification, a relational database-based flux data storage solution, a versatile way to integrate analysisfunction modules, and finally an integrated research environmentfor flux data discovery, visualization, and analysis. This paperintroduces the system design, the recent progress, and a researchuse case. FluxDataONE is promising in improving the researchefficiency, enabling large-scale collaborative research, and acceler-ating the scientific insights for the agricultural and ecologycommunities.

Index Terms—Data interchange, flux data, metadata, onlineanalysis, research environment.

I. INTRODUCTION

F LUX of , , , , and other greenhousegases are characterized above soil andwater surfaces, plant

canopies, and urban or industrial areas, from a single-pointmeasurement using permanent or mobile stations. Of all the fluxmethods, the eddy covariance has been used in agriculturalsciences for over 30 years in the areas of yield research, lightand water use efficiencies, agricultural carbon sequestrationresearch, bio-fuel investigations, crop management, etc. [1].Since the eddy covariance method is one of the most accurateapproaches available to date, hundreds of flux towers have beenestablished worldwide to measure the gases and water vapor in a

variety of agro-ecological zones. The agricultural and ecologicalresearch communities have made big progresses on sharing theflux data through a variety of networks, such as AmeriFlux [2],Asiaflux [3], ChinaFLUX [4], CarboAfrica, 1 CarboEuropeIP, 2

Ozflux, 3 Fluxnet-Canada, 4 KoFlux, 5 JapanFlux, 6 Fluxnet [5],andU.S.–China CarbonConsortium (USCCC).7 These activitieshave been very useful in promoting data exchange among theagricultural and ecological societies.

However, there are four important and desired functions thatare still not fully available among these data networks. The firstone is that there is no dedicatedmetadata description standard forthe eddy flux data. Since the communications among stock-holders may take a pretty long time to ensure the availability ofthe data of interest when involving a large number of sites forvalidation and syntheses, the current manual process of dataexchange needs to be revolutionized to be more scalable. Bydefining, adopting, and promoting a metadata description speci-fication for the eddy flux data, the agricultural and ecologicalsocieties will be able to enable a standardized description of theflux data individually maintained by individual researchers orgroups, to enable a flexible data discovery process through ametadata catalog service, and to further enable catalog federationfor large-scale data sharing and synthesis among these flux datanetworks.

The second desired function is a dedicated flux data inter-change specification. There are a number of tools and softwarepackages that agricultural and ecological researchers use toprocess the instantaneous flux data, and to produce a variety offlux datasets. Therefore, researchers are facing the problems ofconverting the flux data provided by others to be compatible withthe local working environment. By defining and adopting a datainterchange specification, they do not need to convert the datafrom other formats to be in the one adopted in their local workingenvironment. Instead, they only need to take care of the conver-sion between the format and the common standard data format. Adedicated flux data interchange specification benefits the agri-cultural and ecological research communities for effective datareuse and repurpose.

1939-1404 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

Manuscript received November 16, 2013; revised March 02, 2014; acceptedApril 17, 2014. This work was supported by the Ministry of Science andTechnology’s (MOST) 973 project (2013CB956601) of China. (Correspondingauthor: Yuqi Bai.)

The authors are with the Ministry of Education Key Laboratory for EarthSystemModeling, Center for Earth SystemScience, TsinghuaUniversity,Beijing100084,China (e-mail: yan-a12@mails.tsinghua.edu.cn; lvbl12@mails.tsinghua.edu.cn; liu_fang0729@126.com; qing-li11@mails.tsinghua.edu.cn; lingh@tsinghua.edu.cn; yuqibai@tsinghua.edu.cn).

Color versions of one ormore of the figures in this paper are available online athttp://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2014.2321473

1Available: http://www.carboafrica.net.2Available: http://www.carboeurope.org/.3Available: http://www.ozflux.org.au/.4Available: http://fluxnet.ccrp.ec.gc.ca/.5Available: http://www.ncam.kr/page/koflux/database/index.php.6Available: http://www.japanflux.org.7Available: http://research.eeescience.utoledo.edu/lees/research/usccc/.

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The third one is a data storage solution based on relationaldatabase. Physical file is still the common way to long-termarchive, and to exchange the flux data within the agricultural andecological research communities. This brings barriers in effec-tive data analysis, cross-validation, and syntheses. Actually, forindividual research site, the parameters that flux measurementdevice could capture are basically the same for the instantaneousflux data, and for the averaged flux data. Therefore, building aflexible database to long-term archive the flux data, and thenbuilding advanced data analysis function based on matureddatabase language would be very promising in accelerating thescientific insights and in facilitating data exchange.

The fourth significant issue facing the agricultural and eco-logical research communities is the way of doing science in thecoming big data era. Although it is just the averaged flux datafrequently exchanged, and synthesized in the communities, it isstill imperative for flux data producers to keep and archiveoriginal high frequency raw data files. The agricultural andecological communities still heavily rely on manual processesto fulfill data interchange and syntheses studies. We argue thatthis way of doing research needs to be changed [6]. Instead ofasking researchers to accumulate the data of interest locally tofulfill the research studies, we propose that an integrated Web-based research environment for effective flux data discovery andintegrated analysis is promising in improving the researchproductivity, which finally benefits the whole research commu-nities in crop science and ecology.

We selected 10 representative flux networks, such as Ameri-Flux, ChinaFLUX, and Fluxnet.We examined the availability ofthe aforementioned four functions in each of these flux networks,and summarized the results in Table I.

The character of “ ” denotes that the specific function is notavailable in that flux network, and “Y”means that it is available.The finding is that none of these data networks fully supportsthese desired functions.

The purpose of this paper is to present through a case studyhow these significant issues could be taken care of. The restof this paper is organized as follows. The case study of Flux-DataONE is introduced followed by a detailed analysis of theproposed solution to these issues. Section II focuses on the designstrategy followed by the system implementation in detail. Theadvantages of the proposed solutions are discussed leading to theconclusion.

II. FLUXDATAONE PROJECT

A. Project Background

USCCC was established on December 16, 2003 in Beijing,China, as a collaborative consortium between American andChinese institutions that have interests in studying the role ofmanaged ecosystems in the global carbon and water cycles. Theoverall goal is to develop a network of study sites sponsored bythe above institutions in hope that data and results will be sharedso that synthesis can be made at broader spatial scales to assessthe influence of human activities on carbon and water fluxes inthe changing climate.

Like many other flux networks, USCCC relies on manualprocess to promote data interchange and sharing within thecommunity.After 10years of practices,USCCCdecided to furtherenable a more open solution for the flux data archive andmanagement to facilitate the cross-site validation and synthesis.Since July 2013, USCCC has collaborated with the Center forEarth System Science at Tsinghua University on FluxDataONEinitiative. Thegoalof thisproject is todemonstratehowadedicatedmetadata standard for flux data, a common flux data interchangespecification, a relational database-based flux data managementsolution, and an integrated data discovery and analysis researchenvironment could jointly benefit the USCCC members in per-forming large-scale agricultural and ecological studies.

B. Designs of FluxDataONE System

As shown in Fig. 1, FluxDataONEmainly consists of flux datadatabase, flux data metadata database, analysis function meta-data database, catalog service, analysis function library, and thefront-end Web-based user interface. Besides these modules,there are three specifications working behind the things tolink these modules together: flux data metadata specification,flux data interchange specification, analysis function metadataspecification.

The flux data database is a relational database-based solutionfor flux data management [7]. Besides the flux data and the siteinformation, the flux database may also contain the meteorolog-ical data, which usually accompanies the flux data during thephases of capturing, preconditioning, applying corrections, andaveraging.

The flux data metadata database is a relational database-basedsolution for metadata information management. In particular,

TABLE IAVAILABILITY OF THE FOUR DESIRED FUNCTIONS IN MAJOR FLUX DATA NETWORKS

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ISO geographical information metadata were utilized to definethe dedicated metadata standard for the flux data [8].

Analysis function metadata database holds the descriptiveinformation for sharable flux data analysis functions managedin the FluxDataONE. In particular, it identifies the softwarestacks that analysis functions need to be executed, the desiredinput data, and the expected output data.

Catalog service exposes data discovery function for users. Itsearches the metadata information to know which flux datamatches users’ search criteria. OpenGIS Consortium catalogservice standard [9] was adopted in FluxDataONE to enable theflux data discovery function through standard HTTP protocol.

Analysis function library is where the flux data analysisfunctions are archived and executed.

Based on thesemodules, FluxDataONE provides four types offunctions for users through theWeb-based user interface: 1) data/function discovery, 2) data download, 3) data analysis, and4) data synthesis.

Also shown in Fig. 1, there are two types of users that couldinteract with the FluxDataONE: provider and user. Providers arefor those contributing the flux data, or those contributing the fluxdata analysis functions. For users, anonymous users could accessthe FluxDataONE through the GUI interface for flux datadiscovery, while registered users could further download thedata of interest to their local working environment for furtherprocessing, or analyze the data in FluxDataONE using theavailable analysis functions without downloading the originaldata, or even perform the data synthesis work, which mayinvolve multiple sites of data and multiple steps of processing.

III. PROJECT PROGRESS

A. Flux Data Metadata Standard

ISO 19115 Geographic Information Metadata Standard [10]was adopted to propose a dedicated metadata profile to describethe flux data. The top-level UML packages are illustrated inFig. 2.

Modifications and extensions are made to the ISO 19115according to the extension rules described mainly in “AnnexC: Metadata extensions and profiles and Annex F: Metadata

extension methodology.” Two top-level packages: “Spatial Re-presentation Information” and “Portrayal Catalogue Informa-tion” of ISO 19115 are excluded in this profile because eddycovariance flux data carries very limited geographic information.Also, in order to cover necessary information specifically foreddy covariance data, the class “MD_Identification” was modi-fied to incorporate the extended class “MD_SiteInformation”which describes information regarding site, instrument, variable,and environment condition.

The XML Schema implementation for eddy flux metadata isdesigned based on ISO 19139 Geographic information: 2007:Geographic Information—Metadata—XMLSchema Implemen-tation [11]. Fig. 3 presents an XML fragments adhere to the eddycovariance metadata XML Schema.

B. Flux Data Interchange Specification

Data interchange speciation is formulated by defining dataformat to promote data exchange, sharing, and integration withinthe USCCC community. This format mainly specifies the datacontent, data encoding format, organizing method, and data fileformat.

Data content: the data items, orders of these items, and exactname of each items;Data format: format of attribute values, including decimaldigits;Organizing method: length of each variable, naming meth-ods of files, procedures to submit data;Data file format: the CSV format was adopted as theinterchange format.

To boost data sharing activities, policies on data storing,requesting, sharing, publishing, and submitting activities havebeen formalized within the USCCC community. To protectPrincipal Investigator (PI)’s intellectual property, researchersneed to fill in an application form to request for the data ofinterest. Data sharing can be conducted online and authorshipshould be in consensus among related PIs before resultspublished.

Fig. 1. FluxDataONE system architecture.

Fig. 2. Top-level packages of metadata specification proposal for flux data.

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C. Relational Database-Based Solution for Flux Data

A relational database schema was designed to archive the fluxdata, including the accompanied meteorological data.

As shown in Fig. 4, the designed database scheme allows afine-grained storage solution at variable level, thus enablingusers to fetch partial of the flux data and meteorological datafor selected variables, rather than having to download the entiredata. More importantly, maintaining flux data and meteorologi-cal data at variable level makes flux data online analysis possible.

D. Analysis Function Modules

In FluxDataONE, analysis functions are managed as execut-able analysis modules. These modules, along with the descrip-tion information are available for researchers to check whenanalyzing the discovered flux data. These modules are Web-accessible, so that they could be executed on-the-fly to respond toresearchers’ analysis request.

For example, the relationship between net ecosystem exchange(NEE of ) and photosynthetic active radiation (PAR) during

the daytime (when > μ ) is a very useful func-tion in global carbon cycle studies. TheLandsbergModel [12]wasselected to fulfill in FluxDataONE for demonstration purpose

where

maximum rate of photosynthesis (μ );shape parameter representing apparent quantum yield(μ μ quanta);light compensation point (μ ).

The response of daytime NEE to light was analyzed based onthe initial value range of the parameters ( μ <

< μ , > μ μ quanta,> μ ) from an experimental study on selected

mangrove species in China [13].In FluxDataONE, this analysis function was implemented as a

Web-accessible module, so that researchers could access it easily.For many legacy analysis functions that depend on software

packages, such as scripts written in R language, a wrapper isneeded to convert the protocols to be HTTP compliant, so thatthese functions could be accessible in a Web computing envi-ronment. For example, an analysis codewritten inR could still beaccessible in FluxDataONE, after a dedicated wrapper written inJava is deployed.

E. Prototype System

Two USCCC sites in China operated by one of our coauthors(Guanghui Lin), Gaoqiao, ZhangJian, Guangdong province andZhangjiangkou estuary, YunXiao, Fujian province, have beenselected for demonstration purpose in FluxDataONE. MySQLdatabase was selected to archive the flux data. A relationaldatabase schema was designed to maintain both the flux dataand the meteorological data in the database. Flux data andmeteorological data from these USCCC sites have been ingestedinto the database. Data visualization module and two analysismodels written in R have been deployed in the system.

All the archived flux data and meteorological data, the avail-able analysis functions are now accessible through the integrateduser interface, which is shown in Fig. 5.

Fig. 4. Entity relationship diagram for flux and meteorological data inFluxDataONE.

Fig. 3. XML encoding of MD_DataIdentification element.

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IV. RESEARCH CASE STUDY

The efficiency of the FluxDataONE System in acceleratingecological research could be illustrated through a case studydescribed below.

An ecologist, Tom, is interested in knowing the ecologicalvalues of mangroves in tropical regions through quantitativeanalysis of observation data. In particular, he wants to learn thecharacteristics of the NEE and PAR in that area, and theirrelations during the daytime.

Using FluxDataONE, Tom may define the regions of interest,the located geographic region, the IGBP land cover classificationof the site location, the restrictions in time coverage and inter-ested variables to search for data meet these requirements.

As shown in Fig. 6, Tom defined these search criteria inthe search condition panel, and selected NEE and PAR_Avg asthe parameters of interest. After clicking the search button, Tomwill be presented with the list of matched records. As shown inFig. 7, the NEE (μ ) and PAR values captured in theZhangjiangkou site were presented, along with the Timestampinformation.

In the same window, Tommay select the parameter of interestto visualize. As shown in the Fig. 8, the NEE values during theday of June 11, 2012 were plotted.

Clicking each point in that line graphwill show the exact valueand the timestamp information in a pop-up window. Tom cannow easily recognize the diurnal dynamics of NEE of mangroveforest in the Zhangjiangkou site, and to know themaximumNEEvalue, about μ , which indicates the highest daytimerespiration, and the minimumNEE value, about μ ,which represents the peak carbon assimilation rate.

To further examine the relationship between NEE and PARduring the daytime on that day, Tommay turn to the data analysispanel to find predeployed analysis modules to use. As shown in

Fig. 5. FluxDataONE Web-based user interface.

Fig. 6. Search panel.

Fig. 7. Search results panel.

Fig. 8. Data visualization panel.

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Fig. 9, Tom selected “PAR-NEE-Landsberg” module, andexecuted this analysis request. The system invoked this modulewith PAR and NEE values retrieved in the previous step, andfinally presented the regression line graph, and the estimatedvalues.

Since nighttime respiration, daytime carbon assimilation rate,and the parameters obtained from regression analysis of Lands-berg model all provided important information of forest physio-logical process; Tom is now getting more insights about theecological values of mangroves in the Zhangjiangkou site.

This use case clearly shows that FluxDataONE is capable ofenabling flux data management, discovery, visualization, andanalysis in an integrated environment, and this solution candramatically facilitate the overall research efficiency to helpresearchers to quickly gain insights from the data.

V. CONCLUSION

In this paper, issues of metadata standard, data interchangeformat, data storage solution based on relational database, and away of performing integrated data discovery and analysis wereanalyzed. FluxDataONEwas presented to introduce an integratedplatform for the management, visualization, and analysis of fluxdata. A research use case of utilizing FluxDataONE to know theecological values of mangroves in tropical regions is illustrated.

The following conclusions are reached.1) ISO 19115 is useful in defining a dedicated metadata

specification for flux data.2) Relational database could be leveraged to archive the flux

data and the accompanied meteorological data, and tosupport fine-grained data customization and access.

3) Sharing the analysis functions online is doable by deploy-ing the analysis codes and the needed software stacks on

the remote server, and, if necessary, introducing a wrapperto convert the legacy protocol to be Web-accessible.

4) An integrated data archive, discovery, visualization, andanalysis environment is very promising in dramaticallyreducing the amount of data need to transform from thedata provider to the data user, and in accelerating thescientific insights.

REFERENCES

[1] G. Burba, Eddy Covariance Method for Scientific, Industrial, Agriculturaland Regulatory Applications: A Field Book on Measuring Ecosystem GasExchange and Areal Emission Rates. LI-CORBiosciences, 2013, pp. 36–37.

[2] T.A.Boden,M.Krassovski, andB.Yang, “TheAmeriFlux data activity anddata system: An evolving collection of data management techniques, tools,products and services,” Geosci. Instrum. Methods Data Syst. Discuss.,vol. 3, pp. 59–85, 2013.

[3] S. Yamamoto et al., “Findings through the AsiaFlux network and a viewtoward the future,” J. Geogr. Sci., vol. 15, pp. 142–148, 2005.

[4] Y. Fu, L. Zhang,X.Wen, andG.Yu, “Spatial patterns and climate drivers ofcarbon fluxes in terrestrial ecosystems of China—Some new progresses ofChinaFLUX,” AsiaFlux Newslett., vol. 19, no. 3, p. 13, 2013.

[5] D. Baldocchi et al., “FLUXNET: A new tool to study the temporal andspatial variability of ecosystem-scale carbon dioxide, water vapor, andenergy flux densities,” Bull. Amer. Meteorol. Soc., vol. 82, pp. 2415–2434,2001.

[6] Y. Bai and L. Di, “Review of geospatial data systems’ support of globalchange studies,” Br. J. Environ. Clim. Change, vol. 2, pp. 421–436, 2012.

[7] B. Lv, A. Yan, Y. Bai, and B. Xu, “A new generation of data sharingplatform for eddy covariance flux data,” in Proc. 2nd Int. Conf. Agro-Geoinformat., 2013, pp. 139–142.

[8] A. Yan, B. Lv, Y. Bai, and B. Xu, “Metadata standard proposal for eddycovariance flux data,” in Proc. 2nd Int. Conf. Agro-Geoinformat., 2013,pp. 121–125.

[9] OpenGIS Catalogue Service Implementation Specification, OpenGIS Stan-dard 07-006r1, 2007.

[10] Geographic Information—Metadata, ISO Standard 19115, 2003.[11] Geographic Information—Metadata—XML Schema Implementation, ISO

Standard 19139, 2007.[12] J. Chen et al., “Biophysical controls of carbon flows in three successional

Douglas-fir stands based on eddy-covariance measurements,” Tree Phy-siol., vol. 22, pp. 169–177, 2002.

[13] N. Li et al., “Effect of NaCl on photosynthesis, salt accumulation and ioncompartmentation in twomangrove species, Kandelia candel andBruguieragymnorhiza,” Aquat. Bot., vol. 88, pp. 303–310, 2008.

An Yan received the B.S. degree in remote sensingscience and technology from Wuhan University,Wuhan, China, in 2012, and currently pursuing thePostgraduate Studentwith theCenter for Earth SystemScience, Tsinghua University, Beijing, China.

Her research interests include cloud computing-based massive spatial data processing, research cyberinfrastructure, reproducible research, and develop-ment of geospatial information standards.

Baolei Lv received the B.S. degree in environmentalengineering from Tsinghua University, Beijing,China, in 2012, and currently pursuing the Postgrad-uate Studentwith theCenter for Earth SystemScience,Tsinghua University, Beijing, China.

His research interests include regional atmo-spheric pollution and transportation of air pollutantsin China.

Fig. 9. Data analysis panel.

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Fang Liu received the B.S. degree in geographicinformation system from Northeast Forestry Univer-sity, Harbin, China, in 2012, and currently pursuingthe M.S. degree in ecology with the Center for EarthSystem Science, TsinghuaUniversity, Beijing, China.

She has published three scientific articles. Herresearch interests include carbon cycle in coastalwetland and applications of eddy covariance technol-ogy, flux data processing, and estimation of grossprimary productivity by remote sensing measures.

She received the Outstanding Student PaperAward of Northeast Forestry University in 2012 and the Yang Hanxi CareerEnhancement Award on the 4th International Young Ecologist Forum in 2013.

Qing Li received the B.S. degree in biology fromChina Agricultural University, Beijing, China, in2006, and the M.S. degree in marine biology fromTsinghua University, Beijing, China, in 2009, wherehe is currently pursuing the Ph.D. degree in ecology.

Guanghui Lin received the Ph.D. degree in ecologyfrom theUniversity ofMiami,CoralGables, FL,USA,in 1992.

He is currently a Professor with Tsinghua Univer-sity, Beijing, China, in charge of establishing a brandnew research and education program for ecology. Hehas served as the Principal Investigator (PI) for overUS$10 million research grants awarded by Chinesefunding agencies such as Ministry of Science andTechnology, Natural Science Foundation of China,State Oceanic Administration of China, and Chinese

Academyof Sciences.Hehas engaged in plant carbon–water relations, ecosystemcarbon processes, and global carbon cycle research for more than 25 years, andhas published more than 150 papers. He has actively involved in the activities ofvarious professional and international organizations such as IPCC, EcologicalSociety of America (ESA), Ecological Society of China (ESC), and U.S.–ChinaCarbon Consortium (USCCC). His research interests include global changeecology, stable isotope ecology, and coast wetland ecology.

He served as a Lead Author for IPCC 2013 Supplement Guidance onGreenhouse Inventory of Wetlands (2011–2013), the Co-Chairman for USCCC(2012–2014), and a member of Editorial Board for a new online journalEcosystem Health and Sustainability sponsored jointly by ESC and ESA(2014–till date).

YuqiBai (M’06–SM’13) received the Ph.D. degree incartography and GIS from the Institute of RemoteSensing Applications, Chinese Academy of Sciences,Beijing, China, in 2003.

He is the Task Leader of the Group on EarthObservation (GEO)’s Global Earth Observation Sys-tem of Systems (GEOSS) Component and ServiceRegistry team, and the Committee on Earth Observa-tion Satellites (CEOS)WorkingGroupon InformationSystems and Services (WGISS) Integrated SatelliteData Catalogue (CWIC) team. He has more than

10 years of dedicated research on geospatial data integration and systeminteroperability. He has published more than 50 scientific papers and reports.His research interests include cyber infrastructure for global change studies, andin particular, geospatial semantics, and spatial–temporal analysis of climatechange data and camera surveillance data.

Dr. Bai is a member of the CHINA-GEO Science and Technology AdvisoryBoard and the Co-Lead of the GEO CL-01-C4 Task (easy access to, and use of,climate information).

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