Scientific Investigations Report 2007-5199 

U.S. Department of the Interior 

U.S. Geological Survey 

Geoinformatics 2007—Data to 

Knowledge 

Proceedings 

May 17-18 

San Diego, California 
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia: 2007 

For sale by U.S. Geological Survey, Information Services 

Box 25286, Denver Federal Center 

Denver, CO 80225 

For more information about the USGS and its products: 

Telephone: 1-888-ASK-USGS 

World Wide Web: http://www.usgs.gov 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual copyright owners to 


reproduce any copyrighted materials contained within this report.
 

Suggested citation:
 

Brady, S.R., Sinha, A.K., and Gundersen, L.C., editors, 2007, Geoinformatics 2007—Data to Knowledge, Proceedings: U.S. Geological Survey Scientific Investigations Report 2007-5199, 104 p.
 

Manuscript approved for publication September 10, 2007.
 

Prepared by Reston Publishing Service Center.
 

Editing by Marilyn A. Billone.
 

Photocomposition and design by Cathy Y. Knutson.
 

Cover design by Jenifer Bracewell.
 

For more information concerning this report, please contact 


Shailaja R. Brady, U.S. Geological Survey, 911 National 


Center, Reston, VA 20192, srbrady@usgs.gov.
 
III 

Preface 	 	 	 

Conference	Summary: 

The	Geoinformatics	2007	Conference	presented	the	effort	by	the	geosciences	and	information	technology	communities	to	respond	to	the	growing	need	for	utilizing	multidisciplinary	 

geoscience	datasets	and	tools	to	understand	the	complex	dynamics	of	earth	and	planetary	 

systems.	The	vision	of	the	community	of	a	fully	integrated	geosciences	information	network	 

that	provides	free	access	to	high-quality	earth	and	planetary	science-related	data,	tools,	and	 

services	was	highlighted	at	the	meeting.	The	fusion	of	spatial	and	nonspatial	data	from	earth	 

and	planetary	sciences	needed	to	develop	a	coherent	scientific	understanding	of	the	Earth’s	 

four-dimensional	evolution	and	architecture	is	emphasized	in	the	proceedings	volume.	Information	technology	(IT)	research	related	to	knowledge-based	mediation	and	information	integration	 

techniques	for	four-dimensional	data	models;	visualization	of	multiscale,	four-dimensional	information	spaces;	data-level	interoperability;	metadata	modeling	and	interchange;	and	metadata-based	access	to	data	and	services	were	leading	topics	of	discussion	and	presentations.	More	 

significantly,	issues	of	data	management,	moderated	by	A.	Krishna	Sinha	(Virginia	Polytechnic	 

Institute	and	State	University)	and	discussed	by	panelists	Chris	Greer	(National	Science	Foundation),	Linda	Gundersen	(U.S.	Geological	Survey),	Ian	Jackson	(British	Geological	Survey),	 

Lesley	Wyborn	(Geoscience	Australia),	Peter	Fox	(National	Center	for	Atmospheric	Research),	 

Deborah	McGuinness	(Stanford	University),	and	Shinji	Takarada	(Japanese	Geological	Survey),	leading	to	solutions	of	global	challenges	such	as	natural	resources	and	hazards,	provided	 

the	focus	for	the	conference	and	will	continue	to	drive	geoinformatics	research	in	the	foreseeable	future. 

Conference	Sponsorship: 

Geoinformatics	Division	of	the	Geological	Society	of	America,	U.S.	Geological	Survey,	British	 

Geological	Survey,	National	Science	Foundation,	American	Geophysical	Union,	California	 

Institute	for	Telecommunications	and	Technology,	and	San	Diego	Supercomputer	Center	 

Organizing	Committee: 

A.	Krishna	Sinha	(Chair),	Department	of	Geosciences,	Virginia	Polytechnic	Institute	and	State	 

University,	Blacksburg,	Va.;	Linda	Gundersen,	U.S.	Geological	Survey,	Reston,	Va.;	Peter	Fox,	 

National	Center	for	Atmospheric	Research,	Boulder,	Colo.;	Dogan	Seber,	San	Diego	Supercomputer	Center,	University	of	California—San	Diego,	La	Jolla,	Calif. 

Sally	R.	Brady	(Co-Chair),	U.S.	Geological	Survey,	Reston,	Va.;	A.	Krishna	Sinha	(Co-Chair),	 

Department	of	Geosciences,	Virginia	Polytechnic	Institute	and	State	University,	Blacksburg,	 

Va.;	Linda	Gundersen,	U.S.	Geological	Survey,	Reston,	Va.;	Bruce	Johnson,	U.S.	Geological	 

Survey,	Reston,	Va.;	Robert	Raskin,	Jet	Propulsion	Laboratory,	Pasadena,	Calif.;	Ilkay	Altintas,	 

San	Diego	Supercomputer	Center,	University	of	California—San	Diego,	La	Jolla,	Calif.;	Rahul	 

Ramachandran,	Information	Technology	and	Systems	Center,	University	of	Alabama,	Huntsville,	Ala.;	Peter	Cornillon,	Graduate	School	of	Oceanography,	University	of	Rhode	Island,	 

Narraganset,	R.I.;	and	Hassan	Babaie,	Department	of	Geology,	Georgia	State	University,	 

Atlanta,	Ga. 

Financial	support	was	provided	by	the	U.S.	Geological	Survey,	National	Science	Foundation	 

Division	of	Earth	Science	(EAR),	San	Diego	Supercomputer	Center,	California	Institute	for	 

Telecommunications	and	Information	Technology,	and	Virginia	Polytechnic	Institute	and	State	 

University	Foundation. 

Conference	support	was	provided	by	the	Geological	Society	of	America	(Erin	Pitner,	Nancy	 

Carlson),	California	Institute	for	Telecommunications	and	Information	Technology	(Jennifer	 

Zimmerman);		and	Department	of	Geosciences,	Virginia	Polytechnic	Institute	and	State	University	(Mary	McMurray). 
IV 

Contents 

Preface ..........................................................................................................................................................III
 

Envisioning a Geoinformatics Infrastructure for the Earth Sciences: Technological and 


Cultural Challenges..........................................................................................................................1
 

OneGeology—The Birth of a Global Geoscience Spatial Data Infrastructure, or Just Another 


Noble Aspiration?.............................................................................................................................2
 

Building the AuScope Australian Earth Science Grid .............................................................................3
 

A Very Practical Geoinformatics Project: The Reality of Delivering a Harmonized 


Pan-European Spatial Geoscience Database.............................................................................4
 

Integrated Geological Map Database (GeoMapDB) in Geological Survey of Japan, AIST..............5
 

Semantic Web Services in a Virtual Observatory (VO) ...........................................................................7
 

Semantic Mediator Architecture for Environmental Science................................................................9
 

Semantic Integration of Heterogeneous Volcanic and Atmospheric Data........................................10
 

Semantics and Science: Facilitating a Community Centric View........................................................13
 

CyberIntegrator: A Highly Interactive Scientific Process Management Environment to 


Support Earth Observatories........................................................................................................13
 

DIA Engine: Semantic Discovery, Integration, and Analysis of Earth Science Data........................15
 

Customizing a Semantic Search Engine and Resource Aggregator for Different Science 


Domains ...........................................................................................................................................18
 

Towards Debugging Maps Generated by GEON Applications Through Provenance ......................18
 

Visualization Tool for Oracle Spatial.........................................................................................................20
 

Support Collaboration Geosciences Research Through the GEON Portal ........................................20
 

Managing a Parameter Sweep for Earthquake Simulation Research................................................21
 

GeoSciML Testbed 2: Demonstrating the Exchange of Geologic Map Information Using a 


Common Data Transfer Schema and Open Geospatial Consortium Technologies .............22
 

Interactive Immersive Visualization of Geoscience Data.....................................................................22
 

Earth Science Community Implementation Through Iteration and Testing (ITIT) Resources 


Through a Unified Data and Analysis Portal..............................................................................24
 

A Deployable GEON LiDAR Processing and Analysis System .............................................................24
 

LiDAR-in-the-Box: Serving LiDAR Datasets Via Commodity Clusters.................................................25
 

A Petascale Cyberfacility for Physics-Based Seismic Hazard Analysis............................................25
 

An Integration Scheme for Geophysical Studies of the Continental Lithosphere: An Update .......26
 

Atlas of the Cryosphere: A Web Map Service for the Earth’s Frozen Regions..................................26
 

Building the Interface Facility for Centimeter-Scale, 3D Digital Field Geology.................................29
 

Lithospheric Structure of Northern Africa and Western Eurasia........................................................29
 

Deploying Data Portals ...............................................................................................................................30
 

Dynamic Visualization of Geographic Networks Using Surface Deformations with 


Constraints.......................................................................................................................................31
 

Geoinformatics for Geochemistry (GfG): Integrated Digital Data Collections for the Earth 


and Ocean Sciences......................................................................................................................32
 

GIS Interpolation of Stratigraphic Contact Data to Reconstruct Paleogeography: Deriving 


a Paleolandscape Model of the Base Zuni Sequence and Subsequent Cretaceous 

Islands in Central Texas ................................................................................................................34
 

Global Earth Observation Grid (GEO Grid): Development of an IT Infrastructure for the Earth 


Science Using Satellite Data........................................................................................................36
 
V 

HydroXC: A Common Schema for Hydrologic Data Transfer and Object Descriptions ...................37
 

Implementing the Collaboratory for the Study of Earthquake Predictability (CSEP)........................38
 

A Drastic Revision of Active Fault Database in Japan Based on the Redefined Relational 


Data Model......................................................................................................................................38
 

Community Science Ontology Development...........................................................................................39
 

Ontologic Integration of Geoscience Data on the Semantic Web ......................................................41
 

The Critical Zone Exploration Network: Your Growing Community.....................................................43
 

Towards a Reference Plate Tectonics and Volcano Ontology for Semantic Scientific Data 


Integration .......................................................................................................................................43
 

USGS Energy Program Geoinformatics: From Data Management to Information Services...........46
 

Web Services for Geoscience Data: Experiences and Lessons..........................................................47
 

Scientific Workflows for Ontology-Based Data Mining of Geological Data......................................48
 

The Australian Mineral Occurrence Data Exchange Model................................................................49
 

Emerging Web Services at the IRIS Data Management Center (DMC)..............................................51
 

An Experiment Tool for the Multilayered Geoscience Ontology..........................................................52
 

Data Independence and Geospatial Web Services...............................................................................52
 

Using WDO-it to Build a Geoscience Ontology ......................................................................................54
 

WXGURU: An Ontology-Driven Chatbot Prototype for Atmospheric Science Outreach and 


Education.........................................................................................................................................57
 

The U.S. National Geologic Map Database.............................................................................................57
 

A Data Integration and Interoperability Blueprint for USGS ................................................................58
 

Constructing an International Geoscience Interoperability Testbed to Access Data from 


Distributed Sources: Lessons Learned from a GeoSciML Testbed .......................................60
 

The GEON LiDAR Workflow as a Distribution Pathway for Community LiDAR Topography 


Datasets...........................................................................................................................................62
 

Integrating Geologic Data in the NGMDB, a Standards-Based Approach Using GeoSciML 


and Open-Source Tools.................................................................................................................62
 

Geospatial Interoperability: From Sensors to Decision Support .........................................................64
 

Achieving Interoperability in Geosciences .............................................................................................65
 

DIGGS—Data Interchange Standard for Geotechnical and Geoenvironmental Data.....................66
 

Visual Representation of Seismic Data....................................................................................................67
 

The GEON IDV (Integrated Data Viewer) for Data Integration and Exploration in the 


Geosciences....................................................................................................................................67
 

Demonstrating Hydrodynamic Data Assimilation with OpenGL Animations .....................................68
 

From Caves to Optiportals: Evolution and Deployment of Visual Communication for 


Geoscientists...................................................................................................................................69
 

Visually Browsing Georeferenced Digital Libraries...............................................................................69
 

Data Fusion, Compression, and Visualization of Thermal and Visible Imagery for Remote 


Analysis of Geologic Surfaces on Earth And Mars ..................................................................71
 

Sharing Earth Science Information Through Interoperable Approach and 


Cyberinfrastructure........................................................................................................................72
 

A Community Workshop and Emerging Organization to Support a National Geoinformatics 


System in the United States .........................................................................................................75
 

Association of American State Geologists (AASG)-USGS Plan for a National Geoscience 


Information Network......................................................................................................................76
 

The Cultural and Social Challenges of Developing Geoinformatics: Insights from Social, 


Domain, and Information Sciences.............................................................................................78
 
VI 

Hydroseek—A Search Engine for Hydrologists .....................................................................................79
 

CUAHSI Cyberinfrastructure for Hydrologic Sciences .........................................................................80
 

Design and Implementation of CUAHSI WaterML and WaterOneFlow Web Services....................81
 

Hydrologic Information System Server: The Software Stack and the Initial Deployment 


Experience.......................................................................................................................................83
 

Automated Ground Motion Characterization Using Satellite Imagery ...............................................85
 

QuakeML—XML for a Seismological Data Exchange Infrastructure.................................................85
 

The EarthScope Plate Boundary Observatory Distributed Data Management System ..................86
 

EarthRef.org in the Context of a National Cyberinfrastructure for the Geosciences.......................86
 

Phanerozoic Earth and Life: The Paleointegration Project...................................................................88
 

MOAS: Geoinformatic Database for Integration and Synthesis in Coastal Modeling .....................90
 

Automated Multidisciplinary Collection Building...................................................................................90
 

Sensor Web Enablement—Its Impact on Continuous Digital Workflows..........................................92
 

A System for Fast Spatial Searches on the Earth or Sky Using the Hierarchical Triangular 


Mesh.................................................................................................................................................92
 

SODA—Self-Service Online Digital Archive for Unloved Scientific Data..........................................94
 

From Flight Data to Knowledge of the Atmosphere’s Chemistry: An Example from INTEX-NA .....95
 

Patch Reef Analysis Using LiDAR-Derived Metrics at Biscayne National Park, Florida.................97
 

Internet GIServices for Homeland Security ............................................................................................98
 

An Automated Parallel Computing System in the GEON Grid: Applications to Multiscale 


Crustal Deformation in the Western United States ................................................................100
 

Disk-Based Gridding for Large Datasets ...............................................................................................100
 

Geospatial Cyberinfrastructure Solution: Open Source or COTS?....................................................102
 
Geoinformatics 2007—Data to Knowledge 

Edited by Shailaja R. Brady, A. Krishna Sinha, and Linda C. Gundersen 

Envisioning a Geoinformatics 

Infrastructure for the Earth Sciences: 

Technological and Cultural Challenges 

By Linda C. Gundersen1 

1

U.S. Geological Survey, Reston, Va. 

Since its inception in 2000, geoinformatics has been 

envisioned as serving all the earth sciences and enabling the 

understanding of complex systems. For the past 6 years the 

earth and computer science community has struggled with 

clearly defining the path to that vision, obtaining sufficient 

resources, maintaining interest across the broad spectrum of 

scientists that geoinformatics intends to serve, and achieving 

coordination and interaction among scientists nationally and 

internationally. There is hope, however, that we are approaching a true turning point in geoinformatics that has as its 

hallmark a general convergence on a suite of technologies and 

concepts that could begin the process of growing an integrated 

infrastructure. 

Several major technology experiments and community 

database efforts are beginning to bear fruit, and numerous 

workshops and town halls have garnered unprecedented inter

est and agreement in the community. The development of Geo

science Markup Language (GeoSciML) by the Interoperability 

Working Group of the Commission for the Management and 

Application of Geoscience Information (CGI), a commission 

of the International Union of Geological Sciences (IUGS), is 

one such breakthrough. GeoSciML is a geoscience-specific 

XML-based GML (Geography Markup Language) application that supports interchange of geoscience information. In 

recent testing, it demonstrated that it could be used to provide 

the interoperability needed to bring disparate geologic maps 

together. A number of efforts, such as the EarthChem Portal, 

GEON (The Geosciences Network), and the North American 

Geologic Map Data Model, showed that community databases, 

standards, and ontologies can be created and implemented 

cooperatively. The well-attended Geoinformatics Town Hall at 

the American Geophysical Union Meeting in December 2006 

was among the first to articulate a national and potentially 

­

­

international collaboration across the community (Fox and 

others, 2006). 

In early 2007, several workshops occurred among geological surveys nationally and internationally, and a National 

Science Foundation (NSF) workshop among geoinformatics 

practitioners was held. Collectively, these workshops have 

resulted in a more definitive vision and convergence around a 

few key concepts and technologies. Common elements defined 

in these workshops describe an infrastructure that is distributed, interoperable, uses open-source standards and common 

protocols, respects and acknowledges data ownership, enables 

science, fosters communication and collaboration, shares 

resources and expertise, and develops community databases, 

new Web services, and clients that are sustained over time. 

Each of these concepts and technological choices agreed 

upon in these workshops offers a myriad of challenges that 

will now have to be analyzed with agreed-upon resolutions. To 

do this will take a sustained effort, supported by government, 

industry, and academia. It will involve carefully and thoughtfully implementing a real “community of practice” (Wenger, 

1998) in geoinformatics. 

In a recent NSF workshop report (Edwards and others, 

2007), the authors state that “infrastructure is fixed in modular 

increments, not all at once or globally. Because infrastructure 

is big, layered, and complex, and because it means different 

things locally, it is never changed from above. Changes take 

time and negotiation, and adjustment with other aspects of the 

systems involved.” The authors also describe the evolution of 

systems to true infrastructure. They define an evolving spectrum that starts with a single system that is centrally organized 

and controlled, to networks that link systems with control 

partially or wholly distributed among the system nodes, to 

internetworks (or Webs) that are networks of networks based 

on coordination rather than control. These internetworks represent true infrastructure and require communities of practice to 

coordinate and implement. Currently, many systems are being 

built and ad hoc gateways constructed to link systems without 

much coordination. 

Communities of practice are not limited to a community with a common interest or “domain,” but can encompass 

practitioners who share experiences and learn from each other 

across domains. They develop a shared repertoire of resources: 

experiences, stories, tools, vocabularies, and ways of address­
2 Geoinformatics 2007—Data to Knowledge 

ing recurring problems. Coordination, as well as standards of 

practice and reference materials, grow out of this experience. 

The critical benefits of communities of practice include creating and sustaining knowledge, leveraging of resources, and 

rapid learning and innovation. Not only will communities of 

practice help grow the cyberinfrastructure needed to under

stand and analyze complex systems, but they will promote the 

other essential ingredient—communication among scientists. 

The primary barrier to integrated science is the difficulty in 

translating the vocabulary of one scientific field to another. 

A geomorphologist who understands the control geology has 

on the health of vegetation in a riparian habitat has significant 

difficulty translating this information to the ecologist specializing in bird mortality. Simply making data interoperable will 

not create an infrastructure for the earth sciences to conduct 

analyses of complex systems. It will take a sustained, longterm, focused effort across the earth sciences and computer 

sciences to build the tools (databases, gateways, Web services, 

and applications), and to change the culture (common vocabularies, data sharing, and open-source standards). 

References Cited 

Edwards, P.N., Jackson, S.J., Bowker, G.C., and Knobel, C.P., 

2007, Report of a Workshop on History & Theory of Infrastructure: Lessons for New Scientific Cyberinfrastructures, 

NSF Grant 0630263, 55 p. 

Fox, P., Gundersen, L., Lehnert, K., McGuinness, D., Sinha, 

K., and Snyder, W., 2006, Toward broad community collaboration in geoinformatics: Eos, v. 87, no. 46, p. 513. 

Wenger, E., 1998, Communities of practice: learning, meaning, and identity: Cambridge University Press, 336 p. 

OneGeology—The Birth of a 

Global Geoscience Spatial Data 

Infrastructure, or Just Another Noble 

Aspiration? 

By Ian Jackson1 

1

Information Directorate, British Geological Survey, Keyworth, Nottingham, United Kingdom 

Contrary to the thinking of many in positions of power 

and influence in the political and environmental domain, the 

environment is not just restricted to that bit of our world above 

the ground; the subsurface is pretty important too. Think 

earthquakes and landslides; minerals and mining; and groundwater and pollution. Like most things environmental, few of 

these issues respect national frontiers and if, as seems wise, 

we want to assess and address global environmental problems 

­

at a global scale, then we need access to global environmental data. In the topographic domain that often tends to be 

dominant in the global Internet (GI), and in one or two of the 

environmental domains above ground, (for example, meteorology) we have, comparatively speaking at least, extensive 

and relatively consistent data. This is not so in the geoscience domain. Unfortunately, for geoscientists (and the rest of 

society), digital geoscience spatial data across the globe, even 

at small scales, are either unavailable, out of date, of variable 

quality, or inconsistent. 

It is true that in several countries, extensive geophysical 

data exists and there is basic digital geological map data of 

reasonable quality and coverage. But in many more countries, 

it is incomplete or is not present at all—even within Europe 

(which poses an interesting resource and priority challenge 

for a post-INSPIRE European Union and its member states). 

Even where there is good national data, the chances of it being 

interoperable, let alone harmonizable, are small. This is not 

news to those within the geoscience community; many of us 

are well aware that we need to accelerate the development and 

promulgation of simple, basic, and essential digital geological 

map standards and specifications to improve the interoperability and sharing of data. In Europe, North America, and 

Australasia, people are working hard on trying to move 

structural interoperability forward and some of the readers of 

this abstract will be aware of the development of a high-level 

geoscience data model and the interchange format GeoSciML. 

Semantic interoperability, needed to deliver some form of 

scientific homogeneity (in other words, harmonized geoscience data) is another story and will take some very serious 

concentration and effort by the global geoscience community 

in the area of terminology and classification. While geoscientists may thrive on independence and diversity, digital systems 

generally do not. 

Despite the good work and progress with GeoSciML, 

moving the development and promulgation of standards 

forward is a slow and unglamorous process, and, outside 

the domain of informatics, it is regarded as a pretty esoteric 

business whose purpose and value is rarely fully understood. 

Developing a standard tends to be a very abstract occupation, 

somewhat detached from reality; at least that is what many 

geoscientists think. 

Enter the United Nations International Year of Planet 

Earth (IYPE2008) and the germ of an idea to create a 

1:1,000,000-scale global geological map dataset—a concept 

now known as OneGeology and the subject of this abstract. 

At the beginning of 2006, the 1:1,000,000 idea was presented 

somewhat immaturely and at short notice to the General 

Assembly of the Commission for the Geological Map of the 

World (CGMW) in Paris. What if we used IYPE2008 as a 

stimulus to begin the creation of a digital geological map of 

the planet at 1:1,000,000 scale? Could we design and initiate 

a project that uniquely mobilizes geological surveys around 

the world as part of an ongoing IYPE2008 contribution, to 

act as the drivers and sustainable data providers of this global 

dataset? Further, could we synergistically use this geoscien­
tist-friendly vehicle of creating a tangible geological map to 

accelerate progress of an emerging global geoscience data 

model and interchange standard? Finally, could we use the 

project to transfer know-how to developing countries and 

reduce the length and expense of their learning curve, while 

at the same time producing geoscience maps and data that 

could attract interest and investment? These aspirations, plus 

the chance to generate a global digital geological dataset to 

assist in the understanding of global environmental problems 

and the opportunity to raise the profile of geoscience as part 

of IYPE2008, seemed more than enough reasons to take the 

proposal to the next stage. 

Since that CGMW meeting in February 2006, the concept has been disseminated to organizations and individuals 

around the globe and has matured considerably. In addition to 

the support of CGMW, the project has attracted the support 

of the International Union of Geological Sciences (IUGS), 

United Nations Education, Scientific and Cultural Organization (UNESCO), and the International Steering Committee 

for Global Mapping (ISCGM), not to mention the IYPE2008 

Management Team. But crucial to the success of the concept 

is getting geological surveys to sign up and commit their data 

and resources to the cause. At the time of this writing, more 

than 50 surveys from around the world have agreed to participate. My own organization, the British Geological Survey, 

has decided to make the project its prime contribution to 

IYPE2008, as well as offer to continue to play a full role in the 

project’s coordination, and agreed to support the initial kickoff meeting, which took place in Brighton, United Kingdom, 

in March 2007. Several countries, including the United Kingdom, plan to pilot the methodology and make data available 

during 2007-08. 

The proposed methodology differs from the usual method 

of making available geological data for a continent or the 

globe—usually one editor or editorial unit compiling information from a variety of sources and in recent years using 

a GIS to produce the cartographic result. The OneGeology 

proposition is a completely modern paradigm. It is planned as 

a distributed model, and at the most technically sophisticated 

end, will see a Web feature service, a dynamic set of geological map data served mostly on a national basis by individual 

geological surveys and other bodies (for example, the polar 

and marine surveys and research bodies) to a Web portal or 

portals, and as such will be frequently updated and improved 

by the data providers and reflect the most up-to-date data they 

possess. To achieve its goals, the project team will have combined state-of-the-art skills in geoscience data modelling and 

information management with worldwide expertise and experience in geoscience. The project will obviously be closely 

interlinked with the IUGS Commission Working Group, 

developing the global geoscience data model and exchange 

language—GeoSciML. 

The key international players attended the kick-off meeting in the United Kingdom. At this meeting, they endorsed 

and initiated the project and discussed the project plan and 

the specifications for the geological and information systems. 

Geoinformatics 2007—Data to Knowledge  

During 2007, the first test datasets are anticipated to become 

available and we will progressively add data through 2008 

so that we can present the first results at the International 

Geological Congress in Oslo, Sweden, in 2008. What then? 

We geologists work in geological time, so what we are talking 

about here will take no time at all. We will continue to add 

and upgrade the data through time to progressively provide 

the most complete coverage of the planet at our target scale of 

1:1,000,000, and also (more excitingly) add data at a higher 

and more useful resolution. 

Even at the outset there was a realization that the project 

would not be able to obtain 1:1,000,000-scale geological map 

(lithology/chronostratigraphy) data everywhere, and, perhaps, 

in some parts of the world, any data at all. Yes, there will be 

major faults running along many national boundaries (the 

semantic interoperability problem) but what a great way to get 

a long overdue problem tackled! It is here the Google Earth 

philosophy comes into play—Be pragmatic, make available 

what you can now, and aspire to improve it in the future; after 

all, there is no such thing as a geological map that is complete. 

There are, undoubtedly, many more bridges to cross, but 

what was a mere germ at the beginning of 2006 evolved into 

a proposal and that proposal is now a project with an initial 

budget and extensive international support. The hope is that as 

momentum gathers and more become aware of the project and 

its practical and altruistic benefits, they will come on board, 

contributing data and expertise. This presentation will present the progress of OneGeology to date and the technical and 

cultural issues it has encountered. 

Building the AuScope Australian Earth 

Science Grid 

By Lesley A.I. Wyborn1 and Robert M. Woodcock2 

1

Information Services and Technology Branch, Geoscience Australia, 

Canberra, Australia 

2

Division of Exploration and Mining, CSIRO, Kensington, Australia 

In 2006, the Australian Government announced a new 

funding initiative, the National Collaborative Research Infrastructure Strategy (NCRIS). NCRIS aims to provide Australian 

researchers with access to major research facilities, supporting 

infrastructure and networks necessary for world-class research. 

As an element of this strategy, $42.8 million was allocated to 

the Australian Earth Science Research community to build an 

integrated national geoscience infrastructure system called 

AuScope. The NCRIS AuScope funding has several parts. 

One part of the funding is to develop an advanced national 

infrastructure for acquiring and analyzing geophysical and 

geochemical data. Components in this infrastructure include 

a geotransect facility for imaging large-scale cross sections of 

the Earth’s crust, an ion probe for advanced analysis of Earth 

samples, and the development of a virtual library of drill core 
 Geoinformatics 2007—Data to Knowledge 

samples from across Australia. Another part of the AuScope 

infrastructure is the development of a high precision positioning system, which will build an enhanced national geospatial 

reference system. 

To draw together information from this new national 

infrastructure and from other existing sources, the AuScope 

NCRIS funding will also be used to develop a world-leading geoscience geoinformatics network. This network will be 

called the AuScope Grid and will comprise a Data Grid and 

a Compute Grid. Combined, they will provide a distributed 

infrastructure that will enable the dynamic construction of 

an open-access, four-dimensional model of the Australian 

continent. 

The Earth Science Data Grid is a national geoscience data 

network that will enable online access to information from 

new NCRIS geoscience infrastructure and from other sources 

in academia, industry, and government. The Data Grid will use 

open geospatial standards to allow real-time access to data, 

information and knowledge stored in distributed repositories. 

A key objective for the Data Grid is that it will be built on 

“end-to-end” science principles (in other words, open-access 

principles) whereby there will be access to the highly processed information and knowledge, as well as to the original 

raw data and the processing programs used to generate the 

results. 

The goal of the Compute Grid is to facilitate quantitative 

geoscience analysis by providing an infrastructure and tools 

for advanced data mining and online computational modeling and simulation. Computationally demanding geoscience 

programs will be made available as Web services, and distributed across computing and storage resources in a manner that 

requires limited knowledge of the physical infrastructure. 

The key to linking components and resources on the 

AuScope Compute Grid with the Data Grid will be servicebased access to the geoscience information holdings using a 

common service interface and information models, including 

GeoSciML. Further development, maturing, and formalization 

of GeoSciML are essential to the success of the AuScope Grid. 

GeoSciML is being developed through the Interoperability 

Working Group of the Commission for the Management and 

Application of Geoscience Information (CGI), a commission 

of the International Union of Geological Sciences (IUGS). 

Similar information models are also required for geophysical 

and geochemical data. As with GeoSciML, it is desirable that 

these additional information models be developed as open 

standards and under the auspices of the relevant international 

scientific organizations. 

There are no obvious technological barriers to what 

has been proposed in building the AuScope Grid. Nearly all 

the required technical elements have been tested in recent 

years in a series of interoperability testbed projects. Potential 

limitations are now seen as ”social engineering” issues. One 

social limitation to developing the AuScope Grid to its full 

potential is the requirement to bring the relevant communities 

together at an international level, and to work collaboratively 

to develop open standards for information exchange of all of 

the required scientific content. Developing and ratifying these 

standards at an international level is only the first step; they 

also have to be widely accepted and adopted by the community. 

More fundamentally, the proposed grid requires a transition to fully distributed systems whereby all components 

(tools, applications, compute resources, and data) are available 

online as globally distributed resources. This paradigm shift 

contrasts the current culture of “monolithic silos” whereby 

all the required components for a data-mining exercise or for 

a modeling and simulation experiment are hosted by a single 

organization. In some areas, this social transition is proving the most difficult challenge of all as distributed systems 

are sometimes perceived as reducing control of assets and 

resources. Distributed systems also rely heavily on mutual 

trust and open collaboration, and require that once resources 

are exposed as Web services that these are maintained and 

made available 24/7. 

A Very Practical Geoinformatics 

Project: The Reality of Delivering a 

Harmonized Pan-European Spatial 

Geoscience Database 

By Kristine E. Ch. Asch1 

1

Federal Institute for Geosciences and Natural Resources, Hannover, 

Germany 

This paper will introduce and critically consider a project 

whose objective was to produce a harmonized geological 

spatial database for the continent of Europe. As with many 

informatics projects in other scientific domains, the technical 

challenges, while substantial, were significantly less than the 

organizational and cultural challenges. Solutions to all challenges were necessarily pragmatic and the lessons learned may 

prove relevant to other geoinformatics initiatives. 

The IGME5000 (The 1:5,000,000 International Geological Map of Europe and Adjacent Areas) is a project within 

the German Federal Institute for Geosciences and Natural 

Resources (BGR), which is being undertaken under the 

auspices of the Commission for the Geological Map of the 

World (CGMW). Its aim is to create a harmonized geological 

dataset for the whole of Europe and adjacent areas at a scale 

(or resolution) of 1:5,000,000. The project’s initial focus is on 

the pre-Quaternary geology and in addition to coverage of the 

terrestrial areas, it also integrates the much less well-known 

offshore domain. In total, there are approximately 2,570 

area descriptions and 32,000 polygons, of which 27,500 are 

onshore and 4,500 are offshore. 

The project was initiated in December 2004 and its scope 

extends not only to the whole of Europe, but also to areas of 

the Middle East, North Africa, and North America. Forty­
Geoinformatics 2007—Data to Knowledge  

eight national geological surveys and more than 20 academic 

experts from across Europe have made substantial contributions to its goal. 

In line with traditional CGMW projects, the IGME5000 

deliverables included a printed map, which was published 

in February 2006; however, in contrast to previous CGMW 

projects, this printed map was the product of a digital spatial database. The IGME5000 is very much a geoinformatics 

exercise and since June 2006, it has been possible to access the 

dataset via a Web mapping application. 

The IGME5000 faced some significant challenges. Foremost amongst these was the recognition that few, if any, true 

geoscience standards existed (or indeed exist) and that new 

standards needed to be developed. Another significant challenge was the integration of the onshore and offshore—geological domains, which necessitate very different approaches 

to their survey and depiction. Add to these the challenge of the 

logical classification of tectonic and genetic parameters, plus 

the complexities of metamorphic terrains and the “special” 

areas, such as the Alps or the Mediterranean Sea, and the technical and scientific difficulties become evident. 

The organizational and cultural issues proved equally 

challenging. The project team had to deal with “national 

boundary faults” and mismatches; differences in the classification of age and lithology, and colors and symbols; idiosyncratic abbreviations; a plethora of input media; the diversity 

resulting from 48 different countries and geological survey 

organizations across 4 continents; the “variety” of opinion 

from more than 20 academic advisors; and the limitations of 

a small core team in BGR and linked to this a small budget 

(hence, the need to work on a voluntary basis). In addition, 

the complexity of multiple languages and the need to work in 

only one (English), the well-established independent (idiosyncratic?) behavior of geologists coupled with usually inadequate communication between them and informatics experts, 

and, finally, the seemingly strange concept of deadlines, made 

the scale of the challenges apparent. 

The solution to these was pragmatic and as diverse as the 

challenges themselves. The IGME5000 was a marathon, not a 

sprint; the project would take time and would need persistence 

with both contributors and their superiors. The international 

and diverse nature of the participants meant the project would 

need to be inclusive and ensure adequate consultation. Lack 

of budget meant much time would be needed to spread the 

message, and in turn the BGR team would need to inspire and 

be enthusiastic with contributors. There would be a need for 

constant communication and patience. Standards would have 

to be based on the lowest common denominator. Expecting 

perfection was not realistic and accepting less than perfect 

contributions would allow progress. There was a need to maintain interest and, thus, to make sure the project focused on the 

scientific goals of certain experts. Substantial advantage was 

gained in choosing a suitable board of advisors and from being 

part of a global umbrella organization (the CGMW). Keeping 

things simple was essential (for example, a simple data table 

in addition to a sophisticated—and thus unused!—MS Access 

data input template). Last but not least, the language problem 

was, paradoxically, helped by the author being a nonnative, 

“nonperfect” English speaker. 

The IGME 5000 has now delivered a digital spatial 

database of Europe’s bedrock geology, but work on the project 

continues. The Web mapping application will be further developed and optimized. The project and its team are playing a 

role in the new EU Directive—Infrastructure for Spatial Infor

mation in Europe (INSPIRE). The IGME5000 is providing a 

basis for new digital standards for the CGMW. The availability 

of harmonized digital geological data for the whole of Europe 

has led to active involvement by BGR in a European Union 

project investigating geochemical fingerprints for determining 

the origin of food TRACE. The IGME5000 has also started 

to feed valuable practical experience into the new geoscience 

project, which is attempting to produce a spatial database of 

the whole planet—OneGeology. 

In undertaking this project, many lessons have been 

learned. An extensive multinational project with many contributing nations and states requires common standards and 

these standards need to be developed on the basis of a lowest 

common denominator. Cooperation is a matter of communication and patience. Lack of budget does not necessarily mean 

no dedication or contribution, but it will mean that things take 

longer. Many nations mean many cultures. Indeed, adopting 

one language means a constant source of misunderstanding 

with which one has to cope. Set your target high but do not 

be disappointed if you do not reach all your scientific and 

technical objectives in full. Keep things simple and consider 

the end user at all times. Acknowledge and be positive about 

the support you have been given (even if it is not exactly what 

you need). Be aware that IT people and geologists are different 

species and that while they need to work closely together they 

will approach the task differently. Finally, recognize that in all 

of life’s ventures, at some point discussions have to stop and 

hard decisions have to be made; otherwise a project like this 

will never be finished. 

Integrated Geological Map Database 

(GeoMapDB) in Geological Survey of 

Japan, AIST 

By Shinji Takarada1, Daisaku Kawabata1, Ryoichi Kouda1, 

Jun-ichi Miyazaki1

, Yuichiro Fusejima2, and Hisashi Asaue1 

1

Geological Survey oF Japan, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan 

2

Active Fault Research Center, Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan 

Publications and Databases in GSJ 

The Geological Survey of Japan (GSJ) published geological maps at scales of 1:50,000, 1:75,000, 1:200,000, 

­
 Geoinformatics 2007—Data to Knowledge 

1:1,000,000, and 1:2,000,000 during the last 125 years. Other 

maps, such as active fault, active volcano, marine geology, 

hydrogeology, mineral resources, coal, oil and gas fields, 

intensity of aeromagnetic, and geothermal maps, are also published. GSJ published a total of 41 CD-ROM series, including 

1:200,000-scale geological maps in vector and raster formats. 

GSJ established more than 10 databases in the Research Infor

mation Data Base (RIO-DB; http://www.aist.go.jp/RIODB/riohomee.html) of the AIST. The databases include active fault, 

active volcano, seamless digital geological map, geothermal 

drill core, marine mineral, geophysical exploration activity, 

basement rocks, geochemical map, crustal stress, and geological literature databases. These maps and databases have been 

published on printed sheets, CD-ROMs, and normal htmlbased Web sites. GSJ decided to integrate most of these maps 

and databases on a Web-based geographic information systems 

(GIS) to facilitate the accessibility of the geological data of the 

organization in 2005. 

GeoMapDB 

GSJ introduced a new Integrated Geological Map 

Database (GeoMapDB) in September 2006 (see fig. 1; 

­

http://iggis1.muse.aist.go.jp/en/top.htm). The GeoMapDB 

is based on a WebGIS (ArcIMS) technology, which makes 

it possible to browse, overlay, and search geological maps 

online. The purpose of this database is to make many kinds 

of geological maps produced by GSJ accessible to the general public. The database contains geological maps with 

scales ranging from 1:2,000,000 to 1:25,000. The database 

includes the 1:1,000,000-scale geological map of Japan (3rd 

edition), 1:200,000-scale seamless digital geological map of 

Japan (http://www.aist.go.jp/RIODB/db084/index_e.html), 

1:200,000-scale geological map of Japan (raster at 150 dpi 

and vector formats), 1:50,000-scale quadrangle series (raster 

format), and the 1:25,000-scale environmental geologic map 

of the Tsukuba Science City (raster and vector formats). It 

is possible to search information using the attribute tables 

of maps in vector format. Legends and cross sections of the 

1:50,000-scale quadrangle series and environmental map of 

Tsukuba city are available. Links to Quaternary volcanoes are 

also available. Links to other databases, such as geological 

literature, outcrop information, dating, and geological sample 

databases will also be made available soon. Three-dimensional 

(3D) display of the viewing area is also possible. Downloading 

the viewing image at 150 dots per inch (dpi) and original files 

Figure 1. 1:200,000-scale seamless digital geologic map from the Integrated Geological Map Database. 
Geoinformatics 2007—Data to Knowledge 7 

in raster and vector formats is possible. Web Mapping Service 

(WMS) for the 1:1,000,000-scale geological map of Japan and 

1:200,000-scale seamless digital map of Japan is available; 

thus, overlapping borehole data and landslide data from other 

agencies and overlaying on the Google Earth map is possible. 

GSJ decided to contribute the data from GeoMapDB to the 

OneGeology project (http://www.onegeology.org/), which 

aims to make a 1:1,000,000-scale digital geological map of the 

world using WFS. Collaboration with the GEO Grid project 

(http://www.geogrid.org/) is another major activity of the GSJ 

for the next several years. 

Semantic Web Services in a Virtual 

Observatory (VO) 

By Peter Fox1, Luca Cinquini2, Deborah L. McGuinness3, and 

Patrick West1 

1

High Altitude Observatory (HAO), National Center for Atmospheric 

Research (NCAR), Boulder, Colo. 

2

Scientific Computing Division (SCD), National Center for Atmospheric 

Research (NCAR), Boulder, Colo. 

3

Knowledge Systems, McGuinness Associates and Stanford University, 

Stanford, Calif. 

We present a set of four Web services resulting from our 

work on a semantic data framework in the setting of virtual 

observatories. These services allow a client service to search 

for data using three primary selections—parameter, date-time 

range, and instrument—and also to return appropriate service 

links to the actual data (the fourth service). These services use 

a shared understanding of the inputs, outputs, and preconditions as defined by a formal ontology, encoded in the World 

Wide Web Consortium’s Ontology Web Language Recommendation and running in an Internet-accessible environment with 

Web Service Description Language (WSDL) bindings. The 

services can utilize reasoning services just as a user of the Web 

portal is able to. The service client can optionally utilize the 

ontology when it consumes the service for additional knowledge or may be used purely syntactically (like most existing 

Web services). We present these services within a specific 

domain context for the Virtual Solar-Terrestrial Observatory 

(VSTO). 

Introduction 

We are exploring ways of technologically enabling 

scientific virtual observatories—distributed resources that may 

contain vast amounts of scientific observational data, theoretical models, and analysis programs, and results from a broad 

range of disciplines. Simply, the main aim of a VO is to make 

all resources appear to be local and appear to be integrated. 

This is challenging because the information is collected by 

many research groups, using a multitude of instruments with 

varying instrument settings in multiple experiments with 

different goals, and captured in a wide range of formats. We 

must provide a means for an incoming user to discover, locate, 

retrieve, and use heterogeneous, and perhaps diverse, or interdisciplinary data of interest. We also must provide interfaces 

for requests from user applications and machine-generated 

requests for services. 

We present the Web services aspect of our work on 

semantic integration of scientific data (Fox and others, 2006) 

in the context of the VSTO project. VSTO presently covers the 

fields of solar atmospheric physics and terrestrial middle and 

upper atmospheric physics. We used semantic Web technologies to create declarative, machine operational encodings 

of the semantics of the data to facilitate interoperability and 

semantic integration of data. We then semantically enabled 

Web services to find, manipulate, and present scientific data 

over the Internet. We describe our implementation of the Web 

service as part of our Virtual Observatory project (fig. 1). 

Service-Oriented Architecture (SOA) 

There are notable, successful examples of enabling escience using an Internet-based service-oriented architecture 

using Web services. The International Virtual Observatory 

Alliance (IVOA; http://www.ivoa.net) developed a number 

of “simple access protocols” for application interoperability 

that are widely used in the astronomy community, including 

the Simple Spectrum Access Protocol and the Simple Time 

Access Protocol. Also, the Open Geospatial Consortium 

(OGC; http://www.opengeospatial.org) has, via their standards process, developed protocol standards, such as the Web 

Coverage Service (WCS), the Web Feature Service (WFS), 

and the Web Map Service (WMS)—together known as WxS. 

These services are also in wide use in applications needing 

integration via the geospatial coordinate system. We note these 

two examples in this context since they provide light-weight 

semantics via Web services. This means that while not containing formal semantic encodings, their names and basis in 

either particular data types or coordinate referencing and dataproduct types provide hard-coded semantic meaning to those 

clients accessing them. In essence, terms like image, spectrum, 

time, coverage, feature, and map have a well-defined meaning 

in those communities to provide great utility. 

Our need to provide service in an SOA environment 

arises from collaborations we have with the Virtual Ionosphere-Thermosphere-Mesosphere Observatory (VITMO; 

http://vitmo.jhuapl.edu) and Madrigal (http://tmadrigal.haystack.mit.edu/madrigal) projects. 

Semantic Data Framework 

An unexpected outcome of the additional knowledge 

representation and reasoning was that the same data query 

workflow is used across the two disciplines. We are finding 

that it seems to generalize to a variety of other datasets as well 
 Geoinformatics 2007—Data to Knowledge 

Figure 1. VSTO Web services end-point and input example for the query interface initiating an instrument search. The development of 

Web interfaces naturally followed from the Web portal interface functionality. 

and we have seen evidence supporting this expectation in our 

work on other semantically enabled data-integration efforts in 

domains including volcanology, plate tectonics, and climate 

change (Fox and others, 2006). Given the value added by this 

basic knowledge representation and reasoning, we extended 

the method of access to support computer-to-computer inter

faces, particularly via the commonly adopted service-oriented 

architecture implemented as Web services. 

An example of the query instrument service follows in 

figure 2. A consumer of such a service, either another service, or 

client application may parse the OWL as XML without semantic 

meaning and use their own reasoning engine (or ours) to further 

work with the returned information. 

Discussion and Conclusions 

We currently have two clients using VSTO Web services: 

VITMO and the Madrigal Virtual Observatory. Now that our 

Web services are deployed at www.vsto.org, we are in a position to augment the search and query we provide in the VSTO 

­

Figure 2. VSTO instrument query output excerpt returning OWL documents with semantic information on the available instruments 

according to the input selections in figure 2. Figure 2 shows the example end point for the Query Instrument semantic Web service. The 

Web service inputs (all optional) and their types are described, and the end-point service address is given along with a link to the Web 

Services Description Language (WSDL) document content for the service. Two examples are given and below that there is a Query 

Input form that allows a potential user of the service to scope the types of queries that they may wish to make (including the semantic 

filters discussed earlier). Figure 2 contains an excerpt from an example query response beginning with information about 2 of the 13 

valid instruments. 
Geoinformatics 2007—Data to Knowledge  

portal by installing VSTOWeb services at remote locations. 

These services would then be accessed when a user navigates 

the query workflow, resulting in a distributed set of queries 

using Web services displayed to the user. 

We are also beginning work on capturing provenance. We 

plan to leverage the Proof Markup Language (PML-P) (Pinheiro da Silva and others, 2006)—an interlingua for representing provenance, justification, and trust information. Our initial 

work will just use the PML-P portion of the ontology—just 

focusing on where information came from and which services 

were called, later focusing on exposing the actual reasoning 

performed. Once captured in PML, the Inference Web toolkit 

(McGuinness and others, 2004) may be used to display infor

mation about why an answer was generated, where it came 

from, and how much the information might be believed and 

why. 

We have also reviewed the semantic Web services with 

respect to needs for the NSF-funded Geosciences Network 

(GEON) project, the NASA-funded Semantically-Enabled 

Scientific Data Integration (SESDI) project, and the NASAfunded SKIF project, and plan to add these as motivating use 

cases to provide the most robust and smart Web service that 

we can. 

The VSTO project is funded by the National Science 

Foundation, Office of Cyber Infrastructure under the SEI+II 

program, grant number 0431153. The National Center for 

Atmospheric Research is operated by the University Corporation for Atmospheric Research with substantial sponsorship 

from the National Science Foundation. 

References Cited 

Fox, P., McGuinness, D.L., Middleton, D., Cinquini, L., 

Darnell, J.A., Garcia, J., West, P., Benedict, J., and Solomon, S., 2006, Semantically-enabled large-scale science 

data repositories, in Cruz, I., and others, eds., The Semantic 

Web—ISWC 2006, Proceedings of the Fifth International 

Semantic Web Conference, Athens, Ga., November 5-9, 

2006: Lecture Notes in Computer Science, v. 4273, p. 792­

805. 

McGuinness, D., and Pinheiro da Silva, P., 2004, Explaining answers from the Semantic Web: The inference Web 

approach: Web Semantics: Science, services and agents on 

the World Wide Web, v. 1, no. 4, p. 397-413. 

Pinheiro da Silva, P., McGuinness, D., and Fikes, R., 2006, A 

proof markup language for semantic Web services: Information Systems, v. 31 nos. 4-5, June-July 2006, p. 381-395. 

[previous version, KSL Tech Report KSL-04-01.] 

­

Semantic Mediator Architecture for 

Environmental Science 

By Luis E. Bermudez1 and John Graybeal1 

1

Research and Development, Monterey Bay Aquarium Research Institute, 

Moss Landing, Calif. 

Adopting metadata specifications is often insufficient to 

achieve interoperability among geospatial information communities due to the heterogeneity of the values in metadata 

annotations. In geological sample databases, semantic heterogeneities could occur in the rock types, sample technique 

type, sampling platform, and analysis procedures, to name just 

a few. For example, when a sample is collected by hand by a 

diver, the Petrological database (PetDB) calls it “dive,” while 

the SamplesDB database at Monterey Bay Aquarium Research 

Institute (MBARI) uses the term “hand collected.” 

The Marine Metadata Interoperability (MMI) project is 

working to address semantic conflicts. The work is guided 

by community collaborations and supported via the MMI site 

(http://marinemetadata.org). MMI focuses on several activities 

to achieve semantic interoperability: (1) encouraging reuse of 

existing vocabularies; (2) providing best practices for publishing controlled vocabularies so that they are interoperable 

within the Semantic Web; (3) hosting workshops to create and 

map controlled vocabularies; and (4) providing tools and guidance to solve semantic heterogeneities. 

Along these lines, MMI has developed an architectural 

concept and a prototype implementation of a semantic mediation service. In this paper, we present this architecture and 

implementation, and discuss its potential application to the 

geosciences. 

The two basic solutions to solve semantic heterogeneities 

are the mediator-wrapper approach, and the enforced standard 

approach. The wrapper is a piece of software build on top of 

the data sources, which serves the metadata and data via a 

common model. Traditionally, queries in the central system are 

translated to queries in local systems. The enforced-standards 

approach requires that every data source provide the data and 

metadata according to a single standard, including using the 

same exact terms to specify semantic meaning. Clearinghouses 

that harvest metadata in a particular format are an example of 

this approach. 

The MMI proposition to solve semantic heterogeneities is a mixture of the two previous approaches. It requires 

metadata be made available in a standard format, yet allows 

the controlled vocabularies in the system to be heterogeneous. 

We propose that the semantic mediator is a reusable, sharable 

component of a service-oriented architecture (see fig. 1). A 

centralized mediator facilitates lookup services, registry of 

vocabularies, mappings, and queries that other components of 

the system could use. 

This mediator, which is available at http://marinemetadata.org/semor, is being used at Integrated Ocean Observing 

System (IOOS) Tethys (OOS Tethys), which is the Southeast­
10 Geoinformatics 2007—Data to Knowledge 

Figure 1. Semantic mediator. 

ern University Research Association/Marine Metadata Interop

erability (SURA/MMI) demonstration. The semantic mediation service is based on Semantic Web technologies, such as 

OWL and resource description framework (RDF) to store and 

retrieve controlled vocabularies represented in ontologies. It 

has a Web user interface and a Simple Object Access Protocol 

(SOAP) Web service to interact with it programmatically. It 

is currently based on a combination of Sesame and Jena, and 

support for Simple Protocol RDF Query Language (SPARQL) 

Protocol and RDF Query Language SPARQL/SOAP is 

planned in the near future. Mapping of vocabularies is per

forming via the Vocabulary Integration Environmental (VINE) 

tool, a stand-alone application specialized in creating custom 

mappings. Currently, the mappings and rules are loaded to the 

semantic mediator service manually, and the semantic mediator regenerates all the relationships (including inferred ones) 

in the knowledge base every time a new ontology is added. 

These semantic capabilities will prove increasingly useful in a wide range of environmental science applications, as 

more data systems are directly and indirectly linked to provide 

interoperable services. The MMI-developed semantic architectures and tools, along with many others presented on the MMI 

Web site, are targeted to develop interoperable environmental 

data systems on a national and international scale. 

Semantic Integration of Heterogeneous 

Volcanic and Atmospheric Data 

By Deborah L. McGuinness1, Peter Fox2, A. Krishna Sinha3, and 

Robert Raskin4 

1

Knowledge Systems, McGuinness Associates and Stanford University, 

Stanford, Calif. 

2

High Altitude Observatory (HAO), National Center for Atmospheric 

Research (NCAR), Boulder, Colo. 

­

­

3

Department of Geology, Virginia Polytechnic Institute and State University, 

Blacksburg, Va. 

4

Jet Propulsion Laboratory, Pasadena, Calif. 

The vast majority of explorations of the Earth system are 

limited in their ability to effectively explore the most impor

tant (often most difficult) problems because they are forced 

to interconnect at the data-element, or syntactic, level rather 

than at a higher scientific, or semantic level. In many cases, 

syntax-only interoperability is the state-of-the-art. Currently, 

in order for scientists and non-scientists to discover, access, 

and use data from unfamiliar sources, they are forced to learn 

details of the data schema, and other people’s naming schemes 

and syntax decisions. These constraints are limiting even when 

researchers are looking for information in their own discipline, 

but they present even greater challenges when researchers 

are looking for information spanning multiple disciplines, 

including some in which they are not extensively trained. Our 

project, the Semantically-Enabled Scientific Data Integration 

(SESDI), aims to demonstrate how ontologies implemented 

within existing distributed technology frameworks will 

provide essential, reusable, and robust support necessary for 

interdisciplinary scientific research activities. 

Introduction 

Our project is aimed at enabling the next generation of 

interdisciplinary and discipline-specific data and information 

systems. Our initial focus is the integration of volcanology 

and atmospheric data sources in support of investigations into 

relationships between volcanic activity and global climate. 

This work is aimed at providing scientists with the option 

of describing what they are looking for in terms that are 

meaningful and natural to them, instead of in a syntax that is 

not. The goal is not simply to facilitate search and retrieval, 

but also to provide an underlying framework that contains 

information about the semantics of the scientific terms used. 

Our system is expected to be used by scientists who want to 

do processing on the results of the integrated data, thus the 

system must provide access to how integration is done and 

what definitions it is using. The missing element in previous 

systems in enabling the higher level semantic interconnections 

is the technology of ontologies, ontology-equipped tools, and 

semantically aware interfaces between science components. 

We present the initial results of using semantic technologies 

to integrate data between these two discipline areas to assist 

in establishing causal connections, as well as exploring as yet 

unknown relationships. 

Semantic Data Integration Methodology 

Our effort depends on machine-operational specifications 

of the science terms that are used in the disciplines of interest. 

We are following a methodology that we believe is yielding 

candidate reference ontologies in our chosen domains. We 

have identified specific ontology modules that need construc­

­
Geoinformatics 2007—Data to Knowledge 11 

Figure 1. Volcano ontology CMAP fragment. Volcanoes can be classified by composition, tectonic setting, environmental setting, 

eruption type, activity, geologic setting, and landform. The ontology currently contains upper level terms in these areas and is being 

expanded according to the needs of the project and is being reviewed by additional domain experts. The initial focus is on gathering 

terms, putting them into a generalization hierarchy (using “isa” links in the diagram), and connecting the terms through properties (using 

“has” links in the diagram), as well as identifying equivalence relationships (using “sameas” links) and partonomic information (using 

“ispartof” links). 

tion in the areas of volcanoes, plate tectonics, atmosphere, and 

climate. We have begun construction of two of the modules 

along with the help of a set of selected experts in the areas. 

Prior to a workshop, we identify a small set of subject matter 

experts. We also provide some background material for reading about ontology basics. Additionally, prior to our face-toface meetings with experts, we identify foundational terms in 

the discipline and provide a simple starting point for organizing the basic terminology. While we do not want to influence 

the domain experts on their terminology, we find that we 

make more progress if we provide simple starting points using 

agreed-upon terminology. We then bring together a small 

group of the chosen domain experts and science ontology 

experts with a goal of generating an initial ontology containing the terms and phrases typically used by these experts. We 

use our task of researching the impact of volcanoes and global 

climate to focus the discussions to help determine scope and 

level of granularity. 

We held a meeting with volcano experts and generated an 

initial ontology containing terms and phrases used to classify 

volcanoes, volcanic activities, and eruption phenomena. We 

use a relatively simple graphical tool (CMAP) for captur

ing the terms and their relationships. A portion of the initial 

volcano ontology is shown in figure 1 (from Sinha and others, 

2006; McGuinness and others, 2006). 

We held a second workshop to create a plate tectonics 

ontology. We identified domain experts and used the same science ontology experts as used in our volcano ontology meeting. The resulting terminology description is shown in figure 

2. In this meeting, we also focused on gathering the primary 

­
12 Geoinformatics 2007—Data to Knowledge 

Figure 2. Plate tectonics ontology CMAP fragment. Both in preparation for and in followup from the domain workshops, we are 

reviewing relevant existing vocabularies and ontologies. We have reused terminology from SWEET (http://sweet.jpl.nasa.gov/ontology/), 

GEON ontologies (http://kbis.sdsc.edu/GEON/resources/resources.html), and the Virtual Solar Terrestrial Observatory (McGuinness and 

others, 2006) instrument and observatory ontologies. We are also gathering some of the starting points for the atmosphere and climate 

ontologies from SWEET and related ontologies. 

class terms (for example, plate boundary, lithosphere, and so 

on), putting them into a generalization hierarchy, and identifying important properties relating the terms. 

Our candidate datasets for registration with the developed 

ontologies and for use in our data integration use case include 

the WOrld VOlcano DATabase (WOVODAT) in collaboration 

with Yellowstone researchers and the U.S. Geological Survey, 

and the Nevada Test site database in collaboration with Los 

Alamos researchers. We are presently identifying the corresponding atmospheric and climate record databases. Our 

approach will be to establish initially a Web portal based on 

one of the current best-of-breed semantic data frameworks (for 

example, VSTO, GEON) to prototype the access to the data 

from the volcano and atmosphere disciplines separately. Then 

we will provide Web service access to both sources so that the 

statistical application we will utilize for the data integration 

can query and retrieve data. 

Discussion and Conclusion 

We have begun an effort that utilized ontologies to provide the capture term meanings in distinct but related science 

domains with a goal of facilitating research into relationships 

between the domains. We currently have starting points for 

reference ontologies in volcanoes and plate tectonics. We 

have also begun the homework on atmosphere and climate 

and will be holding workshops to generate reference ontologies with domain experts. We also will hold workshops to vet 

the ontologies among the multiple communities. So far, our 

findings are that our methodology for creating starting points 

for reference ontologies is working well in terms of gathering 

terms and relationships, and reaching agreement among the 

initial domain and science ontology experts. 

Based on the successful use of semantics in data integration for the VSTO and GEON projects, our next step for 

SESDI is to articulate a use case that drives the way and 

type of data integration needed to solve a specific scientific 

problem. Our candidate is to examine the statistical relation 
between the height of the tropopause and related forcings. This 

height is very sensitive to forcing so that the fingerprint of 

volcanic and solar forcings are very distinct. 

References Cited 

Fox, P., McGuinness, D.L., Middleton, D., Cinquini, L., 

Darnell, J.A., Garcia, J., West, P., Benedict, J., and Solomon, S., 2006, Semantically-enabled large-scale science 

data repositories, in Cruz, I., and others, eds., The Semantic 

Web—ISWC 2006, Proceedings of the Fifth International 

Semantic Web Conference, Athens, Ga., November 5-9, 

2006: Lecture Notes in Computer Science, v. 4273, p. 792­

805. 

McGuinness, D.L., Sinha, A.K., Fox, P., Raskin, R., Heiken, 

G., Barnes, C., Wohletz, K., Venezky, D., and Lin, K., 2006, 

Towards a reference volcano ontology for Semantic scientific data integration: Eos, v. 87, no. 36, Joint Assembly 

Supplement Abstract IN42A–03. 

Sinha, A.K., Heiken, G., Barnes, C., Wohletz, K., Venezky, D., 

Fox, P., McGuinness, D.L., Raskin, R., and Lin, K., 2006, 

Towards an ontology for volcanoes, in Brady, S.R., Sinha, 

A.K., and Gundersen, L.C., eds., Geoinformatics 2006— 

Abstracts: U.S. Geological Survey Scientific Investigations 

Report 2006-5201, p. 52. 

Semantics and Science: Facilitating a 

Community Centric View 

By Danielle Forsyth1 

1

Thetus Corporation, Portland, Oreg. 

Scientific information is pouring in from satellites, sensors, instruments, cameras, documents, and devices. This raw 

information is processed into an ever-increasing number of 

data products for use by the scientific, policymaking, academic, and corporate communities. Often, these communities 

need to share information for collaboration, yet the languages 

of these communities differ. Communities use different terms 

and relationships to describe and define their information, they 

trust different sources, they have different experts, and they 

rely on different and ever-changing models. 

Understanding complex systems requires cross-community collaboration and historical perspective. Communities 

must be connected so that complex systems can be understood, 

boundary conditions can be examined, and sensitivity to 

potentially impacted or dependent systems can be understood. 

Forcing a common language for these interconnected communities will not happen, and if it did, it would result in a lack of 

needed information fidelity and context. 

Geoinformatics 2007—Data to Knowledge 1 

Semantic approaches to cross-community collaboration 

can facilitate the necessary knowledge sharing and reusability 

to abstract meaning in support of an overall understanding of 

complex systems. 

This presentation demonstrates how different business, 

policymaking, and scientific communities can collaborate 

using multiple domain or problem representations (knowledge 

models). It illustrates the interconnections and information/ 

knowledge sharing between seemingly unrelated communities 

where serendipitous discovery matters. In addition, the presentation demonstrates how these rich semantic models can be 

utilized to find similar and related information and to discover 

unexpected connections between seemingly arbitrary pieces of 

information. 

The presentation centers on a map-based interface that 

allows different communities to visualize changing information and relationships (in a spatial environment) based on 

their domain or problem of interest. Within this semantically 

rich and spatial environment, different groups of users can 

review metadata, make annotations, create new relationships, 

view information history, and filter their views to focus on 

the appropriate level of knowledge and information detail to 

address their questions. New knowledge can be captured and 

community members can be automatically notified of information of interest. 

While semantic models are touched on, the focus of the 

presentation is on their use in filtering information to allow 

users to quickly focus on relevant and needed information in 

their own domains. 

CyberIntegrator: A Highly Interactive 

Scientific Process Management 

Environment to Support Earth 

Observatories 

By Rob Kooper1, Luigi Marini1, Jim Myers1, Barbara Minsker1, 

and Peter Bajcsy1 

1

National Center for Supercomputing Applications, University of Illinois at 

Urbana-Champaign, Urbana, Ill. 

The concept of Earth observatories has been evolving 

over the past decade and it is surrounded with the multitude 

of “informatics” needs for a successful deployment. In the 

context of Earth observatories, informatics refers to problems 

related to expected large amounts of often highly complex data 

that have to be analyzed; information has to be interactively 

extracted from raw data and then understood by domain scientists. In all application areas where Earth observatories are 

being designed and built (for example, Watershed Assessment, 

Tracking and Environmental Results (WATERS), Consortium 

of Universities for the Advancement of Hydrologic Science, 

Inc. (CUAHSI), National Ecological Observatory Network 

(NEON), Geosciences Network (GEON), and Optical Remote 
1 Geoinformatics 2007—Data to Knowledge 

Imaging Observatory of New Mexico (ORION)), scientists 

desire to learn from their data about a spectrum of complex 

phenomena surrounding us; however, the informatics challenges for a domain expert can significantly hinder any learning progress. Regardless of a domain—geologic, hydrologic, 

ecologic, environmental, or sensor—general informatics 

challenges exist related to (a) data volume and computational 

requirements, (b) data, analysis and resource complexity management, and (c) the heterogeneity of information technologies 

supporting scientists. Our goal is to support scientists building 

Earth observatories to overcome these general challenges. 

This paper presents a novel process management environment called CyberIntegrator to support diverse analyses in 

Earth observatories. These analyses are very time-consuming 

and hard to reproduce because of the lack of in-silico scientific process management and because of the diversity of data, 

software, and computational requirements. The motivation 

for our work comes from the need to build the next generation of in-silico scientific discovery processes that require 

(a) access to heterogeneous and distributed data and computational resources; (b) integration of heterogeneous software 

packages, tools, and services; (c) formation and execution of 

complex analytical processing sequences; (d) preservation of 

traces about scientific analyses; and (e) design of secure collaborative Web-based frameworks for sharing information and 

resources. 

The goal of the presented work is to describe a modular 

architecture and key features of a workflow that provides a 

process management environment for automating science 

processes, reducing the human time involved and enabling 

scientific discoveries that would not be possible without supporting software and hardware infrastructure. Our approach 

to solving the above problem is based on adopting objectoriented software-engineering principles, designing a modular software prototype, and focusing on user interfaces that 

simplify complex analyses including heterogeneous software 

integration. 

Figure 1 shows the overall architecture of the developed 

process management environment by bringing together the 

top level features with the low-level object-oriented softwareengineering principles. From the functionality perspective, 

the CyberIntegrator software could be viewed as a system for 

(a) browsing and searching available data, tools, and computational resources; (b) accessing available datasets, tools, 

and computational resources; (c) bringing them together; 

(d) executing one tool at the time or a sequence of tools, (e) 

monitoring and controlling executions, (f) efficiently utilizing available data, tools, and computational resources; (g) 

collecting information, or provenance, about the process flow 

to help later reconstruct the thought process of the scientist; 

and (h) assisting the scientist using the provenance gathered 

by the community. The key architectural components are the 

editor, engine, executors, applications, collections of registries, and optional metadata repository and event broker. 

They are all written in Java. The registries could be viewed as 

repositories of high-level descriptions of available data, tools, 

and resources. The metadata store provides a repository for 

gathered information about workflow execution and it is based 

on a resource description framework (RDF) format. Finally, 

the event broker is a component for handling a stream of data 

or events, and it is based on Java Message Service (JMS) 

application programming interface (API) for sending messages 

between two or more clients. 

Our aim has been to design a workflow that works with 

descriptions (metadata) of data, software tools, and computaFigure 1. The overall architecture of CyberIntegrator. 

tional resources for easy integration and hierarchical organization. This distributed metadata (also called “registries”) 

about data, tools, or resources can be modified with a text or 

Extensible Markup Language (XML) editor. The workflow 

system will pull in all the information from the registries, load 

a requested workflow, and execute the workflow accordingly. 

The benefits of such workflows for domain scientists are (a) 

the simplicity of integrating existing software within the workflow system; (b) the benefits of running, reusing, repurposing, 

and sharing workflows with other scientists; and (c) receiving 

feedback from the system during workflow creation based on 

the provenance gathered. 

CyberIntegrator editor provides a user-friendly interface for browsing registries of data, tools, and computational 

resources; creating workflows in a step-by-step exploration 

mode; reusing and repurposing workflows; executing process 

flows, locally or remotely; aiding research explorations using 

a provenance-to-recommendation pipeline; and incorporating 

heterogeneous tools and linking them transparently. Figure 2 

shows the graphical user interface of CyberIntegrator editor. 

There are three top panes (left to right: Data pane, Tools pane, 

and Resources pane) and one pane on the bottom. The Data 

pane lists all datasets loaded or generated so far for processing. The Tools pane lists the tools currently loaded from local 

and remote registries. The Resources pane lists computational 

resources (executors) available for a particular tool. The bottom pane contains several tabs with information about the 

CyberIntegrator execution. The Help tab contains help text 

about the tool currently selected. The Steps tab contains a 
Geoinformatics 2007—Data to Knowledge 1 

Figure 2. CyberIntegrator. 

list of the tools run so far with information about each execution. The Graph tab includes a graphical representation of the 

sequence of steps. The current workflow can also be saved and 

either reloaded or shared with others later. The scientist can 

ask the system to recommend a tool based on the current data 

by selecting the button above the Data pane. 

The key contributions of our work on CyberIntegrator can 

be summarized as follows: The main computer science novelty 

of our work lies in (1) formalizing the software integration 

framework using object-oriented software-engineering principles; (2) designing a browser-based modeling paradigm for 

step-by-step composition of workflows; (3) gathering provenance during workflow creation and execution; (4) using the 

provenance for tool recommendation feedback for workflow 

autocompletion; and (5) providing capabilities to publish, run, 

monitor, retrieve, and reuse and repurpose workflows from 

local and remote computational resources for long-running 

workflows (for example, referring to large simulation and 

streaming data analyses). The main technical contributions are 

also in testing and demonstrating the prototype process management system with several application scenarios from environmental and hydrologic engineering sciences. Our process 

management prototype is also supporting Waters/CLEANER 

and Consortium of Universities for the Advancement of 

Hydrologic Science (CUAHSI) communities in the context 

of building Earth observatories. The software is available for 

download at http://isda.ncsa.uiuc.edu/download. 

DIA Engine: Semantic Discovery, 

Integration, and Analysis of Earth 

Science Data 

By Abdelmounaam Rezgui1, Zaki Malik1, and A. Krishna Sinha2 

1

Department of Computer Science, Virginia Polytechnic Institute and State 

University, Blacksburg, Va. 

2

Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, Va. 

Introduction 

Geoscientists have generated massive volumes of earth 

science data for decades. Most of the produced data, how­
1 Geoinformatics 2007—Data to Knowledge 

ever, remain isolated “knowledge islands”; the ability to find, 

access, and properly interpret these large data repositories 

has been very limited. This is due to two main reasons: the 

absence of data-sharing infrastructures that scientists may use 

to advertise their data, and lack of a “common language” that 

scientists can use to properly interpret other providers’ data. 

As a result, the discovery, integration, and analysis of earthscience data have remained difficult. This, in turn, has precluded the meaningful use of the available data in answering 

complex questions that require information from several data 

sources. To address this problem, we have developed Discovery, Integration and Analysis (DIA)—a service-oriented, 

Web-based computational infrastructure that enables scientists to utilize semantically enabled technologies to discover, 

integrate, and analyze earth-science data. It also promotes tool 

sharing through Web services. It provides a collaborative environment where scientists can share their resources for discovery and integration by registering them through well-defined 

ontologies (Sinha and others, 2006). DIA is developed using a 

variety of technologies, including the following: Enviromental 

Systems Research Institute’s ArcGIS Server 9.1, Web services, 

.NET, Java, and JNBridge 3. 

Architecture of the DIA Engine 

The DIA engine is a Web-accessible system that provides three classes of functionalities: discovery, integration, 

and analysis. Data discovery enables users to retrieve datasets, while data integration enables users to query multiple 

resources along some common attributes to generate previously unknown information called data products. Data analysis 

may be used to verify certain hypotheses or to refine the data 

product. 

To describe DIA’s architecture (fig. 1), we will use the 

following query as our type example: (Q) Find A-type plutons 

in Virginia and identify the correlation between these plutons 

and their geophysical (for example, gravity) properties. The 

core of DIA’s engine consists of five components: 

User Interface—This is an ArcGIS Server.NET map 

viewer Web application. DIA provides a menu-based interface that enables users to specify a large number of complex 

queries. Map-based queries can be refined by specifying a 

bounding box that identifies a pair of latitude-longitude points 

which delimits the query’s spatial scope. After the query’s 

spatial scope is specified, the user uses DIA’s drop-down menu 

to indicate the filters (A-type igneous rock filter, in our running example) and (or) tools to be applied to the data samples 

discovered in the query’s spatial scope. 

Figure 1. DIA’s software architecture. 
17 

Web Servers—Typically, DIA uses two Web servers. 

The first Web server is responsible for routing users’ queries 

to DIA’s query processor and the second ensures communication between DIA’s query processor and its own map server. 

In a minimal deployment, a single Web server may be used for 

both purposes. In the current DIA’s implementation, we use 

a single instance of Windows IIS Web server as DIA’s Web 

server. 

Map Server—This component is an ArcGIS map server 

that provides maps to DIA’s query processor. 

Registry Servers—These servers could be distributed 

worldwide, and provide directory functionalities (registration 

of data and tools, indexing, search, and so on) The providers 

of resources advertise their resources on registry servers. 

Query Processor (QP)—This is the core component of 

the DIA engine. It is responsible for producing the results for 

users’ queries and delivering them to the Web server. Essentially, the QP consists of two subcomponents: (a) the query 

interpreter and (b) the geology and mapping filters and tools. 

The former is a .NET module that interprets queries and 

identifies the appropriate filters and (or) tools to be invoked to 

answer each query. The latter is a large set of .NET modules 

that perform DIA’s core functionalities, including filters (for 

example, A-Type igneous rock filter), tools (for example, kriging) and map management routines (for example, coloring of 

geological bodies and sample points). Query processing consists of two phases: (a) data and tool discovery and (b) filtering 

and integration. 

Data and Tool Discovery—During this operation, the 

DIA engine identifies and retrieves the resources (data and 

tools) required to answer the user’s query. To illustrate, 

consider the previous A-Type query. When the QP receives 

the query from the Web server, it determines the type of data 

required to answer the query. In this case, the QP determines 

that data associated with the keyword “Geochemistry” is the 

query’s target. The QP then interacts with one or several registry servers to retrieve the needed data. An example of registry 

server is available at http://www.geongrid.org. To interact 

with Geosciences Network (GEON) server, DIA invokes a 

GEON Web service called GEONResources that provides 

functions for searching and getting the metadata information 

for resources registered through GEON portal. When invoking GEONResources, DIA’s QP indicates that it is searching 

datasets registered with the keyword “Geochemistry” and that 

contain data samples in the query’s spatial bounding box. For 

each returned database, the DIA system executes a two-step 

process. First, it builds a virtual query (expressed in Simple 

Ontology-Based Query Language (SOQL)—a language developed by GEON’s researchers at the San Diego Supercomputer 

Center (SDSC)) that requests all the data (in other words, 

columns) that are necessary to apply the filter specified by 

the user. The DIA system then invokes a GEON Web service 

called SoqlToSql that translates this SOQL query into an SQL 

query. In the second step, DIA submits the SQL query to the 

GEON server that interacts with the actual database server, 

Geoinformatics 2007—Data to Knowledge 

gets a record set containing the relevant data samples, and 

returns the data to the DIA engine. 

Filtering and Integration—Data filtering is a process in 

which the DIA engine transforms raw data into a data product. 

After DIA retrieves the datasets relevant to the user’s query, 

it determines whether the filter(s) to apply or tool(s) to use is 

locally available. If so, the filter or tool is applied to the datasets and the query result is displayed to the user. If not, DIA 

searches for the needed filter or tool in registry servers. DIA 

is able to invoke any external tool that is wrapped as a Web 

service. In the case of the given A-type query, the A-Type filter 

is already available in DIA and also made available as a Web 

service for external users. 

Integration in DIA is a process in which the results of 

several subqueries are produced and then overlaid in the user 

interface. In the case of our A-Type query, DIA first follows the same workflow as for determining A-Type bodies 

to produce the result of kriging gravity data. DIA looks up 

registry servers for gravity data in the selected area of inter

est and then retrieves the raw gravity data from its provider(s) 

(for example, http://paces.geo.utep.edu). DIA then determines 

whether a kriging tool is locally available. Since such a tool 

is already included in DIA’s implementation, it is invoked and 

no external registry servers are searched. When the output of 

the kriging tool is generated, DIA overlays it on the previously 

generated results (in other words, A-Type plutons) making it 

possible for the user to have a natural and easily interpretable 

view of the integration’s result (fig. 2). 

­

Figure 2. Semantically enabled integration of data products 

where A-type plutons and gravity fields have been merged 

through the DIA engine. 
1 Geoinformatics 2007—Data to Knowledge 

Conclusion 

We suggest that the semantic integration of data and 

tools can be implemented through the DIA engine, a system 

that enables geoscientists to discover, integrate, and analyze 

earth-science data. DIA also demonstrates the potential of the 

service-oriented design paradigm to enable scientists to share 

tools in addition to data. The DIA engine is now in its final 

pre-release phase. Its beta version is currently accessible at 

http://mapserver.geos.vt.edu/DIA. 

We expect that as the Semantic Web matures, more geoscientists will adopt ontologies as a means for data and service 

sharing and integration. The DIA engine is designed for Webbased geoinformatics systems. These systems would provide 

an infrastructure where scientists worldwide would be able to 

discover, integrate, and analyze data. 

Acknowledgment 

This research is supported by the National Science Foundation, award EAR 0225588 to A.K. Sinha. 

Reference Cited 

Sinha, A.K., Malik, Z., Rezgui, A., and Dalton, A., 2006, 

Developing the ontologic framework and tools for the discovery and integration of earth science data; Annual report, 

June 2006, Blacksburg, Va., Virginia Polytechnic and State 

University, Department of Geosciences, available online 

at http://geon.geol.vt.edu/pubreps/Virginia Tech Annual 

Report 2006.doc/. (Accessed June 12, 2007.) 

Customizing a Semantic Search Engine 

and Resource Aggregator for Different 

Science Domains 

By Sunil Movva1, Rahul Ramachandran1, Xiang Li1, 

Phani Cherukuri1, and Sara Graves1 

1

Information Technology and Systems Center, University of Alabama at 

Huntsville, Huntsville, Ala. 

The goal for search engines is to return results that are 

both accurate and complete. The search engines should find 

only what the users really want and everything they really 

want. Search engines (even metasearch engines) lack semantics. The basis for search is simply string matching between 

the user’s query term and the resource database; thus, the 

semantics associated with the search string is not captured. 

For example, if an atmospheric scientist is searching for 

“pressure” related Web resources, most search engines return 

inaccurate results, such as Web resources related to blood pres

sure. Noesis is a metasearch engine and a resource aggregator 

­

that has been designed to utilize domain-specific ontologies 

to provide specialized scoped search capabilities. Noesis uses 

domain ontologies to help the user scope the search query to 

ensure that the search results are both accurate and complete. 

Semantics are captured in the domain ontologies by defining 

terms along with their synonyms, specializations, generalizations, and related concepts. These domain ontologies are used 

by Noesis to guide the user to refine their search query and 

thereby reduce the user’s burden of experimenting with differ

ent search strings. Noesis also serves as a resource aggregator as it categorizes the search results from different online 

resources, such as education materials, publications, datasets, 

and Web search engines that might be of interest to the user. 

Noesis can be customized for use in different domains by 

configuring it to access different ontologies or to search different online resources. Currently, we have a general purpose 

atmospheric science version of Noesis available, and we are 

creating a coastal ecology version specialized for the Gulf of 

Mexico Research Collaborative, a National Aeronautics and 

Space Administration (NASA) applications project. In addition, Noesis portlets are planned for the Earth Science Infor

mation Partners (ESIP) Federation Environmental Information 

Exchange and Geospatial One Stop. 

Towards Debugging Maps Generated 

by GEON Applications Through 

Provenance 

By Nicholas Del Rio1 and Paulo Pinheiro da Silva1 

1

Department of Computer Science, University of Texas at El Paso, El Paso, 

Tex. 

Tool Overview 

Geoscientists need the capability to understand and 

debug maps generated from the highly distributed Geosciences Network (GEON) applications and workflows in order 

to accept them, particularly when a resulting map exhibits 

unexpected or anomalous properties. On one hand, visualization techniques can help a scientist to understand intermediate and final results of a complex GEON application but not 

the underlying processes that derived these results. On the 

other hand, provenance provides information about sources 

and methods used to derive results, which can also increase 

the understanding and acceptance of GEON-generated maps 

by scientists. Although rarely used in combination, visualization and provenance techniques together may further increase 

geoscientists’ understanding of GEON maps by providing a 

complete picture of their generation. Scientists would be able 

to evaluate final results, derivation processes, and any intermediate result derived during the GEON processes. Probe-It! is 

a single tool that provides geoscientists with the capability to 

­

­
Geoinformatics 2007—Data to Knowledge 1 

Figure 1. ProbeIt! Snapshot. 

visualize provenance associated with GEON map generation 

in order to aid the scientist in the understanding and debugging 

of an unexpected map. 

Because GEON maps can be generated using remote 

resources, such as sensory data, remote databases, and services, assessing the quality and correctness of the resultant 

maps is difficult because of the following: (1) scientists may 

not know the history associated with some data source; and (2) 

scientists may not know details about the underlying services 

applied to their data. Probe-It! addresses those issues by 

providing visual access to both the sources and processes used 

to derive a map. For example, through the use of Probe-It!, 

scientists can move the visualization focus from intermediate 

and final results of a contour map workflow to the associated 

provenance trace back and forth. We believe that providing 

geoscientists with both a description of the map generation 

process and the means to visualize the associated intermediate and final results will allow scientists to determine whether 

their resultant maps are correct. Because the most effective 

visualization varies between scientists, Probe-It! also provides a framework for associating particular visualizations to 

a particular information type. For example, a GEON scientist 

may prefer to view spatial datasets as a two-dimensional plot 

on a map, while another scientist might prefer to view the 

same data as a graph. Probe-It! is flexible enough to facilitate 

a multitude of views. 

Figure 1 highlights ProbeIt!’s justification view, which 

outlines the provenance trail of a contour map workflow that 

consists of three Web services: dataset retrieval service, a 

smoothing service, and a contouring service; the resultant map 

is a contour of gravity data. The provenance associated with 

this workflow execution includes everything from the specified map region, provided by a geoscientist, to the final contour map of the specified region. The arrows indicate dataflow 

between services, while each node of the graph represents an 

invoked Web service and its associated output. 

Evaluation Plan 

In order to verify that our tool Probe-It! aids scientists in 

debugging anomalous results, a moderately sized study comprising of gravity experts around the globe is being initiated. 

Each participant will be asked to identify a correct map. In this 

task scenario, we will present four gravity contour maps of a 

region specified by each subject, three of which have had their 

workflow altered in such a way as to corrupt the final result. 

The scientists are asked to identify the correct map using only 

Probe-It! as a resource. If the subjects can both identify the 

correct map and indicate why the other candidate maps are 

unsatisfactory, then we can claim that our tool provides both a 

comprehensive and digestible trace of a workflow execution. If 

the subjects fail to identify the correct map or error source, we 

can at least get an insight as to what additional functions might 
20 Geoinformatics 2007—Data to Knowledge 

have facilitated success and integrate those missing features 

into our tool. 

Visualization Tool for Oracle Spatial 

By Yinghui Li1 

1

Department of Computer Science, San Diego State University, San Diego, 

Calif. 

This is a teaching tool that is developed for the users who 

are willing to learn Oracle Spatial Database to understand the 

concepts and the formats of Oracle Spatial Database. The user 

interface of the system is written in Java, including Java2D. 

The back-end of the system is using the Oracle 9i Spatial 

Database server. Oracle Java Database Connectivity (JDBC) 

Thin driver is used to establish the database connections. 

There are four modes in this tool: Spatial Data, Spatial 

Query, User Data, and User Query. This tool has online help 

files that introduce how to use these four modes. Users can 

practice using the tutorial modes (Spatial Data and Spatial 

Query modes) and then create their own spatial tables, insert 

their own spatial objects, and do some spatial analysis in User 

Data and User Query modes. Advanced users could use this 

visualization tool to show their graphics. In this tool, users can 

delete tables, modify data, load data from text files, zoom in 

and zoom out the graphics, print preview and print the graphics, and select an object from a list for identification. 

Support Collaboration Geosciences 

Research Through the GEON Portal 

By Kai Lin1 and Chaitan Baru1 

1

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

Successful collaborations are crucial to geosciences 

research. In this demo we will demonstrate all the aspects of 

the MyProjects system in the Geosciences Network (GEON) 

ortal for supporting geosciences collaborations. The goal is 

motivated by the observation that most geosciences projects 

have multiple researchers, often at multiple sites with different 

schedules. It is therefore difficult to share information, coordinate tasks, and maintain consistency. Many successful experiences have proved that modern information technologies, such 

as forum, wiki, and many other tools, offer opportunities to 

reduce this difficulty. We seek to organize these cooperative 

tools in a better way with other cyberinfrastructure technologies to support distributed cooperative work in a geosciences 

environment. 

MyProjects is a system in the GEON portal which allows 

users to create new projects and add other people into the 

project teams. The notion of project is a very flexible concept. It supports not only simple personal tasks but also large 

organizational tasks. Four predefined roles in the system are 

listed as follows, based on decreased privileges: leader, contributor, observer, and guest; that is, a leader can do whatever 

a contributor can do in a project, and so on. A specific role is 

assigned to each team member. 

By default, each project consists of nine main components: configuration management, team, a news board, wiki, 

discussion forums, to-do lists, project planning, problem 

reporting, and some space for sharing resources. Each part has 

several operations that can be applied to it, and each operation 

is associated with a lowest role. A team member can perform 

an operation only if the member’s role is not lower than the 

lowest role of the operation. For instance, four operations 

can be applied to a project team: view team members, view 

team member e-mail addresses, modify team, and modify 

team member role. By default, the lowest role associated with 

viewing team member e-mail addresses is observer; therefore, 

a guest will not be able to see e-mail addressed to other team 

members. 

The configuration management component provides the 

capability of customizing project components and changing 

the lowest role of each operation for the selected components. 

For example, a project may decide not to have any forum and 

wiki and allows only leaders and contributors to view team 

member e-mail addresses. 

Adding new members to a project team can be done by 

sending an e-mail invitation or by inputting people from other 

project teams. Once a person accepts the invitation, that person 

is assigned an initial role as a contributor. But this can be 

changed by a person who can modify the team. 

Project news, including news drafts, unreleased news, and 

current news, can be posted on the news board. By default, 

only project leaders can delete news; contributors can add 

news to the board, edit existing news, and view unreleased 

news; observers can check archived news; and guests can only 

read the current news. Users also can choose whether to send 

released news via e-mail to all project members when the 

news is posted. 

The project wiki is an effective place for mass-collaborative authoring that allows contributors to add, remove, and 

edit, content with links to other resources. Project forums build 

some online discussion groups for contributors to exchange 

open messages on any interesting topics. 

Project task management is supported by three components: to-do lists; project planning; and problem reporting. A 

to-do list usually contains several tasks that need to be done. 

The tasks can be closed once they are completed. Important 

dates in a project’s timeline can be set up as milestones by 

using the project-planning component, so that every contributor knows what needs to be done by what time. Troubles 

with data quality, hardware, and software in a project can 

be reported through the problem report component. When a 

problem is reported, a team member can be assigned to solve 

the problem. A problem can be closed after it is solved by 

reporting what has been completed. 
Geoinformatics 2007—Data to Knowledge 21 

MyProjects provides some storage space for each project 

to share resources with other project members. Contributors 

can create folders and upload files into the folders. The system‘s built-in version-control system lets contributors upload 

new versions to existing files saved in the system. Resources 

found by GEON search also can be saved in the project space. 

A search engine is available in MyProjects to find news, 

discussion messages, and resources in project space. A user 

can choose to search in all the participated projects or just 

within a single project. Some MyProjects functions, like computational job monitoring and resource integration tools, are 

still under design or development. 

Managing a Parameter Sweep for 

Earthquake Simulation Research 

By Choonhan Youn1, Tim Kaiser1, and Dogan Seber1 

1

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

A Grid middleware consists of a set of tools and technologies that allows users to access Grid resources and applications using a common set of protocols and services. It also 

provides seamless computing and information environments 

for science and engineering communities. Within such a Grid 

computing environment, a Grid portal provides a user interface 

to a multitier Grid application development stack, which is the 

Grid middleware. The Geosciences Network (GEON) infrastructure that is naturally distributed with users and resources 

spread across 16 different partner sites in the U.S., provides 

portal, middleware, and data resources to facilitate scientific 

discovery using applications, tools, and services for domain 

scientists through a cyberinfrastructure. It consists of both 

service-oriented Web/Grid framework and application toolkits, 

using a Web service model and a portlet programming model 

to represent applications. Based on those grid environments, 

we have developed the Synthetic Seismogram (SYNSEIS) 

toolkit to be a computational platform that facilitates synthetic 

seismogram calculations in two-dimensional and three-dimensional media for research and educational purposes (fig. 1). It 

is a Web-based simulation and analysis system and is one of 

the services provided within the GEON network. SYNSEIS 

Figure 1. GEON—The Geosciences Network Workbench Web page. 
22 Geoinformatics 2007—Data to Knowledge 

provides information management, computational analyses, 

and simulations all wrapped into Web services and Grid 

computing. The E3D simulation software used by SYNSEIS 

is designed to simulate seismic wave propagation through 

the Earth’s interior. We grid-enabled E3D using our own 

dialect Extensible Markup Language (XML) inputs, running 

crustal models through Web services. The XML inputs for this 

application include: structural information, which contains 

cell dimension; number of time steps; source parameters; 

geology; and number of stations. We demonstrate how one 

can use a simple management scheme to perform a parameter 

sweep and spread the work across a collection of computational resources, using an application that was not specifically 

designed to perform parameter sweeps. In particular, we identify the earthquake simulations in SYNSEIS as an example 

application that can benefit from running on multiple computational resources, and subsequently promote the sharing of 

computational resources among partner sites involved in the 

GEON project. 

GeoSciML Testbed 2: Demonstrating 

the Exchange of Geologic Map 

Information Using a Common Data 

Transfer Schema and Open Geospatial 

Consortium Technologies 

By Boyan Brodaric1, Bruce R. Johnson2, Francois Robida3, and 

International Union of Geological Sciences–CGI Interoperability Working Group4 

1

Geological Survey of Canada, Ottawa, Ontario, Canada 

2

U.S. Geological Survey, Reston, Va. 

3

Bureau de recherches géologiques et minières (BRGM), 3-Avenue Guillemin, Orleans, France 

4

International Union of Geological Sciences (IUGS)–Interoperability 

Working Group of the Commission for the Management and Application of 

Geoscience Information (IUGS-CGI), Ottawa, Canada 

Geoscience Markup Language (GeoSciML) is a standard data schema for exchanging geological features using 

Open Geospatial Consortium (OGC) Web service technologies and standards. It is being developed via open participation in the international community by the Interoperability 

Working Group of the Commission for the Management and 

Application of Geoscience Information (CGI), a commission 

of the International Union of Geological Sciences (IUGS). 

GeoSciML 1.0 is an application of GML (Geography Markup 

Language) and is derived primarily from the North American 

Digital Geologic Map Data Model (NADM, http://www.namdgeo.org) and the eXploration and Mining Markup Language 

(XMML, http://xmml.arrc.csiro.au/). It is being considered as 

the exchange format for OneGeology, a collaborative project 

to deliver, via the Web, geologic maps for the World at scales 

of about 1:1,000,000. 

GeoSciML 1.0 has recently been evaluated in a second 

international testbed. Data providers from eight agencies in six 

countries (Canada, United States, United Kingdom, France, 

Sweden, and Australia) participated in this testbed. Geologic 

map information was served by each agency in GeoSciML 1.0 

format, using OGC Web Mapping Service (WMS) and Web 

Feature Service (WFS) standards. For the testbed, multiple 

Web clients carried out three predefined use cases: (1) viewing 

geologic maps from several data providers and downloading 

geologic data as GeoSciML for a user-selected feature; (2) 

querying multiple maps and downloading geologic data as 

GeoSciML for multiple features; and (3) reclassifying multiple 

geologic maps using a common classification for each of the 

GeoSciML attributes of geologic age and rock type, and displaying the result as a derivative map. 

A live demonstration of the testbed will use both Webbased and desktop clients and present the three use cases from 

multiple data sources. In addition to illustrating the benefits of 

a standard data transfer schema, the demonstration will also 

highlight the need to develop semantic approaches to reconcile 

heterogeneous data content. 

Interactive Immersive Visualization of 

Geoscience Data 

By Oliver Kreylos1, Gerald W. Bawden2, Magali I. Billen3, 

Eric Cowgill3, Bernd Hamann4, Margarete A. Jadamec3, 

Louise H. Kellogg3, Oliver G. Staadt5, and Dawn Y. Sumner5 

1

Department of Computational Science and Engineering, University of 

California—San Diego, La Jolla, Calif. 

2

U.S. Geological Survey, Sacramento, Calif. 

3

Department of Geology, University of California—Davis, Davis, Calif. 

4

Department of Computer Science, University of California—Davis, Davis, 

Calif. 

5

Department of Geology, University of California—Davis, Davis, Calif. 

The geosciences are increasingly challenged to manage, process, visualize, and interpret the large quantities of 

data generated by high-accuracy, high-resolution imaging and 

sensing technologies, or large-scale computer simulations of 

complex phenomena. We are developing and using interactive 

visualization software to view, interact with, and manipulate 

observed and (or) simulated geophysical, geodynamical, and 

geologic data. The innovation of our approach is the highly 

effective use of human interaction in immersive three-dimensional virtual reality (VR) environments. While immersive 

(head-tracked stereoscopic) visualization allows us to detect 

features in large and complex data more effectively, interactive 

tools substantially simplify the construction or manipulation of 

three-dimensional shapes to isolate and identify those features, 

and to perform quantitative measurements of structures emerg­
Geoinformatics 2007—Data to Knowledge 2 

Figure 1. Screen shot from the LiDAR viewer, showing a part of University of California—Davis campus (including the water tower 

and the Mondavi Center for the Performing Arts). The image shows how a user can select features by using a “three-dimensional 

paint brush” (selected points are highlighted in green), and can quantify the position/orientation of features by, for example, extracting 

equations of best-fit planes (planes are visualized as green transparent rectangles). 

ing from the data. We tailor our visualization approach to the 

specific scientific problems to take full advantage of each 

visualization method’s strengths, using both three-dimensional 

perception and interaction with data and ongoing simulations, 

to fully utilize the skills and training of geoscientists exploring 

their data in VR environments. In the process, we are developing a suite of tools that are adaptable to a broad range of scientific and engineering problems. We demonstrate our approach 

on several geophysical and geological datasets, including 

tripod-based and airborne light detection and ranging (LiDAR) 

data, seismic tomography, geodynamics computational 

models, volumetric data from thin serial sections of Archean 

rock samples, and earthquake hypocenters. A LiDAR viewer 

(see fig. 1) allows a user to be fully immersed in point cloud 

data to assess data quality and to analyze complex targets 

that are hard to identify in standard, nonimmersive visualizations. Software mapping tools allow “virtual field studies” in 

regions that are otherwise inaccessible to human geologists. A 

visualization software for gridded volumetric data (see fig. 2) 

focuses on exploratory data analysis by supporting real-time 

extraction of derived shapes, such as color-mapped slices, isosurfaces, or particle traces. Interactive measurement tools then 

allow scientists to quantify their observations. Our software 

is based on a “VR operating system” that can be used with 

Figure 2. Screen shot from the gridded volumetric visualizer, 

showing an early model of a subduction zone underneath Alaska. 

The subducting slab was isolated by an isosurface of viscosity, 

and structures in the surrounding mantle are visualized using 

color-mapped slices at user-defined positions and orientations. 
2 Geoinformatics 2007—Data to Knowledge 

standard computers and a range of immersive three-dimensional environments (for example, GeoWall, ImmersaDesk, 

and CAVE). We have demonstrated that our approaches have 

clear advantages over other commonly used data analysis 

methods. Additional information about our work can be found 

at http://www.keckcaves.org. 

Earth Science Community 

Implementation Through Iteration and 

Testing (ITIT) Resources Through a 

Unified Data and Analysis Portal 

By Yehuda Bock1, Paul Jamason1, Reuy-juin Chang1, 


Feng Pang1, Sharon Kedar2, Danan Dong2, and Brian Newport2
 

1

Scripps Institute of Oceanography, University of California—San Diego, 

La Jolla, Calif. 

2

Jet Propulsion Laboratory, Pasadena, Calif. 

We are in the process of merging the capabilities of three 

NASA-funded projects under the umbrella of the National 

Aeronautics and Space Administration (NASA) Access 

Project, “Modeling and On-the-fly Solutions for Solid Earth 

Sciences (MOSES),” to facilitate data mining and modeling of 

rapidly expanding multidisciplinary geoscience datasets. (1) 

The SCIGN (Southern California Integrated GPS Network)-

REASoN project is focused on the combination, validation, 

archive, and delivery of high-level data products and datamining capabilities from space geodetic measurements, in 

particular from over 600 Canadian Galactic Plane Survey 

(CGPS) stations in Western North America. (2) The QuakeSim 

project is developing linked Web service environments for 

supporting high-performance models of crustal deformation 

from a variety of geophysical sensors, including global positioning system (GPS) and seismic instruments. (3) The Solid 

Earth Natural Hazards (SENH) Research and Applications 

Development Program’s GPS and seismic integration project 

has developed a prototype real-time GPS/seismic displacement meter for seismic hazard mitigation and monitoring of 

critical infrastructure. The focus of the MOSES project is to 

enable direct interaction between modelers and data or dataproduct providers using Web services within a unified portal 

architecture. Modeling applications include, for example, time 

series analysis of continuous and real-time data (for example, 

RDAHMM and st_filter programs) and fault dislocation modeling (for example, Simplex program). Community resources 

include access to extensive infrastructure and distributed data 

archive holdings, an online map server/client linked to a GIS 

database, a “GPS Explorer” data portal that is extensible to 

heterogeneous datasets, and “Geophysical Resource Web 

Services.” We present an interactive display of the current 

capabilities of the unified data portal and solicit feedback from 

community members. 

A Deployable GEON LiDAR Processing 

and Analysis System 

By Efrat Jaeger-Frank1, Sandeep Chandra1, 


Christopher J. Crosby2, Viswanath Nandigam1, 


Ashraf Memon1, J. Ramon Arrowsmith2, Ilkay Altintas1, 


and Chaitan Baru1
 

1

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

2

School of Earth and Space Exploration, Arizona State University, Tempe, 

Ariz. 

Distribution, processing, and analysis of large light distance and ranging (LiDAR, also known as airborne laser swath 

mapping (ALSM)) datasets push the computational limits of 

typical data distribution and processing systems. The high 

point density of LiDAR datasets makes processing difficult for 

most geoscience users who lack the computing and software 

resources necessary to handle these massive data volumes. 

Over the past two years, as part of the Geosciences Network 

(GEON) project, we have developed a three-tier architecture— 

the GEON LiDAR Workflow (GLW)—to facilitate community 

access to LiDAR datasets. The GLW uses the GEON portal, 

a workflow system based on the Kepler scientific workflow 

environment, and a set of services, for coordinating distributed 

resources using emerging Grid technologies and the GEONGrid clusters. The GLW is available to the community via the 

GEON portal and has proven itself as an efficient and reliable 

LiDAR data distribution and processing tool. 

The increasing popularity of the GLW has led to several requests for deployment of the system to additional sites 

and projects. We are currently in the process of creating an 

automatic deployment of the system that requires a minimal 

amount of user intervention (known as a “roll”). As an initial 

phase, we have replicated the processing services originally 

deployed on a GEON cluster at Arizona State University, to 

additional compute clusters at the San Diego Supercomputer 

Center and at the University NAVSTAR (Navigation Signal 

Timing and Ranging) Consortium (UNAVCO) in Boulder, 

Colo. With the GLW deployed on multiple processing clusters, we can improve the system load balancing and provide a 

failover site. We further plan to enhance the system by utilizing a Grid scheduler to map jobs onto the Grid clusters by taking into account the availability of the corresponding compute, 

storage, and networking resources. Deploying the GLW on 

distributed sites also imposes additional requirements on the 

system for increased robustness, and more system monitoring 

information. For example, users are interested in tracking the 

execution state of their LiDAR processing job in real time. A 

number of new features were added to the GLW to address 

these requirements. We use the Kepler “provenance” capability, which collects job provenance data, to enhance the GLW’s 

job-monitoring interface to provide users with live job status 

monitoring. The data provenance is also useful when publishing results and sharing GLW products among scientists. With 
these enhancements, we expect to make the GLW more robust 

and useful to a wide range of earth science users. The GLW 

is currently deployed on 3 sites, and has 126 users who have 

submitted a total of 1,250 LiDAR processing requests for a 

total of over 500 gigabytes of data. 

LiDAR-in-the-Box: Serving LiDAR 

Datasets Via Commodity Clusters 

By Viswanath Nandigam1, Chaitan Baru1, Sandeep Chandra1, 

and Efrat Frank1 

1

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

The Geosciences Network (GEON, http://www.geongrid. 

org) is an National Science Foundation-funded project to create an information technology infrastructure that facilitates 

a collaborative, interdisciplinary science effort in the field of 

earth sciences. GEON facilitates data registration, ingestion, 

and integration of a range of geoscience data types, including 

LiDAR (light distance and ranging) data. LiDAR datasets can 

be used to create high-quality digital earth-surface models, 

which are useful in a variety of geoscience and geospatial 

applications. The recent, rapid increase in the rate of acquisition and popularity of these datasets far outpaces the resources 

available to most geoscientists for processing and using these 

data. 

GEON provides a novel approach for processing and 

distributing LiDAR datasets and derived products using a 

high-performance backend database machine, a portal as the 

front-end user interface, and the Kepler scientific workflow 

system for managing the computations. Currently, the LiDAR 

datasets are stored in an IBM DB2 database running on one of 

the nodes of DataStar, an IBM supercomputer system that is 

one of the computational resources in the TeraGrid. This node 

is linked to a large disk subsystem via a high-end fibre channel 

link. This machine configuration is well suited to handle the 

massive amounts of LiDAR data, which frequently exceed 

several millions of data points per dataset. 

We propose a new approach to hosting LiDAR data based 

on commodity clusters, which can provide a better price/per

formance solution. This is achieved by taking advantage of 

DB2’s “partitioned database” feature. In this approach, the 

LiDAR database tables would be partitioned across multiple 

machines or “nodes.” Each partition is managed by an independent database manager, each with its own data, configuration files, indexes, and transaction logs. This architecture 

provides better scalability; new machines can be added to the 

complex and the database can be expanded across them. In this 

paper, we describe this new parallel database architecture for 

hosting LiDAR data. We refer to this system as the “LiDARin-the-Box” because one of the benefits of this approach is that 

individual researchers will be able to deploy such a system at 

­

Geoinformatics 2007—Data to Knowledge 2 

their sites. We will describe the approach that will be used to 

make the LiDAR in the Box easily deployable. 

A Petascale Cyberfacility for PhysicsBased Seismic Hazard Analysis 

By Philip Maechling1, Thomas Jordan2, J. Bernard Minster3, 

Reagan Moore3, Carl Kesselman4, and The CME Collaboration1 

1

Southern California Earthquake Center, Los Angeles, Calif.
 

2

Department of Earth Sciences, University of Southern California, Los 


Angeles, Calif. 

3

University of California—San Diego, La Jolla, Calif. 

4

Information Sciences Institute, University of Southern California, Los 

Angeles, Calif. 

Current applications of probabilistic seismic hazard 

analysis (PSHA) employ empirical attenuation relationships 

to model the propagation and attenuation of seismic waves 

between the source and receiver; however, these relationships 

cannot easily account for three-dimensional structural variations (for example, basin effects) and source complexities (for 

example, directivity effects). A goal of the Southern California 

Earthquake Center (SCEC) is to use earthquake simulations 

to improve PSHA. For this purpose, SCEC is deploying as 

part of its Community Modeling Environment (CME) a new 

cyberfacility (PetaSHA) that can execute PSHA computational pathways and manage data volumes using the Nation’s 

high-performance computing resources. The objectives are 

to extend deterministic simulations of strong ground motions 

above 1 hertz for investigating the upper frequency limit of 

deterministic ground-motion prediction; improve the resolution of dynamic rupture simulations by an order of magnitude 

for investigating the effects of realistic friction laws, geologic 

heterogeneity, and near-fault stress states on seismic radiation; 

and compute physics-based PSHA maps and validate them 

using seismic and paleoseismic data. The cyberfacility comprises several computational platforms that vertically integrate 

hardware, software, and wetware (technical expertise). One 

of these platforms, CyberShake, employs advanced workflow 

management tools to compute and store the large suites of 

ground-motion simulations needed for physics-based PSHA 

mapping. We are also developing a science gateway for the 

broader community to access the CME simulation capabilities 

and data products. 
2 Geoinformatics 2007—Data to Knowledge 

An Integration Scheme for Geophysical 

Studies of the Continental Lithosphere: 

An Update 

By G. Randy Keller1, Eva-Maria Rumpfhuber1, and 

Aaron Velasco2 

1

School of Geology and Geophysics, University of Oklahoma, Norman, 

Okla. 

2

Department of Geological Sciences, University of Texas at El Paso, El 

Paso, Tex. 

Modern studies of the processes that create and deform 

the continental lithosphere have both fundamental scientific 

and societal implications. With the development of ambitious projects such as EarthScope, the data emerging make it 

possible, in fact essential, to build three-dimensional models 

that have the highest spatial resolution possible, are tied to 

geologic constraints, and provide information on the composition and physical state of the materials that constitute the 

lithosphere. This ambition can obviously be best accomplished 

by measuring a broad range of measurements of P-wave velocity (Vp), S-wave velocity (Vs), density, magnetic properties, 

electrical properties, thermal properties, seismic anisotropy, 

attenuation (Q), temperature, and so on, for volume elements. 

In addition, interfaces that represent features such as stratigraphic boundaries, the Moho, faults, and boundaries of magmatic bodies and other discrete masses must also be mapped 

in order to properly characterize a region of the lithosphere. 

This goal can only be achieved through a highly integrated 

approach that takes advantage of all of the geological and 

geophysical constraints available. In most cases, controlled 

source and natural source seismology have the potential to 

provide the greatest spatial resolution of discontinuities and 

regions with characteristic seismic velocity, anisotropy, or Q. 

Because each of these types of seismic data are measured and 

analyzed by a variety of techniques, developing an integration 

scheme for seismic results is an important first step in building 

integrated models of lithospheric structure. 

The diverse types of seismic data and analysis techniques each have their own sensitivities and spatial resolution, and when used alone, can constrain some aspects of 

the lithospheric structure; however, when used together with 

other types of geophysical and geological data, the combined 

approach has the potential to produce a better constrained 

model that also reflects multiple physical parameters. For 

example, controlled source experiments yield the Vp structure, 

and sometimes Vs structure, of the crust and uppermost mantle 

with the analysis of refraction and wide-angle reflection data. 

In particular, analysis of the PmP phase (Moho reflection) 

yields a good estimate of the average Vp of the crust (Vpave) 

for the crust. In addition to providing an independent measure 

of crustal thickness that complements the wide-angle data, 

receiver function analysis can constrain the Vp/Vs ratio utilizing full-crustal reverberation(s) from teleseismic earthquakes. 

Thus, a simple form of integration involves using the Vp/Vs 

ratio from receiver functions and Vpave from refraction measurements, to solve for the average Vs (Vsave) of the crust. 

When refraction and wide-angle reflection data and several 

receiver functions nearby are available, we have devised 

schemes whereby three-dimensional voxel-based models and 

two-dimensional models with interfaces can be derived using 

tomographic inversion in the first case, and ray-based techniques in the second case. In either case, gravity, magnetic, 

and electromagnetic data can easily add extra constraints. The 

ultimate goal is to add geologic and geodynamic results to 

make the result four-dimensional in nature. 

Atlas of the Cryosphere: A Web Map 

Service for the Earth’s Frozen Regions 

By John Maurer1 

1

National Snow and Ice Data Center (NSIDC), University of Colorado at 

Boulder, Boulder, Colo. 

Introduction 

The National Snow and Ice Data Center (NSIDC) “Atlas 

of the Cryosphere” Web site (http://nsidc.org/data/atlas) 

allows visitors to explore and dynamically map the Earth’s 

frozen regions (figs. 1, 2). Viewed from a polar perspective, 

the available data sources include snow cover, sea ice extent 

and concentration, glaciers, permafrost, ice sheets, and other 

critical components of the Earth’s cryosphere. Users can zoom 

in to a specific region on the Earth as well as overlay country borders, major cities, and other geographic information. 

This site should act as a useful tool in science and education 

efforts surrounding the International Polar Year (IPY) and 

beyond by providing a geographic tool for viewing snow and 

ice on the planet. In addition to providing an interactive Web 

interface, maps and data sources contained in the Atlas of 

the Cryosphere are also accessible via the Open Geospatial 

Consortium (OGC) Web Map Service (WMS), Web Feature 

Service (WFS), and Web Coverage Service (WCS). These 

international specifications provide a framework for sharing 

maps and geospatial data over the Internet. 

This paper will provide an overview of the Atlas of the 

Cryosphere, describe its interoperability with other OGC-compatible software applications, as well as outline some of the 

“lessons learned” in developing an OGC-enabled map server 

from disparate geospatial data sources. 

The development of this application was supported 

by National Aeronautics and Space Administration’s Earth 

Observing System (EOS) program and was developed using 

MapServer, an open-source development environment for 

building spatially enabled Internet applications. 
27 

Figure 1. Example of map available on Atlas of the Cryosphere 

Web site (Arctic view). 

Figure 2. Example of map available on Atlas of the Cryosphere 

Web site (Antarctic view). 

Geoinformatics 2007—Data to Knowledge 

Atlas Features 

The atlas allows for the following activities: (1) dynamically visualizes Earth’s snow and ice; (2) explores the planet 

from a polar perspective for both the northern and southern 

hemispheres; (3) customizes maps by zooming in and out 

and by selecting from a variety of basemaps and overlays; (4) 

views monthly climatologies of snow and sea ice to see how 

and where the cryosphere shrinks and grows over the course 

of a year; (5) looks up definitions for unfamiliar cryospheric 

terms; (6) accesses maps and source data through WMS, WFS, 

and WCS; and (7) provides Really Simple Syndication (RSS) 

feed for keeping informed about atlas additions and modifications. 

Selectable Parameters 

The following parameters may be selected: 

1. Cryosphere-related parameters such as glacier locations; glacier outlines; ice core locations: ice sheet accumulation; ice sheet elevation; permafrost classification; permafrost 

extent; sea ice concentration; sea ice extent; seasonal snow 

classification; snow extent; snow water equivalent; treeline 

(northern limit of forests); and more. 

2. Other parameters such as Antarctic Circle; Arctic Cir

cle; cities; countries; Equator; geographic features (land, sea, 

and ice); International Date Line; latitude and longitude; North 

Pole; South Pole; Tropic of Cancer; Tropic of Capricorn; U.S. 

states; and more. 

Open-Source GIS 

NSIDC has leveraged use of the following open-source 

software packages for the development of the atlas and for 

manipulation of its diverse data sources, such as reformatting 

and reprojecting: 

•	 MapServer—A development environment for building 

spatially enabled Internet applications. 

•	 Geospatial Data Abstraction Library (GDAL)—A data 

translation and processing library for raster geospatial 

data formats. 

•	 OGR Simple Features Library—A data translation and 

processing library for vector geospatial data formats. 

•	 PROJ.4—A cartographic projections library for 

enabling reprojections. 

•	 libgeotiff—A GeoTIFF library for reading and writing 

GeoTIFF information tags. 

Interoperability 

Maps can be generated through the provided Atlas of the 

Cryosphere Web interface. Alternatively, interoperable and 

customizable data access to Atlas maps and source data are 

enabled via the following Open Geospatial Consortium, Inc. 

(OGC) specifications: 

­
2 Geoinformatics 2007—Data to Knowledge 

• 	 Web Map Service (WMS): Provides map and legend 

images of selected data layers and base maps. 

• 	 Web Feature Service (WFS): Provides vector source 

data in Geographic Markup Language (GML) format. 

• 	 Web Coverage Service (WCS): Provides raster source 

data in GeoTIFF format. 

Using these services, maps and data can be accessed for 

your spatial region of interest at your desired resolution or output size. These services are accessible through the construction of a URL string that contains certain required and optional 

parameters for customizing output. Several OGC-compatible clients are also available for handling these interactions 

through point-and-click graphical user interfaces (GUIs), such 

as MapServer, ArcGIS 9 Service Pack 2, ENVI Zoom 4.3.1, 

Google Earth 4, uDig, QGIS, and GRASS, to name just a few. 

Client support for OGC services is constantly improving and 

expanding as popularity for these specifications increases. 

Remote access through a client is normally obtained via 

an OGC “GetCapabilities” URL, as in the following examples 

for the northern and southern hemispheres of the atlas, where 

the “service” parameter can be WMS, WFS, or WCS: 

http://nsidc.org/cgi-bin/atlas_north?service=WMS&request=G 

etCapabilities 

http://nsidc.org/cgi-bin/atlas_south?service=WMS&request= 

GetCapabilities 

Other possible OGC requests include 

• 	 GetMap—Get an image of selected data layers. 

• 	 GetLegendGraphic—Get a map legend for selected 

data layers. 

• 	 GetFeature—Get vector source data in Geography 

Markup Language (GML) format. 

• 	 GetCoverage—Get raster source data in GeoTIFF 

format. 

These requests can include various standardized options 

for customizing output, including the ability to limit results to 

a particular spatial region. For more information, visit http:// 

opengeospatial.org. A summary for applying these requests 

to the Atlas of the Cryosphere is also available at http://nsidc. 

org/data/atlas/ogc_services.html. 

Lessons Learned 

European Petroleum Survey Group (EPSG) Codes 

In order for users to access maps and source data 

remotely via OGC Web Services (OWS), the application must 

list the available map projections for these services. These map 

projections must be specified as EPSG Codes. These codes are 

developed and maintained by the EPSG—now known as the 

International Association of Oil and Gas Producers (OGP)— 

and are a shorthand way of specifying a map projection and 

other coordinate parameters. Unfortunately, requiring the use 

of EPSG codes limits your OWS to the map projections that 

are already defined in the EPSG Geodetic Parameter Dataset. 

This can be especially limiting for polar projections, very few 

of which are currently available. As a result of this limitation, 

NSIDC submitted a request to have several new polar projections added to the EPSG Geodetic Parameter Dataset. This 

request was granted and eventually incorporated into the latest 

release (version 6.12; see EPSG Codes 3408-3413); however, 

these new codes can take several months to be incorporated 

into the various open-source applications and into the OWS 

clients that use them. 

Custom Ellipsoids and Datums 

Several of NSIDC’s datasets are projected on two somewhat unconventional datums, neither of which is available 

in popular image processing or GIS packages such as ENVI 

and ArcGIS, often leading the georeferencing information to 

be ignored when opening GeoTIFFs that are in coordinate 

systems using these datums. This is something to consider 

when attempting to distribute geospatial data that will be 

easily readable for users without extra configuration to be 

handled on their end. If there is not a strong justification for 

using an unconventional datum, one should consider reprojecting onto something like WGS84 prior to distribution, which is 

especially popular and appropriate for global- or large-scale 

earth- science applications. 

Wraparound and Overlapping the Pole 

There are particular issues that need to be addressed 

when attempting to display global data in polar projections. 

If you have a dataset that is in a latitude and longitude (Plate 

Carrée) projection or other such global projections, there can 

be problems when attempting to display these data in a polar 

projection on the fly. Namely, if you have data that overlap 

the ±180 degree latitude line, they will likely be distorted in 

a polar projection because of wraparound at this boundary. In 

addition, data that overlap the pole will also likely be distorted, 

and an annulus will likely prevent data from being viewed 

directly at or near the pole itself. A work-around is to reproject 

and store your global source data in a common polar projection prior to displaying them in a polar-projected MapServer 

application so that the reprojection is not done on the fly. 

Optimizing MapServer Performance 

For improved speed and to avoid unexpected artifacts 

such as those mentioned above, reproject all data sources into 

identical or very similar projections ahead of time rather than 

relying on MapServer to do this on the fly. When accessing 

large raster data files, use a tiling scheme to improve access 

speed, as was done for NSIDC’s 250-meter resolution Moderate Resolution Imaging Spectroradiometer (MODIS) Mosaic 

of Antarctica (MOA) product. This enables the map server 

to access smaller subsets of the larger file at any given time 

rather than always needing to access the entire file. Lastly, 

while incorporating external OWS services into your application promotes interoperability, it can noticeably slow perfor­
mance, depending on the remote server and how greatly their 

projections differ from that of your own application. 

Conclusions 

Between the public release of the Atlas of the Cryosphere 

on February 2, 2007, and the end of March 2007, there have 

been 1,548 unique visitors to the site, already demonstrating its popularity. This project has been an attempt to bring 

together the important large-scale, climatological features of 

the cryosphere into a single, user-friendly, and Web-accessible interface that not only provides dynamic visualization 

but also a flexible and interoperable means for obtaining the 

source data for these features as well, thereby going beyond 

the criticism that many scientists have about applications that 

merely provide “pretty pictures.” By providing a simple Web 

interface, this and other map server applications are also more 

broadly accessible to the general public, compared to other 

geospatial applications that may require special software to 

download and (or) broadband Internet access such as Google 

Earth. Additional cryospheric datasets, supported polar EPSG 

Codes, and interface features will be developed according to 

time and demand. If you have questions, comments, or suggestions, please contact NSIDC User Services at nsidc@nsidc. 

org. 

Building the Interface Facility for 

Centimeter-Scale, D Digital Field 

Geology 

By John S. Oldow1, Charles M. Meertens2, Carlos L.V. Aiken3, 

J. Douglas Walker4, and J. Ramon Arrowsmith5 

1

Department of Geological Sciences, University of Idaho, Moscow, ID 

2

University NAVSTAR (Navigation Signal Timing and Ranging) Consor

tium (UNAVCO), University of Colorado, Boulder, Colo. 

3

Department of Geosciences, University of Texas, Dallas, Richardson, Tex. 

4

Department of Geology, University of Kansas, Lawrence, Kans. 

5

School of Earth and Space Exploration, Arizona State University, Tempe, 

Ariz. 

The acquisition and analysis of field data is the corner

stone of the solid earth sciences and these data provide the 

context and content for most inquiries in geological sciences. Geoscientists have traditionally collected these data by 

analog methods (for example, pencil, pen, and paper). A fully 

digital approach is critical, however, so that information is 

presented in a geospatially referenced frame for data transfer, 

scale and projection manipulation, and registration between 

datasets; this approach requires a cyberinfrastructure for the 

earth sciences. Geoscientists of the future will collect field 

data in a digital environment, which will consist of the following: equipment for acquiring information in a digital and 

­

­

Geoinformatics 2007—Data to Knowledge 2 

georeferenced format; software that allows seamless transfer, 

integration, and exploration of data, and; online processing for 

manipulation of large datasets generated or to be used in the 

field. To implement the transition from the analog-dominated 

system of today to the digital future, we propose to build a 

facility consisting of a pool of shared hardware and software 

supported with instruction by expert users. This facility will 

allow users to build and work in a digital environment that rep

resents the Earth’s surface or landscape in three-dimensional 

with accuracy approaching the scale of a centimeter. 

Lithospheric Structure of Northern 

Africa and Western Eurasia 

By Minoo Kosarian1 and Charles Ammon1 

1

Geosciences Department, The Pennsylvania State University, State College, Pa. 

Although much progress has been made over the last 

few decades towards understanding the structure of the Earth, 

many questions regarding the details of Earth’s lithospheric 

structure remain unanswered. The primary goal of this study 

was to gain a better understanding of upper and lower continental crustal composition and structure to improve our 

knowledge of the tectonic evolution of the Earth. To accomplish this goal, we focused on the estimation of first-order 

seismic structure using receiver functions, and the construction of a library of shear-velocity structures in the vicinity of 

seismic stations across western Eurasia and northern Africa 

using receiver functions and tomography-based surface-wave 

dispersion estimates. 

We used 171 stations recording a total of about 6,000 

teleseismic events producing more than 100,000 seismograms. 

The distribution includes 78 stations in the Middle East and 

Asia, 57 stations in Europe, and 36 stations in central and 

northern Africa. We have examined receiver functions for 119 

stations with the best data for the period of 1990-2004, and 

applied the receiver function stacking procedure of Zhu and 

Kanamori (2000, JGR) to estimate Poisson’s ratio and crustal 

thickness. The structures are classified into five tectonic environments—explicitly shields, platform, Paleozoic orogenic 

belts, Mesozoic to Cenozoic orogenic belts, and rifts based 

on Condie’s (1989) global classifications. The results show 

a slightly lower value of Poisson’s ratio σ = 0.25 for shields 

compared to the orogenic-belts with σ = 0.26. Crustal thickness ranges from 32 to 47 kilometers (km) with an average 

of 38 km and a standard deviation of 3 km for the shields. 

The less well sampled platforms show a wider distribution of 

crustal thickness, ranging from 30 to 58 km with an average 42 

km and a standard deviation of 9 km. Orogenic regions show 

the largest variation in crustal thickness with values from 20 

to 55 km and standard deviations in the range of 8 to 10 km. 

We combined observations obtained in this study with receiver 

functions results from other published analysis. In total, we 

­
0 Geoinformatics 2007—Data to Knowledge 

have integrated observations from 606 stations located in different geologic settings. The compiled results show a value of 

σ = 0.26 for Poisson’s ratio and crustal thickness (H) = 39 km 

for crustal thickness in shields and platforms, and σ = 0.26­

0.27 with H = 35-37 km for the orogenic belts. 

Reference Cited 

Zhu, H., and Kanamori, H., 2000, Moho depth variation in 

southern California from teleseismic receiver functions: 

Journal of Geophysical Research, v. 105, p. 2969-2980. 

Deploying Data Portals 

By Sandeep Chandra1, Kai Lin1, and Choonhan Youn1 

1

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

A fundamental objective of the Geosciences Network 

(GEON) project (http://www.geongrid.org) is to develop 

data-sharing frameworks, and in the process, to identify best 

practices and develop capabilities and tools to enable advances 

in how geoscience research is done. The GEON portal 

framework, which plays a key role in facilitating this objective, is implemented using GridSphere and a portlet-based 

framework that provides a uniform authentication environment 

with access to a rich set of functionality, including the ability 

to register data and ontologies into the system; smart search 

capability; access to a variety of geoscience-related tools; 

access to Grid-enabled geoscience applications; and a customizable private work space from which users have access to the 

scientific workflow environment, which allows them to easily 

author and execute processing “pipelines” for data analysis, 

modeling, and visualization. In this paper, we describe the 

modular hardware and software components that we have 

developed, which make it possible to easily deploy a datasharing portal environment for any application domain. 

In practice, deploying a portal framework like GEON 

portal requires an understanding of the various portal software components and their dependencies that go into engineering such a system. In addition to sites within the GEON 

network, the GEON software infrastructure is increasingly 

being adopted in other projects, such as the Chesapeake Bay 

Environmental Observatory (CBEO), the Network for Earthquake Engineering Simulations (NEES), an Archeoinformatics 

project, and the National Ecological Observatory Network 

(NEON). Based on this experience, we have developed a 

modular packaging of the various components of the system 

to allow easy installation and configuration of the hardware, 

middleware, and other software components. 

The data portal infrastructure consists of the following 

components: 

1. A Portal Server, which runs the portal software. The 

nominal system is a “rack-mounted,” server-class machine 

with 750 gigabyte (GB) raw disk (5 x 146 GB hot-swappable 

Serial Attached SCSI (SAS) drives), dual core 3.0 GHz Intel 

Xeon processors, 4 GB of random-access memory (RAM), 

dual gigabit network interfaces, and redundant power supplies. 

The portal server runs the GEON software stack, including the 

portal software, and provides connectivity to and interoperability among the other GEON systems. 

2. A Data Server, which provides storage and other data 

management services. This system is also a “rack-mounted,” 

server-class machine with dual core 3.0 gigahertz Intel Xeon 

processors. It includes 5 x 300 GB Redundant Arrays of Inexpensive Disks (RAID) SAS drives for a total of 1.5 terabytes 

of raw drive space. The data nodes are configured with RAID 

disks in order to deal with unforeseen disk failures. 

3. A Certificate Authority (CA) Server, which manages 

user accounts. The CA server system has the same basic configuration as the Portal Server, but with 2 GB RAM and the 

disk size reduced to about 36 GB, since the CA Server tasks 

are not input-output intensive. 

The Portal Server runs the Rocks cluster management 

software and a standardized “GEON software stack,” which 

includes the GEON Portal and its dependent libraries, including software tools developed in the GEON project. The 

so-called “core” portal functionality is generic (for example, 

search and data ingestion capabilities) and can mostly be 

leveraged “out of the box” by other projects. The Data Server 

provides the capability to host data registered through the 

portal and also provides other data management services. The 

Data Server runs the SDSC Storage Resource Broker (SRB) 

software, which provides a number of built-in data management services. The Data Server could also host additional data 

management services (for instance, geographic information 

systems (GIS) software). The Portal Server can communicate 

with Web services hosted at remote locations using the standard Web service protocols. 

The CA Server runs the Grid Account Management 

Architecture (GAMA) software, which manages user accounts 

through the portal. The CA Server is installed using the Rocks 

software and the GAMA roll. Once installed, the system is 

fully configured as a CA. The portal software is also preconfigured to communicate with this GAMA server for managing 

user accounts. 

For more information, please see the following: 

1. Ilya Zaslavsky, Chesapeake Bay Environmental Observatory 

(CBEO) Project (http://geon16.sdsc.edu:8080/gridsphere/ 

gridsphere) (Accessed October 11, 2007.) 

2. NEESit – Enabling Earthquake Engineering Research, Education and Practice (http://neesphere.sdsc.edu:8080/gridsphere/gridsphere) (Accessed October 11, 2007.) 

3. National Ecological Observatory Network (NEON) 

(http://neon.sdsc.edu:8080/gridsphere/gridsphere) 

(Accessed October 11, 2007.) 
Geoinformatics 2007—Data to Knowledge 1 

Figure 1. The diplomatic exchange data visualized with analytical tools on the right, and with global context visualization on the left. 

4. Karan Bhatia, Kurt Mueller, and Sandeep Chandra, 

Grid Account Management Architecture (GAMA) 

(http://grid-devel.sdsc.edu/gridsphere/gridsphere?cid=gama) 

(Accessed October 11, 2007.) 

Dynamic Visualization of Geographic 

Networks Using Surface Deformations 

with Constraints 

By Basak E. Alper1, Selcuk Sumengen2, and Selim Balcisoy2 

1

Department of Media Arts and Technologies, University of California— 

Santa Barbara, Goleta, Calif. 

2

Department of Electrical Engineering and Computer Science, Sabanci 

University, Orhanli Tuzla, Istanbul, Turkey 

This paper proposes a visualization system for geographic network datasets, which aims to convey both low-level 

details of the data and high-level contextual information with 

two different visualization modalities. The first modality, 

global context visualization, represents time-series spatial network data within geographic context through a real-time animation of three-dimensional map deformations. It maintains 

spatial framework while providing a qualitative feel of the data 

by only exhibiting dominant and (or) interesting features. The 

second modality provides a set of interactive analytical tools 

based on conventional node and link displays, which reveal 

accurate statistical details of the data on demand. 

The global context visualization technique employs a 

modified graph drawing algorithm based on spring embedders, which position network nodes according to the timeseries data being fed to it. Our contribution lies in projecting 

complex network datasets into a real-time virtual environment 

where the geographic framework is preserved. Applied constraints limit variation of network nodes by favoring inherent 

geographic distribution of nodes. The graph optimization 

solution is reached using an implicit integration scheme and 

allows the system to visualize data in real time. As the position 

of network nodes change, the surface is redrawn to fit on the 

new positions of the nodes. The geographic map projected on 

the surface deforms and enables viewers to read the data variation as a map deformation (fig. 1). This representation gives a 

strong qualitative impression and enables viewers to summarize the nature of the data. 

The first phase of the global context visualization 

employs spring embedders for drawing a graph in which each 

geographic location corresponds to a single node and nonspatial data components correspond to relations between these 

nodes. The output graph visualization reflects input data by 

positioning related nodes closer. In this sense, our technique 

can be compared to force-directed placement methods; however, the proposed technique does not follow force-directed 

placement in any precise sense, but instead exploits its key 

features. The single most important distinction lies in the geographic constraints applied on the system. These constraints 

enable the spring-embedder system to reach a configuration 

that will lead to a deformed map where geographic layout is 

preserved to some extent for assuring intuitive recognition. 

In the second phase, the surface covering the nodes is 

adjusted to fit on the modified positions. As a result, the geographic map projected on the surface deforms and highlights 

variations in the data. This approach exploits a priori knowledge of the viewer about the physically accurate version of the 

map. In other words, map deformation facilitates comprehension of nonspatial variables with respect to the geographic 

framework. Once users have a sense of the overview of the 

data, they can dig into the details by using analytical tools 

provided. Interactively responding to users, these tools expand 
2 Geoinformatics 2007—Data to Knowledge 

informative quality of the visualization through direct manipulation of visualization parameters. They can explore network 

data as height bar animations over three-dimensional maps 

or as arcs showing connections. They are able to filter data 

through selecting nodes or selecting the range of the displayed 

data. 

We examined the proposed method using two different datasets. The first dataset comprises the domestic U.S. 

air flights among 231 airports between 1991 and 2004. The 

second dataset is the diplomatic exchange of data among 128 

nations through years 1815 to 1966 (see fig. 1). 

Geoinformatics for Geochemistry (GfG): 

Integrated Digital Data Collections for 

the Earth and Ocean Sciences 

By Kerstin Annette Lehnert1 and Sri Vinayagamoorthy2 

1

Lamont-Doherty Earth Observatory, Columbia University, Palisades, N.Y. 

2

Center for International Earth Science Information Network, Columbia 

University, Palisades, N.Y. 

The Geoinformatics for Geochemistry (GfG) program, 

a collaborative enterprise by the Lamont-Doherty Earth 

Observatory (LDEO) and the Center for International Earth 

Science Information Network (CIESIN), integrates and 

consolidates the development, maintenance, and operation of 

four closely related Geoinformatics projects, comprising the 

digital data collections for geochemistry (EarthChem, PetDB, 

and SedDB), and the System for Earth Sample Registration (SESAR) that administers global unique identifiers for 

samples. Systems within the GfG program represent core databases for geochemistry and the broader Geosciences, enabling 

data stored in these databases to be discovered and reused 

by a diverse community now and in the future. The systems 

dynamically evolve in response to community needs and technical innovation, and contribute proactively to the construction 

of a digital information infrastructure that supports the next 

generation of Geoscience research and education, by establishing links to other Geoinformatics activities, and by pursuing 

developments in interoperability. 

The GfG program provides the technical infrastructure (hardware, software, and services), the required range 

of expertise (a team of scientists, data managers, database 

administrators, Web application programmers, Web designers, 

and project managers) and the organizational structure for the 

execution of the individual project components. It is managed 

in a professional and sustainable environment that ensures 

reliable services, a high level of data quality, and the long-term 

availability of the datasets. All systems within the program 

are operated in a dynamic modus operandi, continuously 

responding to the needs and demands of the community and 

to changes in technologies, metadata and interface standards, 

data types, policies and procedures for data publication and 

data access, and organizational structures to retain their value 

to the science community. GfG is dedicated to educate and 

train the science community, as well as students and teachers, in the use of the data collections through short courses, 

internships, and lectures, and to advancing the establishment 

of a new workforce for Geoinformatics through training and 

education of project staff. 

The GfG program includes the following elements: 

•	 System engineering and development—Data modeling and database development; development of data 

submission and ingestion procedures; development of 

Web applications; and interoperability interfaces; 

•	 System operation—Database administration; system 

maintenance, security, and backups; risk management; 

•	 Data management—Data compilation/solicitation; data 

and metadata entry and quality control; user support; 

and long-term archiving; 

•	 Education, outreach, and community liaison—Short 

courses, workshops, lectures, and exhibits; and collaboration with other Geoinformatics efforts, nationally 

and internationally; and 

•	 Program and project management—Integrated management of program and its individual projects. 

GfG System Infrastructure and Architecture 

The core infrastructure on which the GfG systems are 

developed and operated is illustrated in figure 1. It consists of 

a set of Web, application, mapping, and database servers; all 

are Sun Microsystems servers. WebLogic application server 

Figure 1. Infrastructure of GfG. 
Figure 2. Service-oriented architecture (SOA) of GfG. 

software is used for developing Web applications. IONIC 

software is used for serving geospatial data and for developing 

mapping and visualization tools. 

Using this infrastructure, we are developing a service-oriented architecture (SOA) to implement an application framework consisting of query models and data cache that support 

Web and interoperability interfaces. This framework is used 

across each individual project, thereby making the operation 

and maintenance of GfG systems more efficient and sustainable. 

At the core of this architecture are the following databases: the new Geo-Chemistry Data Model (GCDM) and the 

SESAR data model. The application software layer consisting of query model and data cache is being developed using 

object-oriented design (OOD) methodologies on a Java/J2EE 

platform supported by a WebLogic application server. Web 

services for querying and serving data are being developed on 

top of the application software layer. The user interfaces are 

built based on Web services, as well as on application software 

modules. The Web services will include interoperability interfaces to serve analytical and geospatial data related to samples 

to external client systems. A conceptual diagram of this service-oriented architecture is illustrated below in figure 2. 

Geochemistry Data Model (GCDM) 

Geochemical data served by the GfG includes a substantially broader range of measurements and materials, such as 

sediment cores, hydrothermal spring fluids and plumes, and 

xenoliths, and requires the following features to be supported 

by the underlying data model: 

•	 Description of spatial and temporal components of 

samples and measurements (for example, depth in 

core, time-series and sensor measurements, and point 

analyses on a microprobe slide); 

•	 Capability to store “derived” (model) types of 

observed values, such as age models for cores or 

end-member compositions for seafloor hydrothermal 

springs; 

Geoinformatics 2007—Data to Knowledge  

• 	 Capability to track relationships between samples and 

subsamples, 

• 	 Ability to integrate data at any level of sample granularity; and 

• 	 Capability to accommodate analytical metadata at the 

level of individual measurements. 

Based on these requirements, we have developed a more 

generic, integrative, and flexible model for geochemical data, 

the Geo-Chemistry Data Model (GCDM), to serve as the core 

data structure for our entire suite of geochemical databases 

(Djapic and others, 2006; Lehnert and others, 2006). This data 

model is compliant with standards defined in GeoSciML, a 

markup language developed by the IUGS Commission for 

Geoscience Information to represent Geoscience information associated with geologic maps and observations (Cox, 

2006). Attributes in GCDM, such as method, sample, and 

item measured, can be mapped to corresponding types within 

GeoSciML; others, such as observation point or observed 

value, can be incorporated into the GeoSciML concepts of 

method, event, and measured value. We will use GeoSciML 

to serve geochemical data via interoperable Web services. We 

have presented and discussed the model with the community 

at various occasions (Geoinformatics, 2006, 2006 AGU Fall 

Meeting, workshop with the IODP Applications Development 

team at TAMU), and received valuable feedback and validation of the model. Updates to the model are in progress and 

will make it even more generic and widely applicable. 

Along with the new data model, we will implement the 

International Geo Sample Number (IGSN), the emerging 

global unique identifier for samples that will allow building enhanced interoperability with other data systems at 

the sample level. SESAR data model is at the core of the 

implementation of IGSN for broad and diverse earth samples 

ranging from holes, cores, and dredges, to individual samples 

and subsamples. SESAR enables the unique identification of 

samples and integration of sample data from various sources 

and systems. 

Web Interfaces 

Each GfG system includes the following Web-based 

interfaces: 

• 	 Query and browse; 

• 	 Visualization and analysis; 

• 	 Administrative; and 

• 	 Data validation and loading. 

Interoperability Interfaces 

To maximize the use of the GfG data collections, we are 

implementing interoperability interfaces using the service-oriented architecture described above, to allow open access to the 

following client systems: 

• 	 Analytical Data Services: Geochemical data access via 

Web services that are based on the XML schema developed by EarthChem to serve complete sample data and 
 Geoinformatics 2007—Data to Knowledge 

metadata. This schema will continue to evolve towards 

compliance with GeoSciML as a community standard, 

and will enable other systems and tools to access GfG 

data in real time. 

• 	 Geospatial Data Services: OGC compliant WMS and 

WFS services for serving sample locations. Selected 

data, metadata, and a link to a sample profile will be 

included. The service will enable any OGC compliant 

client to overlay, visualize, and analyze relevant geospatial data layers from multiple sources in conjunction 

with GfG layers. 

References Cited 

Cox, S.J.D., ed., 2006, Observations and measurements: Open 

Geospatial Consortium, Inc. document OGC 05-087r4, 

version 0.14.7, 168 pages, available online at http://portal. 

opengeospatial.org/files/?artifact_id=17038. (Accessed June 

18, 2007.) 

Djapic, B., Vinayagamoorthy, S., and Lehnert, K.A., 2006, 

Serving geochemical data using GeoSciML compliant Web 

service: Next step in developing a generic geochemical 

database model: Eos, v. 87, no. 52, Fall Meeting Supplement, Abstract IN51B–0813. 

Lehnert, K.A., Vinayagamoorthy, S., Djapic, B., and Klump, 

J., 2006, The digital sample: Metadata, unique identification, and links to data and publications: Eos. v. 87, no. 52, 

Fall Meeting Supplement, Abstract IN53C–07. 

OpenGIS® Web Feature Service (WFS) Implementation 

Specification, Version 1.1.0, Open Geospatial Consortium 

Inc., Document: OGC 06-027r1, Date: 2006-02-12, available online at http://www.opengeospatial.org/standards/ 

wfs/. (Accessed June 18, 2007.) 

GIS Interpolation of Stratigraphic 

Contact Data to Reconstruct 

Paleogeography: Deriving a 

Paleolandscape Model of the Base 

Zuni Sequence and Subsequent 

Cretaceous Islands in Central Texas 

By Shane J. Prochnow1, Melissa Mullins2, 


Stephanie V. Capello3

, Anna F. Perry3, Steven W. Ahr3, 


Isaac T. Westfield3, Kari L. Fallert4, Song Gao4, 


Kirstin T. Hartzell4, and Kenna R. Lang4
 

1

Center for Applied Geographic and Spatial Research, Baylor University, 

Waco, Tex. 

2

Center for Reservoir and Aquatic Systems Research, Baylor University, 

Waco, Tex. 

3

Department of Geology, Baylor University, Waco, Tex. 

4

Department of Environmental Studies, Baylor University, Waco, Tex. 

Geographic information systems (GIS) raster modeling 

technology might constitute a quantum leap in visualization and data interpretation for paleogeographic studies. Our 

research uses raster modeling to interpolate paleotopography 

and stratigraphic thickness maps within a roughly 4,200-km2 

study area in south-central Texas (fig. 1A). Only readily available geospatial data was used. High-resolution geological 

maps (1:24,000 scale) published by the Bureau of Economic 

Geology (BEG) at the University of Texas at Austin were 

digitized and georeferenced using ArcGISTM software products. Paleosurface modeling focused on the following four 

stratigraphic features: (1) basal Zuni sequence boundary set 

on Precambrian granite and Cambrian to Ordovician marine 

sedimentary rocks; (2) fluvial to deltaic Hensel Sand (Lower 

Cretaceous); (3) marine to tidal Glen Rose Formation (Lower 

Cretaceous); and (4) marine Walnut Clay (Lower Cretaceous). 

The basal Zuni sequence boundary is a continental-scale, 

angular unconformity, while the younger surfaces involved 

in this study are relatively conformable. Stratigraphic contact 

traces between these surfaces were converted to three-dimensional (3D) data by extracting elevation values from 28-m 

(meter) resolution digital elevation models (DEM) developed 

by the U.S. Geological Survey (USGS) National Elevation Dataset (NED) using ArcGISTM 3D Analyst. The NED 

has a published vertical accuracy of 7 m to 14 m. ArcGISTM 

Geostatistical Analyst was then used to interpolate paleosurface rasters using the simple kriging method based upon 

12 nearest points along contact traces and their respective 

elevation values. The elevation of the interpolated raster was 

corrected for structural deformation since the Cretaceous using 

the ArcGISTM Spatial Analyst Raster Calculator by setting the 

base of the Walnut Clay (mostly coincident with the upper 

surface of the Glen Rose) as a Z-axis datum and adjusting 

the upper surface of the Hensel Sand and the base of the Zuni 

sequence elevation, respectfully. The interpolated paleosurface 

rasters have estimated vertical root mean square and standard 

error within 5 m, roughly the same as the published NED 

accuracy used for the derivation of elevation. The thickness 

of the Hensel Sand and Glen Rose was also estimated by 

using the Raster Calculator to subtract their corrected bounding surface interpolations. The thickness maps were verified 

by using published measured section data that accompanied 

the original geologic maps. Three-dimensional modeling of 

the paleosurfaces was visualized in ArcGISTM ArcScene. The 

paleorelief for the base of the Zuni sequence in the study area 

was about 351 m. The interpolated paleosurface grid for the 

base of the Zuni sequence has an estimated vertical root mean 

square error of 1.8 m and an average vertical standard error 

of 4.9 m. The total paleorelief for the upper boundary of the 

Hensel Sand is estimated at 356.5 m, but is locally lower in 

relative elevation than the base of the Zuni sequence uncon­
Geoinformatics 2007—Data to Knowledge  

Figure 1. A, The study area is located in central Texas where Paleozoic and Cretaceous bedrock crops out. 

B, The coverage of published 1:24,000-scale geologic maps and the respective contact traces used for this 

study. 

formity. The interpolated upper surface grid for the Hensel grid for the Walnut Clay has an estimated vertical root mean 

Sand has an estimated vertical root mean square error of 3.7 square error of 4.4 m and 1.4 m of average vertical standard 

m and an average vertical standard error of 4.9 m. The total error. The total thickness of the Hensel Sand within the study 

paleorelief for the lower boundary of the Walnut Clay (upper area ranged from 0 to 96.6 m thick. The Hensel Sand is locally 

boundary of the Glen Rose) is estimated to have been 197.0 absent on paleohighlands (interfluves) of Paleozoic rock that 

m, but is locally lower in relative elevation than both the base constitute about 7.5 percent of the study area (fig. 2A). The 

of the Zuni sequence unconformity and the upper surface of Glen Rose ranges from 0 to 183.8 m thick in the study area. 

the Hensel Sand. The interpolated lower boundary surface The area of zero Z-axis values (Glen Rose thickness) indicates 
 Geoinformatics 2007—Data to Knowledge 

Figure 2. ArcGISTM ArcScene three-dimensional model of 

interpolated paleosurfaces. A, The upper boundary of the 

Hensel Sand (Cretaceous fluvial) over the base Zuni sequence 

unconformity showing Paleozoic highland interfluves. View is from 

the northeast. B, Same as A, but including the upper boundary of 

the Glen Rose and showing Cretaceous paleoislands. C, Same as 

B, but shown looking from the south. 

terrestrial exposure above where the marine Glen Rose was 

deposited, probably reflecting islands that persisted throughout 

the deposition of this first marine unit of the Zuni transgression (figs. 2B, C). These islands shifted updip (northwest) 

relative to the Paleozoic interfluves, but still account for about 

7.5 percent of the total interpolated grid cells (fig. 2B). The 

paleotopography at the base of the Zuni sequence may have 

been largely controlled by faulting, but was comparable in 

total relief and slope as the modern topography of the study 

area. The interfluves during the Hensel deposition may 

preserve well-developed paleosols because these features 

were never scoured by Cretaceous fluvial systems, but were 

exposed for long periods of time to the atmosphere, and were 

eventually buried by subsequent low-energy marine sediments. The Glen Rose eventually buried most of the Paleozoic 

highlands (interfluves) in the center of the study area (figs. 

2A, B), but laterally terminates on islands of Hensel Sand in 

the northwestern portion of the study area (figs. 2B, C). This 

suggests that island systems may have existed in the study area 

throughout the deposition of the Glen Rose. The paleoislands 

in the study area were subsequently buried by the locally thin 

(< 2 m thick) Walnut Clay. Again, these paleoislands may better preserve well-developed paleosols since they were buried 

by low-energy deposits and exposed to the atmosphere for 

a longer interval than other areas where the upper boundary 

of the Hensel Sand is preserved. Raster GIS modeling also 

allows for the rapid and consistent calculation of unit thickness, surface slopes, and boundary relationships across large 

areas to better analyze geologic data and serve as a predictive 

tool. This study demonstrates how raster GIS modeling can 

be used, in particular with stratigraphic and paleogeographic 

studies, by spatially interpolating known data into buried, 

obscured, or missing areas. The ability for GIS technology to 

characterize paleogeographic features, including paleoislands, 

has potentially profound implications for geological study. 

These implications include, but are not limited to, identifying 

paleotopographic features for paleontological studies of terrestrial habitats, refining depositional models and interpolating geologic surfaces for petroleum exploration, and studying 

the connection between paleolandscape position and paleosol 

formation and preservation. 

Global Earth Observation Grid 

(GEO Grid): Development of an IT 

Infrastructure for the Earth Science 

Using Satellite Data 

By Shinsuke Kodama1, Ryosuke Nakamura1, 


Naotaka Yamamoto1, Hirokazu Yamamoto1, 


Koki Iwao1, Masashi Matsuoka1, 


Satoshi Tsuchida1, and Satoshi Sekiguchi1
 

1

Grid Technology Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan 

Global Earth Observation Grid (GEO Grid) is aiming at 

providing an E-Infrastructure to understand our Earth with 

more insight and, more precisely, with greater speed and 

ease. Within the E-Infrastrucutre, we are able to share data, 

application programs, and scientific workflows without a deep 

knowledge of information technology (IT), which provides 

the grid technology. GEO Grid securely and rapidly provides 

large archives of earth observation satellite data and integrated 

service with various observation databases and geographic 

information systems (GIS) data, while making them easy to 

use. 

The core contents of the system are the observation data 

from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) developed by Ministry of Economy, 

Trade and Industry (METI), Japan and geoscientific information, such as geological and environment technology data, 

accumulated for a long period of time at the National Institute 

of Advanced Industrial Science and Technology (AIST). As a 

core technology, the GEO Grid provides international standard compliant grid technology and develops systems (fig. 1; 

see also http://www.geogrid.org/). The ASTER data contains 

multispectral images of visible-near infrared region (visible 

near-infrared (VNIR)—3 bands), short infrared region (shortwave infrared (SWIR)—6 bands), and thermal infrared region 

(thermal infrared (TIR)—5 bands) with a spatial resolution of 
Geoinformatics 2007—Data to Knowledge 7 

15 m (for VNIR). We constructed a hard disk-based archive 

system for the ASTER data that reaches more than 1 million scenes. The level 0 ASTER data (raw data) are stored in 

this system and higher products are generated by on-demand 

processing. These products contain orthorectified images and 

15-m resolution Digital Elevation Models (DEMs), generated 

from Band 3N (nadir) and 3B (backward) images. 

We will present several sample applications using ASTER 

data and the GEO Grid system; pyroclastic-flow simulation 

using an ASTER DEM; integration with other satellite images 

and ground truth data for the accurate global land-use change 

detection and carbon-cycle modeling (http://kushi.geogrid. 

org/); and integration with the geologic GIS data. The GEO 

Grid system supports Web Map Service (WMS) to provide the 

ASTER images and maps generated from ASTER data. This 

allows users to integrate ASTER image and GIS data, such as 

geologic maps, easily. We had several experiments in which 

the ASTER image is overlaid on a geologic map provided 

from the Geological Survey of Japan or the Geoscience Network in United States by using WMS. We also plan to support 

the Web Coverage Service (WCS) which allows users to use 

ASTER DEM/Ortho data for scientific analyses. The pyroclastic-flow simulation application provides a possible coverage map of pyroclastic-flow deposits caused by a volcanic 

dome collapse, and that can contribute to make an emergency 

volcanic hazard map. An energy line model is used for calculation of the maximum possible flow distance (Takarada and 

others, 1993). The user can choose collapse point and physical 

parameters of pyroclastic flow and submit a job using a Web 

browser. The resulting map is provided by using the WMS, 

and appears on a Web browser. 

Figure 1. AIST GEO Grid architecture (IT infrastructure). 

HydroXC: A Common Schema for 

Hydrologic Data Transfer and Object 

Descriptions 

By Michael Piasecki1, Bora Beran1, Jon Roe2, and 

Stephanie Liu-Barnes3 

1

Department of Civil, Architectural and Environmental Engineering, Drexel 

University, Philadelphia, Pa. 

2

National Weather Service, Silver Spring, Md. 

3

APEX Digital Systems, Silver Spring, Md. 

The National Weather Service (NWS) Office of Hydrologic Development (OHD) has been sponsoring the development of the HydroXC initiative, which is now in Phase III. The 

overall goal of this initiative, which is also supported by a consortium comprised of members of the hydrologic community 

originating in government (National Weather Service, U.S. 

Geological Survey (USGS), Natural Resources Conservation 

Service, and U.S. Army Corps of Engineers), academia (Duke 

University, Drexel University, University of Pittsburg, and 

University of Virginia), and also private companies (APEX 

Digital Systems, Environmental Science Research Institute, 

Vieux and Associates, Wier and Associates), is to develop a 

general Extensible Markup Language (XML) schema that can 

be used for data transfer between entities interested in hydrologic data. While some efforts are already underway to “schematize” hydrologic information (for example, the HydroML 

effort lead by USGS), most efforts are very specific to a 

certain task prompting the need for an exchange vehicle that is 

generic enough such that any type of hydrologic information 

can be described and packaged in a language that is machine 

readable (in other words, XML). 

The third phase of this project will address pragmatic 

requirements for using the XML schema, and for making it 

more specific to daily needs of hydrologic software users. 

Current research efforts are focused on further evolving the 

HydroXC XML schema that has been derived from the Standard Hydrologic Exchange Format (SHEF) developed at the 

NOAA National Weather Service. To do so, the current thrust 

is to focus on the derivation of several specific hydrologic 

object representations, including a few examples that highlight the new areas of the schema (in other words, a reservoir 

object, a flow rating curve object, a stream reach object, and a 

cross-section object). The focus is on compiling descriptions 

that are useful for data exchange by identifying key attributes 

that are also used by other standards (for example, GML or 

HydroML). Additional work is underway to use the HydroXC 

schema to develop data adapters that are capable of reading 

and writing messages between some proprietary format and 

HydroXC-compliant XML. 

This work will demonstrate the general composition of 

the schema, which is aligned along elements used for defining 

components and example instantiations of the derived object 

descriptions. We will also outline the inclusion of internal 
 Geoinformatics 2007—Data to Knowledge 

standards (like ISO 19115 and 8601, GML, and EPSG codes) 

into this schema and the importance they have in representing 

geospatial and temporal referencing, as well as some of the 

challenges that arrive from attempting to incorporate legacy 

systems. 

Implementing the Collaboratory for 

the Study of Earthquake Predictability 

(CSEP) 

By Maria Liukis1, Danijel Schorlemmer1, Philip Maechling1, and 

Thomas Jordan2 

1

Southern California Earthquake Center, University of Southern California, 

Los Angeles, Calif. 

2

Department of Earth Sciences, University of Southern California, Los 

Angeles, Calif. 

The Collaboratory for the Study of Earthquake Predictability (CSEP) is developing the infrastructure for facilities 

to conduct earthquake forecast experiments. It provides a 

controlled integration environment with standardized software 

stack for the development and installation of forecast experiments. The processing infrastructure has to allow for rapid 

computations using distributed computing facilities, but also 

needs to run on desktop computers for research activities. 

Module design of the CSEP software focuses on reproducibility of any forecast experiment. Furthermore, program codes 

need to be validated and distributed to other than Southern 

California Earthquake Center (SCEC) testing facilities. We 

will discuss the design challenges and present the software 

concept, development strategies, ways for participating in 

development, and the flexibility for customizing our opensource software. 

A Drastic Revision of Active Fault 

Database in Japan Based on the 

Redefined Relational Data Model 

By Yuichiro Fusejima1, Fujika Miyamoto1, and 

Toshikazu Yoshoika1 

1

Active Fault Research Center, Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan 

Outline of the Database 

Active Fault Database of Japan (http://www.aist.go.jp/ 

RIODB/activefault/cgi-bin/index_e.cgi) contains comprehensive information about active faults in Japan, sorted by the 

concept of “behavioral segments” (McCalpin, 1996). Each 

fault is subdivided into behavioral segments based on surface 

trace geometry and rupture history determined through paleoseismic studies. Faults shown on the index map are linked to 

a database of behavioral segments, which contains information about geologic and paleoseismic parameters, including 

slip rate, slip per event, recurrence interval, and calculated 

rupture probability in the future. Behavioral segments can also 

be searched by name or combination of fault parameters. All 

those data are compiled from journal articles, theses, and other 

documents. 

Problems on the Data Input Method 

The database was first formulated in 2002 by the Active 

Fault Research Center, Geological Survey of Japan, National 

Institute of Advanced Industrial Science and Technology 

(AIST). The first edition of the database was launched online 

in March 2005, as part of the Research Information Database 

(RIO-DB), which is managed by AIST. Through the relational 

database management system (RDBMS) of ORACLE 9i, 

many users could easily read information similar to a catalog 

or a handbook, but using a Web browser; however, searching information using composite keywords was not possible. 

Furthermore, the order of the data could not be changed using 

composite attributes. These shortcomings were caused by 

the data input method. The data were entered using the MSExcel spread-sheet software. Attempts were made to solve the 

problem. The inputted data were reconstructed into a pseudo 

data model, defined for a serialized data on two-dimensional 

spread-sheet schema; however, the pseudo data model could 

not solve the problem of data redundancy. Furthermore, standardization of the data specifications also was not possible. 

Because of the high level of data redundancy and the low level 

of standardization, flexible searching functions in the first edition of the database were not possible. 

Figure 1. Top-level data model. Entity relationship diagram in 

IDEF1X. 
Geoinformatics 2007—Data to Knowledge  

Redefined Data Model of the Database 

In general, all data of relational database must be inputted 

by RDBMS on a well-planned and well-designed data model. 

In this study, the data model was redefined to formulate a 

genuine relational database. The redefinition of the data model 

is based on the general contexture of published journal articles 

describing active faults; therefore, the titles of the constitutive 

entities/tables are Survey, Locality, Horizon, Displacement, 

Event, Discussion, Figure, and Reference. Sixty-five tables are 

defined. Many attributes and their data types, domains, and 

other specifications are also defined. Many more tables are 

constructed for appending legend code numbers of these attributes. Other attributes describing metadata are also defined. 

All these tables are normalized and connected by relationships. A top-level data model (entity relationship diagram in 

IDEF1X) is described in figure 1. The completed data model, 

which contains detailed definitions, will be presented in a 

poster at the conference. 

Revision of the Database 

Based on the redefined data model, a data input user 

interface system on an MS Access RDBMS was formulated. 

The new data were entered stepwise by the input system of 

the MS-Access and finally merged into the main RDBMS of 

ORACLE 10g. In August 2006, the database containing a large 

amount of data was revised based on the redefined data model. 

The flexible searching functions, which allow searching using 

composite keywords, other advanced searching methods, and 

changing the order of data using composite attributes, are 

successfully implemented using the revised database. Coincidentally, new field data collected from over 10,000 research 

localities are added into the new Japanese edition database. 

The database also shows metadata, bibliographies, figures, 

chronostratigraphies, and more detailed information. The new 

Japanese edition is presently translated into English. The new 

edition is developed to include analytical and GIS functions 

and more. 

Reference Cited 

McCalpin, J.P., ed., 1996, Application of paleoseismic data to 

seismic hazard assessment and neotectonic research: Paleoseismology, Academic Press, p. 439-493. 

Community Science Ontology 

Development 

By Robert Raskin1, Peter Fox2, Deborah L. McGuinness3, and 

A. Krishna Sinha4 

1

Jet Propulsion Laboratory, Pasadena, Calif. 

2

High Altitude Observatory (HAO), National Center for Atmospheric 

Research (NCAR), Boulder, Colo. 

3

Knowledge Systems, McGuinness Associates and Stanford University, 

Stanford, Calif. 

4

Virginia Polytechnic Institute and State University, Blacksburg, Va. 

Background of the Need for Formal Semantic 

Encodings 

Scientists often spend a majority of their time locating 

and preparing a dataset before it even can be analyzed and put 

to use. Given that 21st century science will be blessed with 

massively large amounts of data, many of which can be used 

together synergistically, a great opportunity is not being realized. Knowing only the syntactic description of a dataset does 

not remedy this situation. Formal semantic encoding potentially enables the use of automated data integration, smart 

search, and interdisciplinary data fusion. 

Best Practices and Modular Ontologies 

Our experiences suggest that ontologies should be modular. Most aspects of science deal with hierarchical specializations of concepts, such as new classes of rock types and subtypes; hence, inheritance is very important. Ideally, concepts 

in an earth- science ontology should also inherit from more 

general ontologies in physics, chemistry, math, space, time, 

and so on. 

Project and Agency Interest 

National Aeronautics and Space Administration (NASA) 

is moving from an instrument-based to measurement-based 

strategy in its archival systems. This approach implies that full 

lineage of a dataset must be preserved to enable cross-platform 

integration. National Science Foundation has initiated calls for 

ontology development through its Office of Cyberinfrastructure. 

Community needs are diverse, but share many common 

elements, such as the desire to read standard data formats and 

associate parameter names with meaningful scientific concepts. At these early stages of ontology development, funded 

work in one community should build upon the work of others, 

rather than be reinvented and not reused. 

Current Ontology Efforts 

Current ontology development work includes the following: 

A. Semantic Web for Earth and Environmental Terminology (SWEET)—An upper-level ontology developed at NASA/ 

JPL with coverage of the entire Earth system (Raskin, 2006; 

Raskin and Pan, 2005). (See fig. 1 for the ontology structure; 

(http://sweet.jpl.nasa.gov.) 
0 Geoinformatics 2007—Data to Knowledge 

Figure 1. SWEET conceptual decomposition. 

Figure 2. GEON planetary ontology framework. 

B. Virtual Solar Terrestrial Observatory (VSTO)—Developed at NCAR with McGuinness Associates with coverage 

of solar atmospheric physics and terrestrial middle and upper 

atmospheric physics (McGuinness and others, in press; http:// 

vsto.org). 

C. Geosciences Network (GEON)—Developed at Virginia Tech with coverage of the solid Earth. (See fig. 2 for the 

ontology structure; http://geon.geol.vt.edu/geon/index.html.) 

D. Marine Metadata Initiative (MMI)—Developed at 

Monterrey Bay Aquarium Research Institute (MBARI) with 

coverage of instrumentation and the marine world. (See fig. 3 

for an excerpt; (http://marinemetadata.org.) 

Ontology Tools for Collaboration of Communities 

There has been a dire need for tools to support ontology 

evolution, validation, reasoning, comparison, merging, and so 

on. This is an emerging area of work for a number of organizations. MMI, for example, has stepped in and created a suite 

of tools for creating, comparing, and harmonizing ontologies 

with the goal of supporting ontologies for marine science, and 

science in general (MMI tools: http://marinemetadata.org/ 

examples/mmihostedwork/ontologieswork). MMI convenes 

workshops where teams generate new ontologies, such as for 

instrumentation. 

The http://www.PlanetOnt.org Web site is a collaborative 

community set up to share ontologies and infuse the experience of others. It provides services for: ontology version 

registration; comparison of ontologies; imported class dependencies; RSS feeds to notify dependent ontology owners of 

potential changes made; and discussion regarding an ontology 

or specific elements within an ontology. It provides a forum 

for identifying best practices, and for getting around specific 

limitations in OWL in a consistent manner. It is open to community involvement and welcomes submissions. 

Figure . MMI ontology schematic. 
Discussion and Conclusion 

Ontologies should be developed collaboratively and 

incrementally from existing work. Ontology normalization 

shares much in common with database normalization; however, there is a further need to distinguish the general concept 

from the more specific. Specialized ontologies should import 

from the more general ones rather than repeat the general 

concepts. The http://www.PlanetOnt.org Web site supports this 

directional structure by identifying dependencies between any 

pair of ontologies. 

References Cited 

McGuinness, D., Fox, P., Cinquini, L., West, P., Garcia, J., 

Benedict, J.L., and Middleton, D., in press, The Virtual 

Solar-Terrestrial Observatory: A deployed semantic Web 

application case study for scientific research, in Proceedings of the 19th Conference on Innovative Applications of 

Artificial Intelligence (IAAI-07), Vancouver, B.C., Canada, 

July 22-26, 2007. 

Raskin, Rob, 2006, Ontologies for earth system science, in 

Sinha, A.K., ed., Geoinformatics: From data to knowledge: 

Geological Society of America Special Paper 397, p. 195­

1990 

Raskin, R.G., and Pan, M.J., 2005, Knowledge representation 

in the Semantic Web for Earth and Environmental Terminology (SWEET): Computers and Geosciences, v. 31, p. 

1119-1125. 

Ontologic Integration of Geoscience 

Data on the Semantic Web 

By Zaki Malik1, Abdelmounaam Rezgui1, and 

A. Krishna Sinha2 

1

Department of Computer Science, Virginia Polytechnic Institute and State 

University, Blacksburg, Va. 

2

Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, Va. 

Introduction 

The World Wide Web was originally created for data 

sharing among scientists. Over the years, the Web has evolved 

from being merely a repository of data to a vibrant environment that allows users to make their data and applications Web 

accessible. As a result, a wealth of information and applications are now available, and their quantitative and qualitative management has become a primary issue. For instance, 

several research initiatives by geoscientists over the years 

Geoinformatics 2007—Data to Knowledge 1 

have produced large amounts of data; however, the ability to 

find, access, and properly interpret these large data repositories has been very limited. Two main reasons for this lack of 

data sharing are the adoption of personal acronyms, notations, 

conventions, units, and so on, by each research group when 

producing data, and the current Web search methods that can 

be understood only by humans or custom-developed applications (Medjahed and others, 2003). Currently, machines 

merely display the data, and they are unable to process it any 

further due to lack of data and application semantics. This 

makes it difficult for other scientists to correctly understand 

the semantics of the data, and makes the automatic interpretation and integration of data simply infeasible. We suggest that 

for enabling the sharing, understanding, and integration of 

geosciences data and applications on a global scale, ontologybased registration and discovery is required. 

Ontologies and the Semantic Web 

The emerging “Semantic Web” is defined as an extension of the existing Web, in which information is given a 

well-defined meaning (Berners-Lee and others, 2001). The 

ultimate goal of the envisioned Semantic Web is to transform 

the Web into a medium through which data and applications 

can be automatically understood and processed. The concept 

of Web services (and other related technologies) is seen as a 

key enabler of the Semantic Web (Alonso and others, 2003; 

McIlraith and others, 2001). A Web service is a set of related 

functionalities that can be programmatically accessed through 

the Web. The convergence of business and government 

activities in developing Web service-related technologies (for 

example, SOAP; Universal Description, Discovery and Integration (UDDI); and WSDL)) is a sign for the large adoption 

of Web services in the near future (Curbera and others, 2002). 

Another key player in the envisioned Semantic Web is the 

concept of ontology. An ontology may be defined as a set of 

knowledge terms, including the vocabulary, the semantic inter

connections, and some simple rules of inference and logic for 

some particular topic. The Semantic Web is expected to offer 

data (organized through ontologies) and applications (exposed 

as Web services) enabling their understanding, sharing, and 

invocation by automated tools. 

Data Ontologies 

Recognizing the potential of the Semantic Web, we have 

defined a “planetary ontology” (through many workshops and 

scientific meetings) to provide the ontologic framework for 

earth-science data at many levels of semantic granularity. The 

planetary ontology includes concepts, concept taxonomies, 

relationships between concepts, and properties that describe 

concepts, as an initial step towards the development of ontologies for earth science. Figure 1 shows the high-level representation of the planetary ontology. High-level packages, such as 

Planetary Material, can be used to represent the nature (physi­

­
2 Geoinformatics 2007—Data to Knowledge 

Figure 1. The planetary ontology framework. 

Figure 2. Semantic Web-enabled geoscience querying engine. 

cal/chemical) and state of substances and their properties. 

This figure also emphasizes the utilization of imported and 

inherited properties from additional packages (for example, 

Physical Properties, Location, and Planetary Structure) to fully 

define the concept of Planetary Materials. Ontologies will 

support the Semantic Web through (1) ease of registration to 

facilitate discovery, and (2) ability to query across multiple 

and diverse databases through interconnected disciplinary 

ontologies. 

We have developed a prototype system as proof of 

concept, which uses the planetary ontology in discovering solutions to complex geoscience questions. Some of the 

geoscience tools that are required for integration within the 

Web environment are registered as Web services. For example, 

“data filtering tools” that distinguish between geologic bodies 

based on Magma Class (for example, A-Type, S-Type, MType, or I-Type) or metamorphic facies assemblages have been 

“wrapped” and registered as Web services. This enables users 

to utilize the tools without detailed knowledge of the operating 

system, development language environment, or the component 

model used to create these geoscience tools. Since only the 

input and output parameters need to be defined for Web service-based applications, it encourages reusability and reduces 

development time, effort, and cost. 

Service Ontologies 

As the Semantic Web matures, and more geoscientists 

adopt this paradigm, it is expected that a number of geoscience 

tools and services will be made accessible as Web services. 

This would require that, similar to data management practices, 

Web services also be ontologically registered. Annotating Web 

services with semantics would ensure that appropriate tools 

(in the form of Web services) are selected in an efficient and 

automatic manner for answering geoscience queries. Domain 

experts would provide formal specifications of geoscience 

concepts, enabling automated Web service usage. Moreover, 

since the Semantic Web is geared towards interactions involving minimal human intervention, a service ontology would 

enable direct service-to-service communication and facilitate 

information transfer. 

To fully understand the need for a service ontology, consider the geoscience query: “Find the chemical composition of 

a liquid derived by 30 percent partial melting (PM) based on 

the average abundances of Rare Earth Elements (REE) of AType plutons in Virginia.” This query clearly requires access to 

a number of datasets and geoscience tools. Figure 2 provides a 

high-level overview of the four steps involved in answering the 

query. These are: finding the A-Type bodies in Va., computing 

the averages, using the REE definitions contained in the element ontology and exporting the data to a PM tool for computation, and displaying the results. The prototype query engine 

(Discovery, Integration and Analysis engine) developed by us 

is able to address the query. 

The discovery of data pertaining to A-Type bodies (a 

class of igneous rocks) and which contains elemental data 

classified as REE requires that the geoscience data be registered to ontologies. The data ontologies now available to 

geoscientists (Sinha and others, 2006) allow access to multiple 

disciplines to fulfill this requirement (see fig. 2). Another 

major requirement for answering the query lies in the discovery of appropriate tools to carry out data filtering through 

both mathematical and domain-specific computations. It is 

expected that geoscientists will develop similar Web services 

(tools), using their own acronyms, and advertise them across 

multiple service registries (UDDI). Discovery of the required 

tools is only possible if the available tools and services have 

defined and precise semantics associated with them; thus, 

similar to data ontologies, “service ontologies” will also be 

required. Service ontologies are used for two purposes: to 

register services and to discover services. An ontology-based 

service description provides metadata information about the 

service provider, such as the service’s categories and subcategories, the service’s address, its parameters and their types, 

the service’s output, the service’s cost, etc. Service registries 
expose ontology-based search interfaces that service clients 

use to discover services appropriate for a given task. Once the 

client selects a given service, the registry provides the service 

description that the client then uses to actually invoke the 

service; therefore, a service ontology will do for Web services 

what a data ontology has done for geoscience data. 

Acknowledgment 

This research is supported by the National Science Foundation award EAR 0225588 to A.K. Sinha. 

References Cited 

Alonso, G., Casati, F., Kuno, H., and Machiraju, V., 2003, 

Web Services: Concepts, Architecture, and Applications: 

Springer Verlag (ISBN: 3540440089). 

Berners-Lee, T., Hendler, J., and Lassila, O., 2001, Semantic 

Web, Scientific American, v. 284, no. 5, p. 34-43. 

Curbera, F., Duftler, M., Khalaf, R., and Nagy, W., 2002, 

Unraveling the Web Services Web: IEEE Internet Computing, v. 6, no. 2, p. 86-93. 

McIlraith, S., Son, C., and Zeng, H., 2001, Semantic Web Services: IEEE Intelligent Systems, v. 16, no. 2, p. 46-53. 

Medjahed, B., Bouguettaya, A., and Elmagarmid, A., 2003, 

Composing Web Services on the Semantic Web: Very Large 

Data Bases (VLDB) Journal, v. 12, no. 4, p. 333-351. 

Sinha, A. K., Malik, Z., Rezgui, A., and Dalton, A., 2006, 

Developing the Ontologic Framework and Tools for the 

Discovery and Integration of Earth Science Data-Cyberinfrastructure Research at Virginia Tech, Annual Report., 

http://geon.geol.vt.edu/geon/index.html. 

The Critical Zone Exploration Network: 

Your Growing Community 

By Michael Hofmockel1, Sue Brantley1, Doug Miller1, and 

Daniel deB. Richter2 

1

Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, Pa. 

2

Nicholas School of Environment and Earth Sciences, Duke University, 

Durham, N.C. 

At Earth’s surface, a complex suite of chemical, biological, and physical processes combines to produce soil from 

bedrock and sediments within the zone that extends from the 

outer limits of vegetation to the lower limits of ground water. 

This weathering engine transforms primary minerals, provides 

nutrients to nourish ecosystems and human society, mediates 

Geoinformatics 2007—Data to Knowledge  

the transport of toxic components within the biosphere, creates 

water flow paths that shape and weaken bedrock, and contributes to the evolution of landscapes at all temporal and spatial 

scales. At the longest time scales, the weathering engine 

sequesters carbon dioxide (CO2), thereby influencing the 

global carbon cycle, long-term climate change, and weathering 

rates. This Critical Zone supports all life on Earth (http://www. 

czen.org/node/254). 

The Critical Zone Exploration Network (CZEN, http:// 

www.czen.org) is a network of people, locations, tools, and 

ideas to investigate processes within the Critical Zone. Any 

group studying the Critical Zone is encouraged to participate. 

The CZEN Web site already provides many communication 

tools, like event scheduling, file upload, forums, list serves, 

and literature databases. A Fall 2007 data and information 

systems workshop is planned to discuss the ongoing collection and compilation of data for CZEN (http://www.czen. 

org/node/215). 

Why join CZEN and create a group (http://www.czen. 

org/node/242)? Participation enables the following: (1) brings 

Critical Zone Observatories (CZOs) into close cyber-proximity; (2) provides economy of scale in technology overhead; (3) 

makes Web resources available to researchers from day one; 

and (4) allows CZOs the resources to build project-specific 

Web products, as the Web-based community-management 

system (CMS) is extendable and flexible. 

Towards a Reference Plate Tectonics 

and Volcano Ontology for Semantic 

Scientific Data Integration 

By A. Krishna Sinha1, Deborah L. McGuinness2, Peter Fox3, 

Robert Raskin4, Kent Condie5, Robert Stern6, Barry Hanan7, and 

Dogan Seber8 

1

Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, Va. 

2

Knowledge Systems, McGuinness Associates and Stanford University, 

Stanford, Calif. 

3

High Altitude Observatory (HAO), National Center for Atmospheric 

Research (NCAR), Boulder, Colo. 

4

Jet Propulsion Laboratory, Pasadena, Calif. 

5

Department of Earth and Environmental Science, New Mexico Institute of 

Mining and Technology, Socorro, N. Mex. 

6

Department of Geosciences, University of Texas at Dallas, Richardson, 

Tex. 

7

Department of Geological Sciences, San Diego State University, San 

Diego, Calif. 

8

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 
 Geoinformatics 2007—Data to Knowledge 

Figure 1. A class diagram representing the framework for linking data to volcanic features and processes. 

Introduction 

When scientific progress depends on integration of data 

across disciplines, it is critical for the users in the diverse 

disciplines to have access to the data in terms they can 

understand and use. Ontologies provide a method for encoding terms, term meaning, and term interrelationships in a 

machine interpretable format. In a geology setting, this means 

that ontologies provide a way of representing geologic terms 

and their associated properties. These encodings can enable 

interoperability and interdisciplinary data integration by allowing end users and agents to access precise, operational term 

definitions. 

In support of a National Aeronautics and Space Administration (NASA)-funded scientific application (Semantically 

Enabled Science Data Integration Project (SESDI); http:// 

sesdi.hao.ucar.edu/) that needs to share volcano and climate 

data to investigate relationships between volcanism and global 

climate, we have generated a volcano and plate tectonic 

ontologies. Our goal is to create reference ontologies—open 

terminology representations meant to be shared and reused 

by a broad community of users interested in the subject area, 

as well as to provide access to key volcanology and plate 

tectonic-related databases. We recognize that many of the 

features and products of volcanism are related to plate tectonic 

processes; thus, the availability of both ontologies allows us to 

map data associated with volcanic events, such as gas and particle ejecta, to plate tectonic settings and processes. Our goal 

is to support investigations into links between volcanism and 

climate change. This goal can be more rigorously addressed 

through semantic integration of data associated with the atmospheric response to volcanism and its plate tectonic setting. 

Volcano Ontology 

The near and far field effects of volcanic activity are common geologic phenomena observed in many parts of the world 

and may include possible catastrophic damage near eruptive 
Geoinformatics 2007—Data to Knowledge  

Figure 2. Class diagram relating plate tectonic features and their products. 

centers, as well as measurable effects on global climate. A 

comprehensive categorization of types of volcanic activity and 

its episodic nature is a challenging scientific goal that requires 

access to many known types of geologic and sensor data. We 

gathered leading volcano and scientific ontology experts to 

capture necessary conceptual relationships between data and 

phenomena associated with volcanism (Sinha and others, 

2006; McGuinness and others, 2006). Figure 1 depicts some of 

the results of this meeting in the form of a concept map. Our 

representation documents the terms and their relationships. 

Such representations are requirements for ontology-supported 

access to data and integration tools. Three high-level classes 

associated directly with volcanism were identified: volcanic systems, volcanic phenomena, and climate. The concept 

of volcanic systems was linked to both plate tectonic and 

geologic environments for providing links between eruptive 

style, magmatic composition, and the location of the volcanic 

field. Additional concepts related to volcanic systems include 

magma plumbing, eruption environment, and the three-dimensional geometry of the volcanic field. Deformation, eruption, landslides, biologic activity, atmospheric disturbance, 

hydrothermal alteration, magma motion, and earthquakes 

were considered to be subclasses of a higher level class called 

phenomena. The two classes—phenomena and volcanic systems—are linked through materials (in other words, magma 

and its products). This organization of concepts contains 

associations with all data types used by geologists to monitor 

active volcanic systems and to study volcanoes preserved in 

the geologic record. 

Plate Tectonics Ontology 

The dynamic interaction of lithospheric plates and their 

interaction with hot spots are closely related to all classes of 

volcanic phenomena. We gathered leading plate tectonics and 

scientific ontology experts and created a plate tectonics ontology that focuses on terminology expected to be used in semantic integration of data associated with volcanic activity and its 

plate tectonic setting (fig. 2). We established a high-level class 
 Geoinformatics 2007—Data to Knowledge 

structure representing the concept of lithospheric plates, as 

well as the subclasses that are associated with such features. 

The concept of a plate was treated as an independent class to 

permit the association of plate boundaries with this concept. 

For example, a divergent plate boundary has organizational 

relationship with a back-arc spreading center and a spreading 

ridge. Similarly, the subclass of convergent plate boundary 

contains convergent margin, which in turn is the parent of 

features such as fore arc, arc axis, or back arc. These are some 

of the more common plate tectonic settings associated with 

volcanism, and its relationship to composition variability in 

volcanic products is one of the key research goals of SESDI. 

It was also established that plates have intraplate and plate 

margin settings which often contain volcanoes associated with 

possible hot spot activity. 

Discussion and Conclusion 

Ongoing climate modeling efforts (Robock, 1989, 1991) 

have ascribed gaseous emissions, especially sulfur dioxide, 

hydrogen sulfide, and hydrogen fluoride, as the most significant gases capable of changing climate over periods of 

decades. For example, ongoing NASA efforts of studying volcanic sulfur dioxide (SO2) loading using a total ozone mapping 

spectrometer (TOMS; Volcanic Emissions Group, http://toms. 

umbc.edu/) show emission relationships between arc and nonarc volcanoes. Our work towards creating reference plate tectonics and volcano ontologies is aimed at facilitating scientific 

data integration in such interdisciplinary settings. Our newly 

developed high-level volcano and plate tectonic ontologies are 

being used to help clarify the relationships between total emissions and plate tectonic settings (noting in particular that these 

relationships are not unique). One conclusion is that the field 

requires a more sophisticated ontology for volcanoes and plate 

tectonics prior to extending current models associating climate 

change with volcanic activity. 

Acknowledgment 

Semantically-Enabled Scientific Data Integration 

(SESDI) is a semantic science data integration project sponsored by NASA Advancing Collaborative Connections for 

Earth-Sun System Science (ACCESS) and NASA Earth-Sun 

System Technology Office (ESTO) under award AIST-QRS06-0016. 

References Cited 

McGuinness, D.L., Sinha, A.K., Fox, P., Raskin, R., Heiken, 

G., Barnes, C., Wohletz, K., Venezky, D., and Lin, K., 2006, 

Towards a reference volcano ontology for semantic scientific data integration, in Proceedings of American Geophysical Union Joint Assembly, Baltimore, Md., May 23-26, 

2006. 

Robock, A., 1989, Volcanoes and climate, in Berger, A., 

Schneider, S., Duplessy, J. Cl., eds., Climate and geo-sciences: A challenge for science and society in the 21st 

century: Dordrecht, Kluwer, NATO ASI Series, Series C, 

Mathematical and physical sciences, no. 285, p. 309-314. 

Robock, A., 1991, The volcanic contribution to climate change 

of the past 100 years, in Schlesinger, M.E., ed., Greenhousegas induced climatic change: A critical appraisal of simulations and observations: Amsterdam, Elsevier, p. 429-444. 

Sinha, A.K., Heiken, G., Barnes, C., Wohletz, K., Venezky, D., 

Fox, P., McGuinness, D.L., Raskin, R., and Lin, K., 2006, 

Towards an ontology for volcanoes, in Brady, S.R., Sinha, 

A.K., and Gundersen, L.C., eds., Geoinformatics 2006— 

Abstracts: U.S. Geological Survey Scientific Investigations 

Report 2006-5201, p. 52. 

USGS Energy Program Geoinformatics: 

From Data Management to Information 

Services 

By David A. Ferderer1, Gregory L. Gunther1, 

Christopher C. Skinner1, and Laura R.H. Biewick1 

1

U.S. Geological Survey, Denver, Colo. 

The U.S. Geological Survey (USGS) Energy Resources 

Program (ERP) is responsible for generating publicly available, science-based assessments on the distribution, quantity, 

and quality of domestic and worldwide energy resources. 

Characterizing energy resources and their distribution promotes responsible use, helps sustain a dynamic economy, and 

supports balanced economic, energy, and environmental policy 

decisionmaking. The ERP also sponsors a Data Management Project to provide information stewardship in support of 

energy assessments; to develop information technology (IT), 

geographic information systems (GIS), and data management 

infrastructure; and expedites access to energy-resource information. This presentation offers an overview of ERP geoinformatic activities conducted by the ERP Data Management Project in support of information stewardship, Internet services, 

and future development of a service-oriented architecture. 

Information management in the ERP involves integration 

and alignment of IT architecture, geospatial data and services, 

and data management processes to meet science requirements. 

These components must also meet project needs, foster data 

stewardship, and increase access to and discovery of products 

and support Internet map services and future service-oriented 

architecture designs. Some components utilized and developed 

include: (1) IT hardware infrastructure (Linux/Dell servers, 

Network Appliance file server, Oracle relational database 

management system, and gigabit networks); (2) GIS data and 

services (geodatabases, metadata documentation, desktop GIS, 
Geoinformatics 2007—Data to Knowledge 7 

map services); and (3) data management protocols (information planning, data mining, data warehousing, inventory, 

product access, and discovery). These activities have produced 

data organization schemas and warehouses, metadata, project 

and product work flows, search and discovery tools, and Enviromental Systems Research Institute map (image) services, to 

name a few. Some of the key project areas where these capabilities, services, and tools have been implemented include the 

National Oil and Gas Assessment, World Petroleum Assessment, and the Gulf Coast Geologic Framework Project. 

The ERP is now transitioning its primary information 

management components into a more open, manageable, and 

flexible information service environment that is based on a 

service-oriented architecture. Successful information services 

and service-oriented architecture rely on metadata documentation, open-source standards, interoperability, catalogues, 

indexes for discovery, and by leveraging the Internet and 

portal technologies. Ultimately, these capabilities can foster 

advanced computing, advanced ontologies, and support knowledge integration and decisionmaking in a complex science 

environment. To this end, the ERP continues to develop more 

robust and functional metadata server architecture, consolidate 

and improve existing Internet map services, develop national 

and global-scale energy-resource service layers and capabilities, incorporate Open Geospatial Consortium standards, 

and is instituting plans to develop ERP portals highlighting 

services, catalogues, and service-oriented architecture capabilities. 

Web Services for Geoscience Data: 

Experiences and Lessons 

By Robert A. Arko1, Andrew K. Melkonian1, 

Suzanne M. Carbotte1, Kerstin A. Lehnert1, 

and Sri Vinayagamoorthy2 

1

Lamont-Doherty Earth Observatory, Columbia University, Palisades, N.Y. 

2

Center for International Earth Science Information Network, Columbia 

University, Palisades, N.Y. 

The lack of accepted standards for data interoperability 

is a continuing challenge in the geoscience community, and 

hampers our ability to make research results broadly available. 

The Open Geospatial Consortium, Inc. (OGC; http://www. 

opengeospatial.org) is attempting to address this challenge by 

developing Web service standards through a consensus process 

among industry, government, and academic partners. These 

standards include the Web Map Service (WMS) to compose 

and display map images from underlying data sources, as well 

as the Web Feature Service (WFS) and Web Coverage Service 

(WCS) to provide direct access to geospatial data. 

WFS provides a simple standard for serving geolocated vector data such as points and polygons. The request is 

expressed entirely in the URL, and the response is delivered 

as an XML object using the Geography Markup Language 

(GML). Such a service can advertise a wide array of useful 

geoscience data, including station locations, physical specimens, event catalogs, track lines, and so on. WFS is currently 

supported by numerous geographic information systems (GIS) 

server products, both commercial (ArcIMS (http://www.esri. 

com) and RedSpider (http://www.ionicsoft.com)) and open 

source (GeoServer (http://www.geoserver.org) and MapServer 

(http://mapserver.gis.umn.edu)). It is supported in GIS clients, 

such as uDig (http://udig.refractions.net) and GeoMapApp 

(http://www.geomapapp.org). 

Deployment of WMS and WFS providers is underway 

throughout the geoscience community. University Navigation Signal Timing and Ranging (NAVSTAR) Consortium 

(UNAVCO; http://www.unavco.org), Incorporated Research 

Institutions for Seismology (IRIS; http://www.iris.edu), and 

the Marine Geoscience Data System (MGDS; http://www. 

marine-geo.org) recently reported results from ongoing collaborative work (fig. 1). The National Geophysical Data Center 

(http://www.ngdc.noaa.gov), Petrological Database of the 

Ocean Floor (PetDB; http://www.petdb.org), and LDEO Borehole Research Group (http://www.ldeo.columbia.edu/BRG) 

have also deployed WFS providers. The Marine Metadata 

Interoperability Project (MMI; htttp://www.marinemetadata. 

org) is pursuing activities, including a formal Open Geospatial Consortium (OGC) Interoperability Experiment for 

ocean-observing data. Examples from these projects will be 

described in detail. 

WFS can provide extensive information for each feature 

instance, including identifier, location, time, elevation, URL 

(in other words, reference for further information), and any 

Figure 1. uDig GIS client displaying integrated results from 

UNAVCO, IRIS, and MGDS providers at Gulf of California (Baja) 

local study site. Features and layers include global positioning 

system campaign surveys and continuous stations, seismic 

stations, earthquake locations, seafloor bathymetry, and 

expedition tracks. 
 Geoinformatics 2007—Data to Knowledge 

Figure 2. GeoMapApp GIS client displaying results from PetDB 

WFS provider. User selected Juan de Fuca mid-ocean ridge study 

site; plotted and colored distribution of potassium oxide (K2O) 

versus silicon dioxide (SiO2). 

number of additional data attributes. For example, the PetDB 

WFS provides a sample feature with an extensive listing of 

geochemical analyses at each instance. A WFS-enabled client 

such as GeoMapApp can load and display PetDB samples on a 

map, and allow the user to color, plot, and intercompare different analytical values (fig. 2). 

As WFS usage increases, several performance issues 

have become apparent. A request for a large number of feature 

instances can return a prohibitively large result that exhausts 

network or memory resources. The GeoMapApp client 

addresses this issue by restricting the user to a defined bounding box (world, ocean, or local study site, as appropriate), thus 

keeping the server request to a manageable size. Further, there 

is no accepted standard for attribute names, units, or ordering. 

GeoMapApp addresses this issue by offering a generic interface for plotting data values, allowing the user to select and 

color attributes according to domain knowledge. 

Scientific Workflows for OntologyBased Data Mining of Geological Data 

By Ilkay Altintas1, Sara Graves2, Rahul Ramachandran2, 

Dogan Seber1, and A. Krishna Sinha3 

1

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

2

Information Technology and Systems Center, University of Alabama at 

Huntsville, Huntsville, Ala. 

3

Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, Va. 

As scientific research becomes more interdisciplinary 

and requires integrative approaches, domain scientists are in 

need of advance technology tools to be able to analyze and 

integrate diversified and voluminous datasets for their research 

activities. Although there are a variety of technology-based 

resources, such as data-mining tools, scientific-workflow-management systems, and portal frameworks, building a complete 

cyberinfrastructure framework that incorporates all these technologies is challenging and requires an extensive collaboration 

among domain scientists and technology developers. In this 

abstract, we explain our approach to develop strategic cyberinfrastructure technologies that will integrate workflow technologies with data-mining resources and portal frameworks 

in a semantically enabled work environment. This is a unique 

effort in the sense that each component will have to be well 

integrated into the system while giving sufficient flexibility for 

the developed services to be applicable in other scientific disciplines. Services to be developed in these efforts will enable 

scientists to manage large and heterogeneous datasets in a 

timely fashion and extract new knowledge from existing and 

future datasets. We chose geosciences as our demonstration 

domain because geoscience data are extremely heterogeneous 

and complex and there is significant expertise and resources 

available to be used in such activities. 

Each cyberinfrastructure component identified in this 

effort is at a sophistication level such that they can now be 

used in an integrative environment. Workflows significantly 

improve data analysis, especially when data are obtained from 

multiple sources and (or) various analysis tools. Given their 

nature, workflows are effective in integrating different technologies and formalize the process; hence, they form a natural 

integration environment. Despite many existing efforts in 

workflow development, integrating workflows with ontologybased data mining provides unique challenges for developers 

and requires collaborative research efforts among technology 

developers. Application of traditional data-mining techniques 

(clustering and classification) has been around for a long time 

and has resulted in extracting novel information from large 

scientific databases (for example, atmospheric sciences and 

genetics), and helped to manage costs and design of effective 

sampling/experimental strategies. As scientific data become 

more complex however, as in the geosciences, it is important 

to relate data to the concepts within disciplines. Data-mining 
services, including developing new ones that deal with sparse 

data, can be applied at different levels of abstraction and help 

the user discover more meaningful patterns leading to a more 

robust capability to answer scientific questions. For example, 

a geoscientist may want to study predicting volcanic eruptions using these types of resources or apply the technologies 

to identify the plate tectonic setting of a former volcano. As 

future steps, we plan to apply the ontology-driven data-mining approach to global geoscience datasets, such as GeoRoc, 

EarthChem, Petros, Pluto, and volcano databases from the 

U.S. Geological Survey and the National Aeronautics and 

Space Administration, towards discovering patterns and trends 

between plate tectonic settings and volcanism not recognized 

by individual scientists. We will utilize workflows in a portal 

environment to integrate semantic data management and datamining technologies seamlessly to facilitate a more comprehensive understanding of the nature of volcanism and its plate 

tectonic settings. 

The Australian Mineral Occurrence 

Data Exchange Model 

By Adele Seymon1, Lesley Wyborn2, Bruce Simons1, 


Oliver Raymond2, Gary Andrews3, Terry Denaro4, 


Greg Jenkins5, Peter Lewis6, James Llorca1, 


Marcus McClenaghan7, Alistair Ritchie1, Jafar Taheri7, 


Ian Withnall4, and Andrew Wygralak3
 

1

GeoScience Victoria, Victorian Department of Primary Industries, Australia 

2

Geoscience Australia, Canberra, Australia 

3

Northern Territory Geological Survey, Northern Territory Department of 

Primary Industries, Fisheries and Mines, Australia 

4

Geological Survey of Queensland, Queensland Department of Natural 

Resources and Water, Australia 

5

Primary Industries and Resources South Australia, Government of South 

Australia, Australia 

6

Geological Survey of New South Wales, New South Wales Department of 

Primary Industries, Australia 

7

Mineral Resources Tasmania, Department of Infrastructure, Energy and 

Resources, Australia 

Introduction 

The Australian Mineral Occurrence Data Exchange 

Model has been collaboratively developed under the leadership of the Australian Government Geoscience Information 

Policy Advisory Committee (GGIPAC). Representatives from 

all Australian Federal, State, and Territory Geological Surveys 

contributed to the model. 

Australian mineral occurrences information is stored in 

individual State and Territory Geological Survey databases. 

The individual organization’s store and maintain information 

about mineral occurrences, such as commodities, historiGeoinformatics 2007—Data to Knowledge  

cal production, endowment, reserves, resources, and mineral 

deposit classification. Each agency’s database has its own 

format, attributes, and vocabularies, and each was developed 

using a variety of software platforms and versions of the software to meet the organization’s individual business requirements. 

Geoscience Australia developed a central database that 

provided a national overview of mineral occurrences, which 

was made accessible through the Australian Geoscience portal 

(http://www.geoscience.gov.au/geoportal/minocc/). To create 

this Web page, data from the States and Territories is currently 

sent to Geoscience Australia and then manually massaged 

and uploaded to the central database. As this database is not 

dynamically linked to the State databases, there are often 

inconsistencies between data at the Federal and the State and 

Territory level for the same deposit or occurrence, depending 

on how long it has been since an upload was completed. 

Drivers for Development of a Mineral 

Occurrence Data Model 

Web services offer an ideal, cost-efficient technology for 

removing both the inconsistencies and the need for data to be 

regularly uploaded to the national database. Web services also 

offer a chance to access the latest and most up-to-date data 

from the originating agency and return the data in a consistent 

format; however, any such Web service requires an agreedupon data exchange standard, and none existed. 

The Mineral Occurrence Data Exchange Model 

The model is a high-level data exchange model for mineral occurrences represented in a unified modeling language 

(UML) that can be extended to cover all Earth resources (fig. 

1). It will enable data on mineral localities to be delivered live 

to the Australian Geoscience Portal and will also facilitate data 

transfer between government, industry, and other organizations. It will enable real-time access to the latest data from 

each Survey. Because it is a standard data model, it will also 

enable a more formal structure for reporting resources and 

reserves that can comply with national and internationally 

accepted reporting codes. The model will require that standard 

vocabularies be compiled for each attribute, and this is work in 

progress. 

The model is compatible with Geoscience Markup 

Language (GeoSciML), the International Union of Geological 

Sciences (IUGS) developed language for exchange of geological map features, and uses patterns and features common to 

GeoSciML. These patterns are based on International Organization for Standardization (ISO) and Open Geospatial Consor

tium (OGC) standards using Geographic Markup Language 

(GML) as an Extensible Markup Language (XML) encoding 

for geographic information. In the ISO model, “features,” or 

real-world objects of interest, are classified into types on the 

basis of a characteristic set of properties. GML provides few 

­
0 Geoinformatics 2007—Data to Knowledge 

Figure 1. A UML diagram of the Mineral Occurrence Data Exchange Model. 
concrete feature types directly, as these are intended to be 

created using the standard components in a domain-specific 

“GML Application Schema.” “Mineral occurrences” is an 

example of a domain-specific schema. Model development 

took place in the graphical UML environment. 

Below is a summary of the key points of the Australian 

Mineral Occurrence Model: 

•	 The model describes earth resources independent of 

associated human activities (for example, mining). 

•	 Caters to a description of earth resources using the following: 

•	 Mineral deposit models that describe the actual 

deposit type (encompassing the Cox and Singer 

classification); 

•	 Mineral systems that describe the processes associated with deposit formation; and 

•	 Supergene processes. 

•	 Utilizes GeoSciML mapped feature to describe spatial 

representation. 

•	 Utilizes GeoSciML earth material to describe host and 

associated materials. 

•	 The model describes a mine as made up of a number 

of mining activities, each of which produce some commodity. 

•	 The model provides the ability to describe commodity 

resources formally or informally, utilizing CRIRSCO 

(Committee for Mineral Reserves International 

Reporting Standards) and including basic JORC Code 

requirements (Joint Ore Reserves Committee—the 

2004 Australasian code for reporting exploration 

results, mineral resources, and ore reserves). 

Participating in Further Developments of the 

Model 

The model can be accessed at https://www.seegrid.csiro. 

au/twiki/bin/view/Xmml/MineralOccurences. 

Any interested parties are welcome to comment and per

haps participate in trying to extend and progress the model to 

becoming an international data exchange standard. 

Emerging Web Services at the IRIS 

Data Management Center (DMC) 

By Bruce R. Weertman1, Joanna Muench1, Linus Kamb1, 

Rob Casey1, and Tim Ahern1 

1

IRIS Data Management Center (DMC), Seattle, Wash. 

IRIS Background 

New demands for interdisciplinary science have broadened the data delivery mission for the Incorporated Research 

­

Geoinformatics 2007—Data to Knowledge 1 

Institutions for Seismology (IRIS) Data Management Center 

(DMC). In the past, the DMC has been charged with archiving 

seismic waveform data and making it available to download 

primarily via batched mechanisms. In recent years we have 

seen the successful addition of a Common Object Request 

Broker Architecture-Remote Procedure Call (CORBA-RPC) 

mechanism named DHI (Data Handling Interface). The 

mission of IRIS has now been expanded to delivering more 

general data products, usable by nondomain experts. Many 

of the building blocks to create these data already exist, but 

in their current format are difficult to combine. At the DMC 

we are working to expose these existing capabilities and to 

develop new ones, linking them together with workflows to 

create a Web-based service-oriented architecture. The DMC 

began providing Web services two years ago, with pilot projects exposing data access and analysis through Web services. 

A new tool, SPADE (Searchable Product Archive and Discovery Engine), uses Web services both for data submission and 

discovery. Enabling interorganizational collaborations is one 

of the strengths of Web services, and IRIS has been working 

with the University Navigation Signal Timing and Ranging 

(NAVSTAR) Consortium (UNAVCO) and the Marine Geology 

Data System at Lamont-Doherty Earth Observatory (LDEO) 

on the Geoscience Web Services (GeoWS) project (http:// 

www.geows.org). The GeoWS project aims to bring together 

datasets from the three organizations by means of Open Geospatial Consortium (OGC)-standard mapping technologies. 

Some of the DMC’s existing capabilities that will be exposed 

as services include data format conversion, plotting, and phase 

analysis. New tools will include hypocenter and tomographic 

retrieval and visualization. Our long-term aim is to provide 

reusable, composable services with programmatic and interactive interfaces, enabling users to easily customize seismic data 

access. The flexibility of a service-oriented architecture will 

enable the IRIS DMC to respond effectively to technology 

changes and demands from the geosciences community. 

IRIS Earthquake Hypocenter Web Service for 

use with the GEON IDV 

The Geosciences Network (GEON) IDV (Integrated Data 

Viewer) (http://geon.unavco.org/unavco/IDV_for_GEON. 

html) is a powerful, free Java-based desktop application that 

allows 3D visualization of complex solid earth-science data. 

The GEON IDV can display earthquake hypocenters encoded 

in Network Common Data Form (NetCDF). We have developed a Representational State Transfer (REST)-style Web 

service for generating such data. The service can rapidly query 

our earthquake event tables for events in latitude-longitude 

bounding boxes and date ranges. Our event tables contain millions of events from multiple contributors and span the time 

range from 1964 to present. The Web service is coupled with 

an easy-to-use map-based Web application that allows users to 

quickly discover what events are in our tables and to download 
2 Geoinformatics 2007—Data to Knowledge 

the information in the NetCDF format. This should be a useful 

tool for educators, scientists, and students. 

An Experiment Tool for the 

Multilayered Geoscience Ontology 

By Kangping Sun1 and Lei Wang1 

1

China University of Geosciences, Wuhan, China 

One of the significant challenges towards the integration 

of geoscience datasets and query for the meaningful geological subjects (such as instances of the earth-material types or 

deposits) from a virtually integrated geoscience database, is 

the need to capture geoscience knowledge with which geologists will be comfortable. This paper reports on an experimental tool that supports compiling multilayered ontology 

(or concept space, as the community refers to it) and outputting geologic query concepts. The end users can browse the 

captured structural knowledge and query geological data in the 

integrated database via the geologic query concepts. 

Basically, the tool supports the compilation of three kinds 

of geologic concept models. The first one is the hierarchical 

concept model, which defines what an independent geologic 

concept is and where it is within the hierarchical concept 

model. The definition methods of the model are very similar 

to the classification schemes of geoscience terminology. The 

classification scheme depends on the geologic term to be classified; thereafter, the definition methods form into the layers 

of the hierarchical model. The second one is the relational 

concept model, which establishes relationships among the 

independent geologic concepts with a more structural style. 

The last one is the query concept model, which builds up 

application-oriented subjects that take the concept localization 

or specification into account. 

The tool can output “geologic concept queries” that 

look like the following: “Select Terrigenous-clastic material. 

Mudrock in the area Lat-long area.areaA” or “Select Washover-fan deposit with the description of lithofaceA or lithofaceB.” 

Initially, the tool is designed for the sedimentary materials: science language for their classification, description, 

and interpretation in digital geologic-map databases (North 

American Geologic Map Data Model Science Language Technical Team, 2004). Almost all the interested terms in it could 

be arranged into one hierarchy under the top-level geologic 

concept model. In fact, it can capture geologic knowledge only 

if the geoscientists agree upon the representations predefined 

in the tool. We believe the tool will be upgraded when more 

geoscience knowledge is captured. 

Reference Cited 

North American Geologic Map Data Model Science Language 

Technical Team, 2004, Sedimentary materials: science language for their classification, description, and interpretation 

in digital geologic-map databases, Version 1.0 (12/18/2004): 

Draft report posted on the North American Data Model Web 

site, http://www.nadm-geo.org/sltt/products.html, 595 p. 

(Accessed October 12, 2007.) 

Data Independence and Geospatial 

Web Services 

By Upendra Dadi1 and Liping Di1 

1

Earth System Science, George Mason University, Fairfax, Va. 

The importance of data independence in a database management system is well recognized in the database community. 

Data independence means that a database can be changed 

structurally without affecting programs processing the data. 

The concept of data independence can play a very useful role 

in the development of geospatial Web service architectures. 

We have created a prototype Web service platform on top of 

the Geographic Resources Analysis Support System (GRASS) 

geographic information systems (GIS) based on the concept of 

data independence. The paper presents the general architecture 

used in developing the Web services. This architecture consists 

of several layers of services. The interfaces of services at each 

layer are independent of the implementation of the lower level 

services. The geospatial modeler, who is also the developer of 

services at the topmost layers, is immune from having to learn 

the specifics of any one particular software package or service. 

The services at the higher levels can be cataloged and made 

discoverable for use in other higher level models. Some of the 

issues that we encountered—granularity of services, statelessness of services, and interoperability between services—are 

also discussed. 

Layered Architecture for Geospatial Web 

Service Development 

One of the important considerations in the design of the 

architecture is that the scientist or model developer should be 

immune to the details of the underlying implementation of the 

software or services. The model developer should be able to 

view the service interfaces available at the conceptual level 

without having to know much about the details of the implementation. At the same time, the flexibility and power of the 

underlying software should not be restricted when incorporated into Web services. In other words, a user of the services 

should be able to achieve all the functionality that can be 

achieved by using the software in a standalone manner. This 
Figure 1. Layered architecture for Web service design. 

idea of conceptual data independence has played a crucial role 

in the design and development of relational databases. Having 

a layered architecture for geospatial Web service development 

can play a similar role. At each layer are services composed 

by chaining services at the lower level or same level. The 

higher level services can be created in such a way that they 

completely hide specifics of the software and services used in 

the lower level. Figure 1 displays the layered architecture used 

in the development of Web Services on top of GRASS GIS. 

The reader is referred to for more details about GRASS GIS. 

GRASS GIS is at Layer 0 in the architecture. 

Layer 1 

Layer 1 consists of “atomic” Web services which are 

directly based on the GRASS commands. Each and every 

service interface emulates a GRASS command. Some simple 

mapping rules, depending on the pattern of the command line, 

were used when going from the command-line interface to the 

Web-service interface. 

Most of the services in this layer are not self-contained, 

although they are independent of each other. In reality, 

however, Web services are supposed to be discrete units of 

code which are independent as well as self-contained. They 

are usually only loosely coupled with each other. In a later 

section, we will argue that services on top of session-based 

command-line software like GRASS are more naturally 

designed as Grid services which have state. The parameters 

used in the above GRASS services—DATABASE, LOCATION, and MAPSET—can be thought of as representing state 

Geoinformatics 2007—Data to Knowledge  

information which is stored on client side and passed during 

each and every call to the service. We will see that in the next 

layer of the stack in figure 1, we can chain the atomic services, 

which can be either stateful or stateless, into higher-level Web 

services that are completely independent and self-contained. 

Specifics of GRASS can be completely hidden from the users 

of the Web services at this layer, unlike services in the present 

layer which require user-level knowledge of GRASS. 

Layer 2 

In this layer, several services from the layer below are 

chained together to create new services. These higher-level 

services are similar to GRASS scripts that are composed of 

several GRASS commands. The interfaces to these services 

are such that they can be looked at the conceptual level by the 

model developer, without regard to the implementation details. 

They follow directly from the definition of the parameter 

being modeled. This will be illustrated with a simple example. 

A service has been developed to calculate Normalized Difference Vegetation Index (NDVI) values from two raster data 

files—near infrared (NIR) and red images. The inputs to this 

service are two raster images—the first containing reflectance 

values from the visible range in Hierarchical Data FormatEarth Observing System (HDF-EOS) format and the second 

containing reflectance values from the NIR range, also in 

HDF-EOS format. The two images are assumed to have the 

same projection, resolution, and bounds. The output from 

this service is the image containing NDVI values in Portable 

Network Graphics (PNG) format. All the services used for creating this service—r_in_gdal, r_mapcalc, and r_out_png—are 

atomic services in Layer 1. 

Layer 3 

In this layer, services developed in Layer 2 are chained 

with other services to create even higher level services. The 

Layer 3 services can also consist of other Web services. The 

other Web services could be Web services based on other 

geospatial software. The OGC data services, such as WCS 

or WFS implementations, could be the data sources for the 

workflows at this layer. Now we will look at an example of a 

scientific workflow that is essentially a service at this layer. A 

simple model for calculating landslide susceptibility based on 

a few parameters, such as slope, aspect, NDVI, etc., has been 

implemented as a Layer 3 service. Figure 2 shows the model 

schematically. 

The services developed in Layer 2 are chained with other 

services to create a workflow which represents the landslide 

model. In the image above, land cover is obtained from a Web 

Image Classification Service (WICS), which is a non-GRASS 

service. Each of the components in the model—NDVI, slope, 

aspect, landcover, and landslide susceptibility—are composed 

as independent Web services. These components form Level 

2 services. They are chained together to create a workflow 
 Geoinformatics 2007—Data to Knowledge 

Figure 2. A simple landslide susceptibility model. 

that represents a landslide model. While one can create the 

model from atomic services itself, to do that requires knowing 

the specifics of GRASS GIS and working at a lower level of 

abstraction. We used Business Process Execution Language 

(BPEL) for creating workflows. 

Conclusion 

This paper presents layered architecture for geospatial 

Web service development over GRASS GIS; however, similar 

architecture may be applicable when converting other geospatial software to Web services. Each geospatial software package has its own set of interfaces with which users can work. 

Usually these interfaces are designed for optimal functioning 

of the software. Bypassing these interfaces to develop coarsegrained services from the software may severely restrict the 

Web service developer from using the full capabilities of the 

underlying software; however, on the other hand, a model 

developer who uses these interfaces directly must learn the 

specifics of the software. A model developer does not and 

probably should not think about any specific software when 

developing a model; therefore, it is better to have multiple 

layers of users. At one layer is the user who is conversant with 

the interfaces. He or she can develop higher-level interfaces 

that completely hide the details of the underlying software and 

that are highly reusable across many different models. These 

service interfaces can be understood at the conceptual level by 

the model developer. The high-level interfaces would be much 

easier for a model developer to use than using implementationdependent low-level interfaces. There is much scope for adding new functionality to the system developed so far. GRASS 

has many interactive commands. It is challenging to convert 

these commands to Web services. An intimate knowledge of 

the GRASS application programming interface (API) may be 

required. So far, there is no software that can act as a client to 

the services developed. For the end user to be able to use the 

services, the development of a general purpose and an extensible geospatial Web service client is needed. 

Using WDO-it to Build a Geoscience 

Ontology 

By Paulo Pinheiro da Silva1, Leonardo Salayandia1, 

and Ann Q. Gates1 

1

Department of Computer Science, University of Texas at El Paso, El Paso, 

Tex. 

Introduction 

Workflow-Driven Ontologies (WDOs) are an approach 

to ontology development based on scientist-level terminology 

such as “dataset” and “methods,” which claims to facilitate the 

scientific process of encoding knowledge from their domains 

(Salayandia, Pinheiro da Silva, Gates, and Salcedo, 2006). In 

addition, resulting ontologies produced from using the WDO 

approach may include properties that enable the automatic 

generation of suggested workflow specifications. These suggested workflow specifications, once refined and endorsed by 

scientists, can be used as a training tool, since they provide 

a graphical representation to which the scientist can easily 

relate. The endorsed workflow specifications can be refined 

into fully computable workflows that facilitate the discovery 

and integration of resources available over cyberinfrastructures. 

WDO-it is a prototype tool developed in Java that supports the WDO approach. Ontologies created with the WDOit tool are encoded in the Ontology Web Language (OWL). 

OWL is the standard ontology language proposed by the 

World Wide Web Consortium (W3C) and the main framework 

language used by the semantic Web community (OWL, 2004). 

In this abstract, we discuss the creation of an ontology about gravity data processing as defined and used in the 

domain of geophysics. The ontology, called GravityWDO, 

has been created with the current version of the WDO-it tool 

available at http://trust.utep.edu/ciminer/software/wdoit/. The 

current state of WDO-it allows suggested abstract workflow 

specifications to be generated from the knowledge provided 

by scientists. We call these abstract workflow specifications 

Model-Based Workflows (MBWs), since they are instantiations on an abstracted workflow model. A graphical representation of the MBWs can be visualized and can serve as a 

training tool by itself. Additional work is underway that will 

allow the MBWs to be migrated to an executable workflow 

language, such as the Modeling Markup Language (MoML), 

the language that the Kepler scientific workflow engine uses to 

represent workflows (Ludäscher and others, 2005). The claim 

is that scientists can relate easier to MBWs than to real executable workflows because they describe only essential properties 

that are required at the scientist level. Nevertheless, WDO-it 

will provide mechanisms to use MBWs as the basis to create 

executable workflow specifications. 
Building Ontologies Using WDO-it 

WDO-it provides three basic modes for building ontologies: (1) brainstorming mode; (2) harvesting mode; and (3) 

relation elicitation mode. In the brainstorming mode, scientists 

have the opportunity to enter concepts from his or her domain 

of interest, where these concepts are classified as either information concepts or method concepts. WDO-it does not use 

the term “concept” in its user interface; instead, it provides a 

very simple interface where scientists can see that information concepts range from raw data (for example, concepts that 

represent data measured in the field) to products (for example, 

concepts that represent models or maps of interest to the 

scientist). Moreover, the interface provides a way for entering 

method concepts that represent the algorithms, applications, 

and tools that are used to retrieve or transform information 

concepts. For example, an application that retrieves gravity 

data from a database about a specified region of interest can 

be classified as an information-retrieval concept. A tool that 

employs the nearest-neighbor algorithm to create a grid of 

uniformly distributed data points from a collection of scattered 

points can be classified as an information- transformation 

concept. These concepts, however, can be already specified in 

some existing ontology that a scientist may want to reuse. In 

this case, concepts in the existing ontology can be imported 

and later classified into information and method accordingly. 

This is referred to as the harvesting mode. 

Any time after at least a method is created in the brainstorm or harvesting mode, the scientist can switch to the relationship elicitation mode to identify relationships between the 

information concepts and the method concepts. These relationships are of the type “IsInputTo” and “IsOutputFrom,” where a 

scientist indicates which information concepts are required as 

input to a method concept, and which information concepts are 

the output of a given method concept. Figure 1 shows a snapshot of the ontology relationship tab. Notice that the tool does 

not show all the relationships available in the ontology being 

created; instead, the user selects a method concept, and then 

the input-information concepts, so that the output-information 

concepts are shown for the selected method concept only. By 

focusing on one method at a time, the scientist can have better 

control of the relationships between concepts, instead of seeing a cluttered diagram that shows all relationships between all 

concepts, which is typical of other general-purpose ontology 

editor tools. Figure 1 shows the creation of a gravity ontology, 

where the scientist selects a method called gridding, and for 

which the information concept “CompleteBouguerAnomaly” 

is shown as its only input; the concept grid is shown as its 

output. 

Additionally, the scientist can create new types of properties that can be used to customize relationships between concepts. For example, a scientist may create a “HAS” property 

that can be used in a relationship to indicate that a “SeismicEvent” has a location and a time concept related to it. This 

functionality is available through the Advanced View button 

shown in figure 1. Moreover, because WDOs are OWL ontoloGeoinformatics 2007—Data to Knowledge  

Figure 1. WDO-it tool; ontology relationship tab. 

gies, more generic ontology editors, such as Protégé (Gennari, 

and others, 2002) and Semantic Web Ontology Overview and 

Perusal (SWOOP; http://www.mindswap.org/2004/SWOOP/) 

can be used. 

Generating Workflow Specifications through 

WDO-it 

Once the scientist has built a WDO about their domain, 

WDO-it can use this WDO to automatically generate a suggested workflow specification for a given information concept 

of interest. For example, if a scientist is interested in obtaining a workflow that would describe the necessary steps to 

create a grid of gravity data, the scientist would choose the 

concept of interest from the information concepts available in 

the captured knowledge about gravity data (in other words, a 

gravity ontology). The WDO-it tool creates a suggested workflow specification (in other words, an MBW) based on the 

relationships available in the captured knowledge (Salayandia, 

Pinheiro da Silva, Gates, and Rebellon, 2006). The scientist is 

presented with a graphical representation of the workflow, and 

the workflow specification can be saved as an OWL file that is 

separate from the WDO. The MBW is not formally considered 

a workflow specification until it is endorsed by a scientist as 

an accurate representation of a process in the scientist domain. 

Corrections and refinements may be needed for the scientist 

to endorse the MBW. The WDO-it evaluation mode is responsible for enabling the scientist to critique, refine, and endorse 

suggested MBWs. 

Figure 2 shows a snapshot of the workflow generator 

tab of the WDO-it tool and the resulting diagram generated 

for the grid-information concept, according to the knowledge 

captured in the loaded gravity ontology. Notice that there are 

some methods that have multiple inputs. Multiple inputs going 

into a method go through an “AND” method, indicating that 

all inputs are necessary for the given method to produce a 

given output. Exclusive-OR (XOR) operators are also used for 

the case where there can be different inputs to a method, but 

only one of them is needed for the method to create an output. 
 Geoinformatics 2007—Data to Knowledge 

Figure 2. WDO-it tool; workflow generator tab. 

References Cited 

Gennari, J., Musen, M.A., Fergerson, R.W., Grosso, W.E., 

Crubezy, M., Eriksson, H., Noy, N.F., and Tu, S.W., 2002, 

The evolution of protégé: An environment for knowledgebased systems development, 32 p. 

Ludäscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., 

Jones, M., Lee, E.A., Tao, J., and Zhao, Y., 2005, Scientific 

workflow management and the Kepler system, in Concurrency and computation: Practice and experience, special 

issue on workflow in grid systems: New York, John Wiley 

and Sons, Ltd., v. 18, no. 10, p. 1039–1065. 

McGuiness, D.L., and van Harmelen, F., 2004, OWL Web 

ontology language overview: World Wide Web Consortium (W3C) Web site http://www.w3.org/TR/owl-features/. 

(Accesssed October 12, 2007.) 

Salayandia, L., Pinheiro da Silva, P., Gates, A.Q., and Salcedo, 

F., 2006, Workflow-driven ontologies: An earth sciences 

case study, in Proceedings of the Second International 

Conference on e-Science and Grid Computing, Amsterdam, 

Netherlands, December 2006: Washington, D.C., IEEE 

Computer Society, 1 CD-ROM. 

Salayandia, L., Pinheiro da Silva, P., Gates, A.Q., and Rebellon A., 2006, A model-based workflow approach for scientific applications, in Gray, J., Tolvanen, J.-P., and Sprinkle, 

J., eds., Proceedings of the Sixth OOPSLA Workshop 

on Domain-Specific Modeling (DSM’06): University of 

Jyvaskyla [Finland] Computer Science and Information 

System Reports, Technical Report TR-37, available online 

at http://www.dsmforum.org/events/DSM06/Papers/20­

Salayandia.pdf. 
WXGURU: An Ontology-Driven Chatbot 

Prototype for Atmospheric Science 

Outreach and Education 

By Rahul Ramachandran1, Sunil Movva1, Xiang Li1, 

Prashanth Anantharam1, and Sara Graves1 

1

Information Technology and Systems Center, University of Alabama at 

Huntsville, Huntsville, Ala. 

Chatbots are computer programs designed to simulate an 

“intelligent” conversation with one or more human users via 

auditory or textual methods. These programs typically scan 

user inputs for keywords and then extract a reply with the 

most matching keywords or the most similar wording pattern from a local database. Chatbots have been successfully 

used in industry as virtual customer-service assistants, guides, 

alternative to FAQs, and so on. One such chatbot is A.L.I.C.E. 

(Artificial Linguistic Internet Computer Entity), which uses 

Artificial Intelligence Markup Language (AIML) to encode 

conversations. AIML is an XML-compliant language with 

a formal specification and W3C XML schema. AIML contains elements to define patterns to match user inputs and the 

subsequent replies. WxGuru (Weather Guru) is a clone of 

A.L.I.C.E., designed as an educational chatbot for atmospheric 

science. WxGuru is unique because its conversational patterns 

have been coupled with a reasoner loaded with atmospheric 

ontology. This coupling allows WxGuru to provide useful and 

knowledgeable replies to user queries regarding any atmospheric science questions. WxGuru design and implementation 

will be presented in this poster. 

The U.S. National Geologic Map 

Database 

By David R. Soller1, Harvey Thorleifson2, and Nancy Stamm1 

1

U.S. Geological Survey, Reston, Va. 

2

Minnesota Geological Survey, University of Minnesota, St. Paul, Minn. 

Since the mid-1990s, the U.S. National Geologic Map 

Database (NGMDB, http://ngmdb.usgs.gov) project has systematically addressed its Congressional mandate to develop 

scientific and technical standards and to provide a national 

archive of geoscience map information. Under this mandate in 

the Geologic Mapping Act of 1992, the U.S. Geological Sur

vey (USGS) and the Association of American State Geologists 

(AASG) have made significant advances in the design and 

development of the NGMDB. (See yearly reports of progress 

at http://ngmdb.usgs.gov/Info/reports/.) 

The NGMDB’s goal is to help users find the information 

they need to address a variety of societal and research applications. Because our users’ range in interest and expertise, from 

the general public to the geologic mappers and geographic 

­

Geoinformatics 2007—Data to Knowledge 7 

information systems (GIS) specialists who prepare maps and 

databases, the NGMDB project began in 1996 by building a 

set of fundamental resources and databases that include the 

following: (1) a careful and responsive customer service capability; (2) a comprehensive geoscience map catalog of nearly 

79,000 products by 350 publishers; (3) the U.S. Geologic 

Names Lexicon (“GEOLEX”), a standard reference for the 

Nation’s stratigraphic nomenclature; and (4) proceedings from 

the 10 annual Digital Mapping Techniques (DMT) workshops. The DMT workshops have provided a unique venue for 

discussion of map and database-preparation techniques and 

Web delivery of geospatial information, and have facilitated 

convergence toward common practices and toward science 

and technical standards for the geosciences. These resources 

receive about 140,000 visits per month from 35,000 users. 

Society, businesses, and private citizens commonly are 

faced with complex, multidimensional issues; in order to 

facilitate the use of geologic information and its integration 

with other types of information (for example, soils, engineer

ing, hydrologic, and cultural), it must be presented in a form 

that is readily comprehensible to the “nongeologist.” In other 

words, the presentation of geologic information must, to some 

extent, be standardized. Geological survey agencies produce 

individual maps, reports, and datasets in a wide variety of 

formats and layouts, each containing specialized scientific 

terminology. Without a doubt, these have proven immensely 

valuable to our users; however, with the advent of GIS and 

Web services, our users demand access to a more integrated, 

comprehensive set of geologic maps and reports. With this in 

mind, the NGMDB project has extensively collaborated with 

U.S. and Canadian agencies to develop essential standards; 

these include the North American Data Model (NADM) 

geologic map data model and lithologic terminologies, and 

the Federal Geographic Data Committee (FGDC) standard for 

map symbolization and for specifying the locational accuracy 

of mapped features. NGMDB also participates in the development of the emerging international standard for exchange of 

spatial geoscience information (Geoscience Markup Language; GeoSciML). 

A principal goal for the NGMDB project is to design and 

build a database of richly attributed, standardized geospatial 

information in vector and raster formats. That database is 

intended to be a distributed system, with nodes hosted by the 

State geological surveys and the USGS, and integrated with 

the existing NGMDB databases described above. Because of 

this project’s requirement to build a national archive that can 

contain geoscience maps from all geological surveys in the 

U.S., a preparatory period was required in order to (1) fully 

discuss among the many participating agencies the technical 

and scientific options for building such a database; (2) forge 

a collaboration among these agencies; and (3) agree upon the 

necessary standards. During this process, essential concepts 

were prototyped in order to support discussion on how to 

proceed (see http://pubs.usgs.gov/of/2001/of01-223/soller2. 

html and http://pubs.usgs.gov/of/2002/of02-202 /). These 

concepts included the requirement for the NGMDB system to 

­
 Geoinformatics 2007—Data to Knowledge 

enable spatial analyses that combined data from the NGMDB 

and other types of databases; this was viewed as essential to 

the wider dissemination and use of geologic information by 

nongeologists who may need, for example, to compare and 

analyze demographic, hydrologic, engineering, and geologic 

data. The project is now building a prototype database and 

portal for this distributed system; we anticipate it will be publicly available in 2007. 

A Data Integration and Interoperability 

Blueprint for USGS 

By Kevin T. Gallagher1, R. Sky Bristol2, 

and Linda C. Gundersen1 

1

U.S. Geological Survey, Reston, Va.
 

2

U.S. Geological Survey, Denver, Colo.
 

Overview 

The U.S. Geological Survey (USGS) Geospatial Information Office is currently leading efforts to develop a long-term 

plan or “blueprint” for data integration, accessibility, discovery, and interoperability across the USGS. The data integration 

blueprint (fig. 1) will include projects that provide metadata, 

data content standards, infrastructure, and informatics that 

enhance scientific techniques, improve data access, provide 

management visibility, advance the strategic directions of the 

USGS science strategy, and connect the USGS to its partners 

and collaborators through participation in international efforts 

to develop a global science and computing platform for the 

21st century. The plan will be comprehensive by design, incor

porating the data integration and scientific tools development 

efforts of all USGS into a single framework with common 

practices and a seamless infrastructure. The USGS will work 

with its partners and national and international cyberinfrastructure activities to develop this framework. 

Background 

The USGS is a world leader in monitoring, assessment, 

and research related to the natural sciences. Coupled with a 

diverse multidisciplinary workforce, extensive monitoring networks, and national- and regional-scale approaches, the USGS 

has carved out a reputation for being the “authoritative source” 

of specific national datasets, such as water quality, cartographic bases, land cover and land use, biological resources, 

and geologic mapping. As the future unfolds, the USGS’s 

ability to map and integrate this data will be critical for the 

advancement of all science directions. Some of the major mission activities that USGS engages in include the following: 

•	 Collect and maintain long-term national and regional 

geologic, hydrologic, biologic, and geographic databases. 

­

Figure 1. Schematic of a service-oriented architecture for integration of USGS data. 
Geoinformatics 2007—Data to Knowledge  

•	 Collect, process, and analyze earth and planetary imagery and remote sensing. 

•	 Develop open-source models of complex natural systems and human interaction with those systems. 

•	 Maintain national and global geologic, biologic, hydrologic, and geographic monitoring systems. 

•	 Archive and preserve physical collections of earth 

materials, biologic materials, reference standards, geophysical recordings, and paper records. 

•	 Develop standards of practice for the geologic, hydrologic, biologic, and geographic sciences. 

The USGS maintains a large number of science datasets 

at local, regional, and national scales. The USGS ability to 

integrate this data is critical to the achievement of Department 

of Interior (DOI) mission objectives in resource protection, 

resource use, and serving communities, and the USGS national 

Federal mission of conducting science and serving earth and 

biological data. Development of a fully integrated science data 

environment will improve the accessibility of science data and 

information within the USGS, across the DOI, and with its scientific partners, collaborators, and customers in other Federal 

agencies and the public. Greater access to a broad range of 

integrated science data will spark new discovery and support 

a wider range of inquiry, better informing and enhancing the 

decisionmaking of managers, policymakers, and stewards of 

the Nation’s resources. 

Examples of some of the long-term national datasets 

maintained by USGS include the following: 

•	 The National Map (topography, orthoimagery, hydrography, and so on); 

•	 MRDATA (comprehensive source of mineral resource 

data); 

•	 The National Geologic Map Database (a standardized 

community collection of geologic mapping); 

•	 NWISWeb (the National Water Information System); 

•	 The National Geochemical Database (collection of 

rock data, stream-sediment data, and data on other 

materials analyzed by the USGS); 

•	 National Geophysical Database (aeromagnetic, gravity, 

and aeroradiometric data); 

•	 National and Global Earthquake catalogs; 

•	 North American Breeding Bird Survey; 

•	 National vegetation and speciation maps; 

•	 National Oil and Gas Assessment; and 

•	 National Coal Quality Inventory. 

The conduct of science is changing worldwide. There is 

widespread recognition that the Earth’s complex natural systems are interrelated and that scientific inquiry must be equally 

integrated to develop new understanding of the implications 

for the environment, land management, resource utilization, 

and policymaking. Complex scientific questions require the 

analysis, integration, and modeling of science data and information from multiple disciplines, locations, and timeframes. 

The USGS and its partners, including industry, Federal, State, 

and local governments, universities and associations, as well 

as international scientific organizations, are beginning to 

connect and integrate the data and research techniques of the 

world’s scientists, making them accessible to a global science 

community and transforming the way in which research, engineering, and education are conducted. Science data integration 

within the USGS is a prerequisite for joining these international efforts to develop a worldwide science collaboration 

and computing platform that can address future environmental 

science challenges. 

For example, phenology is the study of periodic plant and 

animal life cycle events that are influenced by environmental 

changes, especially seasonal variations in temperature and precipitation driven by weather and climate. Phenological events 

record—immediately and empirically—the consequences of 

environmental variability and change vital to the public interest. Variability in phenological events, such as the beginning 

of the growing season, can have important environmental and 

socioeconomic implications for the economy, health, recreation, agriculture, management of natural resources, and natural hazards. Although phenology is a far-reaching component 

of environmental science, it is not well understood. The predictive potential of phenology requires a new data resource—a 

national network of integrated phenological observations. A 

USA National Phenology Network (USA-NPN) is currently 

being designed and organized to engage Federal agencies, 

environmental networks and field stations, educational institutions, and mass participation by citizen scientists. The initial 

phase will establish a continental-scale network focused on 

phenological observations of a few regionally appropriate 

native plant species and nationally cultivated indicator plants. 

The USGS must not only integrate its scientific data to support 

this effort, but must also integrate data from other monitoring activities, such as water availability and soil chemistry, 

to inform larger national issues, such as climate change and 

ecosystems restoration. 

Some of the national and global monitoring systems that 

the USGS maintains include the following: 

•	 National Stream Flow Information Program; 

•	 Advanced National Seismic System; 

•	 National Volcano Early Warning System; 

•	 Debris Flow Warning System; 

•	 Global Terrestrial Network for Permafrost; 

•	 Landsat 5 and 7; 

•	 Biomonitoring of Environmental Status and Trends; 

•	 National Bird Banding Program; 

•	 Land Cover/Land Change Monitoring; 

•	 Famine Early Warning System; and 

•	 National Water Quality Assessment Program. 

Long-Term Vision 

In 2006, the Director of the USGS chartered a team to 

develop a new USGS science strategy. That strategy, entitled 

“Facing Tomorrow’s Challenges: USGS Science in the Coming Decade,” was released in April 2007 and includes six 

major science goals and a special chapter on “New Methods 

of Investigation and Discovery” that provides the following 
0 Geoinformatics 2007—Data to Knowledge 

long-term vision for USGS data integration: The USGS supplies an information environment where diverse and distributed knowledge is accessed and used seamlessly by scientists, 

collaborators, customers, and the public to address complex 

natural science issues. 

The USGS science strategy also lays out the following 

strategic actions to accomplish this long-term vision: 

•	 Incorporate planning for long-term data management 

and dissemination into multidisciplinary science practices. 

•	 Adopt and implement open data standards within 

USGS and contribute to the creation of new standards 

through international standards communities. 

•	 Develop and implement a comprehensive scientific 

cataloging strategy that incorporates existing datasets, 

resulting in an integrated science catalog. 

•	 Develop a sustainable data-hosting infrastructure to 

support the retention, archiving, and dissemination 

of valuable USGS datasets in accordance with open 

standards. 

•	 Develop and enhance tools and methods that facilitate 

the capture and processing of data and metadata. 

•	 Identify and support authoritative data sources within 

USGS programs and encourage development and 

adoption of standards. 

•	 Build and strengthen the internal workforce augmented 

by external partnerships in environmental information 

science. 

•	 Identify and leverage national and international efforts 

that promote comprehensive data and information 

management and foster greater sharing of knowledge 

and expertise. 

•	 Partner with collaborators and customers to facilitate 

data integration across the worldwide science community. 

•	 Partner with collaborators and partners in the development of informatics tools and infrastructure that 

contribute to the evolving global science computing 

and collaboration platform. 

The last three strategic actions are key to successful 

creation of an international cyberinfrastructure for the sciences. One of the ways to achieve this collaboration is through 

the creation and participation in “communities of practice.” 

A community of practice is not merely a community with a 

common interest, but it comprises practitioners who share 

experiences and learn from each other. They develop a shared 

repertoire of resources: experiences, stories, tools, vocabularies, and ways of addressing recurring problems. This takes 

time and sustained interaction. Standards of practice and reference materials will grow out of this experience; however, the 

critical benefits include the following: creating and sustaining 

knowledge; leveraging of resources; and rapid learning and 

innovation. 

Constructing an International 

Geoscience Interoperability Testbed to 

Access Data from Distributed Sources: 

Lessons Learned from a GeoSciML 

Testbed 

By O. Raymond1 and Interoperability Working Group 

GeoSciML2 

1

Geoscience Australia, Canberra, Australia 

2

Commission for the Management and Application of Geoscience Information, International Union of Geological Sciences (IUGS) 

Introduction 

Geoscience data are being generated at exponentially 

increasing volumes, and it is no longer feasible to develop centralized warehouses from which data are accessed. Efficient 

access to such data online in real time from distributed sources 

is rapidly becoming one of the major challenges in building 

cyberinfrastructures for the earth sciences. 

Extensible Markup Language (XML) coupled with 

Web-based data delivery is a proven technology which allows 

access to standardized data “on the fly” via the Internet. 

GeoSciML (Geoscience Markup Language) is a geoscience 

specific, XML-based, GML (Geography Markup Language) 

application that supports the interchange of geoscience information. It has been built from various existing geoscience data 

model sources, particularly the North American Data Model 

(NADM) and Extensible Mining Markup Language (XMML). 

It is being developed through the Interoperability Working 

Group of the Commission for the Management and Application of Geoscience Information (CGI), which is a commission 

of the IUGS. The working group (currently) consists of geology and information technology specialists from agencies in 

North America, Europe, Australia, and Asia. 

The GeoSciML Testbed 

In 2006, representatives from geological surveys in the 

United States, Canada, the United Kingdom, France, Sweden, 

and Australia came together to develop a testbed that would 

utilize GeoSciML to access globally distributed geoscience 

map data (Duffy and others, 2006). 

Data was served from seven sites in six countries with 

several different Web Feature Service/Web Map Service 

(WMS/WFS) software solutions employed. Geological surveys in Canada, the United States, and Sweden used an Enviromental Systems Research Institute ArcIMS platform (and 

in one case a MapServer platform) with a Cocoon wrapper to 

handle queries and transformations of XML documents. The 

British and Australian Geological Surveys employed the open­
Geoinformatics 2007—Data to Knowledge 1 

source GeoServer software to serve data from ArcSDE and 

Oracle sources. The French Geological Survey implemented a 

system using an Ionic RedSpider server for WMS and client, 

and a custom development to implement a WFS. Web clients 

were constructed in Vancouver, Canada, using Phoenix, and 

later in Canberra, Australia, using Moxi Media Internet Mapping Framework (IMF) software to test various use case for 

the WMS/WFS services. Generic Web clients, such as Carbon 

Tools’ Gaia 2, were also used to test some use cases. 

In addition to geologic map data, the testbed also demonstrated the capacity to share borehole data as GeoSciML. Two 

WFSs (French and British) provided borehole data to a client 

able to display the borehole logs. 

System (Open Geospatial Consortium) 

Compliance 

There are three important things to consider when establishing an Open Geospatial Consortium (OGC)-compliant 

interoperability testbed—compliance, compliance, and compliance; however, working at the cutting edge of WFS implementation in the GeoSciML Testbed strained existing WFS 

software implementations to the breaking point. Approaching 

the deadline of the public release of the Testbed, rigorous 

OGC standards compliance became an unrealistic goal. The 

focus of the project necessarily turned from OGC standards 

compliance to ensuring a useful degree of data exchange, 

including display, download, and some simple query functionality. 

In comparison with other data types that have successfully used OGC services, GeoSciML deals with extremely 

complex data; thus, although the GeoSciML Testbed did prove 

that it is possible to make geoscience interoperable, it also 

showed that semantic compliance is not going to be a trivial 

exercise, particularly for the more descriptive components of 

earth sciences. 

Proprietary vendor and open-source software that aims 

to fully support the detail of OGC Web-service specifications 

is still at the developmental stage. The complexity of both the 

WFS query framework and the XML implementation model 

make implementation of such software an onerous task. It may 

eventually be found that WFS as a generic query framework 

over an XML model of GeoSciML’s complexity is not achievable; however, this can only be tested by presenting the OGC 

standards with well conceptually modeled schema in a real 

domain, such as geoscience’s GeoSciML. 

Support for a subset of WFS services was achieved in 

the GeoSciML Testbed, but there is no standard mechanism to 

expose or describe the set of functionality that is implemented. 

In time, vendor and (or) open-source software will likely provide more rigorous and powerful WFS software implementations. The GeoSciML Testbed proved an effective mechanism 

to push further development of software capability in this area. 

Semantic Compliance 

The GeoSciML Testbed highlighted firstly the importance 

of strict compliance to standard vocabularies of controlled 

concepts for true interoperability, and, secondly, the complexity of the concepts that we were tying to standardize and make 

readable by computers. Humans easily cope with a degree of 

fuzziness in data structures or ontologies. It is in our nature 

that many geologists cannot see the problem with attributing a 

sandstone as “cross bedded” or “cross-bedded;” however, it is 

vital to computer-based queries of digital data. 

A lot of work is still to be done (and is underway) in 

the vocabulary arena to make data exchange and query more 

interoperable. A geologist knows that an “igneous extrusive” 

rock and a “volcanic” rock are the same thing, but a computer 

searching for volcanic rocks will not find rocks coded as 

“igneous extrusive” unless rules of equivalence and hierarchy 

are established in complex vocabularies. As with many other 

international initiatives for sharing information, the multilingual aspect has to also be taken into account in any vocabulary 

development. 

So, you have a data model. It is entirely, scientifically 

logical and robust, with complex hierarchical structure and 

vocabularies to accommodate your complex and hierarchical 

data, but, just how practically interoperable is it? 

Participants in the GeoSciML WFS Testbed all provided 

information on the age of the geological units that they served; 

however, the schematic flexibility of the GeoSciML data 

model allows services to provide their age information in fully 

compliant, yet in slightly different ways—as single terms, as 

multiple hierarchical terms, and as maximum and minimum 

terms. This meant that querying and reclassifying the data 

based on age information had to be done differently on each 

dataset without the ability to apply a single standard query to 

all the GeoSciML datasets. 

Usability issues such as these will only be solved with 

the increasing maturity of emerging complex scientific spatial 

data models like GeoSciML. Use cases for data models and 

WFS services must be developed recognizing the capabilities 

of existing and future WFS/WMS and GIS software, as well 

as scientific user needs. However, as the GeoSciML Testbed 

showed, some of the limiting factors in a cutting edge project 

do not become apparent until the project is well underway. 

Implications of the GeoSciML WFS Testbed 

While WFS/WMS standards, data models and supporting 

software are still being developed, demonstrator projects such 

as the GeoSciML Testbed are vital to the progress of interoperability to show users that the technology can deliver access 

to distributed data sources in real time. Further testbeds for 

GeoSciML will result in more robust and functional WFS/ 

WMS services that will become mainstream data delivery services in the near future. Above all, this testbed highlighted the 

complexity of geoscience data and showed that strict adher­
2 Geoinformatics 2007—Data to Knowledge 

ence to controlled vocabularies is essential to making Geoscience data semantically interoperable. 

Reference Cited 

Duffy, T., Boisvert, E., Cox, S., Johnson, B.R., Raymond, O., 

Richard, S.M., Robida, F., Serrano, J.J., Simons, B., and 

Stolen, L.K, 2006, The IUGS-CGI International Geoscience 

Information Interoperability Testbed, International Association for Mathematical Geology, Eleventh International 

Congress, Liege, Belgium. 

The GEON LiDAR Workflow as a 

Distribution Pathway for Community 

LiDAR Topography Datasets 

By Christopher J. Crosby1, Frank Efrat2, J. Ramon Arrowsmith1, 


Viswanath Nandigam2, Han Suk Kim3, Jeffrey Conner1, 


Ashraf Memon2, Chaitan Baru2, and Newton Alex1
 

1

School of Earth and Space Exploration, Arizona State University, Tempe, 

Ariz. 

2

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

3

Department of Computer Science and Engineering, University of California—San Diego, La Jolla, Calif. 

Due to the growing awareness of the power of high-resolution topography from light detection and ranging (LiDAR) 

data for earth-science research, it is becoming increasingly 

common for these datasets to be acquired as a community 

resource. The forthcoming GeoEarthScope LiDAR topography acquisition is an excellent example of this trend. The 

GeoEarthScope acquisition offers an unprecedented opportunity to examine the strain field adjacent to major active faults 

in the western United States at time scales greater than those 

provided by the Plate Boundary Observatory geodetic instrumentation. Modeled after the recent B4 data acquisition, the 

GeoEarthScope LiDAR data is expected to provide digital 

elevation models (DEMs) of one meter or better spatial resolution with scientific-grade geodetic accuracy. These datasets 

will be exceptionally valuable for geologic slip rate studies, 

paleoseismic research, and as a pre-earthquake representation of the landscape should an event occur in the near future. 

These datasets will be utilized extensively and they must be 

available to the EarthScope community as quickly and as easily as possible. 

Traditionally, access to community LiDAR datasets has 

been difficult because of the massive volumes of data gener

ated by LiDAR technology. For example, the recently acquired 

B4 dataset covers nearly 1,000 kilometers of the southern San 

Andreas and San Jacinto faults and contains approximately 

3.7 billion individual LiDAR returns. With the B4 dataset 

­

as a model, the tremendous volume of data generated by the 

forthcoming GeoEarthScope LiDAR acquisition effort could 

potentially be a significant barrier for user-community access 

and processing of these data. 

In order to address the challenges posed by the distribution and processing of community LiDAR datasets, we have 

applied a geoinformatics approach that capitalizes on cyberinfrastructure developed by the GEON project (http://www. 

geongrid.org). The Internet-based resource we have developed, 

the GEON LiDAR Workflow (GLW), is designed to democratize access to these challenging datasets and provides tools to 

enable users to perform basic processing (for example, DEM 

generation) of the data. As a proof of concept, we have made 

four community LiDAR datasets available, including the B4 

data, via the GLW. Our approach utilizes a comprehensive 

workflow-based solution that begins with user-defined selection of a subset of point data and ends with download and 

visualization of DEMs and derived products. In this workflow, 

users perform point cloud data selection, interactive DEM 

generation and analysis, and product visualization, all from 

an Internet-based portal. This approach allows users to carry 

out computationally intensive LiDAR data processing without 

having appropriate local resources. 

In its proof-of-concept capacity, the GEON LiDAR 

Workflow has proven to be a valuable and innovative community gateway for accessing LiDAR topography. As of April 

2007, the GLW had 126 users who processed over 10.5 billion 

LiDAR returns in 1,250 unique processing requests. 

As a result of this success, the GLW has been selected as 

the distribution pathway for the forthcoming GeoEarthScope 

LiDAR datasets. In order to prepare for these new data, we 

are currently in the process of migrating the system from its 

current proof-of-concept implementation to a fully robust, 

production-level, community data portal. As part of this 

migration, we are working on a number of enhancements that 

include improving system stability, documentation, and portal 

usability, as well as adding processing capacity and providing 

new job-monitoring and job-archiving capability. 

The distribution of GeoEarthScope LiDAR topography 

via the GEON LiDAR Workflow represents an excellent 

example of the utilization of cyberinfrastructure to facilitate 

access to computationally challenging community datasets. 

Integrating Geologic Data in the 

NGMDB, a Standards-Based Approach 

Using GeoSciML and Open-Source 

Tools 

By David Percy1, Stephen Richard2, David Soller3, and 

Jon Craigue4 

1

Department of Geology, Portland State University, Portland, Oreg. 
Geoinformatics 2007—Data to Knowledge  

2

Arizona Geological Survey, Tucson, Ariz.
 

3

U.S. Geological Survey, Reston, Va.
 

4

University of Arizona and U.S. Geological Survey, University of Arizona, 


Tucson, Ariz. 

The National Geologic Map Database “phase three” 

prototype builds substantially on the project’s 10-year effort to 

develop standards (for example, contributions to development 

of the North American Data Model, or NADM) for a common 

data structure and controlled science terminology for geologic 

map data. Recently, this effort has been incorporated into an 

international standard proctored by the IUGS Commission for 

the Management and Application of Geoscience Information 

(CGI) with the resulting Open Geospatial Consortium (OGC) 

GML-derived standard, GeoSciML. 

GeoSciML is a transport mechanism and schema 

developed under the auspices of the CGI and demonstrated 

successfully in late 2006 at the International Association for 

Mathematical Geology conference in Liege, Belgium. In that 

demonstration, geologic databases from worldwide participants were queried by a desktop client (not browser-based) to 

show a consistent set of geologic data across disparate datasets 

from different countries or agencies. 

The phase three prototype of the NGMDB integrates data 

from Arizona, the Pacific Northwest (Oregon, Washington, and 

Idaho), as well as from several national datasets. Additionally, 

we demonstrate interoperability with other standards-based 

services, such as the Natural Resources Conservation Service’s 

(NRCS) soils database and National Aeronautics and Space 

Administration’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. 

The prototype is enabled by a custom-built data-import 

tool that allows for matching data in fields from an input-database source, such as Oregon or Washington geologic compilations, to a database schema used as a back end for the Web 

map service (U.S. Geological Survey’s National Geologic Map 

Database-lite or NGMDB-lite). The NGMDB-lite schema is 

a flat-file view of a subset of data from the NGMDB database 

design (Richard and others, 2004). Fields in the input table 

are matched to corresponding fields in NGMDB-lite. Subsequently, unique values from each input field are matched to 

corresponding terms in controlled vocabularies defined by the 

NGMDB. 

After matching fields and terms from the input map 

database to the NGMDB-lite schema and vocabulary, a fully 

attributed Enviromental Systems Research Institute shape file 

is generated. This shape file is then appended to the an aggregated shape file master table for display in an open-source 

mapping framework developed at Portland State University 

and managed on SourceForge as the project Map-Fu (http:// 

sourceforge.net/projects/map-fu/). 

Map-Fu is an Asynchronous Java Script and XML 

(AJAX)/Asynchronous Java Script and JSON (AJAJ)-implemented browser front end to map data that uses JavaScript 

and PHP/Mapscript to interact with individual users to handle 

requests for map data. It includes specific tools for zooming, 

panning, querying, and rendering individual map layers. It is a 

“thick client” that runs in all modern Internet browsers; thus, it 

is inherently cross-platform. 

On the server side, we implement an open-source stack 

that consists of Mapserver and PostGIS (a set of geographic 

information systems extensions for PostgreSQL, a mature 

open-source object-relational database) running atop Apache 

and Linux. Mapserver is configured via map files to display 

and symbolize map data from shape files for quick response 

to user requests. PostGIS is used to answer queries by the user 

and to enable interaction with the database so that registered 

experts can update the database through a GeoWiki. 

The GeoWiki allows users to draw points or polygons 

on a map interface and update information in the database 

on paleontological, engineering properties, hydrogeology, 

or general comments. This facilitates a wider community’s 

participation in making this resource useful for an even larger 

user group. Expert’s data are stored directly in the NGMDB 

table structure. 

As a proof of concept for integrating multiple datasets 

stored locally or remotely, we serve a local dataset that is compiled from all of the above-named sources (with the exclusion 

of Oregon) from a shape file located on the server at Portland 

State University. We have configured the Oregon data as a 

Web Feature Service (WFS) that responds to requests from 

remote servers. This is stored as a reference in the mapfile 

as if it were a remote source, and displayed along with the 

other data simply as another layer. Requests for GeoSciML 

that cross boundaries of local versus “remote” services (for 

example, the border of Washington and Oregon) still return 

standardized fields and science terminology. 

This system is a model for aggregating multiple datasets from many agencies. Some organizations have sufficient 

resources to set up a WFS server and maintain their own 

GeoSciML-compliant data which can be integrated into our 

system. Some organizations lacking these resources, however, 

could simply provide the data to our project for hosting on the 

NGMDB site. A third option is to allow organizations access 

to a virtual server on our system, where they can have their 

own subdomain (for example, id.ngmdb.us - Idaho) and manage their own data as if it existed on their own local server. 

Each of these three scenarios is mediated by our custom Data 

Import Tool, which allows the expert geologist to map their 

data fields for a region to a common schema and the unique 

values contained within to controlled science terminology of 

the NGMDB. 

The end user’s process of data discovery and use will be 

further enabled by the ability to overlay standards-compliant 

WFS and WMS (or generically OWS) services in our mapping 

framework; however, since all of our services will be broadcast as OWS services, any other agency or organization could 

create their own “mashup” with data of their own choosing 

in whatever client they choose, including proprietary desktop 

clients such as ArcExplorer, or any of a number of clients, 

including virtual Earths that will be proliferating in the near 

term. 
 Geoinformatics 2007—Data to Knowledge 

Reference Cited 

Richard, S.M., Craigue, J., and Soller, D.R., 2004, Implementing NADM C1 for the National Geologic Map Database, in 

Soller, David R., ed., Digital Mapping Techniques ‘04—­

Workshop Proceedings: U.S. Geological Survey Open-File 

Report 2004-1451, p. 111-144. (Also available online 

at http://pubs.usgs.gov/of/2004/1451/pdf/richard.pdf.) 

(Accessed June 22, 2007.) 

Geospatial Interoperability: From 

Sensors to Decision Support 

By George Percivall1 

1

Open Geospatial Consortium (OGC), Crofton, Md. 

Geospatial interoperability standards are increasing the 

discovery, access, and use of sensed data in research and 

applications. It is critically important for informed decisions 

that the vast data and processing resources of the geosciences 

community become available as part of the Web. 

The Open Geospatial Consortium (OGC) has developed 

Web-based interoperability extending from sensors to decision support services. OGC interoperability for sensors builds 

on the OpenGIS® Web Map Service (WMS), Web Feature 

Service (WFS), and Web Coverage Service (WCS). It is now 

practical to fit sensors of virtually any type or connection to 

the Web. They can be controlled through open interfaces, and 

their data can be output for an array of uses. 

This paper reviews previously developed OGC Web 

Services to set the stage for description of OGC’s current 

development. Three OGC developments are reviewed: Sensor 

Web Enablement (SWE), Geo-Processing Workflow, and GeoDecision Support Services (GeoDSS). These three developments provide the specifications necessary for the acquisition, 

processing, and tailoring of sensor data using Web services. 

The OGC is an international voluntary consensus standards organization of more than 300 companies, government 

agencies, and universities. OGC members participate in a 

consensus process to develop publicly available geoprocessing interface and encoding standards that enable integration 

of geospatial content and services into enterprise systems 

and that “geo-enable” the Web, wireless and location-based 

services, and mainstream information technology. 

The OGC documents described in this article are available at http://www.opengeospatial.org/specs/. 

OGC Web Services 

OGC Web Services (OWS) are open standards for geospatial interoperability on the Internet. The OWS specifications and architecture have been developed by the members of 

the OGC in an interoperability test and development program 

and adopted in a consensus specification program. Following 

are some of the adopted OpenGIS implementation specifications that are most relevant to Web-based interoperability, 

extending from sensors to decision support services: Web Map 

Service (WMS), Web Feature Service (WFS), Web Coverage 

Service (WCS), Web Map Context (WMC), Catalog Service 

(CAT), and Geography Markup Language (GML). 

Previously developed OGC Web Services, such as WMS, 

WCS, and WFS, are now in the marketplace. Hundreds of 

thousands of map layers are available from WMSs around 

the world. Just as the World Wide Web opened up a whole 

new information space, OGC Web services opens up a vastly 

expanded geospatial Web. 

Sensor Web Enablement 

Members of the OGC are building a unique and revolutionary open platform for exploiting Web-connected sensors. 

For example, such sensors and devices include the following: flood gauges, air pollution monitors, stress gauges on 

bridges, mobile heart monitors, Webcams, and satellite-borne 

earth-imaging devices. In much the same way that HTML and 

HTTP standards enabled the exchange of any type of information on the Web, the OGC Sensor Web Enablement (SWE) 

Initiative is focused on developing standards to enable the 

discovery and exchange of sensor observations, as well as the 

tasking of sensor systems. 

SWE standards that have been built and prototyped by 

members of the OGC include the following pending OpenGIS 

specifications: Observations & Measurements (O&M), Sensor 

Model Language (SensorML), Transducer Model Language 

(TML), Sensor Observation Service (SOS), Sensor Planning 

Service (SPS), Sensor Alert Service (SAS), and Web Notification Service (WNS). 

The sensor Web standards infrastructure defined by these 

specifications constitutes a revolution in the discovery, assessment, and control of live data sources and archived sensor 

data. The goal is to make all types of Web-resident sensors 

discoverable, accessible, and where applicable, controllable 

via the World Wide Web. 

GeoProcessing Workflow 

Making sensor data widely available through the Web 

will allow better-informed decisionmaking. In many cases the 

sensor data must be processed to create the information specifically relevant to a decision. OGC has defined and initially 

developed a set of geoprocessing services that extract context-specific information from sensor data. Typically, several 

geoprocessing services must be applied to sensor data. Such 

chains of services define workflows that can be automated for 

reuse in a variety of communities. OGC has applied workflow technologies to the automation of geoprocessing service 

chains to support decisions based on sensor data. 
Geoinformatics 2007—Data to Knowledge  

Recent OGC Interoperability Program Initiatives have 

developed workflow for decision support. The Web Processing Service (WPS) is a generic interface for multiple types of 

geoprocessing. Several types of WPSs were used to process 

data from WFS and WCS access services. This service chain 

was automated using the Business Process Execution Language (BPEL). 

OGC continues to develop geoprocessing services and to 

refine the practice of chaining the services using BPEL. The 

chaining of geoprocessing services is one method to create 

geospatial information to better inform critical decisionmaking. The ultimate challenge is to enable the geographic imagery collected from different sources to become an integrated 

digital representation of the Earth, widely accessible for 

humanity’s critical decisions. 

Geospatial-Decision Support Services (GeoDSS) 

Traditionally, decision-support systems have been monolithic applications that run on workstation class computers. 

Decision Support Services (DSS) extends this previous body 

of work into the distributed services environment. In the Geospatial Decision Support Services (GeoDSS) Initiative, open 

specifications are being developed that enable decisionmakers to integrate geospatial data and services from a variety of 

sources into the operating environment that best supports optimal decisionmaking. Elements of GeoDSS include the following: Schema Tailoring and Maintenance, GeoSemantic Web, 

Symbology Management, and GeoDSS Integrated Client. 

The key concept of GeoDSS is that a decisionmaker is 

able to sit down at a single workstation, identify any resource 

anywhere, access that resource, bring it into their operational 

context, and integrate it with other resources to support the 

decision process. All of this takes place in a global enterprise 

made up of many different organizations and many different 

information communities. 

It is critically important for informed decisions that the 

vast data and processing resources of the geosciences and 

remote-sensing community become available as part of the 

Web. Application communities need Internet-enabled, interactive, and interoperable access to data from observations and 

models for decision support systems. Bringing data archives 

and active remote sensors online supports the flow of geospatial information supporting policymakers, operational managers (including governments at all levels), and the citizens of 

our world. 

Achieving Interoperability in 

Geosciences 

By David K. Arctur1, Phillip C. Dibner2, and David Schell3 

1

OGC Interoperability Institute, Austin, Tex. 

2

Ecosystem Science Programs, OGC Interoperability Institute, Los Altos, 

Calif. 

3

OGC Interoperability Institute, Wayland, Mass. 

Introduction 

Much has been said in the geoinformatics community 

about the need for interoperability. There are many scientific 

research centers compiling significant data holdings, and many 

of these recognize the need to bridge between multiple scientific disciplines. For example, at a recent National Science 

Foundation (NSF) workshop—Building a National Geoinformatics System (National Science Foundation, 2007)—the 

following was stated by Chris Paola, director of the National 

Center for Earth-Surface Dynamics (NCED): “Important, and 

often costly decisions concerning land management, restoration, and subsurface resources rely on outdated science and 

reasoning by analogy rather than process-based analysis. The 

necessary science comprises elements of geomorphology, 

ecology, hydrology, sedimentary geology, engineering, social 

sciences, and geochemistry, but is not any one of these. The 

foundation of a useful science of Earth-surface dynamics must 

be synthesis across disciplines and scales, and quantitative 

prediction.” 

Similarly, D.A. Miller of the Center for Environmental 

Informatics at The Pennsylvania State University describes the 

“Critical Zone, defined by the outer limits of vegetation and 

the lower boundary of ground water, which reflects a complex interplay between the physical, chemical, and biological 

realms and has become the focus of research among a community of scientists derived from disciplines including, but 

not limited to, ecology, soil science, biology, geochemistry, 

hydrology, and geomorphology. A currently funded NSF 

investigation at Penn State is building tools and infrastructure to promote interdisciplinary research in a developing 

consortium known as the Critical Zone Exploration Network 

(CZEN). CZEN is envisioned as a network of sites, people, 

tools, and ideas...” 

These are just two of a very broad and impressive set of 

research initiatives including EarthRef, Geosciences Network, 

National Ecological Observatory Network, and many others that are, within their respective communities, seeking to 

bridge the differences in classification systems and ontologies, semantics, spatial-temporal scales and reference systems, tools, processes, and other barriers that have inhibited 

interdisciplinary collaboration and integrative research. This 

is clearly a substantial effort for any one research center or 

consortium to undertake, and it is not yet complete. The dream 

and goal remain to carry out integrative studies across multiple 

scientific data centers and consortia, comprising the broadest possible range of disciplines. This is essential if we are to 

understand, for example, the causes and implications of climate change. Facing and coming to terms with the barriers to 

interoperability will also lead to more useful and robust spatial 

data infrastructures (SDI) among regional, national, and global 
 Geoinformatics 2007—Data to Knowledge 

agencies, which will, in turn, greatly improve the ability of our 

governments and other organizations to respond to natural and 

human-caused disasters and emergencies. It is no exaggeration 

to say that the stakes are high, and that no collection of geosciences research data can be considered exempt from the need to 

become interoperable with other scientific data for the purpose 

of interdisciplinary, integrative analysis. 

Steps Toward Interoperability 

The particular challenge here is to provide for unfettered exchange of information among a great many scientific 

endeavors, while still accommodating the unfettered requirements of investigators to organize and present their data as 

per the standards of their own research communities. Fortunately, such advances are being made. Many consortia have 

adopted the cause of interoperability within their subject 

domains. A growing number of research centers are striving 

now to stimulate interdisciplinary research. Bodies of research 

data that can be compiled through substantially automated 

means, such as with satellite imagery, synthetic radars, sensor 

networks, and so on, are often designed around large-scale 

database architectures that lend them to interoperability. Various common schemas based on XML are being developed 

by different consortia, each with a specific application focus 

to meet their varied needs. For data with location and temporal content, a growing number of community-level schemas 

are based on ISO DIS 19136 Geography Markup Language 

(GML; http://www.opengeospatial.org/standards/gml), such as 

GeoSciML (https://www.seegrid.csiro.au/twiki/bin/view/CGIModel/GeoSciML; also NFS 2007, p. 71-73) and ClimateML 

(http://ndg.nerc.ac.uk/csml/). 

Common sets of interacting Web services are also emerging that enable users to find and use the data they seek. The 

Open Geospatial Consortium (OGC; http://www.opengeospatial.org), which developed GML, has also developed open and 

international specifications for online data catalog services, 

Web mapping services (to transfer data as simple graphic 

images), Web feature services (for scalar and vector data), 

Web coverage services (for gridded or field-type data), and 

services for sensor data. What is important about these specifications are that they define interfaces for data exchange; they 

do not require restructuring of existing databases nor changes 

in custodial policy. 

Functional interoperability among Web services is of 

limited use unless the data provided by these services can be 

understood by the greater audience of users and accommodated by an array of client software programs. In the context 

of the complex, multifaceted investigations that one encounters in interdisciplinary work, it is essential to integrate semantics across multiple disciplines. Fortunately, there has been 

much progress in recent years in developing the means for 

expressing semantic content, and in its application, by a great 

many scientific and other information communities. Welldefined, widely adopted ontologies now exist or are emerging 

in many disciplines. 

But this is not an easy process. Cultivation of a productive consortium and engagement with the right stakeholders is not assured. The OGC, however, has had a number of 

successful collaborative experiments with its Interoperability 

Program. Since its first Interoperability Initiative in 1999, the 

OGC has evolved a process that confronts precisely the challenges outlined in this paper. The process to date has enabled 

collaborations of diverse stakeholders, mashups of data from 

multiple different sources, software development by teams of 

skilled programmers (sometimes from competing software 

vendors working together), and complex analyses using data 

from fields as diverse as atmospheric science, toxicology, 

hydrology, geology, and marine science. 

The OGC Interoperability Institute (OGCII) became 

operational in 2006 to promote the use and benefits of the continuing development of interoperable geoprocessing with the 

broader scientific community, as well as public sector agencies 

and research organizations. In order to accomplish these objectives, OGCII works closely with the OGC Interoperability Program to involve researchers in both public and private sector 

in testbed and pilot activities that develop the techniques and 

specifications that enable interoperability. 

It should not be necessary to mobilize vast new funding 

initiatives to address this issue; considerable funds are already 

in place for developing integrative, interdisciplinary datasets, 

tools, and processes. What is needed is the recognition by both 

the research centers and the national agencies of the importance of semantic, as well as functional interoperability, and 

support for ongoing collaboration to achieve this end. 

Reference Cited 

National Science Foundation, 2007, Building a National 

Geoinformatics System: A Community Workshop, Denver, 

Colo., March 14-15, 2007, available online at http://www. 

geoinformatics.info/Download%20Center.html. 

DIGGS—Data Interchange Standard for 

Geotechnical and Geoenvironmental 

Data 

By Daniel J. Ponti1, Thomas E. Lefchick2, and Marc Hoit3 

1

U.S. Geological Survey, Menlo Park, Calif. 

2

Federal Highway Administration, Columbus, Ohio 

3

Civil and Coastal Engineering, University of Florida, Office of Academic 

Affairs, Gainesville, Fla. 

DIGGS—Data Interchange for Geotechnical and Geoenvironmental Specialists (http://www.diggsml.org)—is a developing international standard interchange format for geotechnical and geoenvironmental data. It is being developed under 

the auspices of the U.S. Department of Transportation Federal 
Highways Administration (FHWA) through a collaboration of 

representatives and researchers from 11 State Departments of 

Transportation, the United Kingdom Highway Agency, U.S. 

Geological Survey, U.S. Environmental Protection Agency, 

U.S. Army Corps of Engineers, University of Florida, the 

Consortium of Organizations for Strong-Motion Observation 

Systems (COSMOS), and the geotechnical software industry. 

The goals of DIGGS are as follows: 

•	 Facilitate data exchange among different databases 

within an agency or organization; 

•	 Enable oversight and regulatory agencies to receive 

data from consultants in a standardized format; 

•	 Facilitate exchange of data among practitioners and 

researchers over the Internet; 

•	 Facilitate data quality assurance and quality control 

and promote preservation of valuable subsurface data 

and metadata; 

•	 Facilitate the exchange of data between software packages and providers; and 

•	 Promote the development of data analysis software 

products that are more standardized and compatible. 

DIGGS consists of an Extensible Markup Language 

(XML) schema (Geographic Markup Language-compliant) 

that defines surface, subsurface, and substructure features and 

the associated geological, geotechnical, geoenvironmental, and 

geophysical data that are obtained from field observations and 

field and laboratory tests. Version 1 of the DIGGS standard 

was developed by reconciling and integrating existing geotechnical and geoenvironmental data dictionaries developed by the 

Association of Geotechnical and Geoenvironmental Specialists 

in the United Kingdom (AGS; http://www.ags.org.uk/aboutus/welcome.cfm), the University of Florida, Department of 

Civil Engineering, and by COSMOS, which developed a pilot 

XML-based exchange standard for its Geotechnical Virtual 

Data Center (GVDC; https://geodata.cosmos-data.org/). The 

current schema specifically handles borehole geologic and 

geophysical logs and deep foundations, including an extensive 

suite of associated in-situ and laboratory tests. The DIGGS 

structure is extensible, and planned expansion of DIGGS 

will ultimately cover a much wider range of geotechnical and 

geoenvironmental tests and features. 

DIGGS version 1 is currently being reviewed by a wide 

group of stakeholders and is slated for public release in the 

fall of 2007. Concurrent with the release will also be a number 

of software tools to facilitate data translation and data display. Specifically, public domain software to translate AGS 

flat files to DIGGS XML is in development by the DIGGS 

consortium, and Web-based data previewers that will consume 

DIGGS XML and produce borehole geologic, geophysical, 

and cone penetrometer graphic logs are in development by the 

COSMOS GVDC. Several commercial geoscience software 

developers, including gINT, EarthSoft (EQuIS), and Keynetix 

(HoleBASE), are also making their software compatible with 

DIGGS. 

Geoinformatics 2007—Data to Knowledge 7 

Visual Representation of Seismic Data 

By Amit Chourasia1 and Steven Cutchin1 

1

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

The amount of geoscience data available to researchers 

has seen an exponential growth in the past few decades and 

continues to grow at this rate. This growth is a direct result 

of advancements in monitoring, observation, and recording of data through satellite and field-specific sensors. The 

drastic reduction in compute and storage costs over the same 

timeframe has spurred the majority of this growth. This data 

collected through observation and simulation enable analyses 

at significantly expanded scope and resolution. We present a 

few novel visualization techniques and case studies of simulated and observed seismic data, and also show how existing 

tools from the general animation domain can be applied to 

create visualizations in the geosciences. The case studies are 

visualizations of the TeraShake1&2 and Puente Hills seismic 

simulations, the 1906 San Francisco Earthquake, and a visualization of an experimental data captured from the physical 

shaking of a seven-story building. 

Reference 

Chourasia, Amit, 2007, Digital recreation of a seven-story 

building during an earthquake: The Association for computing Machinery, Inc. (ACM), Crossroads, Computer Graphics, 

Spring 2007-13.3. 

The GEON IDV (Integrated Data 

Viewer) for Data Integration and 

Exploration in the Geosciences 

By Stuart Wier1 and Charles Meertens2 

1

University NAVSTAR (Navigation Signal Timing and Ranging) Consor

tium (UNAVCO), University of Colorado, Boulder, Colo. 

2

UNAVCO Facility, UNAVCO, Inc., Boulder, Colo. 

The Geosciences Network Integrated Data Viewer 

(GEON IDV), provided and developed in part by the Univer

sity Navigation Signal Timing and Ranging (NAVSTAR) Consortium (UNAVCO), is a freely available four-dimensional 

software tool for data visualization and exploration in the earth 

sciences. New complex datasets require a tool with full threedimensional and temporal display capabilities to discover 

and interpret details in the data. Most any earth-mapped data, 

including data with depth extent below or above the surface, 

can be seen in the IDV. The IDV can show data for any area 

on the Earth, any map projection, and any vertical scale, with 

time animation controls (fig. 1). 

­

­
 Geoinformatics 2007—Data to Knowledge 

Figure 1. IDV oblique view of the geophysics of the Yellowstone 

National Park area, including three-dimensional surface relief, 

GPS velocity vectors (magenta), surface fault lines (green), 

caldera outline (red), epicentral focal mechanisms, and an 

isosurface of low P-wave velocity. 

The GEON IDV (called the IDV here) was designed 

to enable data integration, and to foster inter-interoperability across the earth sciences. Diverse datasets from various 

distributed data sources can be viewed simultaneously in 

the same display. For example, the IDV can simultaneously 

display seismicity under St. Augustine volcano (Alaska) using 

data from real-time earthquake catalogs, and real-time 3D 

images of atmospheric ash clouds over the volcano, and using 

data from U.S. National Weather Service WSR88D Level II 

radar data, in true vertical scale, with a three-dimensional 

surface-relief image. 

The IDV recognizes several data-source protocols, 

including local files, URLs, Web catalog servers such as 

THREDDS catalogs and OPeNDAP data servers, Web map 

server, and RSS feeds (such as the U.S. Geological Survey’s 

(USGS’s) “this week’s earthquakes”). Catalogs need not be 

located at physical data locations, offering the possibility of 

distributed data sources, data services, and data users. 

The IDV can be a key part of a Web-based data store and 

provision service. As an example, Incorporated Research Institutions for Seismology (IRIS) has recently built a Web portal 

to their catalog of earthquake epicenter locations and magnitudes. The user can specify earthquakes of interest by latitude, 

longitude, time, and magnitude range. The IRIS service generates a file with the data and provides the URL to the file. The 

IDV can use this service and URL as a data source. IRIS is 

now considering a Web service to convert seismic tomography 

data files, submitted online by their creators, for conversion to 

a format the IDV can use. 

The IDV can be scripted to run unattended and make data 

display imagery, for example, on time as new data arrives, or 

on demand from a Web site. This allows a Web site to provide 

images of IDV displays of data specified by an online user. 

The Web service running the IDV need not be colocated with 

the data source. 

The IDV relies on the power of the network Common 

Data Form (netCDF) file format, which stores multidimensional data, related metadata, and source information. NetCDF 

data files provide complete metadata, including geographic 

location, data units and scaling, time values and formats, 

variable descriptions, data provenance, publication references, 

data creator credits, affiliations, contact information, and 

information to support data discovery by geospatial, temporal, 

and keyword searches. All netCDF metadata is available in the 

IDV. The IRIS tomography data-format converter will convert 

ASCII files of tomography data to netCDF files. 

The UNAVCO GEON Web site (http://geon.unavco.org/ 

unavco/) provides a complete description of the GEON IDV, 

including how to download the software, a tutorial, help, and 

detailed guides to formatting data for the GEON and software 

tools for data conversion to netCDF. 

The UNAVCO portion of this work is funded by the NSF 

Division of Earth Science through the GEON Information 

Technology Research project. 

Acknowledgments 

We thank the science investigators, including Mike 

Ritzwoller, Nikolai Shapiro, Alan McNamara, Francis Wu, 

Corne Kreemer, Bill Holt, Bob Smith, and Greg Waite, and the 

USGS for sharing their results and providing us with valuable 

feedback. 

Demonstrating Hydrodynamic Data 

Assimilation with OpenGL Animations 

By Zepu Zhang1 

1

University of Chicago, Center for Integrating Statistical and Environmental 

Science (CISES), Chicago, Ill. 

We have developed Open Graphics Library (OpenGL) 

animated demonstrations for a hydrodynamic data-assimilation project. The project uses the Princeton Ocean Model to 

simulate wind-driven circulations in Lake Michigan. Hourly 

advection observations at 10 locations are assimilated into 

the model under mass conservation and coastal constraints. 

The demonstration employs a straightforward particle-tracking algorithm and is designed such that (1) it clearly shows 

both the magnitude and the direction of the advection; (2) it 

clearly compares the observed and modeled advections at the 

data sites; (3) it supports zooming, panning, fast forwarding 

and reversing, pausing, and “on-the-fly” adjustment of most 

parameters that control the appearance of the animated advection field; and (4) the demonstrated advection field is switched 

easily at any time between different scenarios (without 
assimilation, with assimilation assuming exact data, and with 

assimilation considering measurement error). 

From Caves to Optiportals: 

Evolution and Deployment of Visual 

Communication for Geoscientists 

By Brian N. Davis1, Christopher F. Polloni2, Jason Leigh3, and 

Luc Renambot4 

1

U.S. Geological Surevey, Sioux Falls, S. Dak. 

2

U.S. Geological Survey, Woods Hole, Mass. 

3

Electronic Visualization Laboratory, University of Illinois—Chicago, 

Chicago, Ill. 

4

Electronic Visualization Laboratory and the Department of Computer Science, University of Illinois—Chicago, Chicago, Ill. 

Virtual Reality 

The goal of virtual reality is to address the influence 

of human factors to allow the human brain to better process 

the data from virtual environments by presenting data in a 

visual form that the human brain has evolved to process. Such 

systems can enable scientists to experience models of reality 

“virtually,” without the expense and difficulty of traveling to 

remote locations. For example, oceanographers can analyze 

and understand the geology and features of the ocean floor 

without traveling in a submarine, and geologists can visualize 

and hypothesize about the geology of Antarctica without having to experience subzero temperatures. 

Consumer-Grade Visualization 

Over time, virtual reality, led by developments at the 

Electronic Visualization Laboratory at the University of Illinois at Chicago, became more capable and realistic; however, 

while achieving additional capability, cost and complexity 

became barriers to deployment in settings usable by every-day 

earth scientists. Cave Automatic Virtual Environment (CAVE) 

technology eventually was ported to smaller, less-expensive 

configurations, but wide acceptance was not achieved until 

the GeoWall Consortium led the effort to make stereo display 

systems affordable and commonplace. 

Software 

Software is always the most difficult and expensive 

component of any computer system, including virtual-reality 

display systems. GeoWall technology did not become widely 

used until after commercial software routinely used by earth 

scientists became available. Recently, free Web browserbased software, such as Google Earth, has increased access to 

Geoinformatics 2007—Data to Knowledge  

three-dimensional stereo visualization to an entirely new set of 

earth-science data consumers. 

Deployment 

Though the National Science Foundation-sponsored 

OptIPuter project (so named for its use of optical networking, Internet protocol, computer storage, and processing 

and visualization technologies) has developed and deployed 

leading-edge networking, grid computing, and visualization 

cyberinfrastructure technologies to again advance the capabilities of scientific visualization, use of these technologies 

by earth scientists will not become widespread until their 

deployment follows the evolution of GeoWall technology. 

This model determines that technology must be affordable, 

comprised of commodity hardware and software components, 

and widely available to enable scientists at different locations 

to collaborate using similar technology. However, the highspeed network access to visual “OptIPortals” developed by 

the OptIPuter project should not be discounted. As network 

bandwidths increase and become widely available, only realtime access to earth-science data using commonplace visual 

communications tools will make virtual reality practical and 

meaningful to earth scientists. Therefore, widespread deployment and use by geoscientists of visual communications systems must embrace the concepts developed by the OptIPuter 

project while evolving toward the cost model of the GeoWall 

Consortium. 

Demonstration

 OptIPuter technology, residing on the University of California at San Diego campus, will be reviewed during this talk, 

and then demonstrated in an informal setting at other times 

during the program. These technologies will include GeoWall, 

HiperWall, and Varrier displays used to prototype future visualization technology capabilities. 

Visually Browsing Georeferenced 

Digital Libraries 

By Angus Forbes1 and Greg Janée2 

1

Map and Imagery Laboratory, Davidson Library, University of Califor

nia—Santa Barbara, Santa Barbara, Calif. 

2

Institute for Computational Earth System Science, University of Califor

nia—Santa Barbara, Santa Barbara, Calif. 

We present a prototype visual browser for georeferenced 

digital libraries that allows a library to be navigated, and 

library content to be discovered, without explicitly formulating 

queries. The browser also provides a seamless transition from 

a synoptic view of library content to examination of individual 

library items. 

­

­
70 Geoinformatics 2007—Data to Knowledge 

A georeferenced digital library organizes information of 

all stripes, from raw data to textual documents, into collections 

of discrete items, each of which has a geographic region of 

relevance or footprint. Among the geosciences there are copious amounts of data that are georeferenced, and it has been 

estimated that over 70 percent of all textual documents contain 

relevant georeferences. The principal goal of a georefenced 

digital library is to take advantage of this metadata, and to take 

advantage of the near-universal reference frames provided by 

cartographic coordinate systems and geographic place names, 

in order to provide spatial search and navigation services and 

to reveal spatial context and relationships. 

The dominant paradigm in information-retrieval systems 

is the query-result cycle: the user first formulates a query; 

submits the query and waits for results to appear (usually in 

the form of a linear list); examines the results (often limited to 

examining one at a time); and perhaps refines the query, thus 

repeating the cycle. The retrieval mechanisms offered by georeferenced digital libraries all follow this model (including the 

Alexandria Digital Library (ADL), Geospatial One Stop, and 

the Geography Network), as well as geographically enhanced 

Web search systems (including Google Local, Yahoo! Local, 

and MSN). These systems typically provide contextual base 

maps to support the query formulation phase, but support 

for visualizing query results is limited, if present at all. Also 

largely missing is the ability to see spatial relationships among 

query results and library content as a whole. ADL provides 

some synoptic graphics, but these are static and not integrated 

with the search system. 

To address these limitations, we have developed a visual 

browser for georeferenced digital libraries that eliminates the 

query and result cycle. Instead, the browser displays reducedresolution versions (in other words, thumbnails and iconic representations) of all items in the digital library over a base map. 

Map pan and zoom controls provide the means of navigating 

the library (fig. 1). 

Of course, displaying all items immediately begs the 

question of how to display the items in a visually coherent 

fashion, especially when large numbers of items overlap and 

even coincide in geographic space. After analyzing and categorizing the types of overlaps encountered in digital library 

collections, we developed a suite of decluttering mechanisms: 

•	 A custom-clustering system that groups items having sizes that are too large or too small or that are too 

dense to be compatible with the current zoom level. 

Clusters of items are represented by variable-sized 

icons to represent the number of library items within a 

region. The clusters are defined both by the geospatial 

range of a collection and the viewing dimensions of the 

browser as follows: 

•	 Two interface controls that dynamically control the 

opacity and size of item footprints. 

•	 A novel control widget that combines selection of 

zoom level with a histogram depicting the numbers 

of items visible at each zoom level. This device 

allows the user to determine at which zoom levels 

items are visible, which is of particular value for 

items that are too small and (or) dense for the current view, as well as for items that are too large for 

the current view (and, hence, effectively invisible 

due to the lack of visible footprint boundaries). 

Our prototype browser is implemented using the Google 

Maps API, and is built on top of the ADL middleware server. 

To support the rapid response times needed for visual browsing, an automated preprocessing step, built on top of core ADL 

library services, computes and caches certain display-related 

information such as clusters and zoom-level histograms. A 

demo version of the browser operating over selected UCSB 

collections is available at http://clients.alexandria.ucsb.edu/ 

ngda/. 

Future development includes integration with other 

(nongeographic) query criteria; refinement of the aforemenFigure 1. Example of map available on Atlas of the Cryosphere Web site (Arctic view). 
71 

tioned clutter-reducing mechanisms; reimplementation using 

the Google Web Toolkit; and integration on top of the National 

Geospatial Digital Archive (NGDA), the funding project for 

this work. 

References 

Hill, L.L., 2006, Georeferencing: The geographic associations 

of information: Cambridge, Massachusetts, MIT Press, 272 

p., available online at http://mitpress.mit.edu/catalog/item/ 

default.asp?ttype=2&tid=11007. (Accessed July 10, 2007.) 

Hill, L.L.,, Janée, G., Dolin, R., Frew, J., and Larsgaard, M., 

1999, Collection Metadata Solutions for Digital Library 

Applications, in Journal of the American Society for Information Science (JASIS), v. 50, no. 13, November 1999: p. 

1169-1181, available online at http://www3.interscience. 

wiley.com/cgi-bin/abstract/66001477/ABSTRACT?CRETR 

Y=1&SRETRY=0. (Accessed July 10, 2007.) 

Janée G., Spatial Footprint Visualization, 2006, available online at http://www.alexandria.ucsb.edu/~gjanee/ 

archive/2006/footprint-visualization/. (Accessed June 25, 

2007.) 

Janée, G., and Frew, J., 2002, The ADEPT Digital Library 

Architecture, in Proceedings of the Second ACM/IEEE-CS 

Joint Conference on Digital Libraries (JCDL), Portland, 

Oreg., July 14-18, 2002, p. 342-350, available online at 

http://portal.acm.org/citation.cfm?doid=544220.544306. 

(Accessed July 10, 2007.) 

Data Fusion, Compression, and 

Visualization of Thermal and Visible 

Imagery for Remote Analysis of 

Geologic Surfaces on Earth And Mars 

By Scott A. Nowicki1 

1

Environmental Studies, University of Portland, Portland, Oreg. 

Global datasets of high-resolution thermal infrared (TIR) 

and visible to near-infrared (VNIR) satellite imagery provide 

opportunities for mapping planetary surface properties at 

resolutions that can be directly applied to field observations, 

with coverage that allows for analysis at regional-to-global 

scales. This information can be ideal for investigating geologic processes, climate variables, and surface history on the 

terrestrial planets, although advanced processing and analytiGeoinformatics 2007—Data to Knowledge 

cal tools are needed to calibrate and display the data in a way 

that is useful for field application. To facilitate the use of these 

multispectral, multitemporal data in a wider scientific community, a new data-fusion technique developed using Mars 

Thermal Emission Imaging System (THEMIS) multispectral 

imagery and modified for use with the Advanced Spaceborne 

Thermal Emission and Reflectance Radiometer (ASTER) 

provides a data product that can be manipulated using commonly available software to map and interpret surface physical 

properties, such as sediment grain size, bedrock exposure, and 

water content of surface materials. 

The term thermal morphology is used here to describe 

the combination of daytime visible reflectance with nighttime brightness temperature, in which the physical proper

ties controlling the diurnal temperature and albedo can be 

directly interpreted. Daytime visible images produce scenes 

in which the reflectivity, topography, and surface roughness 

provide the majority of variation within a field of view (fig. 

1A). Daytime thermal images are similar to daytime visible, in 

which the albedo and morphology dominate the temperature 

variation within a scene (fig. 1B). Nighttime thermal images 

display information related primarily to the thermal inertia 

of materials, in which albedo and topographic information is 

significantly subdued (fig. 1C). Thermal inertia represents the 

ability of near-surface materials to absorb solar energy during 

the day, conduct it into the subsurface, and then release that 

energy throughout the night. A combination of these datasets 

results in a striking image where colorized nighttime thermal 

information is draped over daytime data (fig. 1D). 

Application to ASTER involves the compression of 

coregistered and calibrated three-band VNIR and five-band 

TIR radiance into a single, red-green-blue-color, byte image 

(GeoTIFF). In the thermal infrared emissivity and temperature 

separation, five-band nighttime observations are combined 

to produce a one-band real-number image. Given the normal 

range of diurnally varying temperatures of natural surfaces 

on Earth, the observed range can be linearly converted to byte 

data range (0-255), and retain 0.1oC temperature resolution. 

Daytime observations are equally compressed in data volume 

to present the most thermophysically significant information in a compressed format. ASTER three-band VNIR data 

is integrated and converted to top-of-atmosphere calibrated 

reflectance, producing a single image of minimum data resolution in albedo of 0.004. Conversion of nighttime temperature 

to a standardized hue-based color gradient allows temperature 

to be draped over gray-scale visible reflectivity, resulting in 

a color image which displays the information from those two 

datasets. Retrieval of temperate and albedo values can be made 

visually, with the aid of a color gradient and gray scale. Digital 

separation of temperature and albedo can be performed by 

converting the RGB to Hue Saturation Intensity (HSI) format, 

in which hue is converted to temperature information and 

­
72 Geoinformatics 2007—Data to Knowledge 

Figure 1. ASTER imagery from the Steen Mountains in southeastern Oregon, providing four perspectives used in thermophysical 

analysis. A, Visible albedo is controlled by the reflectivity, roughness, and topography of the surface. B, Daytime brightness temperature 

provides an image similar to VNIR reflectance, because it is primarily controlled by the albedo and insolation. C, Nighttime brightness 

temperature is primarily a function of the thermal inertia, which can be used to constrain the effective sediment grain size, material 

conductivity, and surface-water content. D, Thermal morphology is the combination of the gray-scale-visible (A) and colorized nighttime 

temperature (C) images. 

intensity to albedo. This method allows eight bands of data to 

be compressed into a three-band byte image, and displayed 

in a format that can be interpreted in the field while retaining 

quantitative information for detailed analysis. 

Sharing Earth Science Information 

Through Interoperable Approach and 

Cyberinfrastructure 

By Myra Bambacus1, Marge Cole1, John Evans1, Wenwen Li2, 

Rob Raskin3

, Dick Wertz4, Danqing Xiao2, and Phil Yang2 

1

National Aeronautics and Space Administration Geoscience Interoperability Office, Goddard Space Flight Center, Greenbelt, Md. 

2

Joint Center for Intelligent Spatial Computing, College of Science, George 

Mason University, Fairfax, Va. 

3

Jet Propulsion Laboratory, Pasadena, Calif. 

4

The Federation of Earth Science Information Partnership, Boyce, Va. 

Summary 

Earth-science data and information are generated, collected, and archived at geographically dispersed locations 

and computers by different organizations, including government agencies, companies, and others. To leverage the legacy 

resources for discovering earth-science information and 

knowledge, we need a convenient access to the resources in 

an integral and timely fashion. This paper presents a joint 

effort in developing the Earth Information Exchange (EIE), 

an earth-science portal that supports this need by approaches 

based on interoperability and cyberinfrastructure. Earth 

Science Gateway (ESG), an interoperable portal, is used to 

provide interoperability support to access to heterogeneous 

resources. George Mason University (GMU) Grid, as part of 

the cyberinfrastructure, is utilized to support time-consuming preprocessing, modeling, and decision-support operating 

tools. Semantic search is utilized in bridging different domains 

for sharing cross-domain information and knowledge and for 

refining research results. The functions are integrated into the 

EIE developed by the partnership of National Aeronautics and 

Space Administration (NASA), The Federation of Earth Science Information Partnership (ESIP), and GMU to support the 

objective for facilitating the easily exchange of earth-science 

information. The ongoing effort will also provide a spatial 

Web portal to access and improve earth-science information 

holdings at different government agencies, educational and 

research institutions, and are non-governmental organizations. 

Introduction 

Earth-science data has been massively produced from 

satellite earth observations, insitu sensor detections, computational model simulations, and other sources for use by 

its extensive science and research community. These data 

are most useful when made easily accessible to earth-science researchers and scientists, to government agencies, 

and to society as a whole. To achieve an objective of apply­
Geoinformatics 2007—Data to Knowledge 7 

ing observing and research results about Earth systems for 

improved knowledge gain and more effective decision support, government agencies and nongovernment organizations 

are working together to develop, promote, and implement 

interoperable architectures, and geosciences data-management 

and information-technology approaches to share geospatial 

resources among data producers, distributors, modelers, decision supporters, and decisionmakers. The objective drives 

the delivery and application of earth-system science research 

through integrated systems solutions. Successes have been 

achieved in various forms, as in the following examples: (a) 

the NASA Applied Science Program-sponsored decision-support projects using NASA data through system solutions (Birk 

and others, 2006), and the NASA Geosciences Interoperability Office (GIO) have developed the Earth Science Gateway 

(ESG; Evans and Bambacus, 2005); (b) GMU developed and 

deployed a grid and spatial Web portal platform to support 

geospatial applications (Center for Intelligent Spatial Computing, 2006); and (c) the ESIP Federation initiated the vision of 

an EIE (Federation of Earth Science Information Partnership, 

2005). 

The ESG is a standards-based, Web-services-enabled 

geospatial portal that allows users to discover and access geospatial data and services, and also a prototype for demonstrating and advancing geoscience interoperability. The GMU grid 

and spatial Web portal platform is connected with a community grid system, the Southeastern Universities Research Association Grid (SURAgrid) (Southeastern Universities Research 

Association, 2005) and a nationwide next generation research 

computer network (National LambdaRail (NLR), 2005). The 

ESIP Federation is a network of researchers and associated 

groups that collects, interprets, and develops applications for 

satellite-generated Earth observation information. One practical approach is to develop the EIE. 

Approaches 

The ESG was developed through a public consensusdriven process as a prototype Web services portal to advance 

the discovery, access, and use of NASA’s earth-science data 

products. The ESIP Federation has identified requirements for 

this type of tool within its cluster communities and specific 

scientific research areas. These clusters and research areas 

directly contribute to the U.S. commitments within national 

and international initiatives, such as Global Earth Observation System of Systems (GEOSS). GIO, ESIP Federation, and 

GMU partner in an effort to leverage the three organizations’ 

assets; utilizing emerging technologies on developing and 

advancing ESG and EIE in an interoperable way supported by 

the cyberinfrastructure, as illustrated in the ESG-EIE architecture (fig. 1): (1) meetings among communities are held 

to demonstrate ESG and EIE portal capabilities and solicit 

requirements to develop and improve portlets for EIE; (2) the 

development and evolution of application area portlets are 

lined up with ESIP application clusters and national applications; (3) workshops or telephone conferences are held to demonstrate ESG and EIE portal status; and (4) EIE is maintained 

at http://eie.cos.gmu.edu, for the time being. It will be changed 

to an ESIP domain name when it becomes operational.

 EIE includes support to 12 national application areas 

and relevant technological functions of semantic search, data 

quality, service quality, resource integration, interface and 

Figure 1. Interoperable (ESG) and Cyberinfrastructure (GMU Grid) supported EIE. 
7 Geoinformatics 2007—Data to Knowledge 

Relevant Organizations EIE Application Areas EIE functions 

Environmental Protection Agency and National Aero­ Air Quality Semantic search 

nautics and Space Administration 

National Oceanic and Atmospheric Administration Disaster/Coastal/Water Data quality 

Environmental Protection Agency, National Aeronau­ Carbon/Energy/Educatopm Service quality 

tics and Space Administration, and Department of 

Energy 

Centers for Disease Control, Environmental Protec­ Public Health/Ecological Resource integration 

tion Agency, and National Aeronautics and Space 

Administration 

U.S. Department of Agriculture and National Aero­ Agriculture/Climate Interface and Visualization 

nautics and Space Administration 

Federal Aviation Administration, U.S. Department Aviation/Invasive Species RSS filter 

of Agriculture, and National Aeronautics and Space 

Administration 

Table 1. Earth Information Exchange (EIE) application areas and functions. 

visualization, and RSS filter, with each phase focused on one 

technology area and one to three application domains (table 1). 

Prototypes 

The EIE has been prototyped and is under construction at 

http://eie.cos.gmu.edu. The following figures illustrate selected 

functionalities supported through EIE. The EIE provides data 

from information, knowledge ranged from data, information, 

knowledge, and catalog, to services and applications. 

Figure 2 depicts the EIE user interface. The left side illustrates portlet entry to the 12 national applications and education; earth-science research focus areas will be added thereafter. The middle column highlights the recent breakthroughs 

and phenomena within earth science. The right side provides a 

searching tool to find and navigate the system easily. The tabs 

on top provide overview, collaboration, resources, and a map 

client to view all these different application areas in detail. 

The client also provides access to interoperable geospatial Web services to leverage heterogeneous geospatial 

resources through a service-oriented architecture (SOA), 

providing finding, binding, and chaining functions. It can be 

utilized to support EIE in prototyping earth-science applications in a fast fashion. The semantic search on refining searching results to different catalogs and support quality of services 

is under development and based on the Noesis tool developed 

by University of Alabama at Huntsville (Ramachandran and 

others, 2005). 

Discussion and Conclusion 

This paper presents a joint effort among GMU, ESIP 

Federation, and NASA GIO in leveraging the cutting-edge 

information technologies (such as grid computing and 

Web services) and geosciences interoperability in designing, developing, and operating the EIE. EIE leverages the 

cyberinfrastructure (Ian and Kesselman, 2004), geosciences 

interoperability (Evans and Bambacus, 2005), and spatial Web 

portal (Yang and others, in press) technologies to interoperably access heterogeneous geosciences resources, computing 

power, and existing knowledge to meet the needs of different 

levels of earth-science information users from educators to 

earth scientists. 

Acknowledgment 

The research and development reported is sponsored by 

NASA under grant # NNX07AD99G. 

References Cited 

Birk R., Frederick, M., Dewayne, L.C., and Lapenta M.W., 

2006. NASA’s Applied Sciences Program: Transforming 

Research Results into Operational Success: Earth Imaging 

Journal, v. 3, no. 3, p. 18-23. 

CISC, 2006, The joint Center for Intelligent Spatial Computing, George Mason University, available online at http:// 

landscan.scs.gmu.edu:8080/. (Accessed June 25, 2007.) 

ESIP, 2005, Earth Information Exchange, the Federation of 

Earth Science Information Partnership, available online at 

http://www.esipfed.org/. (Accessed June 25, 2007.) 

Evans, J., and Bambacus M., 2005, NASA’s Earth-Sun System 

Gateway: An open standards-based portal to geospatial 

data and services, in Proceedings of IEEE IGARSS, Seoul, 

Korea: p. 4228-4231. 
Geoinformatics 2007—Data to Knowledge 7 

Figure 2. Earth Information Exchange interface. 

Ian, F., and Kesselman, C., 2004, The Grid: Blueprint for a 

New Computing Infrastructure: Morgan Kaufmann Publishers, 

National LambdaRail, 2005, National LamdaRail, available 

online at http://www.nlr.net/. (Accessed June 25, 2007.) 

Ramachandran R., Movva S., Graves S., and Tanner, S., 2005, 

Ontology-based semantic search tool for atmospheric science, available online at http://ams.confex.com/ams/pdfpapers/102272.pdf. (Accessed June 25, 2007.) 

SURA, 2005, The grid platform of the Southeastern Universities Research Association, available online at http://www. 

sura.org/programs/sura_grid.html. (Accessed June 25, 

2007.) 

Yang, P., Evans, J., Cole, M., Marley, S., Alameh, N., and 

Bambacus, M., in press, The emerging concepts and applications of the spatial Web portal: Photogrammetric Engineering & Remote Sensing (PE&RS ), v. 73, no. 6. 

A Community Workshop and Emerging 

Organization to Support a National 

Geoinformatics System in the United 

States 

By G. Randy Keller1, David Maidment2, J. Douglas Walker3, 

Lee Allison4, Linda C. Gundersen5, and Tamara Dickinson5 

1

School of Geology and Geophysics, University of Oklahoma, Norman, 

Okla. 

2

Deptartment of Civil Engineering, University of Texas—Austin, Austin, 

Tex. 

3

Department of Geology, University of Kansas, Lawrence, Kans. 

4

Arizona Geological Survey, Tucson, Ariz. 

5

U.S. Geological Survey, Reston, Va. 

At the request of the Earth Sciences Division of the 

National Science Foundation a meeting was held in March of 

2007 to explore what direction the geoinformatics community 
7 Geoinformatics 2007—Data to Knowledge 

in the United States should be taking in terms of developing a 

National Geoinformatics System. It was clear that developing 

such a system should involve a partnership between academia 

(in particular, efforts supported by the National Science 

Foundation), government, and industry that should be closely 

connected to the efforts of the U.S. Geological Survey and the 

State geological surveys that were discussed at a workshop in 

February 2007. 

The March 2007 meeting had three main goals: (1) 

define the content of a National Geoinformatics System for 

the United States; (2) identify the technology by which such a 

system could be created; and (3) create a process for moving 

forward to jointly plan and develop such a system. The meeting was designed to be flexible and to emphasize breakout 

group sessions and plenary sessions that encouraged open 

discussion, brainstorming, and forward thinking. 

The major conclusion of the meeting was that the geoinformatics community should proceed to investigate setting 

up a formal organization that is a community of informatics 

providers and scientists whose aim is to enable transformative science across the earth and natural sciences by doing the 

following: 

• Fostering communication and collaboration; 

•	 Enabling science through informatics; 

•	 Engaging other communities (science domains and 

other informatics groups); 

•	 Helping its members work to be more effective science 

information providers; 

•	 Sharing resources and expertise; 

•	 Enabling interoperability; 

•	 Sustaining service to the community over the long 

haul; and 

•	 Providing a mechanism for our community to speak 

with a united voice. 

Association of American State 

Geologists (AASG)-USGS Plan for 

a National Geoscience Information 

Network 

By M. Lee Allison1 and Linda C. Gundersen2 

1

Arizona Geological Survey, Tucson, Ariz.
 

2

U.S. Geological Survey, Reston, Va.
 

National Geoscience Information Network 

In early 2007, the Nation’s geological surveys agreed to 

the development of a national geoscience information network 

that is distributed, interoperable, uses open-source standards 

and common protocols, respects and acknowledges data ownership, fosters communities of practice to grow, and develops 

new Web services and clients. 

Geological surveys have unique resources and missionspecific requirements that include the gathering, archiving, 

and dissemination of data. Together these data represent one 

of the largest, most extensive long-term information resources 

on the geology of the United States. Currently, however, these 

data are available in disparate systems which require time and 

resources to explore, extract, and reformat. Using modern 

information technology and a virtual “service-oriented architecture” that provides common discovery tools and standards, 

the surveys and the general science community will benefit in 

multiple ways. First, online data and other informational products from each survey will be more readily available to the 

world audience and will be more valuable because they will 

be interoperable. Second, data and applications from external 

sources, such as the U.S. Geological Survey’s (USGS’s) more 

than 1,000 databases, catalogs, and inventories, will be readily 

accessible and integratable with each participating Survey’s 

own data system. Third, a large federated data network will 

create inestimable opportunities for the broader community, 

including academia and the private sector, to build applications 

utilizing this huge data resource, and integrate it with other 

data. The work of each geological survey will be enhanced by 

access to these new data and applications. 

By demonstrating national cooperation for data access 

and interoperability among the Federal and State geological surveys, we may be able to serve as a model for broader 

cooperation in geoinformatics across the entire earth-science 

community and linkages to other scientific disciplines, especially those with a geospatial aspect. We intend to coordinate 

the development of this network with other efforts, including 

the National Science Foundation’s “Cyberinfrastructure Vision 

for 21st Century Discovery,” and the emerging academic 

and international efforts in informatics. This “community of 

practice” approach means that we will learn, develop, evolve, 

and coordinate the building of the network with each other and 

our partners. 

When initiated among the geological surveys, any user 

may go to a geological survey (or other participating) Web 

site, enter a distributed science data catalog, and view available data. Because all these data will use a common markup 

language, the user can select and download needed data and 

load them into any number of their own applications, including in-house, freeware, and proprietary commercial products. 

The interface would be seamless and near-instantaneous and 

the original data source would be credited with the download. 

The information network will expand as others participate. 

Roles of Geological Surveys 

On one hand, geological surveys have dual requirements 

to collect, archive, and disseminate data for their stakeholders and customers to use, and on the other, to access data held 

by others that will enable the surveys to better carry out their 
77 

analytical and research duties. Data accessibility enhances this 

two-way exchange of information.

The geological surveys are primary geoscience data 

providers and have mandated responsibilities to collect, organize, and distribute this information to the public. Currently, 

information assets exist in many databases and in many forms. 

Similarly, organizations have implemented a wide variety of 

solutions to manage, process, and support research and data 

stewardship requirements. Some organizations have integrated 

their data to provide products to the public, and others have 

developed accessible Internet map services. Because of the 

large investment in these distributed systems, the emerging 

service architecture must build on existing systems and use 

protocols, standards, and services to help integrate the infor

mation systems and scientific information. 

Geological surveys have unique resources and functions 

as institutions with statutory mandates to collect, archive, and 

disseminate data permanently. These missions can complement and facilitate development of a national geoscience infor

mation network as well as benefit greatly from the result. Geological surveys also contribute to the building of standards of 

practice and fundamental baseline geologic information, such 

as lexicons, geologic maps, and time scales. These contribute 

directly to the overall geoinformatics efforts. The breadth and 

depth of survey-based data are so large that collectively they 

constitute one of the largest, if not the largest, data resources 

in the geosciences—in essence, a national data “backbone.” 

Vision for a National Geoscience Information 

Network 

The surveys agreed to the following principles and 

activities to be undertaken in the next few years to achieve the 

vision: 

•	 Develop a coordinated national geoscience information 

network to access and integrate State survey and USGS 

information resources (databases, maps, publications, 

methods, applications, and data services). 

•	 Function as a “community of practice” in development 

of geoinformatics and the geoscience network. 

•	 Develop prototypes (pilots and testbeds) to show proof 

of concept and to determine realistic levels of effort, 

and compare costs and benefits while providing immediate benefits in the form of user services. 

•	 Build the network through an iterative and evolutionary 

process. 

•	 The basic architecture of the framework should be distributed and leverage existing systems, map services, 

and data, with local autonomy but using standards to 

enable interoperability. 

•	 Review and adopt standards and protocols for developing the network (including metadata). 

•	 New and existing systems should communicate with 

open-source (for example, Open Geospatial Consor

tium-based) protocols to promote interoperability. 

­

­

­

Geoinformatics 2007—Data to Knowledge 

•	 Test and consider accepting GeoSciML (Geoscience 

Markup Language) as a protocol and consider proposing as a standard to Federal Geographic Data Committee (FGDC). 

•	 Recognize there are priority data for which we have 

mission requirements and inherent partnerships 

amongst the geological surveys. Review these and 

adopt service definitions and protocols as appropriate: 

•	 Geologic maps, hazard data and maps, topographic 

data, existing map services; 

•	 Publications and bibliographies. 

•	 Observations and analytical measurements, 

samples, and site information. 

•	 Applications and methods, and analytical tools. 

•	 Legacy analog data. 

•	 Resource data and maps (minerals, energy, water, 

and so on). 

•	 Encourage clients and services to be developed and 

facilitate participation and implementation by others, 

preferably with low overhead, while improving business models and needs; 

•	 Reduce philosophical and cultural barriers that impede 

system development; 

•	 Adhere to a code of conduct that respects and acknowledges data ownership and the work of others. Respect 

intellectual property and data provenance, using 

“branding” in data services to acknowledge data 

sources; 

•	 Develop usage measurements and utilize them in clients and Web services; 

•	 Develop a database citation format; and 

•	 Policy—Acknowledge that geological surveys need 

to recognize interoperable, Web-enabled information 

resources as part of their mission. Seek partnerships to 

leverage resources, and to develop and implement the 

vision. 

References 

Kumar, M., 2006, Geoinformatics 2006: Eos, v. 87, no. 44, 

p. 481. 

Allison, L., Dickenson, T., and Gundersen, L., 2007, Role 

of State geological surveys and USGS in a geophysical 

system for the Nation: A report to the AASG and USGS 

on the results of a workshop conducted February 21-22, 

2007, available online at http://www.geoinformatics.info/ 

Download%20Center.html. (Accessed June 25, 2007.) 
7 Geoinformatics 2007—Data to Knowledge 

The Cultural and Social Challenges of 

Developing Geoinformatics: Insights 

from Social, Domain, and Information 

Sciences 

By Kerstin A. Lehnert1, Paul N. Edwards2, Steven Jackson2, and 

Geoffrey Bowkers3 

1

Lamont-Doherty Earth Observatory, Columbia University, Palisades, N.Y. 

2

School of Information, University of Michigan, Ann Arbor, Mich. 

3

Center for Science, Technology and Society, Santa Clara University, Santa 

Clara, Calif. 

Geoinformatics represents an effort to link a vast, geographically distributed, diffuse, and relatively uncoordinated 

set of existing projects into a genuine infrastructure that is 

envisioned to provide highly reliable, widely accessible capabilities and services in support of scientific work, with the aim 

to facilitate distributed collaboration, democratize the research 

environment, and empower cross-boundary and interdisciplinary scholarship. While the vision of cyberinfrastructure is 

becoming increasingly comprehensive and well defined, the 

path to achieve this vision is still vague and its controls poorly 

understood. 

Insights gained by historians and social scientists from 

the analyses of other kinds of infrastructure, such as railroads, 

telephony, and the Internet, can help guide Geoinformatics 

practitioners to more effectively advance the growth of geoinformatics and cyberinfrastructure in general. The recently 

released workshop report “History and Theory of Infrastructure: Lessons for New Scientific Cyberinfrastructures” 

(Edwards and others, 2007) explains the dynamics, tensions, 

design challenges, and navigation strategies that emerge as 

common patterns and practices of infrastructural development. 

The report emphasizes the relevance of social and organizational factors in infrastructure development, and concludes 

that “robust cyberinfrastructure will develop only when social, 

organizational, and cultural issues are resolved in tandem with 

the creation of technology-based services.” The workshop recommends strategic collaborations between social, domain, and 

information scientists to assist the design of effective navigation strategies that will help realize the vision of cyberinfrastructure. A primary target of these collaborations should be 

the study of existing cyberinfrastructure projects to reveal key 

factors in success and failure. 

We present here initial insights from analyzing the development of successful geoinformatics systems for geochemistry. Geochemistry is a discipline characterized by a culture of 

independent research in the form of small- to medium-scale 

projects, in which data are acquired by human “observers” 

rather than by sensors, often through idiosyncratic data-collection practices and in idiosyncratic formats. Due to the large 

personal effort involved in generating the data, geochemical data are considered private intellectual property, and are 

shared only through publications in the scientific literature that 

guarantee the appropriate credit for data authors. This practice 

has led to a wide dispersion of data in the literature, making 

it difficult for the broad geoscience community to access and 

efficiently use the full range of available data. Data publications are frequently missing contextual information describing 

the complex processes of data gathering that is needed in order 

for other data users to interpret the data. 

In the mid-1990s, domain scientists in the U.S. and in 

Europe independently recognized the need for more efficient 

access to data to support new research endeavors (for example, 

the National Science Foundation-funded RIDGE program or 

the Geochemical Earth Reference Model Initiative) and the 

potential of emerging technologies, such as relational databases and the Web, and started to develop geochemical databases (PetDB, GEOROC, NAVDAT, and EarthRef) that were 

publicly accessible on the Internet. These database projects 

were of limited scope, motivated by the scientists’ personal 

research agendas (PetDB focused on mid-ocean ridge basalts 

and abyssal peridotites; GEOROC initially focused on oceanisland basalts). They were rapidly embraced by relevant parts 

of the community because they provided substantial benefits 

to researchers and educators who no longer had to expend significant efforts to produce their own data compilations, excruciatingly typing data from the literature into spreadsheets. The 

new online databases matched the scientific working environment and workflow, allowing researchers to easily access the 

data, and provided tools to integrate data from hundreds of 

publications into customized datasets within minutes. Since 

the databases went online, several hundred scientific articles 

have cited these databases as the source for datasets used to 

create or test new hypotheses, providing evidence for their 

utility and success. 

The development of the various databases can be 

assigned to the “System Building Phase” in infrastructure 

development, which is characterized by the successful design 

of technology-based services. Even though systems were 

not yet implemented in a sustainable manner, this phase was 

critical to provide a proof of concept to the community that 

broadened support for an advanced data infrastructure in 

geochemistry, and increased awareness within the community 

about deficiencies in the data culture, such as the inconsistent and incomplete reporting of data-quality information in 

publications. 

The database projects next moved into a phase of “System Growth and Stabilization,” during which the systems were 

migrated to more professional and sustainable IT environments, with expert teams that supported development and 

operations. The community increasingly accepted the systems 

as part of their research infrastructure. Finally, the projects 

entered the “Networking and Consolidation Phase” when 

they founded the EarthChem consortium, with the objective 

to better link and integrate the independent data collections, 

nurturing synergies among projects, minimizing duplication of 

efforts, and sharing tools and approaches. Tensions regarding 

ownership, control, and design approaches that arose dur­
Geoinformatics 2007—Data to Knowledge 7 

ing the Networking Phase were surpassed by the substantial 

benefits of the collaboration, such as the broader impact of 

technical or organizational developments, including standards 

and policies, and ultimately led to a more stable and well-considered implementation. The EarthChem “network” is now 

expanding with new partners, and has attained a leadership 

role in the field, advancing a culture change in the geochemistry community and working with other geoinformatics 

projects, societies, editors, and the science community at large 

toward standards for data sharing and data reporting. 

According to our analyses, the following factors have 

been key for the success of the geochemical databases: (a) 

The initial proof of concept systems did not rely on contributions from the community; only investigators experienced the 

benefits of the systems and thus were more readily accepted 

and supported the systems. (b) The systems offered capabilities that did not exist before, and that an individual could not 

achieve. (c) Early collaboration among the database systems 

led to compatible data models and metadata schemes, increasing the impact of the more widely applicable system designs. 

(d) The teams provided the necessary organizational and 

“marketing” components to successfully advance the projects 

(for example, the “wizard-maestro-champion” combination 

noted by historians as a common combination of system-building teams). 

Based on the lesson learned from the geochemistry 

systems, we see three roles that projects need to fulfill in order 

to successfully advance and implement geoinformatics: (a) 

Service provider—The needs and requests of the users need 

to be given the highest priority. It is critical to understand that 

systems are operations, rather than pure research and development efforts. (b) Competent partner—Both data authors and 

data users need to trust the service provider, who needs to 

understand and respond to domain science concerns. (c) Team 

player—In order to advance the infrastructure development via 

networking, project members have to be willing to collaborate, 

share expertise and experiences, and acknowledge others’ 

achievements. 

Community concerns regarding issues of intellectual 

property (credit to data authors), and the impact of geoinformatics on core science funding, as well as the broad implementation of new practices and procedures (for example, 

unfunded mandates of metadata generation) still represent 

major challenges that need to be overcome. 

Reference Cited 

Edwards, P.N., Jackson, S.J., Bowker, G.C., and Knobel, C.P., 

2007, Understanding infrastructure: Dynamics, tensions, 

and design in Report of a workshop on history and theory 

of infrastructure: Lessons for new scientific cyberinfrastructures, January 2007, available online at http://www. 

si.umich.edu/InfrastructureWorkshop/. (Accessed June 25, 

2007.) 

Hydroseek—A Search Engine for 

Hydrologists 

By Bora Beran1 and Michael Piasecki1 

1

Civil, Architectural and Environmental Engineering, Drexel University, 

Philadelphia, Pa. 

Search engines have changed the way we see the Internet. 

The ability to find the information by just typing in keywords 

was a big contribution to the overall Web experience. While 

the conventional search engine methodology worked well for 

textual documents, locating scientific data remains a problem 

since they are stored in databases not readily accessible by 

search engine bots. 

Considering different temporal, spatial, and thematic 

coverage of different databases, especially for interdisciplinary research, it is typically necessary to work with multiple 

data sources. These sources can be Federal agencies which 

generally offer national coverage or regional sources which 

cover a smaller area with higher detail; however, for a given 

geographic area of interest there often exists more than one 

database with relevant data. Being able to query multiple 

databases simultaneously is a desirable feature that would 

be tremendously useful for scientists. Development of such 

a search engine requires dealing with various heterogeneity 

issues. In scientific databases, systems often impose controlled 

vocabularies that ensure homogeneity within themselves, thus 

heterogeneity becomes a problem when more than one database are involved. Having controlled vocabularies at individual 

database level defines the boundaries of vocabulary variety, 

making it easier to solve the semantic heterogeneity problem 

than with the conventional search engines that deal with free 

text. 

Structural, syntactic, and information system heterogeneities emerge as types of additional incompatibilities that these 

systems have to resolve. Structural heterogeneity is generally 

defined as different information systems storing their data in 

different document layouts and formats. In the current state 

of hydrologic data providers, we can speak of HTML tables, 

XML documents, or text files, where the file format alone does 

not guarantee homogeneity since data output can be organized 

in many different ways. Syntactic heterogeneity is the presence of different representations or encodings of data. Date 

and time formats can be given as an example, where common differences are local time versus coordination universal 

time, 12-hour clock versus 24-hour clock, and standard date 

format versus Julian day, which is common in Ameriflux data. 

Whereas information system heterogeneity requires methods 

of communication specifically tailored to interact with each 

data providers’ servers due to the difference in interfaces (for 

example, Representational State Transfer (REST) services 

versus Simple Object Access Protocol (SOAP) services), it 

also encompasses the difficulties from the difference of arguments that each service requires. Sometimes even responses 

and requests have different formats. In the U.S. Environ­
0 Geoinformatics 2007—Data to Knowledge 

mental Protection Agency’s STOrage and RETrieval System 

(STORET), data requests (through available REST services) 

require dates to be provided in Dublin Julian days (days since 

the noon between December 31, 1899, and January 1, 1990) 

while the server returns Gregorian dates with the data. 

We have developed a search engine (http://cbe.cae.drexel. 

edu/search/) that enables querying multiple data sources 

simultaneously and returns data in a standardized output, 

despite the aforementioned heterogeneity issues between the 

underlying systems. This application relies mainly on metadata catalogs or indexing databases, ontologies, and Web 

services with virtual globe and Asynchronous JavaScript and 

Extensible Markup Language (XML) (AJAX) technologies 

for the graphical user interface. Users can trigger a search of 

dozens of different parameters over hundreds of thousands 

of stations from multiple agencies by providing a keyword, a 

spatial extent (in other words, a bounding box), and a temporal 

bracket. 

CUAHSI Cyberinfrastructure for 

Hydrologic Sciences 

By Ilya Zaslavsky1, David Valentine1, and David R. Maidment2 

1

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

2

Center for Research in Water Resources, University of Texas—Austin, 

Austin, Tex. 

The mission of the CUAHSI HIS (Consortium of Universities for the Advancement of Hydrologic Science, Inc., 

Hydrologic Information System) project is development of 

cyberinfrastructure supporting advanced hydrologic research 

and education. It is a collaborative project that involves several research universities and the San Diego Supercomputer 

Center as the technology partner. Over the last three years, the 

CUAHSI HIS team has been researching, prototyping, and 

implementing Web services for discovering and accessing a 

variety of hydrologic data sources, and for developing applications for the desktop and for the Web. 

The CUAHSI HIS system architecture is envisioned 

as a component of a large-scale environmental observatory 

effort, which emerges as a network of seamlessly integrated 

data collection, information management, analysis, modeling, 

and engineering endeavors implemented across disciplinary 

boundaries. The HIS design follows the open services-oriented 

architecture model; that is, it relies on a collection of loosely 

coupled, self-contained services that communicate with each 

other and that can be called from multiple clients in a standard fashion. The core of the system is WaterOneFlow Simple 

Object Access Protocol (SOAP) services, providing uniform 

access to multiple heterogeneous repositories of hydrologic 

observation data, both remote and local. The services follow 

a common Extensible Markup Language (XML) messaging 

schema named CUAHSI WaterML, which includes constructs 

for transmitting observation values and time series, as well 

as observation metadata including information about sites, 

variables, and networks. The information model of WaterML 

follows that of Observation Data Model (ODM, http://www. 

cuahsi.org/his/odm.html), while extending it to handling 

of observation data available as fields. The currently available services provide access to the U.S. Geological Survey’s 

(USGS’s) National Water Information System (NWIS), the 

U.S. Environmental Protection Agency’s (EPA’s) STORET, 

National Climatic Data Center Automated Surface Observing System (NCDC ASOS), Daymet, Moderate Resolution 

Imaging Spectroradiometer (MODIS), and North American 

Mesoscale 12 Kilometer (NAM12K) data, as well as to data 

maintained by users in the ODM format. 

The Web services are accessed from different types of 

clients, including a Web browser, a range of desktop applications such as Matlab, ArcGIS, and Excel, and several programming languages (NET and Java), which were exposed as the 

primary desktop client environments by the CUAHSI userneeds assessment. The general organization of the HIS System 

is shown in figure 1. The Web browser-based client is developed in collaboration with Enviromental Systems Research 

Institute and relies on ArcGIS Server 9.2 for online mapping 

functionality. 

At the physical level, the system being deployed now 

includes a central HIS server and a networked collection of 

workgroup HIS servers. The central node contains observation 

data catalogs for nationwide hydrologic observations repositories maintained at USGS, EPA, NCDC, and other agencies. 

The catalogs are accessible via the Web services supporting 

GetSiteInfo and GetVariableInfo requests, while the data series 

are returned via GetValues SOAP Application Program Interface (API) calls. The organization of Workgroup HIS nodes 

is similar, with additional ability to import local observation 

data into ODM instances, configure the ODM and respective 

Figure 1. The general organization of the HIS system. 
Web services, and serve the newly registered data along with 

the national hydrologic observations. The software stack for 

both the Central and the Workgroup HIS servers is based on 

Windows 2003 server and includes Structured Query Language (SQL) Server 2005, ArcGIS Server 9.2, Visual Studio 

2005, as well as a series of tools and databases developed by 

the HIS team. 

In the first stage of deployment, the workgroup HIS 

servers are designed to be implemented at 11 hydrologic 

observatory testbeds as part of a National Science Foundation 

Water and Environmental Research Systems (WATERS) initiative. This will let us test the HIS tools on diverse hydrologic 

datasets collected by the testbeds and further help develop the 

Cyberinfrastructure for Hydrologic Sciences. More information about the project is available at http://www.cuahsi.org/his. 

Design and Implementation of CUAHSI 

WaterML and WaterOneFlow Web 

Services 

By David Valentine1, Ilya Zaslavsky1, Thomas Whitenack1, and 

David R. Maidment2 

1

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

2

Center for Research in Water Resources, University of Texas—Austin, 

Austin, Tex. 

WaterOneFlow is a term for a group of Web services 

created by and for the Consortium of Universities for the 

Advancement of Hydrologic Science, Inc. (CUAHSI) community. CUAHSI is an organization representing more than 

100 U.S. universities and is supported by the National Science Foundation to develop infrastructure and services for the 

advancement of hydrologic science. CUAHSI Web services 

facilitate the retrieval of hydrologic observations information from online data sources using the Simple Object Access 

Protocol (SOAP) protocol. CUAHSI WaterML (below referred 

to as WaterML) is an Extensible Markup Language (XML) 

schema defining the format of messages returned by the 

WaterOneFlow Web services. 

Background 

Beginning in 2005, the CUAHSI HIS project team 

implemented a variety of Web services providing access to 

large repositories of hydrologic observation data, including the 

U.S. Geological Survey’s (USGS’s) National Water Information System (NWIS) and the U.S. Environmental Protection 

Agency’s (EPA’s) STORET (Storage and Retrieval) database 

of water-quality information. The services gave access to 

station and variable metadata, and observations data stored at 

these sites. As the services were written for each data source 

individually, their inputs and outputs were different across data 

Geoinformatics 2007—Data to Knowledge 1 

sources. The services developed in this ad hoc manner do not 

scale well. As the number and heterogeneity of data streams 

to be integrated in CUAHSI’s hydrologic data access system 

increased, it would become more and more difficult to develop 

and maintain a growing set of client applications programmed 

against the different signatures and keep track of data and 

metadata semantics of different sources. As a result, WaterML 

was developed to provide a systematic way to access water 

information from point observation sites. 

Point Observations Information Model 

In parallel with Web service development, CUAHSI has 

been developing an information model for hydrologic observations that is called the Observation Data Model (ODM). Its 

purpose is to represent observation data in a generic structure 

that accommodates different source schemas. While based 

on the preliminary set of CUAHSI Web services, WaterML 

was further refined through standardization of terminology 

between WaterML and ODM, and through analysis of access 

syntax used by different observation data repositories, including USGS NWIS, EPA STORET, National Climatic Data 

Center Automated Surface Observing System (NCDC ASOS), 

Daymet, Moderate Resolution Imaging Spectroradiometer 

(MODIS), and the North American Mesoscale 12 Kilometer 

(NAM12K) System. 

According to the information model, a data source 

operates one or more observation networks; a network is a 

set of observation sites; a site is a point location where water 

measurements are made; a variable describes one of the types 

of measurements; and a time series of values contains the 

measured data, wherein each value is characterized by its 

time of measurement and possibly by a qualifier that supplies 

additional information about the observation. Figure 1 demonstrates the main components of the model and respective Web 

services. 

WaterML Concepts 

The goal of the first version of WaterML was to encode 

the semantics of hydrologic observations discovery and 

retrieval and implement WaterOneFlow services in a way that 

creates the least number of barriers for adoption by the hydrologic research community. In particular, this implied maintaining a single common representation for the key constructs 

returned on Web service calls related to observations, features 

of interest, observation procedures, observation series, etc. 

An observation is considered an act of assigning a 

number, term, or other symbol to a phenomenon, and a result 

of such assignment. Hydrologic observations are performed 

against many different phenomena (properties of different features of interest), and shall be associated with time measurements (time points or time intervals). The features of interest 

common in hydrologic observations may include points (gauging stations and test sites), linear features (steams and river 
2 Geoinformatics 2007—Data to Knowledge 

Figure 1. CUAHSI Point Observations Information Model, and corresponding Web service methods. 

channels), or polygon features (catchments and watersheds). 

Spatial properties of the features of interest may be further 

expressed in two or three dimensions, in particular via vertical 

offsets against common reference features. The observations 

are made in a particular medium (water, air, and sediments) 

using a procedure. The procedure may represent a multistep 

processing chain, including an instrument (sensor), algorithms 

for transforming the initially measured property (for example, 

“partial pressure of oxygen in the water” may be transformed 

into a measure of “dissolved oxygen concentration”), and 

various techniques for censoring and for quality control of the 

value assignment, including multiple scenarios for assignment 

of no value. Individual observations are organized into observation series (regular sequences of observations of a specific 

variable made at a specific site), which are in turn referenced 

in series catalogs. A series catalog is an element of an observation network, which represents a collection of sites where a 

particular set of variables is measured. A responsible organization can maintain one or more observation networks. 

In addition to point measurements described in the ODM 

specification, hydrologic information may be available as 

observations or model outcomes aggregated over user-defined 

regions or grid cells. While USGS NWIS and EPA STORET 

exemplify the former case, sources such as MODIS and 

Daymet are examples of the latter. In this latter case, as in the 

case of other remote-sensing products or model-generated 

grids, the observation or model-generated data are treated as 

fields, and sources of such data are referenced in WaterML as 

datasets, as opposed to sites. 

The practice of hydrologic observations provides ample 

evidence of complications beyond this general treatment. 

These complications are related to the complex and often 

incompatible vocabularies used by several Federal hydrologic 

observation systems, to different and not always documented 

contexts of measurement and value assignment, to often 

ambiguously defined features of interest, and to complex 

organizational contexts of hydrologic measurement, transformation, aggregation, etc. It is in response to this complexity 

that the CUAHSI WaterML is primarily designed. While some 

of this complexity may be captured within the standards being 

developed under the OGC’s Sensor Web Enablement (SWE) 

activity, the flexibility inherent in such standards may itself be 

a barrier to adoption when the target audience is not computer 

scientists. 

Implementation Context 

WaterML is primarily designed for relaying fundamental hydrologic time-series data and metadata between clients 

and servers, and to be generic across different data providers. 

Different implementations of WaterOneFlow services may add 

supplemental information to the content of messages; however, regardless of whether or not a given WaterML document 

includes supplemental information, the client shall be sure that 

the portion of WaterML pertaining to space, time, and variables will be consistent across any data source. 

Depending on the type of information that the client 

requested, a WaterOneFlow Web service will assemble the 

appropriate XML elements into a WaterML response and 

deliver that to the client. The core WaterOneFlow methods 

include the following: 

•	 GetSiteInfo—For requesting information about an 

observations site; the returned document has a root element of SiteInfoResponse type. 

•	 GetVariableInfo—For requesting information about a 

variable; the returned document has a root element of 

VariableResponse type. 

•	 GetValues—For requesting a time series for a variable at a given site or spatial fragment of a dataset; the 

returned document has a root element of TimeSeriesResponse type. 
The provisional services are available from http://river. 

sdsc.edu. The CUAHSI WaterML description has been submitted as a discussion paper to the Open Geospatial Consortium 

(OGC), and is available from the OGC portal. The project Web 

site is http://www.cuahsi.org/his. 

Hydrologic Information System Server: 

The Software Stack and the Initial 

Deployment Experience 

By Thomas Whitenack1, David Valentine1, Ilya Zaslavsky1, and 

Dean Djokic2 

1

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

2

Environmental Systems Research Institute, Inc., Redlands, Calif. 

Description of the Software Stack 

One of the main outcomes of the CUAHSI Hydrologic 

Information System project is the development of a Windowsbased software stack to support importing, registering, and 

serving hydrologic observation data. The Hydrologic Information System Server (HIS Server) organizes observation 

databases, geographic data layers, data importing and management tools, and online user interfaces into a flexible multitier 

application for serving both national-level and locally maintained observation data. The main components of the distributable software stack (fig. 1), in a typical deployment scenario, 

are a mix of commercial off-the-shelf technologies (COTS), 

and a custom-developed code for Web services, databases, and 

sophisticated online data access. The COTS stack includes a 

Windows 2003 Server operation system with Internet Information Server (IIS), a Structured Query Language (SQL) Server 

2005 for storing observation databases, an ArcGIS 9.2 and 

ArcGIS Server for managing and serving observation site 

maps and other spatial data, and Visual Studio 2005 for imperFigure 1. Components of the HIS software stack. 

Geoinformatics 2007—Data to Knowledge  

sonating and recompiling the map application when required. 

The following are components developed by the HIS Team: 

•	 WaterOneFlow Web services—Services providing uniform query access to remote repositories of 

observation data and data catalogs (U.S. Geological 

Survey’s National Water Information System (NWIS), 

U.S. Environmental Protection Agency’s Storage 

and Retrieval System (STORET), National Climatic 

Data Center’s Automated Surface Observing System 

(ASOS), and so on) and to local Observation Data 

Model (ODM)-compliant databases. In addition, the 

software stack includes an online application for testing local Web services. 

•	 Observation data loader—Software for importing 

observation data and catalogs into ODM instances. 

•	 ODM tools—Software for exploring and editing data 

imported into ODM. 

•	 Data Access System for Hydrology (DASH)—An ASP. 

NET application developed over ArcGIS Server 9.2 

that represents a mapping, querying, and data-retrieval 

interface over observation and GIS databases, and Web 

services. 

In addition, the deployed HIS server contains tutorials and instructions for several workflows supported by the 

system, specifically related to importing, curating, and serving 

observation data on the Internet. 

Importing and Registering Observation Datasets 

The critical capability that the HIS software stack supports is importing and registering user-collected hydrologic 

observation data. The steps in adding a new observation network are as follows (see fig. 2.): 

1. Using the ODM DataLoader, the user (information 

manager in a Water and Environmental Research 

Systems (WATERS) testbed) loads a hydrologic 

observation dataset from an Excel or text file into a 

blank ODM instance in a local SQL Server 2005. As a 

result, a set of ODM-compliant tables will be created 

and populated with observation data and metadata, 

including the Sites, Variables, and SeriesCatalog tables 

upon which the DASH application relies. In addition, 

the user may explore and edit the newly created ODM 

instance using the ODM Tools desktop application. 

2. The user creates a copy of the WaterOneFlow Web Ser

vices template and edits its “web.config” file to point 

to the newly created ODM instance (specifying the 

name of the instance and the connection string). Once 

the new ODM Web service is deployed, the user can 

test it with a Web service testing application to ensure 

that the new service works as expected. 

3. The user creates a point layer of observation stations, 

as either a feature class in a geodatabase, or as a shape 

file, importing point coordinates from the new ODM’s 

Sites table directly, or via GetSites Web service call. 

­
 Geoinformatics 2007—Data to Knowledge 

Figure 2. The HIS Server architecture and steps for adding observation networks. 

4. To configure the DASH application with the added 

observation network, the DASH must be stopped. 

5. Using ArcGIS 9.2, the user adds the new point layer to 

the map configuration file (an .MXD document) used 

by DASH. At this step, the layer’s symbology, scaledependent rendering, labeling, and other components 

of the map display can by modified. 

6. The user edits the Web configuration file of the DASH, 

adding information about the newly created observation network database. For each new network, this 

information includes: NetworkID, NetworkCode, 

NetworkName, NetworkConnectionString, NetworkServicesWSDL, NetworkServicesURL, NetworkServicesGetValuesURL, CreatedDate, LastUpdateDate, 

ResponsibleParty, as well as Disabled and DisabledDate flags. In other words, this block of information 

contains network metadata and pointers to the associated ODM instance and Web services. The NetworkServicesGetValuesURL parameter is included to distinguish between situations where only the site catalog 

is imported into ODM while observation values are 

retrieved from remote repository (for example, national 

repositories such as USGS NWIS, EPA STORET, or 

regional repositories such as TCEQ (Texas Commission on Environmental Quality) or TCOON (Texas 

Coastal Ocean Observation Network)) versus observation data being imported into ODM instance as well. 

7. The user restarts the DASH Web application with the 

updated .MXD and web.config files, and tests it. 

Current Status 

The user interface of the DASH Web application in its 

current version allows online users to query observation networks by location and attributes, selecting stations in a userspecified area where a particular variable was measured during 

a given time interval. Once one or more stations and variables 

are selected, the user can retrieve and download the observation data for further off-line analysis. 

At the time of this writing, the first version of the 

software stack is being finalized, and the San Diego Super

computer Center team is configuring servers to be shipped 

to WATERS testbed projects. At the conference, the initial 

deployment experience will be reported. 

­
Automated Ground Motion 

Characterization Using Satellite 

Imagery 

By Alan Yong1, Susan E. Hough1, Chris J. Wills2, 

and Michael J. Abrams3 

1

U.S. Geological Survey, Pasadena, Calif.
 

2

California Geological Survey, Sacramento, Calif.
 

3

Jet Propulsion Laboratory, Pasadena, Calif.
 

Estimation of local site effects requires precise information about geomorphic and geologic conditions. On the basis 

of our ongoing effort (Yong and others, 2007), we report 

on our progress using multiresolution, remote-sensing data 

to provide preliminary site characterizations for estimating 

ground-motion intensities in future earthquakes. Use of remote 

sensing to determine site effects is especially important in 

many regions of the world, such as Pakistan, Mozambique, 

and Turkey, where local geologic information is either sparse 

or not readily available. Even where local geologic information is available, details in the traditional maps often lack the 

precision or the level of information required for effective 

microzonation. We use readily available satellite-based data 

and state-of-the-art object-based image analysis methods to 

address this problem. Our datasets consist of (1) optical imagery that includes regions in the short-wave infrared (SWIR) 

and thermal infrared (TIR) domains, and (2) relative digital 

elevation models (DEMs), based on stereoscopic-correlation methods, derived from National Aeronautics and Space 

Administration’s ASTER (Advanced Space-borne Thermal 

Emission and Reflection Radiometer) sensors. On the basis 

of geomorphology and geology, we apply automated-feature 

extraction methods to determine the local terrain. Then, on 

the basis of the site-classification schemes from the Wills and 

others (2000) and Wills and Clahan (2006) maps of Califor

nia, we assign shear-velocity-to-30-m-depth (Vs30) values 

to selected regions in California, Mozambique, Pakistan, and 

Turkey. We compare our results to available empirical data and 

discuss the applicability of our site-class assignments in each 

region and the implications of its effects on seismic hazards 

assessment. 

References Cited 

Wills, C.J., and Clahan, K.B., 2006, Developing a map of 

geologically defined site-condition categories for Califor

nia: Bulletin of the Seismological Society of America, v. 96, 

p. 1483-1501. 

­

­

Geoinformatics 2007—Data to Knowledge  

Wills, C.J., Petersen, M., Bryant, W.A., Reichle, M., Saucedo, 

G.J., Tan, S., Taylor, G., and Treiman, I., 2000, A site conditions map for California based on geology and shear wave 

velocity: Bulletin of the Seismological Society of America, 

v. 90, p. S187-S208. 

Yong, A., Hough, S.E., Wills, C., and Abrams, M., 2007, Site 

characterization using satellite imagery: A Progress Report, 

in Proceedings of the 2007 SSA Annual Meeting, Kona, 

Hawaii, April 11-13, 2007, p. 272. 

QuakeML—XML for a Seismological 

Data Exchange Infrastructure 

By Danijel Schorlemmer1 and Fabian Euchner2 

1

Department of Earth Sciences, University of Southern California, Los 

Angeles, Calif. 

2

Swiss Seismological Service, ETH Zurich, ETH Hoenggerberg, Zurich, 

Switzerland 

We report on the progress of the development of 

QuakeML. QuakeML is a flexible, extensible, and modular 

Extensible Markup Language (XML) representation of seismological data that is intended to cover a broad range of fields 

of application in modern seismology. QuakeML is an open 

standard and is developed by a distributed team in a transparent, collaborative manner. The first part of the standard, 

QuakeML—Basic Event Description, will be subjected to 

a Request for Comments process. The standardization process for inventory information and resource metadata is also 

underway. The flexible approach of QuakeML allows further 

extensions of the standard in order to represent waveform 

data, macroseismic information, location probability density 

functions, moment tensors, slip distributions, shake maps, and 

others. 

QuakeML is developed in parallel with a UML representation of its data model. This allows an elaborate software development strategy which uses the unified modeling 

language (UML) class model together with a custom UML 

profile as the basis for automated code generation. With this 

technique, a library of C++ classes is generated which can be 

serialized either to XML (QuakeML) or to Structured Query 

Language (SQL) for persistent storage in a relational database. The XML Schema description is created automatically 

from the UML model with the help of tagged values, which 

describe the mapping from UML class attributes to XML 

representation. The library approach makes it easy for application developers to include QuakeML support in their products, 

since no one source code has to be written. Serialization of 

objects to and from QuakeML format will be supported by the 

Application Program Interface (API). It is possible to use the 

QuakeML library from other object-oriented programming 

languages (for example, Java and Python) using wrappers. 
 Geoinformatics 2007—Data to Knowledge 

The QuakeML language definition is supplemented by a 

concept to provide resource metadata and facilitate metadata 

exchange between distributed data providers. For that purpose, 

we propose a Uniform Resource Identifier (URI)-based format 

for unique, location-independent identifiers of seismological 

resources that are assigned by approved naming authorities. 

QuakeML-Resource Metadata defines a Resource Description Framework (RDF) vocabulary for resource metadata 

description, covering the resource’s identity, curation, content, 

temporal availability, data quality, and associated services. We 

propose to set up a network of registry institutions that offer 

Web services for resolving resource identifiers into corresponding RDF/XML metadata descriptions, and that additionally provide means for resource discovery by offering services 

for searches against resource metadata. 

Currently, the QuakeML development team is bringing together people from University of Southern California 

Eidgenossische Technische Hochschule (ETH) Zurich, GeoForschungsZentrum Potsdam (GFZ Potsdam), U.S. Geological 

Survey, and Incorporated Research Institutions for Seismology 

(IRIS). QuakeML will be used in the Network of Research 

Infrastructure for European Seismology (NERIES) framework 

in Europe by the SeisComp3 software, by the European-Mediterranean Seismological Centre (EMSC), and by the Southern 

California Earthquake Consortium (SCEC) Collaboratory for 

the Study of Earthquake Predictability; it is also under consideration by the Advanced National Seismic System (ANSS). 

We are confident that, in combination with further standardization efforts, the concept of QuakeML can contribute to 

facilitate data exchange and interoperability of seismological 

data providers. 

The EarthScope Plate Boundary 

Observatory Distributed Data 

Management System 

By Greg Anderson1, Mike Jackson1, and Charles M. Meertens2 

1

University NAVSTAR (Navigation Signal Timing and Ranging) Consortium (UNAVCO), University of Colorado, Boulder, Colo. 

2

UNAVCO Facility, UNAVCO, Inc., Boulder, Colo. 

EarthScope is an ambitious, multidisciplinary project 

funded by the National Science Foundation to explore the 

structure and dynamics of the North American continent. The 

Plate Boundary Observatory (PBO) is EarthScope’s geodetic 

component, and will measure the four-dimensional strain 

field resulting from active tectonic deformation in the western 

United States. The University Navigation Signal Timing and 

Ranging (NAVSTAR) Consortium (UNAVCO) is installing 

and will operate the PBO network of more than 1,000 continuous global positioning system (GPS) borehole and laser 

strainmeters, seismometers, and tiltmeters. As of February 

2007, 561 of these stations have been installed. 

The flow of data from these stations is managed from our 

Boulder, Colo., Network Operations Center (NOC), located 

at UNAVCO Headquarters. Automated systems at the NOC 

retrieve data from our stations at least daily, monitor the status 

of the network and alert operators to problems, and pass data 

on for analysis, archiving, and distribution. Real-time network 

status can be found at http://pboweb.unavco.org/soh_map. 

PBO’s analysis centers generate high-quality derived data 

products from PBO raw data. Central Washington University 

and the New Mexico Institute of Mining and Technology 

process raw GPS data to produce initial PBO GPS products, 

including network solutions and station position time series; 

the Analysis Center Coordinator at Massachusetts Institute of 

Technology (MIT) combines these products into the official 

PBO GPS products. Staff of UNAVCO and the University of 

California—San Diego, process data from the PBO borehole 

and laser strainmeter networks and produce cleaned time 

series of shear, areal, and linear strain, Earth tides, pore fluid 

pressure, and other parameters. 

The UNAVCO Facility archives and distributes all PBO 

GPS data products and runs a secondary archive offsite; 

currently, these centers hold over 2.5 terabytes (TB) of PBO 

products. The Incorporated Research Institutions for Seismology (IRIS) Data Management Center and Northern California 

Earthquake Data Center archive and distribute all PBO strainmeter data products, and IRIS archives all PBO seismic data 

products; more than 160 GB of data products are available 

from these archives. These same centers also archive other 

EarthScope seismic and strain data. 

The PBO Web site (http://pboweb.unavco.org) provides 

centralized access to PBO products stored in our distributed 

archives. GPS products may be accessed from http://pboweb. 

unavco.org/gps_data and strain data products from http:// 

pboweb.unavco.org/strain_data. In addition, the individual 

archives provide access to their holdings, both for PBO and 

other networks, through a variety of discipline-specific tools. 

The most exciting development still to come in providing 

access to EarthScope data products will be the creation of the 

EarthScope Portal. This system will be based on Web services 

operated by the EarthScope components that provide access 

to holdings at the EarthScope archives and that are linked to a 

central Web portal. This system will provide a unified system 

for discovery and access to EarthScope digital data products, 

and is planned to be operational by October 2008. 

EarthRef.org in the Context of a 

National Cyberinfrastructure for the 

Geosciences 

By Hubert Staudigel1, Anthony Koppers2, Catherine Constable1, 

Lisa Tauxe2, and John Helly3 

1

Scripps Institution of Oceanography, University of California—San Diego, 

La Jolla, Calif. 
2

Scripps Institution of Oceanography, La Jolla, Calif. 

3

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

EarthRef.org is the information technology (IT) umbrella 

covering four independent grass-roots Cyberinfrastructure 

(CI) initiatives in distinct disciplines of earth and life sciences 

and education: (1) The Geochemical Earth Reference Model 

initiative (GERM, http://EarthRef.org/GERM/) is a consortium 

of leading geochemists who promote understanding of Earth 

chemistry on a planetary scale, by sponsoring scientific workshops and general improvements in CI. GERM was started 

more than a decade ago, and shortly after its inception, it 

played a pivotal role in the creation of the widely used GERM 

Web site and the development of the American Geophysical 

Union’s electronic journal, G-cubed. GERM has also served 

as a forum for the continued development of several independent databases, such as PetDB and GeoROC. The GERM Web 

site offers a range of resources, including compositional data, 

partition coefficient data, and computational tools. It is also a 

source for geochemical publications, such as GERM confer

ence circulars and abstract volumes. (2) The Seamount Catalog (http://EarthRef.org/SC/) is a data repository for seamount 

geographic information, maps, and a wide range of other 

seamount data. It is also the central Web site of the recently 

founded Seamount Biogeoscience Network (SBN; http:// 

EarthRef.org/SBN/) and it aims to provide an equally useful 

CI for disciplines that range from marine geophysics to microbiology to fisheries, with the goal of helping to integrate the 

research from these diverse disciplines on seamounts. (3) The 

Magnetics Information Consortium’s (MagIC; http://EarthRef. 

org/MAGIC/) goal is to provide a data archive and interactive 

visualization Web site for paleomagnetism and rock magnetism. This is a community-driven database, where users can 

upload, search, and use newly derived scientific data as well as 

data from peer-reviewed legacy publications. (4) The Endur

ing Resources for Earth Science Education (ERESE; http:// 

EarthRef.org/ERESE/) project is part of the National Science 

Digital Library (NSDL) and offers a wide range of educational 

resources, in particular, for the teaching of plate tectonics. 

ERESE combines teachers’ professional development with the 

development of digital library resources, and provides Web 

seminars in collaborations with the National Science Teacher’s 

Association (NSTA). These four initiatives have a broad range 

of clients, with dramatically different database and IT needs, 

distinct working styles, and variable levels of comfort with 

CI and IT. However, combining these diverse CI components 

under the EarthRef.org umbrella has been quite cost-effective, and allows each effort to use common IT resources and 

to share software development work. This philosophy has also 

proved rather appealing for its end users: EarthRef.org now 

has over 2,500 registered users, has received 188,000 unique 

users over the last three years, and expects to receive roughly 

120,000 unique users in 2007. 

EarthRef.org has a number of key common features that 

reach across the GERM, MagIC, SBN, and ERESE initiatives, 

­

­

Geoinformatics 2007—Data to Knowledge 7 

many of which we consider important to establish functional 

CI in Geosciences. Some of these features follow: 

Collaboration between IT developers and leading members of the science community—Much of EarthRef.org has 

been developed by, or in close collaboration with, active Earth 

scientists who produce and use data, advise students, and have 

substantive current publication and science-funding activity. 

Their personal and practical experience and perspectives on 

how Earth science is done successfully are a major asset in 

these developments and contribute directly to the resulting 

EarthRef.org Web environment. 

Integrating IT development with science conferences— 

EarthRef.org organizes top-level scientific conferences with 

invited speakers that synthesize the state-of-the-art in a given 

field. Leading scientists involved in major recent scientific 

developments are chosen as keynote speakers for these confer

ences. We use these science conferences to help a community 

hone its science vision, with an eye on the CI needs that will 

ultimately address and solve their “grand challenges.” 

Robust hardware-software requirements—EarthRef.org is 

hosted by the San Diego Supercomputer Center. This hosting 

guarantees deep archiving and safeguarding of all scientific 

data uploaded to the EarthRef.org databases. It also provides 

EarthRef.org with continuous upgrades for the underlying 

hardware, ensuring that its databases and Web sites operate 

with the best technology standards available. 

Laying the foundation for a large-scale cooperation to 

keep scientific content up-to-date—Much care has been taken 

to enable the EarthRef.org users to participate (and ultimately 

take over) data population for legacy as well as primary (new) 

data. To that end we have created various data formats and 

upload wizards that are easy to use via the EarthRef.org Web 

site. Data uploaders have the option of a proprietary hold 

where unpublished data are not accessible to the public. In 

other words, they can keep their data private and view them 

within the context of the existing EarthRef.org databases. 

Users can also provide group names and passwords in order to 

authorize limited groups of coworkers, students, teachers, or 

even reviewers to query and visualize their private data. 

Archiving original records for legacy data for user-based 

quality control and quality assurance—Legacy data entries 

into EarthRef.org are accompanied by several file types, 

including the original scanned image of data tables, and data 

files generated by optical character recognition (OCR) of the 

scanned image. This allows a user to trace data to the original 

source and to explore scanned images as a cause for errors. 

Establishing an information continuum that ranges from 

top science to basic education—Earthref.org, and in particular 

ERESE, work to bridge the gap between science and general 

education through a genuine collaboration between educators and scientists, and by making science database contents 

accessible for education and public outreach. Some of this 

is accomplished through specific contents developed for the 

educational community, but there are also some basic metadata 

that allow educators to screen for specific database contents. 

To this end, all EarthRef.org data contents carry metadata on 

­
 Geoinformatics 2007—Data to Knowledge 

the minimum expert level needed to be able to work with a 

particular digital object. This scale ranges from one (the most 

basic primary school level) to nine (the expert scientist level). 

This relative scale allows educational users to extract objects 

for use in the classroom or curriculum design by browsing 

based on a specified expert level. 

Provide a set of basic CI building blocks that may be used 

by diverse CI efforts—A wide range of features are common 

to all EarthRef.org initiatives, including an online earth-sciences address book, a digital library archive, and an earth-science reference database. The address book allows us to keep 

track of users uploading new data; however, more importantly, 

because it is entirely Google-friendly, it allows users to easily 

find contact information for their colleagues. The EarthRef 

Digital Archive (ERDA) is a multi-purpose digital library 

that can archive any arbitrary digital object (ADO) and does 

provide the basic machinery for deep storing of ADOs and 

for linking data files to the GERM, MagIC, SBN, and ERESE 

portals. The EarthRef.org Reference Database (ERRD) 

contains close to 100,000 references from earth-science 

publications as provided directly by the publishers. This is an 

invaluable resource that allows us to confidently link all the 

user-provided data to the original publisher and to the publications on their respective Web sites. 

Geospatial referencing through Google Maps—Geospatial parameters and data type are amongst the top-rated 

search parameters for geosciences data. The Google Maps 

interface offers an intuitive representation of any parameter. 

We have begun the development of a Google Maps interface 

for seamounts based on a combination of the multibeam data 

and satellite altimetry data stored in Earthref.org. However, 

this interface is equally useful for searching the SBN, MagIC, 

and ERDA databases and has been implemented within each 

of these search portals. 

EarthRef has been built by scientists and educators for 

scientists and educators, and our experience has been that 

much can be done with well-known IT components focusing 

on key practical and some more visionary matters. Practical 

issues include the ease and efficiency of information and data 

acquisition to use, reuse, modeling, and visualization and a 

close link between science and education at all levels. Vision 

issues focus on anticipating future directions of education, science, and, in particular, multidisciplinary science integration. 

This type of vision has to translate into a specific CI design 

with a meaningful metadata structure and ontologies that allow 

us to combine data in new ways. There is a general consensus 

that a successful CI will create an environment that advances 

science in profound ways, helping achieve new levels of 

understanding and addressing the grand challenges. Key to 

such a CI will be community buy-in and ownership. We argue 

that this ownership has to start right from the beginning of the 

development, whereby the “science process” is profoundly 

integrated with CI development. 

Phanerozoic Earth and Life: The 

Paleointegration Project 

By Allister Rees1, John Alroy2, Christopher Scotese3, 

Ashraf Memon4, David B. Rowley5, Judith Totman Parrish6, 

David B. Weishampel7, Emil Platon8, Maureen A. O’Leary9, 

and Mark A. Chandler10 

1

Department of Geosciences, University of Arizona, Tucson, Ariz. 

2

Paleobiology Database, NCEAS, University of California—Santa Barbara, 

Santa Barbara, Calif. 

3

PALEOMAP Project, University of Texas at Arlington, Arlington, Tex. 

4

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

5

Department of Geophysical Sciences, University of Chicago, Chicago, Ill. 

6

Department of Geological Sciences, University of Idaho, Moscow, Idaho 

7

Department of Cell Biology and Anatomy, The Johns Hopkins University 

School of Medicine, Baltimore, Md. 

8

Energy and Geoscience Institute, University of Utah, Salt Lake City, Utah 

9

Department of Anatomical Sciences, Stony Brook University, Stony Brook, 

N.Y. 

10Center for Climate Systems Research, Columbia University, New York, 

N.Y. 

The Paleointegration Project (PIP) within the Geosciences Network (GEON) (http://www.geongrid.org/) is 

facilitating interoperability between global-scale fossil and 

sedimentary rock databases, enabling a greater understanding 

of the life, geography, and climate of our planet throughout the 

Phanerozoic. The key elements of PIP are databases, paleomapping tools, and Web services. 

The following databases are presently in the system (see 

table 1 for temporal distributions): PBDB—The Paleobiology Database (~630,000 occurrences of marine invertebrates, 

vertebrates (including dinosaurs), plants, and microfossils 

from 69,000 collections); DINO—From “The Dinosauria” 

Encyclopedia, 2004 (~4,200 occurrences from 1,200 localities); GCDB—Graphic Correlation Database (~108,000 fossil 

occurrences from 2,000 localities, with over 700 interpreted 

localities that define taxon ranges); PGAP—The Paleogeographic Atlas Project (~135,000 sedimentary rock occurrences 

from 47,000 localities); CSS—Climatically significant sedimentary rocks (~13,000 occurrences from 3,600 localities); 

Table 1. Temporal distributions of databases within the 

Paleointegration Project (PIP). 
OSR—Organic-rich sedimentary rocks (~2,000 occurrences 

from 1,600 localities, including geochemical data). 

The databases are text- as well as map-searchable, 

through the use of age and geography ontologies, linked to 

geographic information systems (GIS) mapping tools. Results 

are viewable on present-day maps as well as paleomaps, and 

can also be downloaded for further detailed analyses. An 

important feature of PIP is the calculation of locality paleocoordinates “on the fly,” based on modern latitude and longitude 

as well as locality age. This was achieved by developing a 

module (Auto Point Tracker, or APT) that enables dynamic 

calculations of locality paleocoordinates and automated plotting on the paleogeographic maps. PIP was designed to ensure 

fast data retrieval, making it especially useful for extensive 

as well as multiple simultaneous searches (for example, in a 

classroom setting). Through the use of Web services developed 

at the San Diego Supercomputer Center (SDSC), the PBDB 

server in Santa Barbara is searchable dynamically within PIP, 

alongside the other databases hosted at SDSC. In addition to 

the PIP features, each PBDB locality record is linked back 

to the original database, enabling the user to either explore 

and analyze the data further within that system (for example, 

using the PBDB statistical tools) or to continue within the PIP 

interface. 

The IT architecture of PIP (fig. 1) shows how the different databases and components interact to produce an 

integrated result and map. Researchers send a query for data 

using the PIP user interface and this query is parsed by the 

PIP middleware. It is then decomposed into several different 

queries sent to the databases, using Java Database’s Applications Program Interface (API) for the ones hosted on SDSC 

servers and using Web Services for querying data hosted at 

remote locations (for example, the PBDB). Once all the results 

are accumulated, they are passed to the APT Web Service for 

“on-the-fly” calculations of locality paleocoordinates so that 

they can be plotted on the paleogeographic maps. 

Geoinformatics 2007—Data to Knowledge  

The Paleointegration Project is already proving useful to 

researchers, teachers, and students. Anyone can now access 

data and tools that were only available previously to a few 

specialists. It should also prove to be an excellent resource for 

a new generation of projects that assimilate both paleoclimate 

models and data for more detailed views of the Earth’s climate 

history. Complex computational tools like Global Climate 

Models (GCMs) simulate details of the Earth’s atmosphere, 

oceans, and land-surface processes that are beyond what proxy 

interpretation alone can provide; however, modelers require 

detailed paleogeographic data, which is used to construct 

the type of boundary conditions used as GCM input. The 

PIP databases will also be useful to paleoclimate modelers 

needing access to proxy climatological data for use in model 

verification. Accessibility to GCMs is now improving through 

programs like the Educational Global Climate Modeling 

Project (see EdGCM at http://edgcm.columbia.edu); thus, 

accessibility to paleo databases is crucial to the development 

of quality data/model integration projects. We emphasize that 

PIP does not replace specialist expertise. It does, however, 

provide another means whereby researchers can develop their 

own scientific queries. 

We envisage continuing to develop PIP with the addition of new datasets, tools, and services. The next phase will 

include the MorphoBank Database, which contains phylogenetic systematics of morphological data (~2,300 anatomical 

images and 23 phylogenetic matrices). The PIP will enable 

phylogeneticists currently using MorphoBank to search seamlessly for relevant fossil taxa in the PBDB and to generate, for 

example, survivorship plots of diversity through time. It will 

also include the online PaleoReefs Database (PARED) developed by Wolfgang Kiessling (Humboldt-University of Berlin), 

which contains data for 3,550 reef complexes throughout the 

Phanerozoic (with details about paleontology, architecture, 

environmental setting, and petrography). Some 35,000 reefal 

taxonomic occurrences from PARED are stored in the PBDB, 

so seamless integration of the additional reef architectural 

and environmental details will be very useful. More tools and 

services will be required as additional databases are integrated, 

to ensure that diverse user needs and interests are addressed. 

We plan to work with projects like PaleoStrat to develop some 

of these new tools and databases within PIP. 

A more complete understanding of the interactions 

between Earth and life through time also requires the integration of geochemical, geophysical, igneous, and metamorphic 

data. From this broader “geoinformatics community” perspective, the Paleointegration Project demonstrates that disparate geologic databases residing on different servers can be 

searched seamlessly using embedded modules, Web services, 

and GIS—structures and tools that will greatly facilitate future 

collaborative efforts. 

Figure 1. The information technology (IT) architecture of the 

Paleointegration Project (PIP). 
0 Geoinformatics 2007—Data to Knowledge 

MOAS: Geoinformatic Database for 

Integration and Synthesis in Coastal 

Modeling 

By Clifford J. Hearn1, Matthew I. Streubert1, and 

Michael A. Holmes2 

1

ETI Professionals, St. Petersburg, Fla. 

2

U.S. Geological Survey, Tampa, Fla. 

Within the last decade, integrated modeling of coastal 

systems has developed as a new methodology through 

advances in the technologies of data acquisition, data synthesis, and simulation modeling. Processing of field and model 

data in multidisciplinary, integrated science studies is a vital 

part of synthesis modeling. Collection and storage techniques 

for field data vary greatly between the participating scientific disciplines due to the nature of the data being collected, 

whether it is in situ, remotely sensed, or recorded by automated data-logging equipment. Spreadsheets, personal databases, text files, and binary files are used in the initial storage 

and processing of the raw data. Network Common Data Form 

(NetCDF) files are created as output from intermediate processing procedures for portability and machine-independent 

sharing of time-series and array-oriented datasets. In order to 

be useful to scientists, engineers, and modelers, the data need 

to be stored in a format that is easily identifiable, accessible, 

and transparent to a variety of computing environments. The 

Model Operations and Synthesis (MOAS) database and associated Web portal were created to provide such capabilities. The 

industry-standard relational database is comprised of spatial 

and temporal data tables, shape files, and supporting metadata, 

accessible over the network through a man-driven, Web-based 

portal, or spatially accessible through ArcSDE connections 

from the user’s local geographic information systems (GIS) 

desktop software. A separate server provides public access to 

spatial data and model output in the form of attributed shape 

files through an ArcIMS Web-based graphical user interface. 

Structured externally to MOAS is a geospatial grid termed the 

“Universal Grid.” This grid, along with temporally referenced 

shape files, reside in an Enviromental Systems Research 

Institute (ESRI) .MXD file and serve as the framework for 

data visualization, manipulation, and utilization, which are 

essential components of the integration and synthesis of costal 

models and data. The Universal Grid provides a user-friendly 

GIS interface to the data within MOAS through point and 

click menus for easy navigation, display, and exportation of 

data. Each cell or group of cells within the grid can be linked 

directly to the database using the “add data” function within 

ArcGIS, then interrogated, analyzed, and compiled within the 

interface. The data can then be extracted in database, spreadsheet, or text-file format. The interface supports rapid display 

of attributes without the necessity of direct user manipulation 

of color bars, parameters, and fields. The interface also supports “on-the-fly” projection changes, and point-click navigation, as well as many other tools under development. This 

approach serves as a viable solution to the ever-growing need 

to access large amounts of data for modeling, mapping, and 

synthesis. Users of the Universal Grid require only minimal 

knowledge or training in GIS to take advantage of its vast 

capabilities. 

The Tampa Bay Integrated Coastal Model is used as a 

case study in this paper, building on the wealth of published 

research and modeling information staged within the estuary. 

The integration and storage of physically collected, synthesized, and modeled data are described in studies concerning 

hydrodynamic, biogeochemical, sedimentary, and hydrological 

processes. 

Automated Multidisciplinary 

Collection Building 

By Caryn Neiswender1, Stephen P. Miller1, Dru Clark1, 

John J. Helly2, Don Sutton2, and John Weatherford2 

1

Scripps Institution of Oceanography, University of California—San Diego, 

La Jolla, Calif. 

2

San Diego Supercomputer Center, University of California—San Diego, 

La Jolla, Calif. 

Geologic disciplines often need to combine both historic 

and current data, observations, and interpretations. If data collections exist, they tend to be homegrown, often drawing on 

the isolated expertise of investigators, with minimal technological support. Expensive to acquire at $25,000 per day, and 

generally impossible to recreate, shipboard data are potentially 

valuable for a wide range of disciplines, far beyond the award 

that funded the original expedition; however, data need to be 

discovered before they can be used, and appropriate metadata 

need to be generated to support effective, wide community 

access. 

Scripps Institution of Oceanography Explorer (SIOExplorer) presents, as a scalable solution, a multidisciplinary 

digital library of over 50 years of shipboard data. Based upon 

an extensible metadata scheme (fig. 1) and implemented with 

technology from the San Diego Supercomputer Center, SIOExplorer enables discovery of data collected onboard Scripps 

Institution of Oceanography (SIO) research vessels. The 

SIOExplorer digital library consists of over 700 SIO cruises, 

with more than 100,000 digital objects, including datasets, 

documents, and images. 

The efforts are being extended to the collections of the 

Woods Hole Oceanographic Institution (WHOI) to include 

cruises, Alvin submersible dives, and remotely operated 

vehicle (ROV) lowerings. The technology also supports the 

efforts of thousands of scientists from dozens of nations with 

the Site Survey Data Bank of the Integrated Ocean Drilling 

Program (IODP). 

Streamlined procedures have been developed to stage 

the data, extract metadata from data files, perform qual­
Geoinformatics 2007—Data to Knowledge 1 

Figure 1. SIOExplorer metadata scheme. Every arbitrary digital object (ADO) is associated with a metadata structure complete with 

descriptive information. Starred entries indicate fields that are managed with controlled vocabularies. 

ity control and error correction, and publish metadata and 

data in a searchable digital library. Discovery tools search a 

PostgreSQL database with metadata, and deliver relevant 

objects from a Storage Resource Broker (SRB) instance. The 

system provides both text-based Asynchronous Java Script and 

XML (AJAX) Web-form and interactive geographical Java 

interfaces. 

Information Infrastructure 

The SIOExplorer is a unique collaboration between scientists, technologists, and educators. The lifecycle of information within the digital library includes data creation, quality 

control and data packaging, publication via the Web, and 

data access/analysis use. To support each of these activities, 

a scalable scientific and technological infrastructure has been 

developed. 

The infrastructure for the digital library is defined by 

a metadata scheme. Currently broken into eight sections, or 

blocks, the SIOExplorer metadata template file (mtf) file comprehensively describes the scientific framework in which the 

digital object was created. 

Interoperability and Controlled Vocabularies 

Interoperability has long been a goal of digital library 

implementations. To facilitate collaboration, tools are developed that can be utilized by other projects, and (or) adapted 
2 Geoinformatics 2007—Data to Knowledge 

to specific implementation needs. One example is the recent 

development of the controlled vocabulary dictionary. In addition to a list of acceptable values for metadata parameters, the 

controlled vocabulary dictionary incorporates a human-readable explanation of each allowed value. Scientists, technologists, and project managers can access this controlled vocabulary dictionary, which enables accurate interpretation of the 

metadata. 

As a member of the Marine Metadata Interoperability 

(MMI) project, SIO promotes the exchange, integration, and 

use of marine data through enhanced data publishing, discovery, documentation, and accessibility. The MMI community 

works to develop best practices and guidance on project development and implementation. Community members include 

metadata experts, scientific researchers, and project managers. 

Automatic Harvesting in an Imperfect World 

Over the years, and across projects and disciplines, there 

has been a tendency for descriptive terminology within metadata to wander. Some of the variation is due to evolution in 

sensor technology, but some may be due to odd abbreviations, 

typographical errors on rolling decks, institutional practices, or 

a momentary inspiration to use a new term. As a consequence, 

we now face challenges in searching digital collections, and in 

designing reusable tools that can be applied to multiple institutions. 

Practical experience with SIOExplorer has enabled the 

development of techniques to assess variations in metadata 

values across collections. The assessment helps to guide the 

development of controlled vocabularies, which in turn can be 

used to enable automatic detection of metadata errors, and in 

some cases automatic correction. 

Controlled vocabularies underlie an emerging set of tools 

that support Web-user interfaces, large-scale automatic har

vesting of metadata and data, project status assessment, workflow management, and overall quality control. They are a key 

resource for user upload code in the IODP Site Survey Data 

Bank, prompting and enforcing appropriate metadata values 

for ocean-drilling proposal-support data. Compared to previous generations of hard-wired code, the access to controlled 

vocabularies allows a project to evolve with flexibility, and the 

code to be ported from one project to another. 

Related links 

•	 SIOExplorer—http://SIOExplorer.ucsd.edu 

•	 SSite Survey Data Bank—http://ssdb.iodp.org/ 

•	 SMarine Metadata Interoperability—http://ssdb.iodp. 

org/ 

•	 SStorage Resource Broker—http://www.sdsc.edu/srb/ 

index.php/ 

­

Sensor Web Enablement—Its Impact 

on Continuous Digital Workflows 

By Peter Loewe1, Jens Klump1, and Joachim Waechter1 

1

GeoForschungsZentrum-Potsdam (DRZ), Potsdam, Germany 

A major challenge for the use of scientific data 

is caused by the absence of continuous digital workflows. 

Research in the earth sciences can span several dimensions 

and orders of magnitude in time and space, crossing scientific 

domain boundaries, which results in the fields’ semantical 

richness. This research led to the accumulation of a tremendous amount of literature, data, and sample collections. So far, 

all the findings have been separated and underutilized due to 

the absence of continuous (digital) workflows providing sustainable data management. The advent of information technology and Internet-based Web services has created new options 

to improve this situation. They offer ways to integrate literature, data, and samples from the source of data in the field or 

laboratory, all the way to their interpretation in the literature. 

Yet, numerous breaks between data capture and data processing remain to be closed. The Sensor Web Enablement (SWE) 

standards currently developed by the Open Geospatial Consortium can be used to close the gaps. They provide integrative 

approaches to environmental monitoring and the capturing 

of real-time data in earth observing systems. The paradigm 

of Sensor Web Enablement extends beyond its application in 

environmental sensor networks. Its ability to model any data 

source or process as a sensor that takes in data and puts out 

processed data, in conjunction with directory and system management services, makes it a universal tool for earth-science 

data. In this paper, we look at the roles of SWE software tools 

provided by the open-source software communities for the 

creation of continuous digital workflows in the earth sciences. 

These workflows can be used for monitoring and data capture, 

data processing and modeling, creation of added value chains, 

and new forms of publication. 

A System for Fast Spatial Searches on 

the Earth or Sky Using the Hierarchical 

Triangular Mesh 

By Gyorgy Fekete1 

1

Department of Physics and Astronomy, The Johns Hopkins University, 

Baltimore, Md. 

Spatial searches represent the most frequent search patterns, both in astrophysics and in earth sciences. In each of 

these areas, scientists use a multitude of different coordinate 

systems, describing the distribution of both point-like and 

extended objects over the sphere. Many cutting-edge science 

projects today involve multiple sources, and thus require a 
detailed fusion and federation of these datasets. Very often 

drop-outs (objects that are not detected in one of datasets) are 

particularly interesting, since they could represent objects that 

are quite “small.” Such searches require a detailed knowledge 

of what area of the globe has been covered by a particular 

instrument (Earth- or satellite-bound). Once the common 

area (the intersection of the respective footprints) has been 

determined, we need to find the relevant objects in this area, 

and perform a spatial cross match, and find all detections that 

may correspond to the same physical object. All this requires 

a rather sophisticated set of tools. Requirements are rather 

stringent; satellite orbits consist of long stripes over the sky, 

and the required accuracy is determined by the resolution of 

the best detectors. We need to track objects over hundreds of 

degrees with a resolution of a few milliarcsecs. We need a 

framework that can be used in several different contexts, like 

stand-alone user applications, Web services, and Web applications, and that can also be embedded into existing commercial 

database systems. We have been working on this problem for 

over 12 years. In the mid-1990s we wrote a simple C-based 

package called the Hierarchical Triangular Mesh (HTM) that 

is still being used by several observatories and NASA centers. 

We then substantially rewrote the library to C++. This package 

is currently in use at multiple institutions and projects worldwide. 

We address two separate mathematical issues. The first is 

about the correct abstract mathematical representation of complex spherical regions, and about implementations of various 

set operations (union, intersection, and difference), morphological functions (dilation and erosion), and scalar functions 

(area). Beyond the optimal algorithms for these operations, 

there are also serious challenges related to numerical precision; for instance, determining whether two planes exactly parallel. The second issue is a discrete pixelization of the sphere. 

For fast spatial searches we use data structures suitable for 

fast search algorithms. We use the sphere quadtree to build a 

hierarchy of triangle-shaped pixels (trixels) on the globe, thus 

the name—Hierarchical Triangular Mesh (HTM). 

There are many ways to represent shapes on the surface 

of the unit sphere. We chose a system where there are no singularities at any pole, employing three-dimensional Cartesian 

unit vectors to describe locations on an idealized (unit radius) 

sphere. The most basic building block is the half space, a 

directed plane that splits the three-dimensional space into two 

halves. It is parameterized by a normal vector “n” and a scalar 

“c” (distance from the origin). When intersected with the unit 

sphere, a half space describes a spherical cap. Inclusion of 

a point inside such a cap is decided by a simple dot-product 

computation. 

A so-called “convex” is the intersection of many half 

spaces. A rectangle is described by a convex of four half 

spaces, the sides of the rectangle. A region, the most general 

shape, is simply a union of convexes. 

The normal form of a region is, therefore, a union of 

intersections. Very often the shapes are more conveniently represented by other well-known primitives, such as rectangles, 

Geoinformatics 2007—Data to Knowledge  

spherical polygons, circles, and the convex hull of a point set; 

using these familiar terms, we provide a full complement of 

shape functions that convert descriptions into the normal form. 

As always, the devil is in the details. Floating-point computations are subject to rounding errors; a number of mathematical issues arise from these inherent uncertainties in our 

project. What two points or half spaces should be considered 

identical? When is a point exactly on an arc? Computer geometry libraries working in Euclidean space sometimes avoid 

these issues by utilizing exact arithmetic, mathematical formulas on fractional numbers represented by integer numerators 

and denominators; however, this slower work-around is not an 

option for solving the spherical geometry due to the normalization constraint, which involves taking the square root of the 

coordinates. We use the IEEE-754 standard, double precision 

numbers, and derive the limitations from the actual number 

representation. At the core of many formulas is the cross 

product to find perpendicular directions. Colinear vectors have 

vanishing cross products and numerically this can be tested in 

a robust way by comparing the largest coordinate to the double 

precision. If it is too small, its square root cannot be taken to 

normalize the vector and the indefinite perpendicular direct 

means colinearity. A similar problem is solving for the roots 

of two circles on the sphere. If the computation is inaccurate, 

the roots will not be on the circles numerically. The sweet spot 

for double precision numbers is at about the tolerance level of 

108

 radians, which corresponds to a few thousandths of an arc 

second. This is about a foot in size on the surface of the Earth. 

There are two basic kinds of spatial constraints in a 

query: (1) “Is this object within some distance of a target?” 

and (2) “Is this object inside some region?” 

Both kinds involve costly geometrical computation, so 

we devised a mechanism that eliminates objects from consideration quickly. Crude boxing techniques, which elect candidates by boxing latitude and longitude values, work well only 

if the region of interest is shaped like a rectangle aligned with 

the latitude and longitude grid; however, they are less effective for other shapes, like those that contain the pole or narrow 

stripes that have a high inclination to the primary great circles. 

The main idea is that we implement a coarse filter whose job 

is to reject all objects that are certain to fail the spatial constraint, and use the fine filter for only the few false positives. 

The goal is to make the coarse filter as good as possible but 

also as fast as possible. 

In a database that has a user-defined function called 

AngularDistance and a table of Objects that have columns 

named ObjID, Lat and Lon, a simple query that selects objects 

within half a degree of a point (Lon = 12, and Lat = -30) is as 

follows: 

select ObjID from Objects o where 

o.AngularDistance(12, -20, o.Lon, o.Lat) < 0.5 . 

The costly AngularDistance function would be evaluated 

for each object in the table. Instead, if we implemented the 

coarse filter as a function that produces a table that would be 
 Geoinformatics 2007—Data to Knowledge 

efficiently computable on the fly, then the query is augmented 

with a join as follows: 

select ObjID from Objects o join CircleCover(12, -20, 

0.5) c on o.HtmId between c.lo and c.hi 

AND AngularDistance(12, -20, o.Lon, o.Lat) < 0.5 . 

The second example presumes the existence of an HtmID 

column and the CircleCover table-valued function that returns 

rows of low, high values that constrain the possible HtmId values. Only those objects that pass the first constraint are given 

to AngularDistance for the precise calculation. The function 

CircleCover here produces a table that represents a circular 

region of interest centered on the given location and radius; 

however, our methods are capable of expressing arbitrary 

shapes with the full richness of the Region-Convex-Half space 

paradigm. The essence of the idea is that the inside of every 

trixel is turned into a range query in a database over the HtmId 

values. We can exploit the relational database engine’s ability 

to use the HtmID as an index for very rapid operation. 

With the advent of modern silicon-based detectors, the 

way observational science is done is changing rapidly. To 

take full advantage of the avalanche of data, we need scalable 

information systems that provide reliable access via interoper

able interfaces and efficient search capabilities. For a low-cost 

scientific data analysis infrastructure, all of the above components are required at the same time. Our approach is to wed 

state-of-the-art database and Internet technologies to novel 

algorithmic developments in order to enable fast and seamless 

access to collaborative science archives. The framework serves 

both the earth and space sciences communities. 

SODA—Self-Service Online Digital 

Archive for Unloved Scientific Data 

By Rex Sanders1 

1

U.S. Geological Survey, Santa Cruz, Calif. 

SODA (Self-service Online Digital Archive) is a project 

under development at the U.S. Geological Survey (USGS) for 

archiving “unloved” scientific data. 

Background 

USGS collects many thousands of gigabytes of new data 

annually. Many data types have well-defined processing and 

archiving paths, but many do not—our so-called “unloved 

data.” Unloved data types usually fall into two classes: data 

types that have not traditionally shown national significance 

(for example, marine sediment analyses), and data types created from new technology and research (for example, airborne 

and land-based light detection and ranging (LiDAR) surveys). 

­

Unloved data are difficult to find, difficult to access, and often 

vanish completely upon the retirement or departure of key 

scientists and technicians. 

Scientists with the best intentions frequently fail to 

archive their data well enough. One scientist carefully created and labeled three copies of digital core photos on a total 

of 90 CD-R discs. Three years later, none of these disks were 

readable because the label adhesive had corrupted the data 

layer. As a nonprofessional archivist, he did not know the risk 

associated with using sticky labels on CD-R discs. 

Goal and Use Cases 

The SODA project wants to make archiving unloved scientific data easier than having to burn another CD-R disc. 

We are building our system around two use cases— 

archiving data and finding data. 

To archive data: 

1. Point your Web browser to “soda.usgs.gov,” and click 

the “Submit Data” button. 

2. Fill out a form with data type, format, minimum metadata (or more), and select a public release policy. 

3. Upload your data. 

4. Get a “permanent” link pointing to the data and metadata, with initial internal-only access. 

5. Archivist reviews the data, metadata, and release 

policy. 

6. If review passes, archivist enables public access to data 

and metadata according to release policy. If review 

fails, archivist contacts you for corrections. 

To find data: 

1. Point your Web browser to “soda.usgs.gov,” and click 

the “Find Data” button. 

2. Fill out form to search for data using any metadata 

fields, including geographic region, data type, or 

author. 

3. Get links to download the data and metadata directly to 

your desktop. 

Design Features 

SODA will have other features, including: 

• 	 Design for users—Using SODA will be easier than 

burning another CD-R; 

• 	 Design for reuse by other Web-based tools, including 

ArcGIS, Geospatial One-Stop, MRIB (http://mrib.usgs. 

gov), InfoBank (http://octopus.wr.usgs.gov/infobank), 

and so on; 

• 	 Design for longevity using open standards and simple 

technology; 

• 	 Design to scale to large numbers of very large files; 

• 	 Separate searches for USGS-only and public data and 

metadata; and 
• 	 Archivists can easily add new data types, data formats, 

metadata forms, and release policies. 

Benefits 

Some of the anticipated benefits of SODA include the 

following: 

• 	 Improved access to data and metadata by USGS scientists and the public; 

• 	 Scientists and technicians will be able to archive data 

easily and immediately; 

• 	 Improved data preservation; 

• 	 Reduced data rescue; 

• 	 Scientists will be able to cite permanent links in published papers; and 

• 	 Scientists and technicians will not need to respond to 

data requests. 

SODA is intended to be the scientific data archive of last 

resort, dependent on the cooperation of overworked scientists 

and technicians to keep valuable data from being lost forever. 

As such, we cannot require very much effort to archive the 

data; the process must be simple and self-explanatory. Our 

reduced metadata and approval requirements disappoint many 

mainstream data archivists, but capturing more data with some 

metadata is better than capturing no data. 

SODA is not intended to replace any other USGS dataarchiving mechanism, including open-file reports, data series 

reports, or online databases such as NWIS (http://waterdata. 

usgs.gov/nwis). 

Technical Design 

SODA technical design is based on the following principles: 

• 	 SODA servers will be organizationally and geographically distributed, and locally run, but centrally searchable. 

• 	 SODA servers will be easy to set up and run by local 

information technology (IT) personnel, using inexpensive hardware and software. 

• 	 SODA is much more than hardware and software— 

SODA will include processes and procedures to ensure 

the longevity of the data archive. 

• 	 A SODA “cookbook” will enable IT personnel to set 

up and run a SODA server with little outside support. 

• 	 A central SODA server will enable searches and 

retrieval across the distributed SODA servers. 

Current Status of the SODA Project 

SODA has been under development with minimal funding since early 2006. After surveying commercial and opensource projects, we concluded that writing our own software 

and designing our own system would best meet our needs. 

Geoinformatics 2007—Data to Knowledge  

We have a core developer team with three members, and an 

e-mail-based advisory group with about 45 members, all 

working at the USGS. 

We have an initial, nonarchival prototype running. We are 

using the prototype to work out many technical, user-interface, 

process, and procedural issues. 

We anticipate release of our first production SODA server 

by the end of 2007. A few months after that release, we anticipate release of the SODA “cookbook” to enable other sites to 

set up and run local SODA servers. Development of the central 

search system and other SODA features is unscheduled, 

dependent on acquisition of further resources. 

We will consider joint development of SODA with nonUSGS partners. 

From Flight Data to Knowledge of the 

Atmosphere’s Chemistry: An Example 

from INTEX-NA 

By V.E. Delnore1, J.H. Crawford1, A.A. Aknan2, and C.C. Brown2 

1

Science Directorate, National Aeronautics and Space Administration Langley Research Center, Hampton, Va. 

2

Science Systems and Applications Incorporated (SSAI), Hampton, Va. 

Abstract and Concept 

This paper describes how proper management of airborne 

data contributes to an increase in knowledge of the chemistry 

of Earth’s atmosphere. Before the mission, historical data are 

used by mission scientists to design the airborne campaign. 

During the field phase, flight planners use new data from each 

flight to lay out following flights. Post mission, the newly 

acquired data are archived for maximum accessibility to a 

wide range of interested parties, to include educators and the 

interested public, as well as the mission participants. Throughout all phases of the mission, data specialists maintain close 

cooperation with mission scientists, flight planners, principal investigators, and potential users to ensure that the data 

become useful knowledge. This paper focuses on this process 

as successfully implemented for INTEX-NA (defined below), 

and cites an example benefiting the National Oceanic and 

Atmospheric Administration (NOAA). 

The Field Mission 

INTEX (the Intercontinental Chemical Transport Experiment) is an atmospheric chemistry field mission seeking to 

understand the transport and behavior of gases and aerosols on 

transcontinental and intercontinental scales and their impact 

on air quality and climate. INTEX is a National Aeronautics 

and Space Administration (NASA) contribution to a larger 
 Geoinformatics 2007—Data to Knowledge 

Figure 1. The flight lines indicate the many regions of the world sampled during the heritage Global Tropospheric Experiment (GTE) and 

the recent INTEX campaigns. The broad arrow represents the flow of data through the Langley Research Center’s archive facility and, 

ultimately, to scientific and public users. 

global effort—International Consortium for Atmospheric 

Research on Transport and Transformation (ICARTT). A 

particular focus of the 2004 phase of INTEX (“INTEX-NA,” 

for North America) is to quantify and characterize the inflow 

and outflow of pollution over that continent. The red lines in 

figure 1 below show the track lines of the INTEX-NA flights, 

designed to sample a wide variety of chemical species over 

and around the continental U.S. 

Data Collection and Archiving 

Data management for the final phase of INTEX-NA has 

been completed. The Web-based Principal Investigator Data 

Registration system and Web-based data archive proved to be 

invaluable to mission scientists in making revisions to their 

data both in the field and after returning home, as they refined 

instrument calibrations and algorithms. NASA-sponsored 

aircraft serving as instrument platforms for the hundreds of 

datasets included the DC-8, SkyResearch J-31, and Proteus. 

The archive also hosts datasets from ground stations, satellites, national lightning-sensor and ozonesondes networks, and 

air-mass trajectory and model calculations. These data are now 

publicly available on the Web at http://www-air.larc.nasa.gov/ 

missions/intexna/intexna.htm. The data have also been incorporated into a plotting tool at http://www-air.larc.nasa.gov/cgibin/2Dplotter and into a digital atlas tool at http://www-air. 

larc.nasa.gov/cgi-bin/datlas. The latter application displays 

altitude profiles (as well as statistical summaries) of chemical species measured on the DC-8 for the entire INTEX-NA 

mission. The archive also hosts merge products standardized 

to a common time base for many species. Sixty-second merges 

were created during the field phase of the mission and used 

for planning subsequent flights. For the preliminary and final 

phases of the mission, merges were created for a very wide 

variety of chemical species as needed by mission scientists. 
Links to the INTEX-NA mission summaries, data archive, 

and analysis tools can be accessed at http://www-air.larc.nasa. 

gov/missions/intexna/intexna.htm. Among these is the Satellite 

Predictor Tool at http://www-air.larc.nasa.gov/tools/predict. 

htm, which yields the subtracks of an orbiting spacecraft, 

along with the footprint paths of sensors selected by the user. 

This was particularly useful for planning flights to support 

validation of satellite instruments. 

Data Management and Formatting 

To support INTEX-NA, the Langley Research Center’s 

Research, Education, and Applications Solution Network 

Cooperative Agreement (REASoN-CAN) team worked very 

closely with NOAA’s Aeronomy Lab, also an ICARTT mission partner, in developing the ICARTT data protocol and 

formats. The Langley group then took the lead in developing a 

Web-based data scanning and archiving system consisting of 

several tools that worked together to achieve full automation. 

Mission scientists received instant feedbacks that proved very 

useful for uploading their data files. The archiving system was 

then expanded to archive data for many additional platforms 

sampled during the ICARTT mission. Future refinements of 

this system will be undertaken with the cooperation of international data users’ working groups. 

Enabling Science: An Example of Moving Data to 

Knowledge 

Making use of data merge and overlay tools developed 

by the Langley REASoN team, a NOAA researcher, Owen 

Cooper, combined data from the INTEX ozonesonde network 

(IONS), the National Lightning Detection Network, and Measurement of Ozone on Airbus In-service Aircraft (MOZAIC) 

observations from commercial aircraft. These data were 

interpreted by Cooper with the FLEXPART dispersion model 

to establish the link between lightning emission of nitrogen 

oxides and the large increases in ozone observed over North 

America during the ICARTT experiment. 

Acknowledgments 

The REASoN-CAN Award—Through its REASoN-CAN 

grant, “Synergistic Data Support for Atmospheric Chemistry 

Field Campaigns,” Langley Research Center provides data 

management to support flight planning during tropospheric 

chemistry field missions. After the field phase of a mission, 

Langley’s researchers gather corroborating data from many 

sources and provide these data to the scientific and educational community in forms that encourage further research 

and analysis, and to support NASA’s outreach programs. The 

award is provided through NASA’s Program Executive for 

Data Systems, Science Mission Directorate. 

Geoinformatics 2007—Data to Knowledge 7 

Patch Reef Analysis Using LiDARDerived Metrics at Biscayne National 

Park, Florida 

By Monica Palaseanu-Lovejoy1, John Brock2, 

Amar Nayegandhi1, and Wayne Wright3 

1

ETI Professionals, St. Petersburg, Fla.
 

2

U.S. Geological Survey, St. Petersburg, Fla.
 

3

National Aeronautics and Space Administration Wallops Flight Facility, 


Goddard Space Flight Center, Wallops Island, Va. 

This study uses submerged topographic data and coral 

reef rugosity estimates derived from National Aeronautics and 

Space Administration’s (NASA’s) Experimental Advanced 

Airborne Research LiDAR (EAARL) for Biscayne National 

Park, Florida (Brock and others, 2004, 2006). The purpose 

is to evaluate the capability of NASA EAARL to describe 

patch reef variability and habitat complexity. Over 1,000 patch 

reefs were analyzed using mean neighborhood statistics and 

reclassification of LiDAR and slope data. The area of each 

reef was divided into the following categories: (1) base or reef 

footprint, (2) side of the reef, and (3) top of the reef. Differ

ent mean metrics were derived for each reef category from the 

submerged topography and rugosity data. Scatter plots of reef 

depth versus rugosity, relative relief, shape index, perimeter, 

area and volume, respectively, suggested that the multivariate 

data is bimodal. A mixture of two log-normal distributions 

suitably approximated the reef depth distribution. The depth 

at which the two log-normal distributions intersect was used 

to divide the patch reefs in shallow (less than 7.77 m) and 

deeper (7.77 to 14 m) reefs, respectively. The results showed 

that shallow patch reefs had a tendency to be bigger with a 

smaller relative relief than the deeper patch reefs. Topographic 

complexity, or rugosity, increased with depth for shallow reefs. 

In contrast, for deeper reefs, rugosity decreased with depth. 

An independent component analysis was carried out on 

principal components derived from the patch reef metrics to 

determine if depth was the single most important factor to 

influence reef physical variability and habitat complexity. 

Principal components, although uncorrelated, are only partly 

independent (Hyvarinen and Oja, 2000). Two distinctly different independent components emerged from the analysis of 

seven principal components that described over 95 percent of 

data variability. We demonstrate that one independent component can be a function of patch reef rugosity while the other 

independent component is most likely a function of reef geometry and depth. These two independent components divide the 

patch reefs population in the following three depth classes: (1) 

from 2 to 6 m, (2) from 6 to 9.5 m, and (3) from 9.5 to 14 m, 

respectively. The deepest class correlates with the tail data not 

modeled by the log-normal mixture distribution. 

Independent component analysis is more sensitive than 

simple multivariate analysis in assessing data variability. 

Multivariate analysis confirmed two major different popula­

­
 Geoinformatics 2007—Data to Knowledge 

tions—shallow and deeper reefs—with divergent rugosity 

correlations, but similar behavior of other reef metrics, such 

as perimeter, area, and volume. Independent component 

analysis suggests that three classes may be more appropriate 

to describe patch reef variability and habitat complexity in 

Biscayne National Park. 

References Cited 

Brock, J.C., Wright, C.W., Clayton, T.D., and Nayegandhi, 

A., 2004, LIDAR optical rugosity of coral reefs in Biscayne 

National Park, Florida: Coral Reefs 23, p. 48-59. 

Brock, J.C., Wright, C.W., Kuffer, I.B., Hernandez, R., and 

Thompson, P., 2006, Airborne lidar sensing of massive 

stony coral colonies on patch reefs in the northern Florida 

reef tract: Remote Sensing of Environment, v. 104, p. 31-42. 

Hyvarinen, A., and Oja, E., 2000, Independent component 

analysis: Algorithms and applications: Neural Networks, v. 

13, nos. 4-5, p. 411- 430. 

Internet GIServices for Homeland 

Security 

By Ming-Hsiang Tsou1, Tong Zhang1, and John Kaiser1 

1

Department of Geography, San Diego State University, San Diego, Calif. 

Homeland Security and Web GIS 

This paper will illustrate the potentials of applying Internet Geographic Information Services (GIServices) to improve 

homeland security intelligence works. In the post-911 era, 

homeland security has been one of the primary missions for 

all levels of U.S. governments. The homeland security intelligence is of critical importance in preventing terrorist attacks, 

responding to the natural or human disasters, and recovering from the hazard damages. A Web-based spatial decision 

support system can combine dynamic geospatial information 

and Web mapping services from multiple Web servers located 

in Federal, State, and local agencies. The dynamic integration of Web-mapping services and information can provide 

more accurate and effective information for decisionmaking 

processes. Such information exchange is essential to pre-emergency planning, critical first-response actions, relief efforts, 

and community recover. Furthermore, such information can 

greatly enhance daily operations and cooperation among agencies in meeting homeland defense responsibilities. 

The NASA REASoN Project Showcase 

This paper will introduce a National Aeronautics and 

Space Administration (NASA) REASoN (Research, Education 

and Applications Solution Network) project, called “A Border 

Security Spatial Decision Support System,” as a showcase of 

Internet GIS applications for homeland security tasks. This 

project is the collaboration between San Diego State University and the San Diego Sector of U.S. Border Patrol Agency 

(http://geoinfo.sdsu.edu/reason). This project seeks to establish 

Figure 1. The interoperable database framework for Web-based spatial decision support systems. 
Geoinformatics 2007—Data to Knowledge  

Figure 2. The integrated Internet GIServices (combining NWS and NHSS with local San Diego GIS layers). 

and implement an integrated Web-mapping service that will 

allow rapid integration of geospatial data among participating 

agencies. By utilizing a standardized Web-mapping interface 

(Open Geospatial Consortium’s Web Map Server Interfaces 

Implementation Specification) and popular vendor-based 

frameworks (Enviromental Systems Research Institute’s 

ArcIMS services), individual participating agencies can implement their own data systems and services while maintaining an 

aggregated, system-wide interoperability through multiple data 

warehouses and Web-based decision support systems (fig. 1). 

Real-Time Web Mapping from Multiple 

Resources 

To test and evaluate the feasibility of the proposed 

framework, the REASoN project selected the National 

Weather Service’s (NWS’s) (http://www.nws.noaa.gov) and 

U.S. Geological Survey’s (USGS’s) Natural Hazard Support 

System (NHSS) (http://nhss.cr.usgs.gov) to combine with local 

San Diego Border Region GIS layers. Real-time or near-realtime data from NWS and NHSS can be rapidly fetched and 

distributed to the Web GIS application and be viewed by decisionmakers. Figure 2 illustrates an ArcIMS mapping service 

screenshot which combines NWS, NHSS, and local GIS data 

in the San Diego region to demonstrate the potentials of Web 

GIS-spatial decision support systems. 

Discussion and Future Directions 

Beyond GIS data and mapping services, Internet GIServices can support customizable spatial-analysis functions in 

the future. The newly developed ArcGIS server by ESRI or 

open-source GIS tools offers powerful GIS analysis tools at 

the server side. It is clear that desktop GIS tools are migrating 

to the Web platform. It is totally applicable to develop comprehensive GIS analysis online tools by either using commercial 

Application Development Framework or by adopting a lower 

level of programming models, such as Java-based applications. 

Another future direction of Internet GIServices for homeland 

security is the adoption of mobile GIS with wireless communication devices. Mobile GIS can provide critical geospatial 

information on the ground and can be sent to the Internet GIS 

server for data update and announcement in a timely manner. 

The servers can also respond to the inquiries made by the client devices to assist in-field actions. 

To summarize, the needs of enhancing homeland security provide great opportunities for the future development of 

Internet GIServices. The recent progress of GIS technologies 

and Web services can facilitate the easy adoption of Internet 

GIServices in all major homeland security tasks. Besides the 

technology development of Internet GIServices, it is also 

important to promote the awareness of geospatial technology in various levels of decisionmaking and to facilitate the 

collaboration between GIS researchers and homeland security 

staff. 
100 Geoinformatics 2007—Data to Knowledge 

An Automated Parallel Computing 

System in the GEON Grid: Applications 

to Multiscale Crustal Deformation in 

the Western United States 

By Mian Liu1, Huai Zhang1, Youqing Yang1, and Qingsong Li1 

1

Department of Geological Sciences, University of Missouri—Columbia, 

Columbia, Mo. 

In the past decade, supercomputing power has become 

available to most researchers in the form of affordable 

Beowulf clusters and other parallel computer platforms. However, to take full advantage of such computing power requires 

developing parallel algorithms and related codes, a task that 

is often too daunting for geoscience modelers whose main 

interest is in geosciences. As part of the Geosciences Network (GEON) effort, we have been developing an automated 

parallel computing system built on open-source algorithms 

and libraries. Users interact with this system by specifying the 

partial differential equations, solvers, and model-specific properties using a high-level modeling language in the input files. 

The system then automatically generates the finite element 

codes that can be run on distributed or shared-memory parallel machines. This system is dynamic and flexible, allowing 

users to address a large spectrum of problems in geosciences. 

We demonstrate this modeling system with a suite of geodynamic models that simulate multiscale crustal deformation in 

the western United States, ranging from timescale-dependent 

faulting in the San Andreas fault system to strain localization 

and active deformation in the western U.S. Cordillera. We 

show that this system may facilitate integration of high-performance computing with distributed data grids in the emerging 

geoscience cyberinfrastructures. 

Disk-Based Gridding for Large 

Datasets 

By Steven Zoraster1 

1

Austin Subsurface Modeling, Inc., Austin, Tex. 

Abstract 

As computer speed and memory increase, the amount 

of data to be processed grows even faster, requiring more 

sophisticated algorithms to run on the new hardware. Terrainand subsurface-formation modeling have experienced this data 

explosion, along with a demand for larger output models. In 

this paper, I present a mathematically sophisticated, gridbased surface-modeling algorithm that makes little demand 

on computer random-access memory (RAM). The algorithm 

is competitive with other gridding algorithms on small- or 

medium-size datasets. For large problems, those with tens of 

millions of input data points and output grids with billions of 

nodes, this algorithm produces a solution where many others 

are just starting or have already crashed. 

Introduction 

The algorithm presented here, named Basin Gridding, is a 

disk-based, mathematical algorithm for gridding large datasets. The algorithm is an extension of an in-core, iterative, BSpline algorithm (Lee and others, 1997). My implementation 

follows the workflow of the original algorithm, and retains the 

accuracy of that algorithm. Like that algorithm, Basin Gridding implements a one-directional, multilevel solution, working with coarse grids during early iterations and increasingly 

finer resolution grids during later iterations. The difference is 

that Basin Gridding requires minimal RAM. Because of the 

conservative use of RAM, Basin Gridding increases the size of 

datasets that can be modeled and the size of the grids that can 

be produced. The price paid for conservative use of RAM is 

reliance on disk-based storage and the associated time penalty. 

Central-processing unit CPU and wall-clock times for this 

algorithm are shown in table 1. 

Previous Work 

A working assumption for all two-dimensional gridding is that the data is sampled from an elevation function F, 

mapping R2 -> R1, and that the derived model approximates 

F to some degree of accuracy over R2. This problem has been 

studied for decades. It is possibly impossible to describe all 

types of gridding algorithms. Reviews can be found in papers 

and books by Foley and Hagen (1994), Franke and Nielson 

(1991), and Jone sand others (1986). A recent, general purpose 

algorithm for solving large gridding problems using matrix 

formulations and iterative techniques has been presented by 

Billings and others (2002). 

A tradeoff rarely addressed for gridding algorithms is 

dependence on RAM and dependence on hard-disk memory. 

Control Points Grid Nodes 

(millions) 

CPU 

(minutes) 

Wall Clock 

(minutes) 

89,000 .3 .2 .3 

250,000 4.6 .4 .5 

250,000 18.5 .5 1 

5,700,000 8.3 5.7 11 

5,700,000 133.0 17 78 

30,000,000 380.0 50 205 

145,000,000 4,876 170 600 

Table 1. Algorithm central-processing unit (CPU) and wall-clock 

times. 
In related domains, such as computational geometry, these 

tradeoffs are considered. For example, sweep-line algorithms 

for finding intersections between members of a set of line segments are both memory efficient and a natural way to organize 

computations. Fortune’s (1989) sweep-line based, two-dimensional triangulation is an algorithm that could be used for 

surface modeling that would make relatively small demands 

on RAM. I do not know if it is being used anywhere for that 

reason. 

For Basin Gridding, the criteria for algorithm selection 

was an ability to model large datasets and create large models 

in small amounts of RAM, without sacrificing mathematical 

properties such as smoothness, C2 continuity, and accuracy. I 

wanted, at least in theory, an algorithm that could solve a gridding problem of any size. Basin Gridding is the result. It is an 

extension of an in-core, hierarchical B-Spline gridding algorithm. By trading computational time for disk access, I created 

an algorithm with the good qualities of the in-core algorithm 

with reduced dependence on RAM. 

Hierarchical B-Spline Gridding 

In-memory algorithm 

Creating a grid-based surface model with B-Splines 

requires the calculation of a lattice of control points over a grid 

at the same (delta x, delta y) resolution. A multigrid approach 

is often followed, starting with a coarse 4-x-4 grid that is 

refined at the end of every iteration until the target grid row 

and column spacing are achieved. Lattice values are derived 

so that the evaluation of a local set of control lattice values, 

used to weigh a set of third-order basis functions, produces an 

estimate of surface values in each grid cell. At the final grid 

resolution, this compact support avoids global influence from 

purely local data distributions or local surface roughness. In 

addition, B-Spline surfaces using third-order basis functions 

are C2 continuous, which promises high-quality interpolation 

between regions with different data densities. 

This general hierarchical B-Spline workflow is shown in 

figure 1. Each square block represents an intermediate grid at 

some row and column spacing in the multigrid progression. 

Each refinement, represented by an oval, creates a new grid 

with twice as many rows and columns at one-half the spacing 

of the previous grid’s rows and columns. Before updating, a 

feature of the refinement process is that the lattice values of 

the new finer grid represent the same surface model as the grid 

that was refined. So refinement supplies a more localized way 

to change lattice weights to better fit the available data at small 

cost. 

As shown in figure 1, updating for each data point 

involves changing local lattice weights stored at the nodes of 

a 4-row-by-4-columns subset of the entire grid centered at the 

cell holding the data point. Each individual update makes the 

implicit surface better fit that data point’s z value. For sparse 

data relative to grid row and column spacing, this algorithm 

Geoinformatics 2007—Data to Knowledge 101 

Figure 1. Algorithm general work flow showing embedded 4-x-4 

grid. 

interpolates control point z values exactly. Even at the final, 

densest grid resolution, however, there may be multiple control 

points within two grid increments of many grid nodes; therefore, the final grid will seldom interpolate all the input data 

exactly. Instead it will pass a smooth, representative surface 

model through such dense collections of data. 

Basin Gridding Algorithm 

The local 4-x-4 updating of lattice values is the algorithm 

feature that allows it to store most columns of the current grid 

model on disk during each iteration. For any one control point, 

updating modifies nodes in only four adjacent grid columns. 

Grid columns to the left of these four, and columns to their 

right, might as well be kept on disk. If the data is sorted on 

its x coordinate, then lattice updates will flow across the grid 

from left to right. As each new control point is reached, the 

four grid columns of interest will be either the four currently 

in memory, or a set of four adjacent columns to their right. 

This local focus, combined with sorting of the input data, 

breaks the RAM constraint. The focus on a few columns is 

indicated by the narrow rectangles inserted in each grid block 

in figure 2. These rectangles represent grid columns that are 

in memory at any one time. These processing rectangles move 

across the grid from left to right. Columns of the grid to the 

left of the window have been updated and can be written to 

disk. Columns to the right are still on disk, not yet updated, 

ready to be read into memory. 

The “moving window” scheme discussed above is also 

applied to the “refinement” step. Refinement also proceeds 

from left to right. At any instant, a few columns of the grid at 
102 Geoinformatics 2007—Data to Knowledge 

Figure 2. Disk-based work flow. 

the “input” resolution will be in memory while being used to 

generate higher density columns of the output grid. The output 

grid columns have twice as many nodes as the input columns. 

This is not a problem because only three input columns need 

to be read into memory to create two output columns. Again, 

the output columns are written to disk after creation, the 

refinement process moves to the right, and another input grid 

column is read into memory. 

In fact, the description above simplifies algorithm data 

structures and mathematical operations. Because multiple data 

points may influence the updating of a single grid node, the 

original in-core algorithm performs the summation of update 

information in two auxiliary grids, and only at the end of an 

iteration does it update the grid lattice values. That means that 

Basin Gridding must keep four columns from three grids in 

memory at one time. The actual updating of the lattice values 

in a left-hand column of the lattice grid is computed from the 

left-hand column of the auxiliary grids, just as the examination 

of a new control point reveals that one or more new (rightside) columns of the control lattice need to be read from disk, 

and left-side lattice columns need to be updated and written 

to disk. New columns for the auxiliary grids are created as 

needed and node values in those two columns start as zeros. 

Left-hand columns of the auxiliary grids that are no longer 

needed are simply deleted from RAM. 

Basin Gridding incorporates other enhancements. Grid 

refinement is a distinct operation. That means there is a lattice 

grid read from and written to disk twice for every iteration 

level. I avoid this by interleaving grid refinement with imposing data on the grid. As grid columns leave the processing 

window—as the window moves right—the algorithm keeps 

two extra left-side columns in memory, does refinement in 

RAM, and then writes two new columns of the refined grid 

lattice. The result of all the enhancements discussed above 

is a gridding algorithm constrained only by free space on the 

computer’s hard disks. 

Discussion and Conclusion 

An algorithm has been presented for creating rectangular 

grid-based surface models using disk storage to increase the 

size of the datasets that can be modeled and the size of the 

models that can be produced. The method applies multigrid BSpline gridding in a novel way such that internal storage space 

is required for only a few columns of the target grid and one 

x, y, z triple of data at any one instance. The general workflow and mathematical details for memory-based hierarchical 

B-Spline gridding are honored, so there is no loss of accuracy 

from this use of disk storage. 

References Cited 

Billings S.D., Beatson, R.K., and Newsam, G.N., 2002, 

Interpolation of geophysical data using continuous global 

surfaces: Geophysics, v. 67, p. 1810-1818. 

Foley, T., and Hagen, H., 1994, Advances in scattered data 

interpolation: Surveys on Mathematics for Industry, v. 4, p. 

71-84. 

Fortune, S., 1987, A sweepline algorithm for Voronoi diagrams: Algorithmica, v. 2, p. 153-174. 

Franke, R., and Nielson, G.M., 1991, Scattered data interpolation of large sets of scattered data: International Journal of 

Numerical Methods in Engineering, v. 15, p. 1,691-1,704. 

Jones, T.A., Hamilton, D.E., and Johnson, C.R.,1986, Contouring geologic surfaces with the computer, in Computer 

methods in the geosciences: New York, Van Nostrand 

Reinhold, 315 p. 

Lee, S., Wolberg, G., and Shin, S.Y., 1997, Scattered data 

interpolation with multilevel B-Splines: IEEE Transactions 

on Visualization and Computer Graphics, v. 3, p. 228–244. 

Geospatial Cyberinfrastructure 

Solution: Open Source or COTS? 

By Tong Zhang1 

1

Department of Geography, San Diego State University, San Diego, Calif. 
Geoinformatics 2007—Data to Knowledge 10 

Internet GIServices and Geospatial 

Cyberinfrastructure 

Internet-based Geographic Information Servcies (GIServices) are definitely important components within the geospatial cyberinfrastructure. The goal of providing interoperable, 

intelligent, and scalable Internet GIServices is consistent 

with the underlying driving force of building the geospatial 

cyberinfrastructure; therefore, the research on building the 

well-performed geospatial cyberinfrastructure will pose challenges on the enhancement of current Internet GIS technologies. Internet-based GIService solutions are primarily two 

strategies—open source and commercial off-the-shelf (COTS). 

From both open-source and commercial-off-the-shelf development contexts, there are advantages and disadvantages demonstrated in the development of geospatial cyberinfrastructure. 

The methodology to integrate the advantages of two solutions 

while downplaying the downsides should be an interesting 

research topic. 

The Geospatial Cyberinfrastructure Vision 

The National Science Foundation is leading the research 

toward the cyberinfrastructure, which is an unprecedented 

initiative to construct underlying and supporting facilities in 

order to advance scientific research, decisionmaking, and collaboration by integrating any available and useful resources. 

The cyberinfrastructure initiative can be implemented in 

Geography and Geosciences domains as well. The so-called 

geospatial cyberinfrastructure is a vision that will be used as 

the blueprint of the future geospatial technology in the next 

few decades. Similarly, the geospatial cyberinfrastructure 

should be able to allow the researchers to conduct high-end 

research supported by a variety of computing resources in 

a collaborative manner. The goals may involve facilitating 

geographic decisionmaking, promoting productivity, preserving the environment, securing the homeland, and advancing 

geosciences education. 

Internet GIServices and Their Roles in the 

Geospatial Cyberinfrastructure 

Internet GIServices evolved from the rudimentary, static 

Hypertext Markup Language (HTML), prerendered map 

images, to interactive Web mapping and toward distributed 

GIServices. Current Internet GIServices are rapidly moving 

toward providing high-performance complex data and analysis 

services. This transition makes Internet GIServices a perfect 

technology to create global-scale geospatial cyberinfrastructure. The geospatial cyberinfrastructure heavily relies on the 

advancement of Internet GIServices. 

Open-Source Solutions and COTS Solutions 

Both the open-source and COTS Internet GIS solutions 

can be traced back to the early stage of static Web mapping. 

The commercial products, such as Enviromental Systems 

Research Institute’s (ESRI’s) ArcIMS, have been widely used; 

the open-source packages have also gained popularity in the 

last few years. 

A Review of Open-Source Strategy 

Open-source software development aims to produce computer programs which are free of charge. More importantly, 

the source codes are open to the public. Internet GIServices 

has long been an active area of open-source development. 

Lately, open-source Internet GIServices have gained a tremendous rise in popularity. The open-source packages vary, from 

the Internet GIServer and geographic information systems 

(GIS) database management system, to GIS analysis tools. The 

Open Geospatial Consortium (OGC) specifications work as 

the de facto standards for the entire open-source GIS community. 

A Review of COTS Strategy 

Commercial Internet GIServices solutions still dominate 

the market. COTS Internet GIServices software has been 

extensively using the computing technologies—from Common 

Gateway Interface (CGI), ActiveX, Java, and dynamic HTML 

(DHTML), to Asynchronous Java Script and Extensible 

Markup Language (XML) (AJAX) and Web services. Commercial Internet solutions also allow flexible customization of 

interfaces and functions. 

A Comparison 

As for costs and development transparency, open-source 

solutions prevail. Open-source software becomes competitive 

for GIS vendors in terms of reliability and functionality; however, COTS software still has advantages over the open-source 

counterparts, especially from the perspective of end users. The 

reluctance of going open source can be contributed to the fact 

that open-source software is relatively more difficult to configure and customize. The technical supports, product liability, as 

well as complete documentation, explain the larger portions of 

market share by the COTS solution. Given these challenges, 

the recent creation of the Open Source Geospatial Foundation 

could be seen as a major event for joint efforts compared to 

previously uncoordinated endeavors. A comparison will be 

given for the most popular open-source and COTS products 

covering the aspects of system architectures, costs, platform 

neutral, Open Geospatial Consortium (OGC) compatibility, 

performance, spatial data formats, customization and development, installation, administration, and configuration, Web 
10 Geoinformatics 2007—Data to Knowledge 

mapping effectiveness, spatial analysis functions, security, and 

reliability. 

The Integration of Internet GIS in the Geospatial 

Cyberinfrastructure 

The development of the geospatial cyberinfrastructure 

requires the integration of Internet GIS technologies with other 

supporting components. 

Current Practices 

Current geospatial cyberinfrastructure practices are 

extensively using Internet GIS to deliver a variety of services, including metadata cataloging, Web mapping, GIS data 

processing, and visualization, as well as high-performance 

GeoComputation and geocollaboration. Both open-source and 

COTS software can be used in the current geospatial cyberinfrastructure development. As an example, take the Geosciences Network (GEON) ArcIMS is used as an online mapping 

tool. 

Challenges and Discussion 

Geospatial cyberinfrastructure involves many computing 

technologies that interact with Internet GIS. Integration should 

be a big concern. To seamlessly work with other components, 

Internet GIS may have to comply with international standards 

from both GIS and computer science communities. In addition, some emerging technologies, such as Grid computing, 

may give rise to further problems. The large-scale scientific 

research projects demand stable, reliable, secure, and scalable 

Internet GIServices. The complexity of geospatial problems 

and decisionmaking will continue to introduce obstacles in 

developing domain-specific applications. 

A Hybrid Methodology 

Simply put, the hybrid methodology selects the most 

suited solution for a given task within geospatial cyberinfrastructure. The COTS strategy has been leading the technical 

development of Web-mapping user interface, server administration, and configuration, as well as service security. The 

high reliability of commercial products makes them better 

candidates to deliver the most frequently used GIS functions, 

such as Web mapping, data management, and simple geovisualization and analysis. The standardized and well-defined 

tools can be developed by commercial packages. However, 

some specific applications and requirements (for example, 

performance, interoperability, security, and reliability) would 

demand that developers go into a lower level of development; 

in these instances, open-source solution could be selected. 

Based on this methodology, a Web portal prototype 

design is presented with specific software tools (fig. 1). The 

design is feasible and applicable with all available technologies. 

The Prospect 

The future geospatial cyberinfrastructure will promote 

the development of Internet GIS by providing both opportunities and challenges. Neither open-source solutions nor COTS 

solutions will prevail; they will continue to compete with each 

other. 

Figure 1. A Web portal prototype design based on the hybrid 

methodology. 
Brady and others—Geoinformatics 2007—Data to Knowledge—Scientific Investigations Report 2007-5199 

Printed on recycled paper