Cover. Image of Europe produced by the Moderate Resolution Imaging Spectrometer (MODIS) aboard the National Aeronautics and Space Administration’s (NASA’s) Terra satellite. This image is part of the U.S. Geological Survey’s Digital Image Gallery 

(http://eros.usgs.gov/imagegallery/).
Geoinformatics 2008—Data to Knowledge

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

Scientific Investigations Report 2008–5172

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2008

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:

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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.

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by the USGS. Abstracts submitted by researchers from academia and from other State, Federal, and international 

agencies are published as part of these proceedings but do not necessarily reflect the Survey’s policies and views.

Suggested citation:

Brady, S.R., Sinha, A.K., and Gundersen, L.C., eds., 2008, Proceedings, Geoinformatics 2008—Data to Knowledge, 

Potsdam, Germany, June 11–13, 2008: U.S. Geological Survey Scientific Investigations Report 2008–5172, 76 p.
iii

Preface

The Geoinformatics 2008 Conference brought together many of the leading researchers 

from many countries to share new insights into the status of informatics-based research, with 

the common goal of working towards meeting geoscience-based societal challenges. The vision 

of the participants of a fully integrated geoscience cyberinfrastructure was highlighted at the 

meeting. Information technology research presentations emphasized the significant gains in the 

implementation of collaborative environments, portals, workflows, visualizations, semantics, 

and Web-based engines for visualization and integration. The application of many of these technologies was identified within the ongoing, multinational OneGeology project. The emphasis 

on developing collaborations between institutions, agencies, and countries was the most significant outcome of the conference. Collaboration is likely to be the intellectual driver of the future 

as geoscientists and computer scientists join to meet the global challenges of resource discovery 

and management, climate change, and natural disasters.

Conference Sponsorship:

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

National Science Foundation, German Research Center for Geosciences, American Geophysical Union, British Geological Survey, Federal Institute for Geosciences and Natural Resources 

[Germany], American Geophysical Union (Earth and Space Science Informatics), European 

Geosciences Union (Earth and Space Science Informatics), Electronic Geophysical Year (eGY), 

and the Committee on Earth Observation Satellites’ Working Group on Information Systems 

and Services.

Organizing Committee:

Kristine Asch, Federal Institute for Geosciences and Natural Resources, Hannover, 

Germany; Sally Brady U.S. Geological Survey, Reston, Va.; Peter Fox, National Center for 

Atmospheric Research, Boulder, Colo.; Linda Gundersen, U.S. Geological Survey, Reston, Va.; 

Ian Jackson, British Geological Survey, Nottingham, United Kingdom; Jens Klump, German 

Research Center for Geosciences, Potsdam, Germany; Peter Löwe, German Research Center 

for Geosciences, Potsdam, Germany; Bernd Ritschel, German Research Center for Geosciences, Potsdam, Germany; Márta Nagy-Rothengass, European Commission, Brussels, Belgium; Andrea Schwerdtfeger, German Research Center for Geosciences, Potsdam, Germany; 

A. Krishna Sinha, Virginia Polytechnic Institute and State University, Blacksburg, Va.; Dogan 

Seber, San Diego Supercomputer Center, La Jolla, Calif.; and Lesley Wyborn, Geoscience 

Australia, Canberra, Australia.

Financial support was provided by the U.S. Geological Survey, National Science Foundation Division of Earth Science, and the German Research Center for Geosciences.

Conference support was provided by Geological Society of America (Nancy Carlson, Eric 

Nocerino).

v

Contents

Preface ...........................................................................................................................................................iii

Oral Session I..................................................................................................................................................1

Global Challenges and the Challenges for Geoscience Informatics (Keynote).........................1

Answers to Earth System Science Questions—The Evolution of Informatics at 

the U.S. Geological Survey ....................................................................................................2

Using Remote Sensing and GIS Techniques to Monitor the State of the Greenland 

Ice Sheet...................................................................................................................................3

Use of Remote Sensing Data in Searching for Hydrocarbon Deposits .......................................6

Geoinformatics Mapping of Renewable Energy Resources and Systems in the 

Philippines ................................................................................................................................6

Network of Research Infrastructures for European Seismology (NERIES)—

Progress Review .....................................................................................................................7

OneGeology—The Global Context for a European E-Geoscience Project.................................8

Implementation Plan for the Geoscience Information Network (GIN) ........................................9

GeosciNET—A Global Geoinformatics Partnership .....................................................................11

A Three-Dimensional GIS Model Based on Crystallographic Principles Dedicated 

to Spatial Analyses in Geosciences...................................................................................12

Avizo—Three-Dimensional Visualization Framework ..................................................................13

Tunisian Structural Extrusion Revealed by Numerical Geomorphometry.................................14

Project Towards Simultaneous Visualization for Various Kinds of Geoscience Data 

on Google Earth .....................................................................................................................15

GIS-Morphometry—A GIS Framework for Digital Tectonic Geomorphology Studies............16

Geoinformatics and Nature Parks ...................................................................................................17

Geoinformatics on the Front Lines—Purdue University’s Inaugural Geoinformatics 

Course .....................................................................................................................................19

Oral Session II ..............................................................................................................................................20

A Cyberinfrastructure-Based Portal for Topographic Data Access, Processing, 

and Community Interaction .................................................................................................20

Standardizing Interfaces for External Access to Data and Processing for the 

National Aeronautics and Space Administration’s Ozone Product 

Evaluation and Test Element (PEATE) ................................................................................21

The Open GeoSpatial Consortium Web Coverage Service Standard for Unified 

Sensor, Image, and Statistics Data Services....................................................................22

Orchestrating Grid-Computing-Enabled Web Processing Services ..........................................24

Building a Geospatial Web Portal Based on Service-Oriented Architecture...........................24

Coming of Age—The Positive Legacy of Free and Open-Source Software 

Geographic Information Systems.......................................................................................26

Data Integration Using the Image Grand Tour...............................................................................28

A Collaborative Environment for Climate Data Handling .............................................................31

Globalization of Geoscience Information—Developing Collaboration to Sustain 

Growth (Keynote) ..................................................................................................................31

The German Research Center for Geosciences’ Information System and Data 

Center—Portal to Geoscientific Data, Information, and Knowledge ...........................33

Scientific Application Portal Development for Research and Education in 

Cyberinfrastructure...............................................................................................................36
vi 

Neptune—Developing a Digital Information Infrastructure for Micropaleontology 

in the 21st Century ..................................................................................................................37

The EarthScope Data Portal .............................................................................................................38

Enhancing Core Drilling Workflows Through Advanced Visualization Technology ................40

An Analysis of Landscape Change Based on Remote Sensing and Geographic 

Information Systems in the Jinghe Basin, China .............................................................41

The GEOROC Database as Part of a Growing Geoinformatics Network ...................................42

Directory Interchange Format (DIF) Metadata and Handling at the German 

Research Center for Geosciences’ Information System and Data Center..................43

Network of Research Infrastructures for European Seismology (NERIES)—Web 

Portal Developments for Interactive Access to Earthquake Data on a 

European Scale .....................................................................................................................46

Semantically Enabled Registration and Integration Engines (SEDRE and DIA) for 

the Earth Sciences................................................................................................................47

Oral Session III.............................................................................................................................................51

Semantic Provenance for Image Data Processing.......................................................................51

Effective Future Use of Current Remotely Sensed Data Sets to Study Long-Term 

Climate Changes....................................................................................................................54

Peer-Reviewed, Open Data Publication as a Means of Data Quality Management in 

Research.................................................................................................................................55

Long-Term Availability of Geoscience Data ...................................................................................55

Towards an OpenEarth Framework (OEF).......................................................................................58

Integration of Hydrologic Observations from Government and Academic Data 

Collections with the Consortium of Universities for the Advancement of 

Hydrologic Sciences (CUAHSI) Hydrologic Information System..................................60

Metadata and Semantics in the Astronomical Virtual Observatory...........................................62

Comparison of Different Land-Use Object Classes by Means of Semantic Similarity 

Measurements.......................................................................................................................63

Semantic Web Technologies for Value-Added Services at the German Research 

Center for Geosciences’ Information System and Data Center....................................66

Sensor-Based Landslide Early Warning System (SLEWS)—Development of a Spatial 

Data Infrastructure With Integrated Real-Time Sensor Data as a Basis for 

Early Warning Systems Exemplifying Landslides ............................................................69

Ontological Geosciences ..................................................................................................................71

A Volcano Erupts—Semantic Data Registration and Integration ..............................................72
Geoinformatics 2008—Data to Knowledge

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

Oral Session I

Global Challenges and the Challenges for 

Geoscience Informatics (Keynote)

By Ian Jackson1

1

British Geological Survey, Keyworth, Nottingham, United Kingdom.

At a meeting in late April 2008, at the Royal Society in 

London, the Chief Scientific Advisor to the United Kingdom 

(UK) Government set out the key global policy challenges 

facing mankind. The following list is predictable but no less 

worrying: population growth, urbanization, poverty alleviation, technological change, food supply, energy demand, water 

resources, security, infectious diseases and, compounding 

them all, climate change. Some of the predictions are frightening: 60 percent of the world’s population will live in urban 

environments in 2030 as compared with only 30 percent in 

1950—and that is 60 percent of a very much larger global 

population than exited in 1950.

Where does geoscience come in? Well, if we (society) 

want clean water, a house that won’t fall down, fuel for our 

cars, and a safe site to dispose of our waste, then we need to 

know about the rocks and processes beneath our feet. If our 

urban or rural homes are in a part of the world where earthquakes, volcanoes, and tsunamis are possibilities, then the 

need to understand those rocks can be a matter of life or death. 

Unfortunately for society, information about the rocks isn’t 

always up-to-date, joined-up, understandable, and (sometimes) 

even available at all in all parts of the world. As a result, the 

essentials of life, such as clean water, building materials, 

and precautions against natural disasters, are that much more 

difficult to provide. We in the geosciences have some serious 

challenges to contemplate.

Whether we work in universities, geological surveys, 

other government agencies, or commerce, contributing to finding solutions to the challenges above has to be our overriding 

mission. So how can we in the geoscience informatics domain 

contribute best and what are the specific challenges that this 

creates for us if we want to raise our game? What are the areas 

in our science and our approaches to them that we need to 

change, improve, and do more of?

Taking a lead from the word “challenge”, these areas and 

approaches can (hopefully memorably) be defined by 10 words 

beginning with the letter “C” (and here I must acknowledge and 

apologize for taking liberties with the discussions at a recent summit on geoinformatics in Rome, and with the English language!).

•	 Communicate—We need to make sure our science is 

available to all the stakeholders whether they have a 

degree in geoscience or computing or not.

•	 Content—We need to give a higher priority to data 

management because, arguably, the biggest problem 

we face is the lack of quality data, not the lack of computing power.

•	 Collaborate—We need to be able to share and integrate 

our information; therefore, we need interoperability 

and much improved taxonomies and semantic control.

•	 Coordinate—We need to manage our efforts in a coherent and cost-effective way with minimal duplication 

and fewer turf wars.

•	 Consistency—We need to develop standards and best 

practices and then comply with them so we can cut effort 

and add value to otherwise insular data and models.

•	 Chart—We need to audit and map the data resources 

we have and make them discoverable.

•	 Currency—We need to exploit new technologies, in 

particular Web technologies, but we also need to focus 

on the problem and not get seduced by the technology.

•	 Competencies—We need to ensure that we have the 

right skills and therefore we also need to ensure that 

we are providing the proper education and training to 

meet the challenges.

•	 Contribute—We need to proactively share our knowhow with each other and listen to and understand the 

needs and contexts of those in the developing world.

•	 Change—We need to be prepared to be more agile and 

more flexible; we need to accept that change (as well 

as the increased pace of change) is a fact of life and 

that more than any time in the past we probably won’t 

be able to do as we have always done.
2 Geoinformatics 2008—Data to Knowledge

This presentation will, drawing on national and international examples, explore these challenges and the issues they 

raise.

Answers to Earth System Science Questions—

The Evolution of Informatics at the U.S. 

Geological Survey

By Linda C. Gundersen1

1

U.S. Geological Survey, Reston, Va.

The U.S. Geological Survey’s (USGS’s) science strategy 

(U.S. Geological Survey, 2007) identified data integration as 

one of its crosscutting strategic science directions and states 

the following: “The USGS will use its information resources 

to create a more integrated and accessible environment for its 

vast resources of past and future data. It will invest in cyberinfrastructure, nurture and cultivate programs in Earth-system 

science informatics, and participate in efforts to build a global 

integrated science and computing platform.”

USGS is constructing a service-oriented architecture 

(SOA) for all USGS data and science applications, which is a 

complex challenge for a 129-year-old institution that has been 

collecting earth science data since its inception. This effort 

requires operating on many aspects of architecture creation 

simultaneously while dealing with extensive legacy analog 

and digital data. Projects are underway that include everything 

from building a federated database warehouse, developing 

Web services for discovery of data, creating communityspecific data models, and building integrated-earth-system 

scientific applications. The complex issues of governance, 

platforms, standards, and active engagement in international 

and national informatics efforts require the development of the 

SOA to be collaborative, iterative, and experimental. The following provides highlights of projects underway to create this 

service-oriented architecture for earth-system science.

Data Discovery

As a first step, USGS is engaged in evaluating the agency’s 

data holdings and is creating a searchable, spatially enabled 

database tentatively named the “National Digital Catalog 

(NDC)” This catalog will provide discovery tools, metadata, a 

geospatial interface, and other information related to the nature 

of the materials or data. A Web-based pilot was recently developed to test hardware and software technology. This pilot, the 

Geospatial Management Information System (GMIS), provides 

one-stop access to different sources of USGS data, including 

detailed information on the thousands of USGS science projects 

being conducted around the world. The system allows the user 

to search information by topic and geographic area. Another 

part of the NDC under development is a catalog service for the 

physical material collections of the State geological surveys 

and Department of Interior bureaus that will contain searchable 

metadata on data and materials such as core, rock samples, well 

logs, engineering data, and maps.

Map Services

The USGS has created (and is constantly revising and 

updating) national, regional, and topical map-based data in a 

wide range of scales and resolutions. The National Map (http://

nationalmap.gov/) provides imagery from a variety of sources, 

elevation and hydrographic data, geographic names, and landcover data. The Mineral Resources On-Line Spatial Data service 

(http://mrdata.usgs.gov/) provides access to national databases 

and maps of mines, historical mining, mineral occurrences, 

geochemistry of rocks and sediments, lithology, geology, and 

geophysics of the United States. Both the National Map and the 

Mineral Resources On-Line Spatial Data service include map 

browsers, download sites, and Web services.

Integrated Science Applications

The PAGER (Prompt Assessment of Global Earthquakes 

for Response) system is an automated system developed by the 

USGS (http://earthquake.usgs.gov/eqcenter/pager/) to integrate 

multiple data streams; rapidly assess the number of people, 

cities, and regions exposed to severe shaking by an earthquake; 

and inform emergency responders, government agencies, and 

the media about the scope of the potential damage. PAGER 

monitors the National and Global Seismological Networks; 

retrieves shaking intensities reported by people in the epicenter 

region via USGS’s online “Did You Feel It?” system; generates 

a site-specific ground-motion amplification map; and computes 

the population affected at each intensity level. Within 15 to 30 

minutes, depending on the location and size of the earthquake, 

PAGER produces regional ground shaking estimates using the 

reported intensities, the site-specific ground-motion amplification map, and seismic-wave-attenuation equations that account 

for the variations of seismic shaking intensity with magnitude, 

distance, and depth. Final information is distributed in multiple 

formats and through multiple media, including Google’s online 

map display services, automatic e-mail alerts, and community 

standard Extensible Mark-up Language (XML) format.

Two significant USGS collaborative efforts that bring 

global communities together to address global-scale societal 

issues are the Famine Early Warning Systems Network (FEWS 

NET) and the Delta Research and Global Observation Network 

(DRAGON). The FEWS NET grew out of an effort started by 

the U.S. Agency for International Development (USAID) in the 

wake of the devastating 1985 famine in Ethiopia. Today it is a 

modern network of multiple agencies supplying multiple data 

streams from both ground observations and satellite remote 

sensing. These data feed into a series of Web services and programs to produce products ranging from key vegetation indexes 

to sophisticated daily flood models. The network identifies 

and provides early warnings for famine in sub-Saharan Africa, 
Geoinformatics 2008—Data to Knowledge 3

Afghanistan, Central America, and Haiti. The USGS Earth 

Resources Observation and Science (EROS) Data Center (USGS 

EDC) works in cooperation with USAID, the National Aeronautics and Space Administration (NASA), the National Oceanic 

and Atmospheric Administration (NOAA), and Chemonics 

International, Inc., to provide the data, information, and analyses 

needed to support the FEWS NET activity. NASA and NOAA 

are responsible for the collection and processing of satellite 

data that provide the spatial coverage and temporal frequency 

necessary for monitoring both vegetation condition and rainfall. 

Chemonics maintains a staff of field representatives responsible 

for key field observations and monitoring regional and countryspecific conditions. USGS EDC provides end-to-end data 

management, processing, and analyses; GIS and remote-sensing 

technical support; crop and flood modeling; and long-term data 

archive and distribution services. The Africa Data Dissemination 

Service (ADDS) provides additional Web services. 

DRAGON is an effort recently initiated by USGS to 

create a global science framework for comparing, integrating, 

and predicting the key drivers and management practices in 

large delta ecosystems. Large delta ecosystems provide the 

habitat for a broad diversity of flora and fauna as well as lifesustaining agriculture, commerce, and fisheries for hundreds 

of millions of people. The project will require an extensive 

effort to (1) make large volumes of ecological, hydrological, 

geological, and biogeochemical information interoperable; (2) 

create a common data and discovery portal; and (3) develop 

community tools and models through a global “community 

of practice” in delta system management. The pilot program 

partners the USGS with the Chinese Qingdao Institute for 

Marine Geology to develop the conceptual frameworks for the 

Lower Mississippi Valley simultaneously with those for the 

Huang He River. The resulting joint comparative model will 

be expanded to include river deltas in the Netherlands, Russia, 

Vietnam, and other countries with similar deltaic systems.

Reference Cited

U.S. Geological Survey, 2007, Facing tomorrow’s challenges—U.S. Geological Survey science in the decade 2007–

2017: U.S. Geological Survey Circular 1309, 70 p.

Using Remote Sensing and GIS Techniques to 

Monitor the State of the Greenland Ice Sheet

By Meredith C. Payne1 and Anne W. Nolin2

1

College of Oceanic and Atmospheric Sciences, Oregon State University, 

Corvallis, Oreg.

2

Department of Geosciences, Oregon State University, Corvallis, Oreg.

As we strive to tailor hypotheses related to global 

climate change while assessing the possibility of an anthropogenic driver, it becomes crucial to constantly monitor the 

world’s most climatically sensitive areas. Examples of such 

areas include glaciers and ice sheets whose record melting 

is impacting communities on a global scale. In some cases, 

regions that rely upon glacial water as a principle source of 

fresh water are witnessing the rapid dwindling of resources. In 

other cases, rising sea level, to which the melting of glacier ice 

contributes, is threatening low-lying communities. Unfortunately, as is the case with the Greenland ice sheet, many such 

areas are remote and dangerous, making spatially and temporally comprehensive field measurements cost prohibitive. 

Hence, we must rely on remotely sensed measurements from 

aircraft and satellites in order to fill in our knowledge gaps left 

by sparse field measurements.

The Multi-angle Imaging SpectroRadiometer (MISR) 

instrument (operational since 2000) is on board the Earth 

Observing System (EOS) satellite, Terra, which is in a sunsynchronous polar orbit. MISR is uniquely suited for studying 

the poles because of the continuous, overlapping coverage of 

data taken by its nine pushbroom cameras that are arrayed at 

fixed angles ranging from 0° to 70.5° (from nadir) and symmetric about the nadir camera. Each camera has four filters: 

red, green, and blue (in the visible), and a near-infrared (NIR) 

at 866 micrometer (μm) wavelength. The multi-angle views 

in conjunction with the 275-meter (m) resolution (available at 

the visible red wavelength on all nine cameras) can be used to 

compute a proxy of surface roughness of the observed target 

on a scale that is comparable to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m data (Nolin 

and others, 2002). We have elected to use MISR’s red-filtered 

C-cameras (60.0° fore and aft) in order to define and investigate a proxy for ice surface roughness based on forward 

and backward scattered radiation, which we call the Normalized Difference Angular Index (NDAI). We define NDAI for 

Greenland to be fore C-camera red-channel values subtracted 

from the aft C-camera red-channel values divided by their 

sum. Because the forward-viewing camera is seeing forwardscattering radiation while the aft camera sees backscattered 

radiation (the sun is to the south), the forward scattering is 

associated with generally smooth surfaces and backward scattering dominates when an observed surface is rough (Nolin 

and Payne, 2007). Therefore, in an NDAI image, values range 

from -1 to 1 and rougher surfaces appear brighter.

As a case study of the NDAI proxy measurement, we 

chose to study a region in western Greenland encompassing 

Jakobshavn Glacier (69.2° N, 50.2° W, 40 m elevation), which 

is one of the fastest moving glaciers in the world and one that 

drains a significant percentage of the Greenland ice sheet. Its 

area is greater than 9,200 square kilometers (km2

) (Rignot and 

Kanagaratnam, 2006). Our study site extends upglacier in the 

inland ice to Summit (72.6° N, 38.5° W, 3200 m elevation), 

which is the highest point on the Greenland ice sheet. We 

reviewed all available MISR images of the Greenland ice sheet 

for blocks 30 to 35, paths 8 to 10 during the 2000 to 2007 

sunlit seasons across this transect. We determined 2004 to be 

the year when our study site was least obscured by clouds. 
4 Geoinformatics 2008—Data to Knowledge

Nevertheless, completely cloud-free images over the entire 

region of study were impossible to come by. We investigated 

the application of the Radiometric Camera-by-camera Cloud 

Mask (RCCM) product, provided by the National Aeronautics 

and Space Administration’s (NASA) Langley Atmospheric 

Science Data Center (ASDC) to the radiance images, but 

found the mask to be overly strict when distinguishing cloud 

pixels from ice pixels. Hence, using digital image processing 

along with geographic information system (GIS) techniques, 

we devised a method of creating composite images of NDAI 

and of top of the atmosphere (TOA) bidirectional reflectance 

factor (BRF) encompassing the early- (April and May), 

mid- (June and July), and late- (August and September) ablation season (fig. 1). These composite NDAI and reflectance 

images, along with their corresponding gradient images (midseason composite minus early-season composite, and late-season composite minus mid-season composite) were examined 

to establish a pattern whereby the coastal regions are observed 

to grow progressively rougher throughout the ablation season. 

Ice surface roughness is intensified by (1) seasonal snow and 

ice in the ablation zone melting back to reveal underlying bedrock, (2) melt ponds forming upglacier in the wet-snow (slush) 

and percolation zones (as defined by Benson, 1960; Long 

and Drinkwater, 1994), (3) sastrugi (jagged erosional features 

caused by wind) morphology becoming more pronounced, 

and (4) melting and collapse of snow and ice bridges to reveal 

the highly irregular crevasse topography beneath. Although 

changes are not as dramatic upglacier towards Summit (melt 

ponds do not appear in the dry-snow zones), changes towards 

a rougher surface are observed mid-season in the percolationzone, as NDAI pixels have greater values and become brighter 

compared with the early-season. As expected, there is little 

observed change in the near proximity of Summit over the 

sunlit season because it lies in the conjectured dry-snow 

zone as defined by Benson (1960) and Long and Drinkwater 

(1994). In the late-season images (August and September) 

after snowfall has resumed (especially over the wet-snow and 

percolation zones), NDAI values are observed to drop, but not 

fall as low as the early-season (April and May) values. Study 

of TOA reflectance images reveals the exact opposite relationship of pixel values throughout the time series: the pixels 

become darker (lower values) during mid-season and brighten 

Figure 1. Images showing 

the ice-surface roughness 

of a portion of the Greenland 

ice sheet. Images A through 

C show the Normalized 

Difference Angular Index 

(NDAI), a proxy for icesurface roughness, for the 

2004 ablation season (AprilMay, June-July, and AugustSeptember composites, 

respectively). Higher NDAI 

values signify rougher 

surfaces. Images D through F 

are the composite reflectance 

images for April-May, JuneJuly, and August-September, 

respectively. In images D 

through F, greater reflectance 

values signify smoother 

surfaces, which demonstrates 

the opposite relationship to 

the NDAI. Black pixels on the 

inland ice in images D through 

F indicate no data. 
Geoinformatics 2008—Data to Knowledge 5

after fresh snowfall towards the end of the sunlit season. These 

relationships are illustrated in figure 2, in which NDAI and 

reflectance values from each of the three respective composite 

images are plotted along a straight transect from Jakobshavn 

to Summit.

We are encouraged enough by these results to proceed 

with production of similar NDAI composite images for the 

entire Greenland ice sheet for all years where enough lowcloud-percentage images are available. We hope to use these 

products to expand our analyses of the evolution of glacier 

zones during the operational lifetime of the MISR instrument 

to possibly include identification of glacier zones (such as the 

superimposed-ice zone) that are invisible to radar (Nolin and 

Payne, 2007). Furthermore, we believe that these products will 

enrich the already plentiful MISR dataset, which is publicly 

available for use in analyses.

References Cited

Benson, C.S., 1960, Stratigraphic studies in the snow and firn 

of the Greenland ice sheet: Pasadena, Calif., California 

Institute of Technology, unpublished Ph.D. dissertation, 

213 p.

Long, D.L., and Drinkwater, M.R., 1994, Greenland observed 

at high resolution by the Seasat-A scatterometer: Journal of 

Glaciology, v. 40, p. 213–220.

Nolin, A.W., Fetterer, F.M., and Scambos, T.A., 2002, Surface 

roughness characterizations of sea ice and ice sheets—Case 

studies with MISR data: IEEE Transactions on Geoscience 

and Remote Sensing, v. 40, p. 1,605–1,615.

Figure 2. Graph showing Normalized Difference Angular Index (NDAI) values (left vertical axis) and reflectance values (right vertical 

axis) along a straight-line transect from Jakobshavn (left side of plot) to Summit (right side of plot) for the April-May, June-July, and 

August-September images as shown in figure 1. The contrasting relationship between NDAI and reflectance is noticeable, as is the 

trend of the ice surface growing rougher from spring to late summer, and then becoming smoother during late summer and early fall 

when snowfall recommences. 
6 Geoinformatics 2008—Data to Knowledge

Nolin, A.W., and Payne, M.C., 2007, Classification of glacier zones in western Greenland using albedo and surface 

roughness from the Multi-angle Imaging SpectroRadiometer (MISR): Remote Sensing of Environment, v. 107, 

p. 264–275.

Rignot, E., and Kanagaratnam, P., 2006, Changes in the velocity structure of the Greenland ice sheet: Science, v. 311, 

p. 986–990.

Use of Remote Sensing Data in Searching for 

Hydrocarbon Deposits

By Yuri Baranov,1 Sergey Kulapov,1 Ekaterina 

Denisevich,1

 Maxim Vanyarkho,1 and Denis Filatov1

1

Space Information Lab, Gazprom Research Institute (VNIIGAZ), Moscow, 

Russia.

For an oil and gas company, providing the right to 

develop the planet’s interior is preceded by efforts to identify economic expediency prior to licensing. Lack of studies 

of new oil- and gas-producing regions and limited access to 

existing information result in problems in expediting licensing 

for exploration and development of new regions.

Estimates of oil and gas in a new region can be performed using a complex analysis of widely available geological, geophysical, and remote-sensing data (Alexeev and others, 

1988; Baranov and others, 1989; DeMers, 1999).

The advantage of using remote-sensing data is they 

provide unique information that cannot be retrieved by 

any other method. Remote-sensing is also a cost-efficient 

manner of gathering information while searching for liquid 

hydrocarbons, which in turn ensures efficient geological 

prospecting by means of innovative high-end technologies. 

Because remote-sensing methods (especially those that 

analyze landscapes) determine hydrocarbon geochemical 

anomalies observed in space images rather than the deposits 

themselves, they can be applied to any type of accumulations, including unstructured oil-and-gas fields. In addition 

to remote sensing techniques, other methods such as (1) 

analyzing spectral characteristics of images, (2) determining 

particular anomalies in the infrared spectrum, (3) lineament 

analysis, and (4) photogrammetry are also employed (Baranov and others, 2003).

In this paper, we show that the use of remote-sensing data 

substantially reduces the costs of geological prospecting, in 

particular because of the localization of potentially productive 

areas. We also demonstrate remote sensing to be extremely 

powerful from the standpoint of ecological monitoring and 

industrial safety of licensed areas, development objects, and 

hydrocarbon transport.

References Cited

Alexeev, A.S., Pyatkin, V.P., and Dement’ev, V.N., 1988, Automatic image processing of Siberian ecoterritories: Novosibirsk, Russia, Nauka, 224 p.

Baranov, Y.B., Grushin, R.V., Kruchkova, T.A., Baranova, L., 

and Feygin, A.E., 2003, Aerospace methods for monitoring 

of cryopedology conditions of northern oil-and-gas deposits 

of YANAO, in Proceedings, Gazprom—Maintenance of 

safety of infrastructure objects on permafrost soil territories, 

Tyumen, Russia, 2002: Moscow, Gazprom, p. 19–24.

Baranov, Y.B., Sokolovskiy, A.K., and Fedchuk, V., 1989, On 

some issues of regional geology of Precambrian of Aldan, 

in Proceedings, Russian Conference on Methods of Remote 

Sensing Data and Space Information Processing: Ryazan, 

Russia, p. 81–82.

DeMers, M., 1999, Geographical information systems—The 

basics: Moscow, Russia, Data+, 490 p.

Geoinformatics Mapping of Renewable Energy 

Resources and Systems in the Philippines

By Carlos Miniano Pascual,1 Phebe Marcos Pasion,2

and Irma Pascual Acebedo3

1

Department of Agricultural Engineering, Mariano Marcos State University, 

Batac, Philippines.

2

Management Information Services, Mariano Marcos State University, 

Batac, Philippines.

3

Affiliated	Non-Conventional	Energy	Center,	Mariano	Marcos	State	University, Batac, Philippines.

Accurate assessment of renewable energy resource data 

(such as for solar-, water-, wind-, and biomass-generated 

power) are important in order to (1) assess the availability of 

such resources, (2) mitigate global climate change, and (3) 

determine the size, cost, and life cycle of renewable energy 

systems technologies. Knowledge of the spatial distribution of 

the renewable energy resources allows for a more cost-effective design and operation of such systems. The goal of our 

study is to develop a renewable energy resource assessment 

for the Philippines that incorporates and builds upon the current understanding of the spatial distribution of each resource.

In order to assess solar energy, we used a high-resolution, 

global-satellite-derived, cloud-cover database for creating a 

climatological solar radiation model. In the case of hydrologic 

resources, the total volume can be specified according to the 

flow rate, and the effective elevation (head) can be measured 

through use of a digital elevation model (DEM).The availablility of the wind-power resource is defined in terms of 

the wind-power-density value, which is expressed in watts 
Geoinformatics 2008—Data to Knowledge 7

per square meter. This value is based on wind speed and air 

density. To estimate biomass resources, some conservative 

assumptions were made in order to calculate a practical and 

reliable estimate; for example, outputs of straw and stalks, 

which were left over after a harvest, were calculated based on 

crop outputs and the ratio of grain production to stalk mass. 

These assumptions were related to the type of processing 

done for a particular commodity by researchers and planners. 

Other geographic data from land remote-sensing satellites, 

digital land-use and boundary maps, as well as hydrometeorological data were gathered (downloaded), converted, and 

compiled as input databases and base maps. ArcView geographic information system (GIS) software was used for the 

query-based spatial-data analysis. Visual Basic 6 programming 

was used to develop graphic user-interface programming to 

compile the georeferenced input data, as well as to create the 

graphic user interface that linked relational databases to GIS 

query modules. The long-range energy alternative planning 

system (LEAP) program was used to account for how renewable energy is consumed, converted, and produced in a given 

region or economy under a range of alternative assumptions 

on population, economic development, technology, price, and 

so on. An estimate of greenhouse-gas emissions will also be 

presented in order to quantify its mitigation effect on climate 

change. This assessment provides data that may be helpful to 

researchers, planners, developers, and investors in establishing successful commercial renewable energy technologies 

that can be adapted to mitigate climate change in the Philippines. A geoinformatics-based decision-support system was 

developed to build wealth of georeferenced data and information on renewable energy resources (sun-, water-, wind-, and 

biomass-generated) for policy research and development on 

energy resources. Georeferenced databases and thematic maps 

are major outputs that show various indicators of assessment, 

monitoring and evaluation, and efficiency that are useful for 

energy research, planning, and policy options. Such activities are necessary for both intermediate and long-term energy 

development plans for the country. The use of satellite remote 

sensing, geographic information systems (GIS), and global 

positioning systems (GPS) are associated with renewable 

energy resource management. Additionally, geoinformaticsbased mapping tools and statistical analysis are required to 

share information through the World Wide Web. We present a 

graphical user interface using ArcView GIS, which includes 

a mapping system architecture to share information about 

renewable energy resources.

The combined use of satellite remote sensing, GISs, and 

GPSs with graphic user programming language has proven 

to be a very valuable and indispensable geoinformatics tool 

for gathering, organizing, retrieving, and storing georeferenced data that will be used for subsequent retrievals and 

analyses. Efforts to create a cadre of experienced geoinformatics professionals and students and to conduct advanced 

collaborative research projects, symposia, and partnerships at 

local and international levels in the Philippines also will be 

discussed.

Network of Research Infrastructures for 

European Seismology (NERIES)—Progress 

Review

By Rémy Bossu,1 Torild van Eck,2 Domenico Giardini,3

Stefan Wiener,3

 and the NERIES Consortium4

1

Euro-Mediterranean Seismological Center (EMSC), Bruyères le Châtel, 

France.

2

Observatories and Research Facilities for European Seismology 

(ORFEUS), De Bilt, The Netherlands.

3

Swiss Federal Institute of Technology, Zurich, Switzerland.

4

NERIES, Royal Netherlands Meteorological Institute, De Bilt, The Netherlands.

NERIES (http://www.neries-eu.org) is a research infrastructure project addressing observational seismology in its 

broadest sense and providing opportunities far beyond the consortium members alone. Many elements within this Integrated 

Infrastructure Initiative (I3) European Commission project are 

currently being realized, and a wide-scale collaboration with 

other projects within Europe and the United States has been 

established. This presentation will provide an overview of the 

current status of the project, its ongoing and planned activities 

in 2008, and the opportunities provided.

NERIES so far has accomplished the following: (1) the 

Virtual European Broadband Seismic Network (VEBSN) 

has been extended to more then 250 broadband stations, 

(2) two deep-sea ocean-bottom seismometer systems have 

been operating in the Mediterranean for nearly one year, (3) 

homogeneous earthquake ground shaking maps (shakemaps) 

can currently be produced at several European observatories, 

(4) prototypes of portal elements have been launched for 

broadband-waveform retrieval services, earthquake-parameter 

services, historical earthquake data, and European tomography model-review and site-response software. In addition to 

numerous small meetings, NERIES also organized focused 

workshops and meetings to promote coordination on a European scale; these events addressed such topics as the acceleration of data exchange, European observatory coordination, 

software developments for Web application, and historical 

seismology. Grants for European earth scientists have been 

provided and will continue to be available for research visits at 

several institutes.

Project collaborations involving NERIES have been 

set up with the following: (1) the EarthScope program, for 

Web portal developments; (2) the U.S. Geological Survey, 

for rapid parameter exchange and shakemap developments; 

(3) the German-Indonesian Tsunami Early Warning System 

(GITEWS) program, for waveform and parameter handling; 

(4) the Seismic Early Warning For Europe (SAFER) program, 

for rapid warning; (5) the Southern California Earthquake 

Center (SCEC) for hazard assessment as a function of time; 
8 Geoinformatics 2008—Data to Knowledge

and (6) other global partnerships, for standardization issues 

such as Extensible Mark-up Language (XML).

One of the goals of NERIES is to design and develop a 

Web portal, which would be the uppermost layer that provides 

rendering capabilities for the underlying sets of data. The portal would offer tools and services related to earthquake data to 

the earth-science community and to the public. The proposed 

portal is presented in separate posters with a demonstration of 

the alpha version.

OneGeology—The Global Context for a 

European E-Geoscience Project

By Ian Jackson1 

1

British Geological Survey, Keyworth, Nottingham, United Kingdom.

In February 2006, a deceptively simple concept was 

put forward. Could we use the International Year of Planet 

Earth (IYPE2008) as a stimulus to begin the creation of a 

digital geological map of the planet at a scale of 1:1,000,000? 

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 geoscientist-friendly vehicle of creating a 

tangible geological map to in turn accelerate the progress of 

an emerging global geoscience data model and interchange 

standard? Finally, could we use the project to transfer knowhow to developing countries and thereby 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, plus the opportunity to 

raise the profile of geoscience as part of IYPE2008, seemed to 

be more than enough reasons to take the proposal to the next 

stage.

In March 2007, in Brighton, United Kingdom, 81 

geoscientists from 43 countries and 53 national and international bodies gathered together to consider whether they 

would be prepared to collaborate in order to create a global, 

interoperable, geological map dataset. The participants 

unanimously agreed to the Brighton “Accord” and kicked off 

“OneGeology,” an initiative that now has the support of 78 

nations. The agreed OneGeology mission is “to make Webaccessible the best available geological map data worldwide at a scale of about 1:1 million, as a Geological Survey 

contribution to the International Year of Planet Earth.” (See 

http://www.onegeology.org/what_is/mission.html/.) The aim is 

to create dynamic, digital, geological map data for the world 

with an initial target scale of 1:1,000,000, but the project is 

pragmatic and accepts a range of scales and the best available 

data. The geological map data are being made available via a 

distributed Web service, using Web Map Service (WMS) and 

Web Feature Service (WFS). Geological surveys are dynamically “serving” the data for their territories to a Web portal. 

OneGeology is accelerating the global introduction of the 

foundation technologies necessary for the dynamic interchange of geoscience data and allows real-time access to the 

latest version of information and knowledge from the geological surveys of the world.

Since those early days in 2006, OneGeology has grown 

to be an international project that has progressed not only in 

its scientific and technical goals by launching the first version 

of its Web map portal with map data from many nations, but 

it has also attracted substantial scientific, public, and media 

interest around the world and spawned continent-wide activity 

and projects. A major project involving 29 partners and 20 

nations has now been funded by the European Commission. 

The project—OneGeology-Europe—has a budget of 3.25 million Euros and will directly support the INSPIRE directive that 

is creating a spatial-data infrastructure for Europe. The project 

will start in September 2008. The project proposal has a very 

straightforward case: geological data are a key environmental 

dataset, which is essential to the health and wealth of society. 

Although rich geological data assets exist in the geological 

surveys of each individual Member State, they are extremely 

difficult to discover, to obtain, to use, and to integrate with 

each other. Geological spatial data are necessary for, among 

many other things, the prediction and mitigation of landslides, land subsidence, earthquakes, flooding, and pollution. 

INSPIRE spatial data themes are grouped into Annexes (I, II, 

III). Geology is a key dataset in INSPIRE’s Annex II; it is also 

fundamental to the Annex III themes of natural risk zones, 

energy, and mineral resources. Geological spatial data are 

needed for (1) ground-water and soil protection directives, (2) 

the Global Monitoring for Environment and Security (GMES) 

program, (3) the Global Earth Observation System of Systems 

(GEOSS) program, and (4) the development of the Shared 

Environmental Information System (SEIS) by the European 

Environment Agency. The proposed OneGeology-Europe 

project will make geological spatial data held by the geological surveys of Europe more easily discoverable and accessible 

via the Web.

OneGeology-Europe will produce a Web-accessible, 

interoperable, geological spatial dataset for the whole of 

Europe at a scale of 1:1,000,000, which is based on existing data held by the pan-European geological surveys. The 

project will develop uniform standards for basic geological 

map data and make progress towards harmonizing the dataset 

(an essential first step to addressing integration at higher data 

resolutions). The project also will accelerate the development and deployment of a recent international interchange 

standard for geological data, Geoscience Mark-Up Language 

(GeoSciML), which will enable the sharing and exchange of 

the data within and beyond the European geological community. OneGeology-Europe will facilitate the re-use and 

addition of value by a wide spectrum of users in the public and 

private sector and will identify, document, and disseminate 

strategies for reducing the technical and business barriers to 
Geoinformatics 2008—Data to Knowledge 9

re-use. The project plans to address the multilingual aspects of 

access through a multilingual discovery portal. By identifying 

user and provider communities and raising awareness within 

them, OneGeology-Europe will move geological knowledge 

closer to the end-user, where that knowledge will have greater 

societal impact and ensure fuller exploitation of the key data 

that have been gathered at huge public expense. The project 

intends to provide examples of best practice in the delivery 

of digital, geological spatial data to users, such as those in the 

insurance, property, engineering, planning, mineral resource, 

and environmental sectors. OneGeology-Europe will also help 

Europe play a leading and pivotal role in the development of 

a global geoscience spatial data infrastructure. Geoscience, 

like all environmental domains, is worldwide in its nature and 

reach, and through this project, Europe will make a crucial 

contribution to and advance the OneGeology-Global project.

In summary, OneGeology addresses head-on the challenges of interoperability and open standards and will improve 

access to, and exploitation of, rich and relevant digital content. 

In bringing together an extensive network—geological surveys 

with proven capacity and stability, plus users and stakeholders 

from a cross section of key sectors—and raising awareness 

within that network, OneGeology will assist with disseminating best practice while also identifying and addressing weaknesses, barriers, gaps, and opportunities. Although the problems and opportunities that geology raises are transnational, 

many of the issues are currently being tackled on a local basis 

and in a disconnected way by individual nations. OneGeology, 

at a global and European level, seeks to address that.

Implementation Plan for the Geoscience 

Information Network (GIN)

By M. Lee Allison,1 Linda C. Gundersen,2 Stephen M. 

Richard,1

 and Tamara L. Dickinson2

1

Arizona Geological Survey, Tucson, Ariz.

2

U.S. Geological Survey, Reston, Va.

Rationale for a Geosciences Information 

Network

Many of the challenges to creating an Earth science 

cyberinfrastructure are not technical but organizational and 

cultural in nature. Recent workshops have focused on how to 

achieve cooperation, integration, and community governance 

of a geoinformatics system. The critical stumbling blocks to 

creating a wide-reaching geoinformatics component of the 

cyberinfrastructure for the sciences are (1) agreements on 

common standards and protocols, (2) the engagement of a vast 

number of distributed data resources, (3) practices for recognition of and respect for intellectual property, (4) a simple data 

and resource discovery system (distributed integrated catalogs), (5) mechanisms to encourage development of Web service tools for analyses, and (6) business models for continuing 

the maintenance and evolution of information resources.

Geoscience Information Network (GIN)

The Association of American State Geologists (AASG) 

and the U.S. Geological Survey (USGS) agreed in 2007 that 

“the nation’s geological surveys develop a national geoscience 

information framework 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” (Allison 

and Gundersen, 2007; Allison and Dickinson, 2008). The 

AASG and USGS subsequently formed an interagency steering committee to pursue the design and implementation of the 

Geoscience Information Network (GIN). The National GIN 

concept involves four modular components:

1. Agreement on open-source standards and common protocols through the use of Open Geospatial Consortium 

(OGC) standards.

2. A data-exchange model that, to begin with, will use Geoscience Mark-up Language (GeoSciML) (Cox and Richard, 

2006; Richard and the Commission for the Management 

and Application of Geoscience Information Interoperability 

Working Group, 2007), which is based on the OGC-compliant Geography Mark-up Language (GML).

3. Prototype data discovery tools or catalogs such as the tentatively named “National Digital Catalog” (NDC), which 

is being developed under the USGS’s National Geological 

and Geophysical Data Preservation Program (NGGDPP), 

and the National Geologic Map Database (NGMDB).

4. Data integration software tools developed or planned by a 

number of independent projects that can be used in various applications including meeting GIN goals.

The “lack of a national (U.S.) civil Earth information 

strategy” was noted by Gail and others (2007). They argue that 

the Global Earth Observation System of Systems (GEOSS) and 

the U.S. Group on Earth Observations fall short in addressing 

the Nation’s Earth information needs. Instead, they call for the 

United States to “commit to a National Earth-Information Initiative to re-evaluate the national process of collecting and using 

civil Earth information, including the effectiveness of governmental organizations, the relationship between government functions and private sector activities, and the ability to effectively 

connect scientific developments to societal uses.” We believe 

implementation of the GIN will effectively achieve this goal.

National Digital Catalog

As part of the NGGDPP, State geological surveys are 

compiling inventories of collections that they maintain, or 

that are outside the surveys but are available to be archived, 
10 Geoinformatics 2008—Data to Knowledge

or that are at risk of being lost. Next year, the States will start 

compiling metadata records that describe, at the individual 

sample level, contents of these collections. Linking these data 

resources to the network using data interchange tools is a 

primary initial target of the GIN.

Completing GIN

The GIN implementation plan will enable basic network 

operation by (1) establishing service definitions, standard 

protocols, and best practices through community workshops, 

and (2) instituting the network architecture by means of a 

series of test bed systems. The first test bed will focus on services for serving interpreted geospatial features (for example, 

a geologic map) in the context of the International Union of 

Geological Sciences Commission for the Management and 

Application of Geoscience Information (IUGS CGI) Interoperability Working Group’s GeoSciML development. Priorities 

for subsequent service development will be established by a 

steering committee; one high-priority candidate is to serve 

observation data recorded at point locations (for example, 

samples, chemical analyses, boreholes). Test-bed-network 

nodes initially will be implemented and tested on a single 

server; after a demonstration for the community, the service 

will be “rolled out” to other nodes in the network. We will 

seek expressions of interest from State geological surveys and 

individual USGS programs to participate in each test bed.

The network will use data discovery services that are 

being implemented as part of the NGGDPP and the NGMDB. 

Web services will enable integration of GIN data with other 

applications and data sources.

Sustainability

Like the Internet, a successful information network will 

create a tipping point at which users and providers will see the 

network as critical to their basic functions. When that happens, 

populating and maintaining that network become necessary 

costs of doing business. Few organizations are mandated to 

maintain a Web site, yet most realize that without one, they 

essentially do not exist in today’s environment. We are quickly 

moving to a similar situation for sharing data in an interoperable manner.

The AASG-USGS workshop participants acknowledged 

the need to recognize that providing and using interoperable, 

Web-enabled information resources as part of their mission 

should be sufficiently compelling to support network maintenance and development just as they currently do for Web 

sites. Once the framework of GIN is built and the test beds are 

demonstrated successfully, we expect that other data providers 

and users will find compelling needs that will prompt the use 

of the network for a wide variety of specific tasks, which will 

in turn help fund the full implementation and expansion of the 

GIN. We also expect that each network participant will include 

costs for expanding their contributions to the GIN in their base 

operating costs and grant proposals in the same way costs for 

Web site activities are funded.

Education and Training

We plan a “circuit rider” approach wherein GIN technical 

staff members are dedicated to providing potential network 

participants with technical training or to actually carrying out 

the technical work themselves by “riding the circuit” among 

them for short durations. The original circuit riders were 

preachers in the late 1700s who rode a circuit through frontier 

regions of the United States to serve rural populations who 

had no churches. Similarly, our circuit riders will travel (in 

person or electronically) to organizations that want to join the 

GIN but need assistance or training. The circuit rider’s services will be free but will need to be prioritized by the steering 

committee. Our goal is to give each State geological survey 

and USGS program the ability to write GeoSciML protocol 

“wrappers” to translate their datasets and to guide them on the 

server configurations that are necessary for the datasets to be 

discoverable by GIN users. For geological surveys or programs without the technical expertise to handle these chores, 

the circuit rider would carry them out either onsite or remotely 

as required. Various online services exist to facilitate a virtual 

environment for the circuit riders to work interactively in real 

time with network participants, including shared access to 

computers or servers while writing code or tutoring on code 

development.

A help desk will provide no-cost remote assistance to 

providers and users. The goal is to provide service not only to 

the initial geological survey data providers but also to other 

organizations that want to be early adopters of the GIN opportunities.

Mechanisms for Change and Adaptation in 

Technologies

The challenge to creating a dynamic, flexible, community-based network is defining and maintaining sufficient 

standards to make the network effective and reliable while 

keeping it open to new developments. The GIN will be defined 

by collections of service definitions, interchange formats, 

and vocabularies that are established (to the extent possible) 

independent of any particular hardware, operating system, or 

lower-level network protocols. Adoption of new technology 

will only require the implementation of network elements in 

a new environment, ideally with no change to any network 

service definitions or protocols. The architecture allows for the 

use of multiple conventions for different user groups.

Acknowledgments

This project is supported by National Science Foundation award 0723437 to the AASG, and by the USGS NGMDB, 

through support of S.M. Richard.
Geoinformatics 2008—Data to Knowledge 11

References Cited

Allison, M.L., and Dickinson, T.L., 2008, Final report—A 

Workshop on the Role of State Geological Surveys and U.S. 

Geological Survey in a Geological Information System for 

the Nation: Arizona Geological Survey Open-File Report 

08–01, 22 p.

Allison, M.L., and Gundersen, L.C., 2007, Association of 

American State Geologists (AASG)-USGS Plan for a 

National Geoscience Information Network, in Brady, 

S.R., Sinha, A.K., and Gundersen, L.C., eds, Proceedings, 

Geoinformatics 2007—Data to Knowledge, San Diego, 

Calif., May 17–19, 2007: U.S. Geological Survey Scientific 

Investigations Report 2007–5199, p. 76–77.

Cox, S.J.D., and Richard, S.M., 2006, A formal model for the 

geologic timescale and global stratotype section and point, 

compatible with geospatial information transfer standards: 

Geosphere, v. 1, p. 138–146.

Gail, W.B., Lane, N.F., and MacCauley, M.K., 2007, A U.S. 

Earth information strategy?: Imaging Notes, v. 22, no. 2, p. 

45–46.

Richard, S.M., and the Commission for the Management and 

Application of Geoscience Information Interoperability 

Working Group, 2007, GeoSciML—a GML application for 

geoscience information interchange, in Soller, D.R., ed., 

Digital Mapping Techniques ‘06—Workshop proceedings: 

U.S. Geological Survey Open-File Report 2007–1285, p. 

47–59.

GeosciNET—A Global Geoinformatics 

Partnership

By Walter S. Snyder,1 Kerstin A. Lehnert,2 Emi Ito,3

Ulrich Harms,4

 and Jens Klump4

1

Department of Geosciences, Boise State University, Boise, Idaho.

2

Lamont-Doherty Earth Observatory, Columbia University, Palisades, N.Y.

3

Department of Geology and Geophysics, University of Minnesota, Minneapolis, Minn.

4

German Research Center for Geosciences, Potsdam, Germany.

GeosciNET is an emerging partnership of existing 

geoinformatics organizations that are collaborating to provide a more effective data system. Current members are: 

CoreWall (http://www.corewall.org), Geoinformatics for 

Geochemistry (GfG; http://www.geoinfogeochem.org), 

System for Earth Sample Registration (SESAR; http://www.

geosamples.org ), PaleoStrat (http://www.paleostrat.org), 

and the International Continental Drilling Program (ICDP; 

http://www.icdp-online.org). With the existing membership, 

GeosciNET can offer a comprehensive, integrated system for 

data acquisition in the field, data dissemination, archiving, 

visualization, and data integration and analysis. The system 

will enable a single researcher or a group of collaborators 

to keep track of, visualize, and virtually archive any type of 

geologic sample, the data produced from that sample, and subsamples taken from the original sample. Samples can be solid, 

liquid, or gas; can be from an outcrop or drilled or dug from a 

pit; or may even be virtual (for example, a spectral analysis of 

an outcrop detected by satellite imagery).

GeosciNET is envisioned as a model to advance the 

larger, ongoing process of building a global geoinformatics 

system and it is open to new partners that would expand the 

scope and impact of the partnership. Lehnert and others (2008) 

stressed that geoinformatics needs to be developed as a linked 

system of sites that provide to users a library of research data 

and tools to discover and access, integrate, manipulate, analyze, and model interdisciplinary data without corrupting the 

original data. The “grand challenge” for geoinformatics is to 

build such a linked system. Enforced networking of geoinformatics systems (the “top-down” approach) has not been 

perceived well by academics that tend to value bottom-up 

systems that can be more responsive to users’ needs. There are 

major roadblocks to building networks within government and 

academic domains as well as between them partly because of 

perceived differences in their respective modes of operation 

and partly due to inadequate funding. Few links exist today 

among various geoinformatics efforts, so it is difficult for 

anyone to know where various data are (data discovery), to 

integrate them (interoperability), and to view diverse data in a 

synthetic and dynamic way (visualization). GeosciNET’s aim 

is to eliminate the obstacles so that users can take advantage of 

geoinformatics resources and value their benefits. Once these 

benefits are understood by the user community, the barriers 

that currently exist in building a larger geoinformatics system 

will start to erode.

We are organizing GeosciNET to advance coordination, 

complementarity, and interoperability and to minimize both 

the duplication of efforts and the overlap of scope among the 

involved partner systems in order to streamline the development and operation of geoinformatics efforts. We believe that 

by advancing the development and data holdings of its member groups, the overall value of each site will be significantly 

enhanced and will better meet the needs of the users. We are 

jointly developing a plan that outlines the proposed interaction among the projects and their specific responsibilities for 

building a network of data, services, and tools. Our goal is 

to establish an integrated, apparently seamless network that 

can be offered to the community of users to support science 

and education programs. This collaboration is based on a 

memorandum of understanding predicated on the idea of 

mutual benefit. Specific responsibilities of partner projects 

are based on maximizing this mutual benefit and the technical capabilities of the partners. For example, the Antarctic 

Drilling Program (ANDRILL), ICDP, and others are work-
12 Geoinformatics 2008—Data to Knowledge

ing with CoreWall to enhance technologies that can then be 

implemented by their projects. One key need that has been 

identified by the user community is that CoreWall needs to 

be able to push data out to other databases, including but 

not restricted to PaleoStrat and GfG members. PaleoStrat is 

partnering with the International Congress on Carboniferous and Permian (ICCP), the Permian and Carboniferous 

Subcommissions of the International Commission on Stratigraphy (ICS), and GeoSystems (http://www.geosystems.org) 

to support the newly formed Upper Paleozoic Paleoclimate 

Working Group. EarthChem (http://www.earthchem.org), 

which is a core member of GfG, is building a Geochemistry 

Information Network and has established a consortium of 

international partners with the goal of accessing globally distributed collections of geochemical data via the EarthChem 

Portal. All of these activities will enhance the effectiveness 

of all GeosciNET partners.

A major focus for GeosciNET is to support individual 

researchers and projects that do not have their own dedicated 

data management, education, and outreach programs. One 

of the greatest challenges for geoinformatics lies in being 

perceived as a friendly resource by its users where they can 

easily link their observations and analyses to other users and 

integrate them with other data. These data, when viewed 

holistically, provide a continuum of information that allows 

the geoscience community to address fundamental questions 

of earth processes. The data can provide us with information 

about the environment or natural resources that we did not 

know we had or with questions we did not know to ask.

Despite the importance of data (legacy or otherwise), 

there currently are no convenient mechanisms that enable 

users to easily input their data into databases. Although the 

developers of some efforts such as the GfG databases, PetDB 

and SedDB have worked hard to compile such data, only the 

users’ active participation can capture the major part of the 

overall legacy and new data. User participation requires the 

proper tools such as a translator that can recognize tags and 

parse the data accordingly, and incentives such as tools and 

more data for enhanced data synthesis and analysis. GeosciNET will be experimenting with these mechanisms. Efficient 

capture of legacy and new digital data is part of the larger data 

preservation “grand challenge” that includes physical samples; 

there are many government and academic drilling core and 

sample repositories that can benefit from SESAR, CoreWall, 

and other components of GeosciNET.

Reference Cited

Lehnert, K.A., Harms, U., Ito, U., Klump, J., and Snyder, 

W.S., 2008, Promises, achievements, and challenges 

of networking global geoinformatics resources—Experiences of GeosciNET and EarthChem: Geophysical 

Research Abstracts, v. 10, 2 p., available only online at 

http://www.cosis.net/abstracts/EGU2008/05242/EGU2008-

A-05242-1.pdf/. (Accessed July 31, 2008.)

A Three-Dimensional GIS Model Based on 

Crystallographic Principles Dedicated to Spatial 

Analyses in Geosciences

By Benoit Poupeau,1 Benoit Deffontaines,1 and 

Olivier Bonin2

1

Department of Earth Materials and Engineering Geology, University of 

Paris, Marne-la-Vallée, France.

2

Object Design and Generalization of Topographical Information (COGIT) 

Laboratory, National Geographic Institute, Saint Mandé, France.

A three-dimensional geographic information system 

(GIS) enables the integration and coherence of multiple 

sources of data from different providers while respecting and 

representing the end user’s choices in terms of geometry and 

topology. Current three-dimensional GISs use unique topological and geometrical modeling as a rule (Zlatanova, 2000; 

Coors, 2003). This feature of a three-dimensional GIS makes 

queries easier to compute from topological models such as the 

“close to close” way of relating geometric primitives of one 

object or its neighbor objects; however, this homogenization 

leads to the loss of the model’s specificities, leading to heavy 

computations for conversion of the data. Additionally, it does 

not compensate in an automated way for issues arising from 

data acquisition and modeling.

This paper proposes a model for analysis based on a 

three-dimensional GIS. The proposed approach allows queries to be made on one object (intra-object analysis) or a set of 

objects (inter-objects analysis) even if the geometrical coherence between objects is not perfect. This model, which is based 

on the principles of crystallography, analyzes the symmetric 

features of each object in order to describe its structure, such as 

the way in which geometric primitives are arranged together. 

This first abstraction (that is, the structure of the object, which 

allows handling of the object independent of its geometry) gives 

a global overview of the object. One of the advantages of the 

structure is to make some queries easier, such as the roof extraction of a cavity or the three-dimensional building simplification.

A second abstraction, the bounding crystalline mesh or 

lattice unit in the terminology of crystallography, is obtained 

through the analysis of symmetric elements (plans, axes, or 

centers). The lattice unit is, in crystallography, the envelope of 

the smallest parallelepiped, which is a structure that preserves 

the geometric properties. The lattice unit is used like a threedimensional bounding box adapted to the object shape and 

allows relationships of geographical objects, regardless of 

their geometric dimension. Relationships between geographical objects follow two principles:

1. Each geographical object is subject to gravity; therefore, 

emptiness is not allowed in the model. The main objective 

is to connect non-adjacent objects (for example, a house 

that is not in contact with a Digital Terrain Model) or to 

ensure relationships between the parts of different objects 
Geoinformatics 2008—Data to Knowledge 13

(for example, connect the roof of a geological layer with 

those of an included cavity).

2. If a lattice unit intersects another lattice unit, we assume 

that these lattice units are adjacent. This principle is formalized with proximity spaces (Naimpally and Warrack, 

1970). This mathematical theory constitutes, in topology, 

an axiomatization of notions of “nearness”).

With the help of lattice units, two graphs are computed. 

The first one is an incidence graph, which describes relationships between objects and makes it possible to establish the 

interrelationship between them. The second one, a temporal 

graph, represents, for one object, the evolution of its relationship with its own environment.

This model has been used and validated in different applications, such as three-dimensional building simplification (Poupeau and Ruas, 2007), and in a context of a coal basin affected 

by anthropic subsidence due to extraction (Gueguen, 2007).

References Cited

Coors, V., 2003, 3D GIS in networking environments, in

Environments and Urban Systems: Amsterdam, The Netherlands, Elsevier, p. 345–357.

Gueguen, Y., 2007, Etude des mouvements de surface en environnement minier à partir d’interférométrie radar et identification des origines des déformations—L’exemple du bassin 

Nord/Pas-de-Calais: Marne-la-Vallée, France, University of 

Paris, unpublished Ph.D. dissertation, 209 p.

Naimpally, S.A., and Warrack, B.D., 1970, Proximity spaces: 

Cambridge Tracts in Mathematics and Mathematical Physics 59, 128 p.

Poupeau, B., and Ruas, A., 2007, A crystallographic approach 

to simplify 3D building, in Reports of participants, Twentythird International Cartographic Association Conference, 

August 4–10, 2007, Moscow, Russia: Moscow, Russia, 

Federal Geodetic and Cartographic Service, 1 CD-ROM.

Zlatanova, S., 2000, 3D GIS for urban development: 

Enschede, The Netherlands, International Institute for GeoInformation Science and Earth Observation, unpublished 

Ph.D. dissertation, 222 p. and appendix.

Avizo—Three-Dimensional Visualization 

Framework

By Peter Westenberger1

1

Visualization Sciences Group, Mercury Computer Systems, Dusseldorf, 

Germany.

Avizo software is a powerful, multifaceted tool for visualizing, manipulating, and understanding scientific and industrial data. Wherever three-dimensional (3D) datasets need to 

be processed in material sciences, geosciences, or engineering 

applications, Avizo offers abundant state-of-the-art features 

within an intuitive workflow and an easy-to-use graphical user 

interface.

Avizo XGreen Package—3D Visualization 

Framework for Climatology Data

In these times, when climatology data have become more 

and more complex with respect to size, resolution, and the 

numbers of chronological increments, the German Climate 

Computing Center in Hamburg has chosen Mercury Computer Systems to develop a software extension called XGreen, 

which is based on their visualization framework Avizo (formerly known as “amira”).

XGreen provides domain-specific enhancements for 

Avizo, such as the following:

•	 Network Common Data Form (NetCDF) CF-1.0 

Reader—

•	 Support for regular, rectilinear, rotated, and curvilinear grids.

•	 Support for large data.

•	 Streaming of time-dependent data.

•	 Use of main memory for caching NetCDF data.

•	 Geographical projections—

•	 Cylindrical, equidistant, spherical, Mollweide, and 

many other projections.

•	 Earth module—

•	 High-level textures, three levels of detail.

•	 Scalable elevation and bathymetry.

•	 Continental outlines and country borders.

•	 Fast hardware-based bump shading for multiple scalar 

quantities on two-dimensional (2D) slices.

•	 Particle advection and trajectories.

•	 Volume rendering for rectilinear grids.

•	 Several other unique interactive visualization techniques.

Avizo Earth Edition—3D Visualization Framework 

for Geoscience Data

Avizo Earth Edition is the software suite that includes 

Avizo and all its extensions for interactive exploration, visualization, analysis, comparison, and presentation of geoscience 
14 Geoinformatics 2008—Data to Knowledge

Figure 1. A visualization of 1 time step out of 1,440 in an ocean 

model simulation. The colors represent the temperature near 

the surface of the Atlantic Ocean, with blue being the coolest 

and red being the warmest. The embossing illustrates the 

current’s velocity. Data courtesy of the Max Planck Institute 

for Meteorology and the German Climate Research Center in 

Hamburg, Germany.

data. This 3D visualization framework is an ideal tool, allowing the user to import, manage, interact with, and visualize 

geoscience data from multiple sources within a single environment.

Avizo Earth Edition includes the whole Avizo feature set 

plus an advanced SEG-Y (file format used by the Society of 

Exploration Geophysicists) data importer, and the XLVolume 

Pack, which manages and visualizes very large amounts of 

volume data, up to terabytes.

Avizo Earth Edition addresses several geosciences needs 

with the following features:

•	 Quality control of 3D seismic data.

•	 Multidiscipline project review.

•	 Final project presentation.

•	 Core sample analysis.

•	 Rapid application development

The Earth Edition delivers advanced technologies and 

extensions suitable for efficient, multi-data visualization of 

large quantities of geosciences data by means of the following 

features:

•	 Ultimate memory management technology to interac imate memory management technology to interactively explore out-of-core datasets.

•	 Versatile data management capabilities through the 

SEG-Y wizard, the OpenSpirit plug-in, and userdefined readers.

•	 Readers for horizons, fault sticks, and fault surfaces.

•	 Advanced seismic visualization by interactive investigation using inline, crossline, time slices, oblique 

slices, and fence slices (random slices); uses slice 

animation to identify hidden features.

•	 Interactive region of interest, with progressive image 

quality during 3D navigation.

•	 Embossing to enhance feature identification (fig. 1).

•	 Advanced colormap editor to interactively change 

colormap transparency curve “on the fly;” several 

ready-to-use colormaps for instantaneous attributes, 

velocity, and other geoscience data.

•	 Horizon visualization and quality control using timedepth colormap.

•	 “GeoBody” segmentation through Avizo’s advanced 

segmentation editor.

•	 Data query from seismic volumes using PointProbe, 

LineProbe, and SplineProbe

•	 Multidiscipline, multiviewer project review—Visualize 

seismic volumes, wells, horizons, faults, and reservoirs 

in a single 3D environment, and manage exploration 

using a dedicated “project tree” view.

•	 Professional project presentations using a large set 

of tools, including DemoMaker, CameraPath, MovieMaker, 3D Annotation, virtual trackball, and highresolution snapshots, and more.

•	 Project review in immersive theaters is also possible 

using the Avizo XScreen extension which provides 

scalability on clusters and virtual reality systems.

Tunisian Structural Extrusion Revealed by 

Numerical Geomorphometry

By Benoit Deffontaines,1 Tarek Slama, Jr.,2 Noamen 

Rebai,2

 and Mohamed Moncef Turki2

1

Earth Materials and Engineering Geology Laboratory, University of Paris, 

Marne-la-Vallée, France.

2

Department of Geology, University of Sciences of Tunis, Tunis, Tunisia.

Neotectonics may be revealed by detailed numerical 

geomorphic analyses of topography. Tunisia is an excellent case example of a country affected by active tectonics; 

numerous earthquakes have struck the region, and faults, 

folds, and associated structural features have been mapped 

by geoscientists. Previous geomorphic studies of the region 

and the development of new indicators in this current study 
Geoinformatics 2008—Data to Knowledge 15

have lead us to propose a new structural scheme for analyzing the Tunisian tectonic setting. We propose herein an 

eastern extrusion model of central Tunisia based on the 

northward migration of the African plate toward Eurasia. 

This model is based on drainage analyses, drainage network 

classifications, specific analyses of the digital terrain model 

(DTM), summit-level surface analyses, and other analyses 

which were integrated in a geographic information system 

(GIS) (Deffontaines, 1990, 2000; Deffontaines and others, 

1994) as well as previous studies (Sokoutis and others, 2000; 

Bouaziz and others, 2002). 

We propose that the well-known diapir fault line 

(Medjerda-Tunis fault zone) corresponds to a major left-lateral, transtensive, northeastward-trending fault zone, which 

acts as the major northern boundary of the central Tunisia 

extrusion. The zone is identified in the field as a compressive structure that resulted from the continuous uplifting 

of the elongated northeast-southwest-trending salt diapirs 

that parallel this major transcurrent extrusion.. The zone is 

associated with the well-known northeast-southwest-tending 

Gafsa-Gabes fault zone, which is characterized by numerous 

en echelon folds and acts as a major right-lateral fault zone 

that bounds the southern part of the central Tunisia extrusion (Bouaziz and others, 2002). Within central Tunisia, the 

north-south axis of the zone (also known as the Al Abiod 

fault zone) appears to be a reactivated graben that is closely 

associated with the eastern extrusion of central Tunisia 

and differentiates the high Atlasic and low eastern Tunisian domains (Bouaziz and others, 2002). This geomorphic 

approach is limited by the difficulty of distinguishing the different tectonic phases within the long, northward migration 

of the African plate because the topography reveals cumulated effects; however, the development of a more advanced 

system is underway. 

Geomorphometry appears to be an excellent tool for 

understanding, at a regional scale, the geodynamic setting of 

this northeastern part of northern Africa where the AfricanEurasian collision is characterized by an eastern extrusion in 

the central part of Tunisia. Further work is proposed, such as 

studies of bathymetry, gravimetry, and magnetism; and a quick 

offshore seismic-reflection survey that would help locate the 

exact continuation of the Tunisian extrusion’s major tectonic 

boundaries.

References Cited

Bouaziz, S., Barrier, E., Soussi, M., Turki, M.M., and Zouari, 

H., 2002, Tectonic evolution of the northern African margin 

in Tunisia from paleostress data and sedimentary record: 

Tectonophysics, v. 357, p. 227–253.

Sokoutis, D., Bonini, M., Medvedev, S., Boccaletti, M., 

Talbot, C.J., and Koyi, H., 2000, Indentation of a continent 

with a built-in thickness change—Experiment and nature: 

Tectonophysics, v. 320, 243–270.

Project Towards Simultaneous Visualization for 

Various Kinds of Geoscience Data on Google 

Earth

By Yasuko Yamagishi,1 Hiromichi Nagao,1 Seiji 

Tsuboi,1

 Katsuhiko Suzuki,1 Hajimu Tamura,1 Hiroshi 

Yanaka,2

 and Tadahiro Hatakeyama3

1

Institute for Research on Earth Evolution, Japan Agency for Marine-Earth 

Science and Technology, Yokohama, Japan.

2

Fujitsu Limited, Chiba, Japan.

3

Okayama University of Science, Okayama, Japan.

Scope of the Project

Numerous types of data are available throughout the 

community of solid-earth scientists. New insight into the 

structure of and activity in the Earth’s interior could be gained 

by a simultaneous interpretation of multidisciplinary data. 

The Japan Agency for Marine-Earth Science and Technology 

(JAMSTEC) also has been accumulating various bodies of 

geoscience data (obtained by observations both on land and in 

the oceans) related to the solid earth. In order for cross-disciplinary research to occur, it is necessary, as a first step, to visualize simultaneously these different types of data in a common 

platform. Google Earth is a powerful tool for the simultaneous 

visualization of data; it converts geoscience data into keyhole 

mark-up language (KML), a format that is expected to be 

available for everyone.

The Institute for Research on Earth Evolution (IFREE), 

which is a data collection center within JAMSTEC, adopted 

Google Earth as a common browser for databases produced by 

the solid-earth science community and has been promoting a 

project to develop the conversion tools to produce KML files 

easily and quickly. Here, we introduce the conversion tools 

developed in the project.

Released Conversion Tools from the Project

The present target data to be converted to KML files 

are seismic tomography data, geomagnetic data, geochemical data of rocks, and navigation data from JAMSTEC’s 

research vessels. The conversion tool for the seismic 

tomography data has already been released as Web application software and is available from the Pacific 21 Website 

(http://www.jamstec.go.jp/pacific21/). It is possible to download, for example, a KML file of an arbitrary horizontal and 

vertical cross section of the seismic tomography model of the 

Earth’s mantle proposed by Obayashi and others (2006), Isse, 

Suetsugu, and others (2006), and Isse, Yoshizawa, and others 

(2006), setting parameters such as which cross section and 

area is to be displayed using Google Earth.
16 Geoinformatics 2008—Data to Knowledge

A conversion tool for geochemical data from the 

following two databases is also available: Geochemistry of Rocks of the Oceans and Continents (GEOROC, 

http://georoc.mpch-mainz.gwdg.de/georoc/), and Petrological 

Database of the Ocean Floor (PetDB, http://www.petdb.org/), 

which archives analytical data (major element composition 

and isotope ratios, for example) for rock sampled from the 

ocean floor.

As a part of this project, we have been developing a 

conversion tool that enables us to view the global or local geomagnetic field on Google Earth. This tool makes it possible to 

generate a KML file from a geomagnetic field model, which 

is given as a table of spherical harmonic coefficients such as 

those used by the International (or Definitive) Geomagnetic 

Reference Field and the National Geophysical Data Center 

720 models.

In this presentation, we show examples of KML converters for seismic tomography models, geomagnetic field models, 

the geochemical data of rocks, and navigational data for 

JAMSTEC vessels. We also show a simultaneous comparison 

of these data on Google Earth.

Summary

Cross-disciplinary cooperation between various fields 

in the solid-earth sciences becomes more and more important 

in order to understand the Earth’s interior. A simultaneous 

visualization of different types of geoscience data is essential 

in order to achieve this purpose, and we consider Google Earth 

to be the best solution for a data browser. We plan to provide 

more conversion tools such as Web and (or) Java applications 

not only for the data presented here but also for other types of 

data. We believe our conversion tools will elevate the use of 

simultaneous visualization as a research tool and enable new 

discoveries about the Earth’s interior.

References Cited

Isse, T., Suetsugu, D., Shiobara, H., Sugioka, H., Yoshizawa, 

K., Kanazawa, T., and Fukao, Y., 2006, Shear wave speed 

structure beneath the South Pacific superswell using 

broadband data from ocean floor and islands: Geophysical 

Research Letters, v. 33, 5 p.

Isse, T., Yoshizawa, K., Shiobara, H., Shinohara, M., Nakahigashi, K., Mochizuki, K., Sugioka, H., Suetsugu, D., Oki, 

S., Kanazawa, T., Suyehiro, K., and Fukao, Y., 2006, Three 

dimensional shear wave structure beneath the Philippine 

Sea from land and ocean bottom broadband seismograms: 

Journal of Geophysical Research, v. 111, no. B6, 13 p. 

Obayashi, M., Sugioka, H., Yoshimitsu, J., and Fukao, Y., 

2006, High temperature anomalies oceanward of subducting 

slabs at the 410-km discontinuity: Earth and Planetary Science Letters, v. 243, p. 149–158.

GIS-Morphometry—A GIS Framework for Digital 

Tectonic Geomorphology Studies

By Tarek Slama, Jr.,1 Benoit Deffontaines,2Noamen 

Rebai,1

 and Mohamed Moncef Turki1

1

Department of Geology, University of Sciences of Tunis, Tunis, Tunisia.

2

Earth Materials and Engineering Geology Laboratory, University of Paris, 

Marne-la-Vallée, France

Digital terrain modeling and landform analysis are carried out by use of (1) general geomorphometry of a digital 

elevation model (DEM), (2) digital drainage network analysis, 

(3) digital image processing, (4) lineament extraction and 

analysis, (5) spatial and statistical analysis, and (6) threedimensional surface modeling. These approaches define the 

growing field of digitally analyzed tectonic geomorphology. 

Most of its investigations include visualizing geology imagery with topography, raster calculations using map algebra 

procedures, and the integration of fault-model calculations 

to determine surface uplift. Analysis of DEMs by means of 

geomorphometry provides an efficient tool for recognizing 

fractures and for quantitatively characterizing the morphotectonics and the morphodynamics of an area.

Topographic attributes extracted from the DEM allow for 

the general and specific characterization of relief in terms of 

coupled and inherent interactions between surface processes 

and tectonic activity. The DEM manipulations illustrate the 

basic evidence of deformation and surface processes recorded 

in the topography. In addition, digital drainage network 

analysis and digital image processing allow, via morphometric parameters extraction and integration, for morphotectonic 

investigations of the landform. The large number of quantitative attributes, however, requires a well-organized digital system to ensure, numerically, systematic extraction and manipulation. The implementation of an adequate framework using 

available geographic information system (GIS) technology is 

the motivation for and the primary goal of our work.

A GIS architecture and database has been designed 

and developed to ensure many quantitative procedures and 

approaches. They are originally based on landform investigations and geomorphological characteristics which were translated into mathematical and numerical algorithms. The general 

framework of the developed GIS is composed of three core 

“morphomtric components” as follows: (1) DEM-morphometry, (2) digital drainage network (DDN)-morphometry, and (3) 

digital image (DI)-morphometry. A large set of morphometric 

parameters are extracted from these GIS substructures; however, classical operations and GIS functions also are ensured, 

such as data integration and interoperability, data management 

and spatial analysis, topology, and interactive visualization. 

This GIS-Morphometry framework was developed using the 

ArcGIS package, the ModelBuilder tool, and Python programming language. The GIS object-oriented technology was used 

to extract automatically most of the morphometric parameters 
Geoinformatics 2008—Data to Knowledge 17

and attributes. A comprehensive geodatabase was structured 

for the purposes of data integrity and effectiveness of data 

manipulation. The system’s completely digital nature ensures 

that it will be flexible so that it can grow and evolve as new 

data, processing procedures, and modeling and visualization 

tools become available. A set of morphotectonic and morphostructural maps of test sites in northern Tunisia was created. 

The neo-morphodynamic model of folded and faulted structures was particularly emphasized and mapped.

Geoinformatics and Nature Parks

By Peter Löwe,1Claudia Eckhardt,2 and Ralf Löwner1

1

German Research Center for Geosciences, Potsdam, Germany.

2

Geo-Naturpark Bergstrasse-Odenwald, Lorsch, Germany.

Introduction—Google Earth Blazes the Trail

This paper describes the mutually beneficial relationship geoinformatics can have with nature parks—to provide 

location-based up-to-date information for park visitors based 

on decentralized data and knowledge repositories. Geoinfor- eoinfor- eoinformatics-based applications such as Google Earth have become 

widely accepted for everyday use during the last few years. 

They have been readily accepted by both laypersons and 

academia. In this paper we highlight how similar geoinformatics-driven approaches can be applied to nature parks. This 

approach enables nature park staff to help visitors understand 

global change processes on a local scale. The approach is 

based on new databases and services that are maintained and 

operated by online user communities and by new technology 

for local and regional applications.

Nature Parks—Potential and Challenge

Geology forms the foundation of any park’s ecosystem by setting the stage and providing the context for its 

local natural and historic heritage; therefore, having visitors 

acknowledge the beauty of the geologic features and having 

park staff communicate information about the geologic setting 

of those features to the visitors are interactions that are central 

to any nature park concept. Some nature parks in Europe are 

members of the European Geoparks Network and the Global 

Geoparks Network, which is supported by UNESCO. These 

organizations have an obligatory, strong focus on informing 

the public about the parks’ geological and cultural heritage 

and protecting both in order to achieve sustainable regional 

development. The following quote by Mike Soukup (Associate Director for Natural Resource Stewardship and Science, 

National Park Service, United States), as cited in Higgins 

(2007), highlights the current status: “Intuitive decisionmaking might have sufficed in the twentieth century, (but) it 

certainly will not ensure that the natural systems (the wildlife 

and the scenery) of national parks will be maintained unimpaired throughout the 21st century.”

Good management of nature parks requires a sound 

understanding of the available physical settings and anthropogenic infrastructure, including the scientific aspects. They 

need to be appropriately communicated to park visitors and 

academia.

An ongoing dialogue between geoscientists and park 

resource-management staff about research needs, projects, 

and grant appeals is crucial in order to introduce the means for 

knowledge management, data access, and representation of 

the facts and findings (fig. 1); however, according to Higgins 

(2007), even within U.S. system of national parks, where landscape interpretation was first introduced as a means to communicate nature and science to the public, about 90 percent 

of the parks still lack an onsite geoscientist. This indicates the 

huge potential for contributions from the field of geoinformatics.

Geo-Naturpark Bergstrasse-Odenwald

We present the Geo-Naturpark Bergstrasse-Odenwald, 

in Lorsch, Germany, as a real-world scenario that depicts the 

potential benefits of applying geoinformatics to nature parks. 

The park covers the Odenwald mountain range between 

Frankfurt and Heidelberg and stretches from the Rhine River 

valley to the wine-producing region of Franconia. The park 

was the first nature park in Germany to achieve “Geopark” 

status on the national, European, and global level. The park 

promotes “protection by usage,” preservation of heritage and 

knowledge, and the empowering of local enterprises (that is, 

sustainable tourism) under the crosscutting objective of sustainable regional development. 

Real-World Examples of the Potential Impact of 

Geoinformatics

Apart from basic management tasks such as visitor statistics and trail management, which can be supported by offthe-shelf software solutions, geoinformatics can help to open 

fields of research and communication that otherwise could 

not be addressed with the available resources. We provide two 

examples.

Contemporary Regional Geology—A Challenge

Lots of regional field expertise and knowledge needs to 

be stored, managed, and communicated within a nature park. 

In the case of Bergstrasse-Odenwald, this is a significant 

challenge because the park’s area is covered by geologic maps 

constructed by three different geological surveys, one each for 

the States of Hesse, Bavaria, and Baden-Württemberg.

The implementation of geoinformatics software to integrate the growing number of geodata infrastructures (GDIs) on 

both State and regional levels could be modeled after the efforts 
18 Geoinformatics 2008—Data to Knowledge

reported by Baru and Lin (2008). Despite the improvements in 

data access, the issue of data being kept up to date remains. For 

the Bergstrasse-Odenwald area, most of the available geologic 

data were charted more than a century ago. It will be a challenge 

for the future to integrate crowd-sourced contemporary observations into a regional geodatabase, but a method similar to that 

described by Ramm and Topf (2008) may be possible. “Crowd 

sourcing” describes an activity that is jointly undertaken by 

a community of individuals who share the same interests and 

pool their resources to accomplish the overall task. In our case, 

the mapping-related geological observations made by people 

spread out over the whole park area could be used to create new 

geologic or geomorphological map products. 

Telling Stories From Data Queries

In geoinformatics, providing query results to the user is 

the last step in a process and typically yields a map product. 

Yet from the nature park perspective, the challenge to communicate scientific facts just begins at this stage. For communication “in the field,” computer-independent activities (relying 

on vision, touch, smell, and so on) are still preferred when 

interacting with park visitors. Also, the challenge to communicate findings in a barrier-free manner needs to be addressed 

(Ludwig, 2005). In order to better convey the science of the 

park to the public, dedicated Web Processing Services (WPS) 

will help to enable the on-demand production of derivative 

products such as conventional maps, global positioning system 

(GPS) tracks, multi-thematic lenticular products, or even 

jigsaw-puzzles (fig. 2).

Conclusion

Recent developments in geoinformatics can make a significant contribution to (1) the management of a nature park’s 

scientific information by enabling access by both park staff 

and visitors to local spatial data repositories using services 

such as Sensor Observation Service (SOS) and WPS, and (2) 

Figure 1. Interaction between 

park visitors, park staff, and 

geoinformatics advisors.

Figure 2. Using a three-dimensional jigsaw puzzle (Geocubes) to 

communicate remote sensing products to the public.
Geoinformatics 2008—Data to Knowledge 19

the on-demand creation of adequate map-related products 

(fig. 2). The use of free and open-source software (FOSS) 

tools enables access to geoinformatics software without significant financial investments. Research results shared through 

park interpretive staff and interest groups will enhance the 

experience for all park visitors by helping to communicate 

the current scientific understanding of the park to the general 

public.

References Cited

Baru, C., and Lin, K., 2008, Mediating among GeoSciML 

resources: EGU Geophysical Research Abstracts, v. 10, 

2 p., available online at http://www.cosis.net/abstracts/

EGU2008/11390/EGU2008-A-11390.pdf. (Accessed 

August 5, 2008.)

Higgins, Bob, 2007, Geoscience research in U.S. National 

Parks: Eos, Transactions of the American Geophysical 

Union, v. 88, no. 21, p. 226.

Ludwig, Thorsten, 2005, Kurshandbuch Natur- und Kultu Kurshandbuch Natur- und Kulturinterpretation: Bidungswerk interpretation Web site at 

http://www.interp.de/. (Accessed August 5, 2008.)

Ramm, Frederik, and Topf, Jochen, 2008, OpenStreetMap—

Using and improving the free world map: Berlin, Germany, 

Lehmanns Media, 288 p.

Geoinformatics on the Front Lines—Purdue 

University’s Inaugural Geoinformatics Course

By C.C. Miller1 and Michael Fosmire2

1

Earth and Atmospheric Sciences Library, Purdue University, West Lafayette, Ind.

2

Physical Science and Engineering Libraries, Purdue University, West 

Lafayette, Ind.

In 2007, the National Science Foundation’s (NSF) Cyberinfrastructure Council published their “Cyberinfrastructure 

Vision for 21st Century Discovery.” In it, the NSF lays out a 

plan for directing and funding the initiatives designed to take 

the many and rapid advances in (mostly) large-scale computing architectures, communication and data transfer protocols 

and very large data storage capabilities and build from them a 

coordinated, integrated, interoperable cyberinfrastructure that, 

among other virtues, “serves as an agent for broadening participation and strengthening the nation’s workforce in all areas 

of science and engineering” (NSF Cyberinfrastructure Council, 2007 p. 6). Much of what the NSF proposes addresses 

the challenges of distributed, high-performance computing at 

the scale and scope one would expect from an NSF response 

to a revolution; yet, they’ve taken care to acknowledge in 

their plan the fact that, although an electronic infrastructure 

is an increasingly vital component of scientific research, 

these machines still require engineers, of sorts: scientists who 

can care for the data drawn from and fed into the electronic 

machines that produce or consume them. The Council also 

states, “In the future, U.S. international leadership in science 

and engineering will increasingly depend upon our ability to 

leverage this reservoir of scientific data captured in digital 

form” (NSF Cyberinfrastructure Council, 2007, p. 22). The 

Council goes on to state that “ongoing attention must be paid 

to the education of the professionals who will support, deploy, 

develop, and design current and emerging cyberinfrastructure” 

(NSF Cyberinfrastructure Council, 2007, p. 38).

In other words, just as high-end computing architectures 

must be relied upon to process and manipulate and share data, 

there is an equally important amount of human finesse that 

goes into the preparation, consumption, interpretation of, and 

care for those data. To this end, faculty from Purdue University Libraries and the Department of Earth and Atmospheric 

Sciences cooperated to offer an inaugural course in Spring 

2008 entitled “Geoinformatics.” The course was designed 

to be a discipline-independent overview of emerging trends 

and issues in the geosciences that fall within the purview of 

geoinformatics. The course was intended to fill a need within 

the emerging universe of cyberinfrastructure that will “both 

demand and support a new level of technical competence in 

the science and engineering workforce and in our citizenry at 

large” (NSF Cyberinfrastructure Council, 2007, p. 37). The 

instructors intended the course to fit somewhere within that 

hotspot between (1) cyberinfrastructure and the Semantic Web 

and (2) the future of data and rapidly developing, increasingly 

geospatially savvy technologies. The intended focus of the 

course was on the “workforce” component of the NSF vision 

(NSF Cyberinfrastructure Council, 2007, chapter 5). The plan 

was to begin teaching our next generation of scientists about 

the stores of data available in online systems, the power and 

limitations of those networked tools and data structures, and 

the importance of “good data hygiene.”

Data are only as interoperable as the scientists who are 

willing to understand the technologies, adhere to standards, and 

share their work with others. The provenance of data—who 

collected them, how they collected them, and whether and how 

they have been verified—is an important factor for researchers 

to consider before incorporating external data into their analyses, but it is just as important when it comes time to convey 

these data and the results of data analyses back into the community via the growing cyberinfrastructure-based sharing and 

dissemination systems and digital libraries. The course instructors therefore emphasized the concept of data management and 

sharing mechanisms throughout the course. All coursework was 

put into the context of the greater world of geodata and geodata 

issues. Course modules ranged from data collection and massaging (such as the development and use of a global positioning 

systems (GPS), or statistics and databases) to the more semantic concerns of metadata, ontological structures, and systems 

designed to assist with data stewardship and sharing.
20 Geoinformatics 2008—Data to Knowledge

In this presentation, one of the instructors (a geographic 

information system (GIS) librarian) of Purdue’s “Geoinformatics” course will (1) discuss the factors that influenced the 

development of the course, (2) briefly describe the modules, assignments, and technologies that were introduced in 

the course, and (3) address the successes and failures of an 

attempt to introduce the gargantuan world of geoinformatics 

to a diverse (by background and technical skill) body of 13 

students.

An infrastructure is only as good as the ability of societies and cultures to develop and deploy solutions upon it. 

Likewise, future scientists will only be willing to work within 

the bothersome restrictions necessitated by an adherence to 

standards and interoperability if they have been trained and 

have been convinced of the benefits of doing so. Stated in 

analog terms, perhaps one last time, even the fantastic technology of the book was lost on the illiterate. The “literacy skills” 

required of scientists in the interdisciplinary, high-grade 

cyberinfrastructure future proposed by the NSF and others 

are perhaps more nascent than the revolution itself. This is 

geoinformatics on the front lines: the lessons learned by the 

students ideally will have prepared them to move further into 

their respective domains with their eyes open to the opportunities that exist (or will exist) for advancing disciplinary or interdisciplinary research, and the lessons learned by the course 

instructors speak to the difficulties of moving geoinformatics 

itself into the future and the importance of doing so.

Reference Cited

National Science Foundation Cyberinfrastructure Council, 

2007, Cyberinfrastructure vision for 21st century discovery: 

Arlington, Va., National Science Foundation, 57 p. (Also 

available online at http://www.nsf.gov/pubs/2007/nsf0728/

index.jsp.) (Accessed on August 5, 2008.)

Oral Session II

A Cyberinfrastructure-Based Portal for 

Topographic Data Access, Processing, and 

Community Interaction

By Christopher J. Crosby,1 Viswanath Nandigam,1

Chaitan Baru,1

 Newton Alex,2 J. Ramon Arrowsmith,2

and Ashraf Memon1

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.

Over the past decade, there has been dramatic growth 

in the acquisition of publicly funded high-resolution topographic and bathymetric data for scientific, environmental, 

engineering, and planning purposes. Because of the richness 

of these datasets, they are often extremely valuable beyond 

the initial application that drove their acquisition and thus are 

of interest to a large and varied user community; however, 

because of the massive volumes of data produced by highresolution mapping technologies such as light detection and 

ranging (LiDAR), it is often difficult to manage and distribute these datasets via the Internet. Furthermore, the datasets 

can be technically challenging to work with because they 

require specific software and computing resources that are not 

readily available to many users. Through a variety of cyberinfrastructure tools, we have launched an initiative to build 

an online portal (the Open Topography Portal, or OpenToPo, 

at http://www.opentopography.org) that provides integrated 

access to high-resolution topographic data and Web-based 

processing tools and enables the community of users to share 

knowledge, experiences, and resources. OpenToPo builds 

upon the cyberinfrastructure-based system developed in the 

Geosciences Network GEON LiDAR Workflow (GLW) project during four years of collaboration between earth scientists 

at Arizona State University and computer scientists at San 

Diego Supercomputer Center.

OpenToPo will use the GLW as its core cyberinfrastructure-based system to provide online access to multibillionpoint, high-resolution, LiDAR topography datasets. To address 

the distribution of both LiDAR point data as well as standard, 

high-resolution digital elevation models (DEMs) produced 

from LiDAR and provided by the data vendor, we have developed multiple pathways for users to access data. We employ a 

Google Maps- or Google Earth-based interface to allow users 

to browse and download standard, tiled digital elevation data. 

For users who wish to explore the full potential of the LiDAR 

data, we provide access to the raw LiDAR point data as well 

as a suite of DEM generation tools to enable users to generate 

custom DEMs to best fit their science applications. Through 
Geoinformatics 2008—Data to Knowledge 21

these multiple pathways, we are able to service various user 

communities and thereby democratize access to these challenging community datasets.

Given the diverse applications of these datasets, the relative inexperience of the user community, and the technical difficulty of working with the data, OpenToPo’s goal is to offer 

not only access to data and processing tools but also to provide 

an environment for users to (1) learn about the datasets and 

data processing and (2) network and interact with fellow users. 

OpenToPo will use blogs, discussion forums, and wikis to 

encourage communication and interaction between users. We 

also hope to leverage the collective knowledge of the user 

community to build a system that provides processing recommendations and guidance based on what other users already 

have accomplished.

Currently, OpenToPo serves five datasets totaling over 

7 billion data points and approximately 2.5 terabytes. This 

system has been selected as the primary distribution pathway 

for LiDAR data acquired by the GeoEarthScope component of 

the National Science Foundation-funded EarthScope project 

(which will entail more than 20 billion additional points and a 

significantly larger user community). OpenToPo’s predecessor, the GLW, has over 180 users who have processed over 49 

billion LiDAR returns and downloaded more than 6,000 DEM 

tiles. Future OpenToPo development includes expanding the 

dataset distribution and processing approach to develop a more 

generic workflow that will permit users to query, process, and 

calculate common derivatives for DEMs of various resolutions 

and origins. We are currently seeking collaborators to host 

additional datasets in the system.

Standardizing Interfaces for External Access 

to Data and Processing for the National 

Aeronautics and Space Administration’s Ozone 

Product Evaluation and Test Element (PEATE)

By Curt Tilmes1 and Albert J. Fleig2

1

Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, Md.

2

PITA Analytic Sciences, Bethesda, Md.

The National Aeronautical and Space Administration’s 

(NASA) traditional science data-processing systems have 

focused on specific missions and on providing data access, 

processing, and other services to the funded science teams 

selected for those specific missions. Recently, NASA has been 

modifying this stance by changing its focus from “Missions” 

to “Measurements.” Where a specific Mission has a discrete 

beginning and end, the Measurement considers long-term data 

continuity across multiple missions. Total column ozone, a 

critical measurement of atmospheric composition, has been 

monitored for decades on a series of Total Ozone Mapping 

Spectrometer (TOMS) instruments. Some important European 

Space Agency (ESA) missions also monitor ozone, including 

the Global Ozone Monitoring Experiment (GOME) and the 

Scanning Imaging Absorption Spectrometer for Atmospheric 

Chartography (SCIAMACHY). With the U.S-European 

cooperative launch of the Dutch Ozone Monitoring Instrument (OMI) on NASA’s Aura satellite, and the launch of 

ESA’s GOME-2 instrument on its Meteorological Operational 

(MetOp) satellite, the ozone monitoring record has been further extended.

In conjunction with the U.S. Department of Defense 

(DoD) and the National Oceanic and Atmospheric Administration (NOAA), NASA is now preparing to evaluate data 

and algorithms for the next generation Ozone Mapping and 

Profiler Suite (OMPS), which will be launched as part of the 

National Polar-orbiting Operational Environmental Satellite 

System (NPOESS) Preparatory Project (NPP) in 2010. NASA 

is constructing a Science Data Segment (SDS) that will be 

used to evaluate the various NPP data products and algorithms.

The NPP SDS Ozone Product Evaluation and Test Element (PEATE) will build on the heritage of the TOMS and 

Ozone-Monitoring Instrument (OMI) mission-based processing systems. The overall measurement-based system that 

will encompass these efforts is the Atmospheric Composition 

Processing System (ACPS). We have extended the system to 

include access to publicly available datasets from other instruments where feasible, including non-NASA missions as appropriate. The TOMS and OMI systems were largely monolithic 

and provided only a very controlled processing flow from the 

raw data gathered by the satellite to the ultimate archive of 

specific operational data products. The ACPS will allow more 

open access using standard protocols, including Hypertext 

Mark-up Language (HTTP), Simple Object Access Protocol/

Extensible Mark-up Language (SOAP/XML), Really Simple 

Syndication (RSS), and various Representational State Transfer (REST) incarnations. Outside users can be granted access 

to various modules within the system, including an extended 

data archive, the ability to search metadata, and production 

planning and processing tools.

Data access is closely controlled. Certain datasets may 

be designated as being available to the public or restricted 

to groups of researchers or limited strictly to the originator. These finely tuned controls can be used, for example, 

to release one’s best validated data to the public but restrict 

access to the version of data that may be processed with a 

newer, unproven algorithm until it is ready for release.

Similarly, the system can provide access to algorithms, 

both as modifiable source code (where possible) and as fully 

integrated, executable algorithm plug-in packages (APPs). 

This will enable researchers to download publicly released 

versions of the processing algorithms and easily reproduce the 

processing remotely, while interacting with the ACPS. The 

algorithms can be modified, which enables better experimentation and rapid improvement. The modified algorithms can be 

easily integrated back into the production system for largescale bulk processing to evaluate the improvements.
22 Geoinformatics 2008—Data to Knowledge

The ACPS includes complete provenance tracking of 

algorithms, data, and the entire processing environment. The 

origin of any data or algorithm is recorded and the history of 

the processing chains are stored such that a researcher can 

understand the entire data flow. Provenance is captured in a 

form suitable for the system to guarantee scientific reproducibility of any data product it distributes, even in cases where 

the physical data products themselves have been deleted due 

to space constraints. We are currently working on Semantic 

Web ontologies for representing the various types of provenance information.

A new Web site consolidating information about measurements, processing systems, and data access has been 

established to encourage interaction with the overall scientific 

community. We will describe the system, its data processing 

capabilities, and methods the community can use to interact 

with the system.

The Open GeoSpatial Consortium Web Coverage 

Service Standard for Unified Sensor, Image, and 

Statistics Data Services

By Peter Baumann1

1

School of Engineering and Science, Jacobs University, Bremen, Germany.

Motivation

In the modular geospatial and location-based services 

specification set of the Open GeoSpatial Consortium (OGC, 

http://www.opengeospatial.org), the foundation for handling 

coverages is laid down in the Web Coverage Service (WCS) 

standard (fig. 1); its current version 1.1.2 (Whiteside and 

Evans, 2008) was adopted by the OGC Technical Committee 

in April 2008. WCS offers basic services such as spatial and 

temporal subsetting, range (commonly also called band or 

channel) subsetting, scaling, and reprojection. Further services, such as Sensor Web Enablement (SWE) (Botts and others, 2006) and Web Processing Service (WPS) (Schut, 2005) 

build upon the coverage model introduced by WCS.

Actually, WCS is not just one specification, but a core 

of services with optional add-on services (extensions) that the 

implementer may offer. Among the extensions under construction are data format encodings, so-called “transactional” 

services for updating coverages (because WCS per se is purely 

focused on data retrieval), and a coverage processing extension that offers a processing request language with server-side 

evaluation.

In this contribution we present this processing extension—the Web Coverage Processing Service (WCPS) emerging standard (Baumann, 2008a,b). The author is co-chair of the 

WCS Working Group and the Coverages Working Group, and 

chair of the WCPS Working Group.

The OGC Coverage Model

OGC’s conceptual model of a coverage is based on ISO 

19123:2005 (International Organization for Standardization, 

2005) and OGC Abstract Specification Topic 6 (Open Geospatial Consortium, Inc., 2007). Definitions in both of those 

documents are rather generic and include, for example, any 

currently known type of coverage. For practical reasons, the 

notion of a coverage in WCS refers only to gridded coverages 

(for the time being).

A coverage offered by a server consists of the following: 

(1) a locally unique identifier, (2) the value array itself, (3) its 

domain (a description of its spatial and temporal extent), (4) 

its range (the data type of the coverage’s “pixel” elements), 

(5) a list of coordinate reference systems in which the coverage can be addressed, (6) an optional set of null values, (7) 

interpolation methods which can be applied to this coverage 

when needed by an operation, and (8) metadata (part of which 

are optional).

The WCPS Processing Language

WCPS offers an XQuery-oriented coverage request 

language for describing coverage manipulation. The design of 

the request language was guided by the experience gained in 

developing and formalizing database array query languages 

(Baumann, 1999).

The basic request structure consists of a loop over a list of 

coverages offered by the server, followed by an expression indicating the desired processing of each coverage, as shown below:

for var_1 in ( coverage_1_1, coverage_1_2, ... ), var_2 in 

( coverage_2_1, coverage_2_2, ... ), ... [ where filterPredicate( 

var_1, var_2, ... ) ] return processingExpr( var_1, var_2, ... )

For example, the difference between red and nearinfrared channels in the coverage “ModisScene” encoded in a 

Tagged Image File Format (TIFF) file would be as follows: 

for m in ( ModisScene ) return encode( abs( m.red - m.nir ), 

“TIFF” )

In the example above, the resulting TIFF file is returned to 

the client immediately. By using the “store()” function, a coverage result alternatively can be stored server-side for subsequent 

download by the client. The response in this case is a Uniform 

Resource Locator (URL, or Web address) under which the file is 

accessible. The following request implements this:

for m in ( ModisScene ) return store( encode( m, “TIFF” ) )

The list of operations allows for spatial and temporal 

subsetting, changing pixel values via arithmetic or further 

operations, scaling, reprojection, summarization, and derivation 

of new coverages (such as histograms or convolutions). The following example returns not a coverage, but a single number that 

represents the average of the squared differences between red 

and near-infrared channel of a Modis scene. Note that the result, 

one floating point number, does not need to be encoded:

for m in ( ModisScene ) return avg( sq( m.red - m.nir ) )

In practice, WCPS covers a range of statistics, images, 

and signal processing functionality. As an exercise, we have 
Geoinformatics 2008—Data to Knowledge 23

implemented a Web Map Service (WMS) over WCPS. The 

language is “safe in evaluation,” which means that no single 

request can ever block the server for unlimited time.

WCPS Implementation

The WCPS reference implementation is based on the multidimensional raster database management system “rasdaman” 

which stores multidimensional coverages of unlimited size in 

standard relational databases and adds a raster query language 

(similar to the standard database language SQL) for retrieving 

and manipulating them. Extensive server-side optimization (in 

particular, algebraic rewriting and hardware and software parallelization) is then performed. Next, raster objects are partitioned 

internally into tiles and are optionally compressed.

Rasdaman has been operational for several years and 

serves, for example, airborne image maps that are dozens of 

terabytes in size, three-dimensional geophysical data models, 

and four-dimensional climate simulation results. As proof 

of the concept, a Web Map Service (WMS) has been implemented on top of WCPS (fig. 2). An online demonstration is 

available at http://www.earthlook.org.

WCPS Service Embedding

The WCS Processing Extension (Baumann, 2008b) 

embeds WCPS into the WCS suite by defining an additional 

request type, ProcessCoverages, which specifies how clients 

can send WCPS requests to a server and how they receive the 

processing results. 

Outlook and Future Work

Now that WCS has become a stable and mature specification, the standardization group’s work is now concentrated 

on modularization and adding optional extensions to accommodate the different user communities. For example, atmospheric researchers from OGC’s Geo-interface to Atmosphere, 

Land, Earth, and Ocean netCDF (GALEON) Network (see 

http://www.ogcnetwork.net/galeon) are actively contributing. 

Among the extensions planned or already under development are (1) extensions to allow the incorporation of irregular 

grids (currently WCS, and therefore also WCPS, only consider 

regularly gridded or raster data), and (2) extensions that allow 

for any number of spatial and temporal dimensions (currently 

only two, three, and four dimensions can be considered). All 

in all, work is plentiful, hence, experts in the fields of, for 

example, computers or earth sciences are invited to join the 

standardization group and contribute to shaping the future 

standards of our communities. 

References Cited

Baumann, Peter, 1999, A database array algebra for spatiotemporal data and beyond, in Proceedings, Fourth International Workshop on Next Generation Information Technologies and Systems (NGITS ‘99): Lecture Notes on Computer 

Science, v. 1649, p. 717.

Baumann, Peter, ed., 2008a, Web Coverage Processing Service 

(WCPS), version 1.0.0: Open Geospatial Consortium, Inc., 

document 08-068.

Figure 1. Web Coverage Service (WCS) extraction from a threedimensional Advanced Very High Resolution Radiometer (AVHRR) 

satellite image. This image was retrieved from a time-series 

mosaic consisting of about 10,000 single images.

Figure 2. Web Map Service (WMS) access to the HakonMosby underwater volcano area (one bathymetry layer, three 

submersible seafloor video mosaic layers)
24 Geoinformatics 2008—Data to Knowledge

Baumann, Peter, ed., 2008b, Web Coverage Service (WCS) 

Processing Extension, version 1.0.0: Open Geospatial Consortium, Inc., document 08-059r2.

Botts, M., Robin, A., Davidson, J., and Simonis, I., eds. 2006, 

Sensor web enablement architecture: Open Geospatial Consortium, Inc., document 06-021r1.

International Organization for Standardization, 2005, ISO 

19123:2005—Geographic information—Schema for coverage geometry and functions: Geneva, Switzerland, International Organization for Standardization, 65 p.

Jerosch, K., Ludtke, A., Schluter, M., and Ioannids, G.T., 

2006, Automatic content-based analysis of georeferenced 

image data—Detection of Beggiatoa mats in seafloor video 

mosaics from the Hakon-Mosby mud volcano: Computers 

and Geosciences, v. 33, no. 2, p. 202–218.

Open Geospatial Consortium, Inc., 2007, Abstract specification topic 6—The coverage type and its subtypes: Open 

Geospatial Consortium, Inc., document 07-011, 67 p.

Schut, Peter, 2005, Web Processing Service Implementation 

Specification, version 1.0.0: Open Geospatial Consortium, 

Inc., document 05-007r7, 87 p.

Whiteside, A., and Evans, J.D., eds., 2008, Web Coverage Service Implementation Standard, version 1.1.2: Open Geospatial Consortium, Inc., OGC document 07-067r5, 133 p.

Orchestrating Grid-Computing-Enabled Web 

Processing Services

By Bastian Schaeffer1 and Bastian Baranski1

1

Institute for Geoinformatics, University of Münster, Münster, Germany.

Existing spatial data infrastructures (SDIs) are mainly 

focused on data retrieval, data processing, and data visualization. An SDI based on open standards, (for example, those 

of the Open Geospatial Consortium (OGC)) mostly supports 

the retrieval and visualization of data through Web services; 

however, the geospatial-data processing (otherwise known 

in the SDI community as “geoprocessing”) is normally performed by humans with more or less proprietary and monolithic 

geographic information systems (GISs). With growing network 

capacity and processing power, some efforts were made to integrate stand-alone geoprocessing applications and their expert 

functionality into a Web service environment and, therefore, 

enable Web services to execute geoprocessing tasks. The OGC’s 

Web Processing Service (WPS), which became an official standard in late 2007, is a major attempt to address this issue in a 

standardized way. The WPS specification defines a standardized 

interface to publish and perform geospatial processes over the 

Web. Such a process can range from a simple geometric calculation (for example, a simple “intersect” operation) to a complex 

simulation process (for example, creating a global-climatechange model). To speed up the processing of large amounts 

of data and perform complex calculations (for example, when 

doing a weather forecast simulation), the use of grid computing or related methods and technologies is a good choice for 

achieving high calculation performance, for improving service 

availability, and for guaranteeing a different quality of service.

Even though grid computing or distributed computing is not 

a new approach, only some research has been done on combining OGC’s Web service (OWS) standards implementations in 

such a manner. The intrinsic complexity of geospatial data (also 

known as “geodata”), however, often requires the use of several 

processing steps to address a given problem. Grid computing can 

be applied to one dimension to improve the performance of each 

step, but to automate the whole business process, orchestrated 

geoprocessing workflows have to be built. This would create a 

second dimension and enable the creation of high-speed, fully 

automated, value-added geoprocessing workflows.

This paper will give an introduction to the new OGC 

WPS standards and will present an approach on how to combine this specification with grid computing. This approach will 

be combined with an introduction to the orchestration of these 

grid-based WPS in workflows.

Finally, the presented approaches will be validated by 

means of a real-world scenario. A geoprocessing workflow 

will be presented that solves a given problem in the field of 

fire protection in southern Spain. The Business Process Execution Language (BPEL) will be used to design this workflow, 

which will contain some grid-based WPS Web Services. Modeling, execution, and the visualization of results will all be 

performed in a single integrated environment and will prove 

the usefulness of these new approaches.

Building a Geospatial Web Portal Based on 

Service-Oriented Architecture

By Peisheng Zhao,1 Liping Di,1 Weiguo Han,1 Yaxing 

Wei,1

 and Xiaoyan Li1

1

Center for Spatial Information Science and Systems, George Mason University, Greenbelt, Md.

Geoscience research and applications often involve analysis of a large volume of geospatial data. Traditionally, scientists 

spent a lot of time installing and learning a variety of software 

on local machines, searching for and collecting the data from 

various sources, and preprocessing and analyzing the data on 

local machines. This “everything-locally-owned-and-operated” 

paradigm makes the analysis and application of geospatial 

data very expensive and time-consuming. Recent advances in 

Service-Oriented Architecture (SOA) are shifting the geospatial 

data and analysis from the “everything-locally-owned-andoperated” paradigm to the “everything-shared-over-the-Web” 

paradigm (a “Web-and-service-centric” paradigm). Currently, a 
Geoinformatics 2008—Data to Knowledge 25

significant number of geospatial datasets are available that use 

Web services, such as the Web Coverage Services (WCS) of 

the Open Geospatial Consortium (OGC) (Whiteside and Evans, 

2008). These services perform various geospatial analysis 

functions. By embracing geospatial content and capabilities 

within the context of the SOA, we have developed a SOA-based 

geospatial Web portal, which is a fully extensible portal system 

for discovering, retrieving, analyzing, and visualizing geospatial 

data and other types of data obtained from networks. The most 

distinguished characteristic of this portal is that it is designed to 

use distributed software, hardware, and applications to provide 

services from a number of different sources, thereby enabling 

the following: (1) a single point of access to geospatial information and processes over the Web, (2) Web-service-based analysis 

in which all functions are provided through interoperable Web 

services, and (3) user customization and collaboration by integrating and chaining together user-specified Web services.

The SOA uses loosely coupled and interoperable Web 

services to implement system requirements. All the services 

within the SOA are independent with self-described interfaces 

so that they can be accessed in a standard way without knowledge of how the service actually performs its tasks. Moreover, 

the SOA can support the integration and orchestration of 

distributed services into a composite service. The presented 

portal includes four main components: Web portal, catalog 

service, data service, and Web-processing service.

The Web portal follows the model-view-controller 

(MVC) design pattern, which is a commonly used software 

engineering architecture, to implement the following:

•	 User portal—Storing the current state of the portal in 

an OGC Web Map Context (WMC) (Sonnet, 2005) 

document that can be imported again later to restore 

the portal’s state.

•	 Data management—Retrieving geospatial data from 

a remote service and temporarily storing them on the 

map server in a network-accessible location.

•	 Data analysis—Selecting or integrating a preferred 

processing service to perform data analysis.

•	 Workflow—Allowing the user to build a chain of services to perform a task.

•	 Data visualization—Allowing the user to set up preferences for displaying the data, such as the building a 

sequence of coverages, deciding which subsets of data 

to show, and creating an image palette.

Data services provide users with a common data environment in which they can use data in an interoperable manner. 

OGC’s WCS provides intact multidimensional and multitemporal geospatial data as a “coverage” in order to meet the 

requirements of client-side rendering, scientific model inputs, 

and other clients beyond simple viewers. OGC’s WFS (Web 

Feature Service) (Vretanos, 2005) supports the networked 

exchange of geographical vector data as “features” encoded 

in Geographic Mark-up Language (GML). OGC’s Web Map 

Service (WMS) (De la Beaujardiere, 2006) provides geospatial 

data as a “map,” which is generally rendered in spatially referenced pictorial image formats (such as PNG, GIF, or JPEG) 

that are dynamically generated from real geographical data.

In distributed computing environments, a catalog service 

plays the role of “directory” in helping with the registration and 

discovery of data and services. The OGC’s Catalog Service for 

Web (CSW) (Nebert and others, 2007), an Electronic Business 

Registry Information Model (ebRIM) profile for Web-based 

geospatial catalog services, has been implemented to register, 

discover, and access a wide variety of distributed resources 

(for instance, geospatial data, applications, and services). CSW 

searches distributed catalog services such as the Group on Earth 

Observations’ Global Earth Observation System of Systems 

(GEOSS) Clearinghouse, the National Aeronautical and Space 

Administration’s (NASA) Earth Observing System Clearinghouse (ECHO), and George Mason University’s GeoBrain 

catalog. With regard to the geospatial data metadata descriptions 

in ISO 19115 (International Organization for Standardization, 

2003), the CSW makes some further extensions to accommodate the ISO model. The class “Dataset” has been added to the 

ebRIM to provide a flexible way to describe network-accessible 

data. The CSW supports a variety of classification methods to 

enable the service publisher to indicate the domain to which a 

service belongs at publication time, including the definitions 

from OGC specifications, ISO 19119 (International Organization for Standardization, 2005), and NASA’s Global Change 

Master Directory (Olsen and others, 2007).

A Web Processing Service (WPS) provides a domainspecific computational model, which might be a simple spatial 

calculation or a complex global climate-change model, to enable 

users to perform data analysis over the network. For manipulating 

and analyzing vector and raster geospatial data, the Web portal we 

present here provides more than 20 built-in WPSs and more than 

50 relevant operations that are based on Open Source Geospatial 

Foundations’s Geographic Resources Analysis Support System 

(GRASS). The WPSs can also be chained together to perform 

more complex analysis task. Users can access these services to 

perform data analysis and data mining of any OGC-compliant 

online data source. Moreover, the portal is able to integrate new 

Web services dynamically in order to provide users with an open 

and fully extensible environment. If a user has his or her own 

geospatial processing service and would like to use it to perform 

data analysis, he or she can integrate that service into the portal to 

build a unique portal. If the service is registered into the catalog 

service, other portal users will benefit from it. Hence, the more 

users are involved, the more powerful the portal becomes.

References Cited

De la Beaujardiere, Jeff, ed., 2006, OpenGIS Web Map Server 

Implementation Specification: Wayland, Mass., Open Geospatial Consortium, Inc., document 06-042, 85 p., available 

only online at http://www.opengeospatial.org/standards/

wms/. (Accessed August 25, 2008.)
26 Geoinformatics 2008—Data to Knowledge

International Organization for Standardization, 2003, ISO 

19115—Geographic information—Metadata: Geneva, Switzerland, International Organization for Standardization, 140 p.

International Organization for Standardization, 2005, ISO 

19119—Extensions of the service metadata model: Geneva, 

Switzerland, International Organization for Standardization, 

4 p. [Amended in 2008.]

Nebert, Douglas, Whiteside, Arliss, and Vretanos, Panagiotis, 

eds., 2007, OpenGIS Catalogue Services Specification: 

Open Geospatial Consortium, Inc., document 07-006r1, 218 

p., available only online at http://www.opengeospatial.org/

standards/cat/. (Accessed August 25, 2008.)

Olsen, L.M., Major, G., Shein, K., Scialdone, J., Vogel, R., Leicester, S., Weir, H., Ritz, S., Stevens, T., Meaux, M., Solomon, 

C., Bilodeau, R., Holland, M., Northcutt, T., and Restrepo, 

R.A., 2007, NASA/Global Change Master Directory (GCMD) 

earth science keywords, version 6.0.0.0.0: Greenbelt, Md., 

National Aeronautics and Space Administration, available 

only online at http://gcmd.nasa.gov/Resources/valids/archives/

keyword_list.html/. (Accessed August 25, 2008.)

Sonnet, Jerome, ed., 2005, Open Geospatial Consortium, 

2005a, Web Map Context Implementation Specification: Wayland, Mass., Open Geospatial Consortium, 

Inc., document 05-005, 30 p., available only online at 

http://www.opengeospatial.org/standards/wmc/. (Accessed 

August 25, 2008.)

Vretanos, P.A., ed., 2005, Web Feature Service Implementation Specification: Wayland, Mass., Open Geospatial 

Consortium, Inc., document 04-094, 131 p., available only 

online at http://www.opengeospatial.org/standards/wfs/. 

(Accessed August 25, 2008.) 

Whiteside, Arliss, and Evans, J.D., eds., 2008, Web Coverage Service (WCS) Implementation Standard: Wayland, Mass., Open Geospatial Consortium, Inc., document OGC 07-067r5, 133 p., available only online at 

http://www.opengeospatial.org/standards/wcs/. (Accessed 

August 25, 2008.)

Coming of Age—The Positive Legacy of 

Free and Open-Source Software Geographic 

Information Systems

By Peter Löwe1

1

German Research Center for Geosciences, Potsdam, Germany.

Introduction

Software projects evolve during their development 

and usage lifecycles through various stages. For successful 

projects, this evolution results in improvements regarding 

code quality, reliability, and performance. Because of these 

processes, projects which were started in the past can be 

considered as “legacy” with respect to the very latest information technology approaches. Usually the term “legacy” is used 

in a derogative way. Several Free and Open-Source Software 

(FOSS) projects have had several iterations of these cycles 

and could be rightfully addressed as “legacy” while they still 

serve their intended purpose. The example of the Geographic 

Resources Analysis Support System (GRASS), which is a free 

and open-source geographic information system (GIS), is used 

in this presentation to showcase a software project that has 

a positive legacy. This kind of legacy is a valuable asset for 

software development. In this type of system, “cutting edge” 

technologies are incorporated once those technologies have 

withstood the test of time; at the same time, code functionality is ensured by the careful maintenance of existing code. We 

will show how the rise of integrated development environments (IDEs) will help to ensure the future: saving projects 

by employing a gradual yet strategic increase in the developer 

base.

Perceptions of Legacy 

The online, user-written encyclopedia Wikipedia 

(http://www.wikipedia.org) describes two ways in which the 

term “legacy” can be applied to “software,” two terms which 

seem to be almost mutually exclusive: On the one hand (from 

an innovation-friendly point of view), application programs 

are considered “legacy” when they continue to be used 

because the user does not want to replace or redesign them. On 

the other hand (from a problem-solving perspective), the term 

“legacy” can be applied to software that is actually performing 

its tasks effectively even for very large amounts of input data, 

which indicates a fully developed tool, which then makes the 

term sound more positive. We will show that when it comes 

to software development, there are at least two other factors 

related to the term “legacy.”

The Legacy of GRASS GIS—Alive and Kicking

The GRASS GIS project has been evolving for more than 

two decades. The project was started by the U.S. Army Corps 

of Engineer’s Construction Engineering Research Laboratories 

(CERL) and lasted from 1982 to 1995. Afterwards, the code 

was handed over to academia, where a new phase of development began. Licensing under the general public license (GPL) 

in 1999 resulted in a dramatic increase in its development. 

Over the years, the functionality evolved from a raster-based 

GIS onwards to include floating point operations, a topologybased vector model, volume support, and, finally, the inclusion 

of Open Geospatial Consortium-based services. Apart from its 

traditional use as a desktop GIS, GRASS is also used as part 

of the backend of other applications, such as Quantum GIS 

(QGIS) and the Java Geographic Resources Analysis Support 
Geoinformatics 2008—Data to Knowledge 27

System (JGRASS) (developed using the uDig system). Both of 

these open-source GIS tools provide easy-to-use graphical user 

interfaces (GUIs) for standard desktop GIS tasks such as data 

queries or map production. They shield the user from most of 

the underlying complexity of GRASS GIS. For Internet-based 

mapping, GRASS can be used with the University of Minnesota’s MapServer (UMN Mapserver) or as a standalone OGCbased Web Processing Service (WPS) such as George Mason 

University’s GeoBrain Online Analysis System (GeOnAs) or 

pyWPS (a Python-language WPS available through Wald Intevention). These tools allow Web Mapping Services (WMS) or 

Web Processing Services (WPS) (for custom data processing) 

to be provided to their end-user communities; both services 

are available through the World Wide Web (WWW).

Coding Paradigm 

The highly modular code paradigm follows the approach 

of (scriptable) Unix shell commands. The reference code 

base, which is hosted by the GRASS servers, consists of more 

than 300 ANSI C-modules, with additional add-on scripts and 

modules that are independently provided and hosted by user 

communities. The current version consists of about 500,000 

lines of source code. “Write access” to the code repository has 

been controlled since 2006 by the GRASS Project Steering 

Committee.

Serial Code Development 

A relatively small multinational group of constant contributors voluntarily adds new features and updates old ones. 

The majority of the GRASS software-development community is highly fluent in the code structure and uses text-based 

tools for development such as the text editors “vi” or “emacs.” 

For newcomers, this situation results in a steep learning curve. 

Additionally, the constant overall development of libraries 

forces the contributors of add-on C-modules to adapt their 

code. As a consequence, many C-based add-on-modules 

become defunct over time, while shell-style scripts remain 

usable because the C-module interfaces remain unchanged 

despite the internal changes.

Arguing the Case for Extended IDE Usage 

During the last several years, IDEs became widely available for code development. They allow for easy navigation in 

large code repositories and collaborative development. Many 

programmers consider the availability of IDEs as a given; 

therefore, it makes sense to apply the know-how regarding 

GRASS-development to IDEs, such as the Eclipse C/C++ 

Development Toolkit (CDT) (fig. 1). Also, due to the availability of code-tracking and refactoring tools, add-on modules 

can be much more easily updated to the latest standards by 

their developers. The decision of the GRASS GIS community to stick to the C-language code, in the spirit of a positive 

legacy, allowed for the successful deployment of a native version of GRASS for Microsoft Windows. Because IDEs such 

as Eclipse CDT are platform independent, active code can be 

developed on non-Linux systems, such as Microsoft Windows. 

With respect to the “dark side” of legacy, such as the pending 

retirements of the current pre-IDE developers and the resulting loss in knowledge and skills, it is even more important 

to enable IDE-based development to help document the code 

while there is time. 

An IDE can be used as a convenient front end for source 

code development, maintenance, and building binary executable files. The issue of software legacy can be characterized 

by two distinctive aspects: (1) the overall quality of the source 

code itself, including knowledge preservation by means such 

as comments, and (2) the mechanisms (“toolchains”) required 

to derive executable files, which are executed according to 

control files (such as a “makefile” for C/C++ programming). 

For the latter, GRASS GIS relies on a custom makefile structure. Standard approaches such as autoconfig/automake for 

the configure/make/install toolchain (or cmake as an alternative) are not supported (yet). This can be perceived as a case 

of positive legacy (stemming from the “if it works, don’t fix 

it” approach), yet this approach could create a bottleneck for 

platform-independent development. Fortunately, the toolchain based on a custom makefile for building GRASS GIS 

binary executable files can be encapsulated in order to be used 

within Apache’s Ant toolchain. Ant is commonly used for Java 

applications and is part of the Eclipse IDE. Use of Ant allows 

developers to manage the whole software development process of checking out the latest sources from the SVN, editing 

the code, and configuring and building the executable files in 

one IDE, thereby making the IDE truly platform independent.

Conclusion 

Over the last two decades, GRASS GIS has proven to 

be a geographic information system for professional use. It 

continues to grow and is very much alive. The development 

approach of the community is basically conservative, yet 

it integrates additional technologies once they are considered mature and relevant. The usage of IDEs is expected to 

extend and rejunevate the developer community. IDEs will 

also broaden and speed up the development process because 

they enable active development non-Linux systems. The use 

of IDEs is an example of the positive, stabilizing effects of 

trusted software which “just works well” and can continue to 

be developed on various platforms.

Outlook 

A tool to describe processing chains would be desirable 

to in order to document and manage the community-inherent 

knowledge about how to enable GRASS modules to cope 

with complex tasks. Usually, every task can be accomplished 

in various ways, some of them more efficient than others. 
28 Geoinformatics 2008—Data to Knowledge

Although knowledge of the solutions remains only marginally 

archived, the loss of experienced members from the user community will result in massive losses of knowledge and skills. 

A likely tool for helping the community retain knowledge 

and skills is CyberIntegrator (CI, developed by the University of Illinois’ Image Spatial Data Analysis Group), which 

is a workflow-based system that supports interactive workflow creation, connection to external data and event streams, 

provenance tracking, and incorporation of workflow fragments 

and functionality from other systems and applications. Trials 

for the integration of CI and GRASS GIS are underway. Once 

such tools have been tied into the GRASS GIS environment, 

the knowledge and skills behind this community-driven FOSS 

Geoinformatics project will be saved and its continued development ensured for years to come.

Data Integration Using the Image Grand Tour

By Bradley C. Wallet1 and G. Randy Keller1

1

School of Geology and Geophysics, University of Oklahoma, Norman, 

Okla.

Introduction

Geoscience, like many disciplines, is experiencing an 

overabundance of data. A geoscientist often has access to a 

large variety of data including gravity anomalies, magnetic 

anomalies, digital elevation grids, laser altimetry, multispectral 

imagery, geological maps, and seismic velocity images. The 

challenge is to take these varied and often disparate sources 

Figure 1. Screen capture showing the use of the Eclipse IDE with the C/C++ Development Toolkit, which provides an integrated 

development environment (IDE) for the C/C++ programming language. The view shows that the current Geographic Resources Analysis

Support System (GRASS) geographic information system (GIS) source code has been downloaded from the project’s software source 

code repository (based on CollabNet’s Subversion (SVN) tool for software versioning) and has just been compiled via the Apache-Antwrapped GRASS-building chain. The resulting binary executable file has just been invoked as the last step of the processing chain. 
Geoinformatics 2008—Data to Knowledge 29

and to integrate them together in a manner that increases 

knowledge and understanding of the underlying structures and 

processes in the Earth.

Visualizing the integrated sources is inherently difficult 

due to the high dimensionality of the data. When visualizing 

the data as an image, there are a number of different color 

models that will support the display of multidimensional 

spatial data (the most common model being red-green-blue, or 

RGB); however, most color-display models only allow for a 

maximum of three different components. Although there has 

been work in visualizing more than three dimensions of spatial 

information, these newer approaches generally add only a few 

additional dimensions. Furthermore, the increased ability to 

visualize dimensions comes at a cost of increased complexity 

and difficulty in analysis and understanding.

Dimensionality Reduction

A common method of addressing the difficulties of 

visualizing multidimensional data is to reduce the dimensionality by methods such as linear projections. Perhaps the 

most popular method of doing this is principal component 

analysis (PCA). PCA is a common technique in remote sensing and is often applied to multispectral imagery. Guo and 

others (2006) applied it to spectral decomposition of seismic 

data with excellent results; however, PCA has some serious 

limitations as its goal is to maintain the maximum variance in 

the projected data. The reliance upon variance is troubling in 

many remote sensing applications because the feature of interest may only be a small portion of the dataset. In such a case, 

the variance of the overall dataset is likely to be dominated by 

the background or noise. As such, PCA may be optimally the 

wrong method because it may maximize the noise. Additionally, the global nature of PCA means that local dependencies 

are generally ignored in favor of global trends. The most 

interesting limitation of PCA is the lack of involvement of the 

implied spatial nature of structure in spatial data. Organization 

of the data in the spatial view of the data is not considered in 

PCA.

Image Grand Tour

The Image Grand Tour (IGT) is an interactive method for 

reducing dimensionality when the data are spatial in nature 

and can be visualized as an image. The IGT involves defining 

a smooth trajectory in the set of all possibly linear projections. 

This trajectory may be chosen according to a number of criteria, including denseness or maximum coverage in a finite time. 

The data are then viewed in image form in a smooth manner. 

The result is a form of “data movie.” Geoscientists can then 

visualize the data from all possible angles and look interactively for interesting views that offer insight into the data. The 

IGT has been applied to a number of areas, including medical 

imaging, land mine detection, and multispectral imaging. Wallet and Marfurt (2008) demonstrated the value of applying the 

IGT to interpreting spectral decomposition information from 

seismic data. They demonstrated that the IGT yielded information that was not readily apparent using PCA alone. In doing 

this, they constructed single, grayscale views that showed 

features that increased their understanding of the data.

Application

We illustrate the IGT technique by applying it to a land 

seismic survey acquired over south Texas in the United States. 

Spectral decomposition was calculated on the seismic volume 

resulting in 85 spectral components ranging from 5 cycles per 

second (hertz, or Hz) to 90 Hz. We recognized a horizon containing a fluvial-deltaic system and flattened the data. We then 

applied PCA to these 85 images and retained the first eight 

PCA images (fig. 1). We noted that several channel features 

can be seen in the first six principal components, or eigenspectra, while only random noise (either geological or seismic) can 

be seen in eigenspectra 7 and 8.

Examining other eigenspectra revealed that significant 

information was present in images other than the first three 

(fig. 1); however, by the time the seventh eigenspectrum was 

examined, little information appeared to be visible. We thus 

chose to run the IGT using the first six eigenspectrum because 

they appeared to capture all of the values in the dataset.

Running the IGT revealed structures that were not readily 

apparent when examining just the first three eigenspectra. We 

stopped our tour any time we identified a feature of geologic 

interest. The tops of figures 2A and 2B show the six coefficients 

applied to each eigenspectra at the current tour location. Several 

small channels appeared that were difficult to see in figure 1. 

Figure 2A presents various meandering channels, several of 

which were not clearly visible, or were absent, in the first three 

eigenspectra. Figure 2B clearly shows a distinct view of a single 

meandering channel. Although this channel was visible in previous images, this view provides better, more localized views of 

the channel edges. These results illustrate the value of combining information gathered from multiple tour projections.

Conclusions

The IGT is a valuable method for interactively integrating multiple data sources. Because the geoscientist controls 

the projections, the process is geared towards views that are 

interesting from a heuristic definition rather than predefined 

criteria related to variance. Using the IGT, it is possible to 

construct views of data that reveal insight that is not apparent 

using other methods.

Acknowledgments

We thank Anadarko Petroleum for permission to use its 

seismic data in this study. This study was partly supported by 

the National Science Foundation via the Geosciences Network 

(GEON) project.
30 Geoinformatics 2008—Data to Knowledge

References Cited

Guo, H., Marfurt, K.J., Liu, J., and Dou, Q., 2006, Principal components analysis of spectral components: Society of Exploration Geophysicists Expanded Abstracts, v. 25, p. 988–992.

Wallet, B.C., and Marfurt, K.J., 2006, A grand tour of multispectral components—A tutorial: The Leading Edge, v. 27, 

p. 334–341.

1 2 3 4

5 6 7 8

Figure 1. An example 

of output from a principal 

component analysis (PCA) that 

was input to the Image Grand 

Tour from a portion of a seismic 

survey from south Texas. 

The first eight eigenspectra 

(principal components) 

represent the vast majority of 

the energy in the 85 spectral 

components. North is oriented 

towards the top. Note that 

different channel features 

appear stronger in different 

components. Eigenspectra 7 

and 8 show very little channel 

information. Eigenspectra 

2 and 3 are quite sensitive 

to the north-south-trending 

acquisition footprint.

Figure 2. Image Grand Tour 

(IGT) projections. A, An IGT 

projection that illuminates 

very narrow meandering 

channels (yellow arrows) and 

a northwest-trending major 

channel that corresponds to 

deeper valley fill (magenta 

arrow). B, An IGT projection 

that is generally featureless 

except for the channel 

indicated by the green arrow.
Geoinformatics 2008—Data to Knowledge 31

A Collaborative Environment for Climate Data 

Handling

By Stephan Kindermann1 and Martina Stockhause2

1

German Climate Computing Center, Hamburg, Germany.

2

Max Planck Institute for Meteorology, Hamburg, Germany.

The Collaborative Climate Community Data and Processing Grid (C3Grid) project, which began in September 2005 as 

part of the German grid initiative (D-Grid), is currently building a climate-data handling infrastructure to support scientists 

in finding, analyzing, processing, and sharing climate datasets. 

The purpose of the infrastructure is to track the entire data 

cycle, from the discovery of input data to the publication and 

archiving of the results.

The approach consists of three layers: common data discovery layer, data access layer, and data manipulation layer:

•	 Data discovery is based on harvesting ISO 19139 

(International Organization for Standardization, 2007) 

metadata descriptions into a central metadata catalog.

•	 The underlying complexities of data access, providerspecific data extraction, and pre-processing steps are 

hidden by a simple Web service interface.

•	 Data processing can be triggered in a collaborative grid 

environment that provides computing resources and 

both short- and long-term data storage components.

There are three main problems the project encountered 

while building the infrastructure:

1. Establishing a consistent security layer—On the one hand, 

there is a clear need for a federated data authentication 

and authorization (AA) infrastructure, which prevents 

user identity and role management outside of the user’s 

home organization. On the other hand, the collaborative 

environment uses grid technology with a public key infrastructure (PKI) and a virtual organization- (VO-) based 

AA. The C3Grid approach merges Shibboleth’s Security 

Assertion Mark-up Language (SAML) information with 

grid- and VO-based AA mechanisms. After a prototype 

phase with plain Web services, C3Grid is now moving toward the implementation of Web-service resource 

framework (WSRF) services.

2. Generation and quality control of ISO 19139-compliant 

metadata—The discovery of information currently is 

handled mostly by large data providers. Yet, substantial 

support is necessary to allow (1) small data providers to 

join the infrastructure and (2) workflow providers to generate appropriate metadata for the resulting data (including data provenance information). Therefore, within 

C3Grid, tools were developed to provide automatic data 

provenance tracking, semiautomatic data archiving, and 

quality checks of discovery metadata.

3. Enable flexible but modular processing—Complex 

scientific workflows, which are composed of predefined 

modules, need to be supported by the infrastructure. Support requires both the availability of sufficient workflow 

descriptions and additional description-of-use details for 

the data. The handling and generation of such metadata 

can be built on collective experience and developed tools, 

but also needs additional agreements and information 

services.

In general, a major challenge in the project is to find 

or develop legal agreements that reflect an elaborate balance between technical progress and manageable effort. The 

established data and computing-service providers want to 

re-use their current implementations in order to minimize the 

maintenance of their software and the labor required to adapt 

to changes that are necessary when building the infrastructure. 

Yet, integrating collaborative environments always requires 

the creation of prototypes and the adoption of not-yet-established technologies. Different technological pathways have to 

be merged with respect to the specific needs of the existing 

scientific community and the future needs of intercommunity 

cyberinfrastructures. In this presentation, the key experiences 

and design decisions within the C3Grid project are discussed.

Reference Cited

International Organization for Standardization, 2007, ISO/TS 

19139:2007—Geographic information—Metadata—XML 

schema implementation: Geneva, Switzerland, International 

Organization for Standardization, 111 p.

Globalization of Geoscience Information—

Developing Collaboration to Sustain Growth 

(Keynote)

By Kristine E. Ch. Asch1

1

Federal Institute for Geosciences and Natural Resources, Hannover, 

Germany.

Universities, geological surveys, and other agencies 

around the world have similar goals and activities, issues 

and challenges in the field of geoscience information. The 

rapid development of technology (especially Web services), 

the massive explosion of data, and the increasingly diverse 

requirements of users across all sectors (governments, scientists, commerce, and the public) introduce new demands and 

challenges. Add to this the global and transnational issues—

sustainable energy resources, mineral resources, agriculture, 

ground water, transport, catastrophic natural hazards such 

as earthquakes and tsunamis, and last (but not least) climate 

change—where geoscience has a critical role to play and it 

becomes obvious that there is a pressing need for us to work 
32 Geoinformatics 2008—Data to Knowledge

together to sustain growth. Although the field of geoscience 

informatics provides many good examples that exploit developments in information technology to further global cooperation, we still have a long way to go in efficiently sharing 

information, experience, and expertise so we can analyze and 

synthesize data and knowledge across political and continental 

boundaries with a minimum of barriers and work in international teams on research projects that add value.

There already are some excellent examples of collaboration: (1) global initiatives such as the U.S. Environmental Protection Agency’s Global Earth Observation System of Systems 

(GEOSS), the European Information Services for Environment 

and Security’s Global Monitoring for Environment and Security (GMES) project, the European Union’s Infrastructure for 

Spatial Information in the European Community (INSPIRE), 

and the International Union of Geological Science Commission for the Management and Application of Geoscience 

Information’s (IUGS-CGI’s) Geoscience Mark-up Language 

(GeoSciML); (2) cooperation between governmental and 

nongovernmental organizations such as the European Geoscience Unions and the Commission of the Geological Map of 

the World (CGMW); and (3) specific international projects 

such as OneGeology and Australia-based eWater. Several of 

these initiatives and groups have made good progress and the 

challenges above are very much part of their discussions and 

agendas; however, significant questions remain: How effective 

are the current collaborations? Given the enormity of the challenges listed in the first paragraph, can we do better? This presentation will use the approach of a “Strengths, Weaknesses, 

Opportunities, and Threats (SWOT)” analysis to examine that 

question and, within that analysis, closely examine two specific examples: IUGS-CGI and its development of GeoSciML, 

and the European Union’s INSPIRE.

Strengths and Opportunities

The strengths of the actors (participants) in this domain 

include our rich and diverse expertise, different viewpoints, 

extensive datasets, state-of-the-art computing applications, 

and access to high-performance computing. There are existing 

projects, networks, and collaborations that provide a foundation for future integration. 

The opportunities are considerable because information 

and informatics are what joins the sciences together and there 

has never been a more opportune need for multidisciplinary 

science. The wish to bring together and integrate data means 

that there are opportunities for the development of standards for 

interoperability and harmonization. Some of these strengths and 

the opportunities will be illustrated by reviewing the work on 

GeoSciML and INSPIRE. GeoSciML is being taken forward by 

the IUGS-CGI through its active working group on interoperability. INSPIRE is an example of the positive growth of spatial 

data infrastructures; ultimately, it will create an infrastructure 

for environmental spatial data across Europe that will (1) set 

forth data discovery methods and data and network specifications, and (2) enable sharing of both data and systems.

Weaknesses and Threats

In spite of our successes, we must acknowledge weaknesses, too. Because there are so many actors, it is difficult to 

know just who is working in our domain and what they are 

doing. Can we identify and map them? The different cultures 

and viewpoints of these actors and their different goals often 

lead to “re-inventing the wheel,” a duplication of effort, and 

ultimately, a defense of territory. A subsidiary question must 

be asked as well: Does the academic reward system, with its 

emphasis on individual merit and esteem through papers and 

citation indexes, actually discourage collaboration? Finally, 

the geoscience information domain has many different actors, 

but there is little or no clear collective vision or clear leadership behind which all these actors can unite. Can we (or 

indeed, do we want to) change that which is now, essentially, a 

liberal system?

Perhaps the most significant threat is inertia—we do 

nothing and all the weaknesses persist. We do take advantage of the potential opportunities, and so our branch of the 

sciences remains marginal. Some may also see it as a threat 

that, unless we (academics, and governmental and nongovernmental agencies) take a lead, commercial companies and the 

market will move on and ignore us and our work.

The Results of the SWOT Analysis

So what do we need to do to make the collaboration more 

effective (that is, what are our opportunities)? We can begin 

by addressing our weaknesses and exploiting our strengths. 

One of these weaknesses, and perhaps the one that needs to 

be addressed first, is to simply understand who is doing what 

in our domain. There are an amazing number of groups and 

initiatives in the area of geoscience information and geoinformatics. With each meeting like this, we seem discover more 

new initiatives, groups, and individuals who are working in 

the same fields and on the same problems. Second, we need to 

embrace more enthusiastically the work on spatial data infrastructures (SDIs), which is work ignored in some scientific 

quarters because it is lead by geographers, not earth scientists. 

Third, the major scientific unions need to take a stronger lead 

in their involvement with global projects such as GEOSS, 

and they need to learn to interact more with regional political bodies like the European Commission. Many in our field 

feel there is a need for these organizations to give geoscience 

informatics a much higher priority and profile than they have 

to date. Without the lead of these organizations to provide the 

“mutual” discovery and “glue money” (funding for meetings, 

publications, and others means of sharing information), then 

we will make much less progress than we deem is necessary or 

society expects of us.

Our greatest opportunity for improving the situation lies 

in our own hands. That means that we in the geoscience information and informatics community should proactively look to 

develop collaboration that transcends the different sectors of 

our own domain (academia, geological surveys, and agen-
Geoinformatics 2008—Data to Knowledge 33

cies), use our transferable technology and techniques to extend 

beyond that into the different sciences, and last (but not least) 

work even more intensively across political boundaries.

The German Research Center for Geosciences’ 

Information System and Data Center—Portal to 

Geoscientific Data, Information, and Knowledge

By Bernd Ritschel,1 Vivien Mende,1 Hartmut Palm,1

Lutz Gericke,1

 Sebastian Freiberg,1 Ronny Kopischke,1

and Christian Bruhns1

1

German Research Center for Geosciences, Potsdam, Germany.

The German Research Center for Geosciences’s Information System and Data Center (ISDC) portal is integrating 

important data management services for satellite missions 

such as German Research Center for Geosciences’ Challenging Mini-Satellite Payload (CHAMP), Germany’s TerraSAR-X 

(an X-band synthetic aperture radar satellite), and the Gravity 

Recovery and Climate Experiment (GRACE, a joint partnership between the German Aerospace Center and the National 

Aeronautics and Space Administration (NASA)), as well as for 

international geodetic projects like the International Association of Geodesy’s Global Geodynamic Project (GGP), and the 

German Research Center for Geoscience-Dresden Technical 

University’s global positioning system (GPS) data reprocessing (GPS-PDR) project (Flechtner and others, 2003; Reigber 

and others, 2003; Ritschel and others, 2003, 2006, [2008]). 

The main components of the ISDC portal system are the portal 

framework, a content management system, a user and product management application, as well as detailed monitoring 

and statistics software. The new ISDC product philosophy 

describes the usage of product-type and data-product-related 

metadata documents written in Directory Interchange Format Extensible Mark-up Language (DIF XML) (Mende and 

others, 2007, 2008; Ritschel and others, 2007) for a standardized documentation of product type and data-product-specific 

information.

The entrance to the new ISDC portal 

(http://isdc.gfz-potsdam.de) is defined by a three-fold graphical user interface (GUI) as shown in figure 1. The portal 

framework is based on the open-source software PostNuke 

(http://postnuke.com). In addition to the portal framework 

and standard components (such as user registration, content 

management, and a user forum), most of the components were 

developed by the ISDC team.

At present, the ISDC is managing almost 300 different 

product types covering various geoscience domains such as 

geodesy, geophysics, atmospheric physics, and ionosphere 

physics. One third of these product types are accessible by 

public users and user groups. The other product types are only 

for operational, restricted, and internal uses. More than 11 

terabytes of data and 16 million data products are in long-term 

storage and are accessible online through the ISDC product 

archive. There are currently 1,760 international users and 

user groups, a number that has been increasing exponentially 

since the start of the new ISDC portal in March 2006. Besides 

Germany, most of the users and user groups are from China, 

followed by United States, India, and Japan.

Figure 1. The German 

Research Center for 

Geosciences’ Information 

System and Data Center 

graphical user interface. The 

left panel is the navigation 

portlet with a list of links to 

the main parts of the portal, 

such as projects, product 

type description, the Content 

Management System CMS, 

and so on. The middle panel 

contains links to the main 

content. The right panel 

contains links to the auxiliary 

information portlet.
34 Geoinformatics 2008—Data to Knowledge

The ISDC is managing almost all parts of a scientific data 

product’s lifecycle. Because of the standardized ISDC product 

philosophy, almost 300 different product types can be managed by a uniform process. As mentioned already, all product 

types are described by standardized parent DIF (NASA’s 

Directory Interchange Format for the Global Change Master 

Directory, or GCMD) metadata documents (fig. 2) (Mende 

and others, 2007, 2008, this volume; Ritschel and others, 

2007). This means that all product-type-dependent information (such as entry identification, entry title, parameters or 

keywords, topic category, data center, summary, personnel, 

instrument, and quality) and the temporal and spatial coverage 

of the whole data set are recorded in DIF files, which describe 

product-type and data-product-related metadata in one file 

only. In order to deal with data products (single data files) 

of a specific product type in a more efficient way, a further 

development of the DIF standards was necessary. In addition 

to the “old” ISDC metadata standards, the product-dependent 

metadata of all new ISDC product types are stored in separate 

data-product DIF metadata documents. Detailed information about the new ISDC product philosophy based on XML 

structures is found in Mende and others (2008, this volume). 

The data input and output management is operated by ISDC’s 

file transfer software components (“data pumps”), which are 

not only designed for the transfer of data but also for importing information to the ISDC product catalog (fig. 2). In order 

to keep ISDC data and information sustainable, science-driven 

data review processes are necessary; unfortunately, they are 

not occurring on a regular basis.

In order for improvements to (1) the interoperability of 

the relational database-based ISDC catalog (which consists of 

information about product types and data products), and (2) 

the XML document-based ISDC metadata collection to occur, 

the additional use of standardized service-oriented-architecture- (SOA-) driven concepts would be helpful. Using XML 

for metadata related to ISDC data and data products provides 

an opportunity for the smooth transformation of metadata 

documents from one standard to another. An example of this 

benefit is the Extensible Stylesheet Language (XSL) transformation from ISDC’s metadata written as DIF XML to Open 

Geospatial Consortium- (OGC-) and ISO 19115-compliant 

metadata for importing into Web-based systems such as the 

open-source Catalogue Service for Web (CSW) software 

“deegree” (http://www.deegree.org) (Braune and others, 2003; 

Data Processing Centers Information System and Data Center

Figure 2. Diagram illustrating product philosophy and metadata 

processing at the Information System and Data Center (ISDC). 

ISDC products are generated in different processing centers. 

Important sources of information about the content and structure 

of data files are often stored in data-file headers, or in processing 

software (programs), or in proprietary metadata files, or, 

sometimes, it is stored only in scientists’ heads; therefore, not 

only is it necessary to collect and store this information, but it is 

also necessary to be able to read the wide variety of different 

data formats for different data products. The ISDC is dealing 

with almost 300 different product types and many of them use 

different approaches for the storage of metadata. The only way 

that ISDC can overcome these challenges is with its own product 

philosophy, which describes a method of using a standardized 

metadata schema for the storage of metadata and the creation of 

an ISDC product. Standardized ISDC products contain one data 

file (or a small collection of data granules) and one appropriate 

DIF metadata file. In this manner, the management of ISDC 

products is realized by software that collects metadata by parsing 

only the standardized DIF metadata files. There is no need to look 

into the header of different types of data or to deal with additional 

metadata files. The collected metadata are stored in a database, 

whereas the complete products are in long-term storage in the 

ISDC data archive.
Geoinformatics 2008—Data to Knowledge 35

International Organization for Standardization, 2003; Voges 

and Senkler, 2005; Burgess and others, 2006). 

Most of the ISDC interfaces are based on committeedriven standards and techniques. In addition, there are many 

community-driven activities and developments, which have 

been used in composing the interactive Web 2.0. Currently, the 

ISDC team is studying such Web 2.0 techniques as tagging and 

social navigation for use at the ISDC, and appropriate user interfaces already are in development. The combination of Web 2.0 

techniques with Semantic Web languages such as Web Ontology 

Language (OWL) and the Simple Knowledge Organization System (SKOS) is offering new ways to represent data, information, and knowledge stored at the ISDC. Detailed information 

about ISDC’s research in Semantic Web technologies is given 

by Ritschel and others (2008, this volume).

References Cited

Braune, S., Czegka, W., Klump, J., Palm, H., Ritschel, B., and 

Lochter, F.A., 2003, Applications of metadata in conformity 

with ISO 19115 for catalogue services dealing with environmental and geoscientific geodata: Zeitschrift für Geologische Wissenschaften, v. 31, no. 1, p. 37–44.

Burgess, P., Palm, H., Ritschel, B., Bruhns, C., Freiberg, S., 

Gericke, L., Kase, S., Kopischke, R., Loos, S., and Lowisch, 

S., 2006, Implementing modern data dissemination concepts in 

the ISDC Portal, in Observation of System Earth from Space, 

Status Seminar, Bonn, Germany, September 18–19, 2006: 

Potsdam, Germany, Geotechnologien, available only online at 

http://isdc.gfz-potsdam.de/. (Accessed August 11, 2008.)

Flechtner, F., Ackermann, C., Meixner, H., Meyer, U., Neumayer, K., Ritschel, B., Schmidt, A., Schmidt, R., Zhu, S., and 

Reigber, C., 2003, Development of the GRACE Science Data 

System, in Program and abstracts, Observation of System 

Earth from Space, Status Seminar, Munich, Germany, June 

12–13, 2003: Geotechnologien Science Report 3, p. 48–50.

International Organization for Standardization, 2003, ISO 

19115—Geographic information—Metadata: Geneva, Switzerland, International Organization for Standardization, 140 p.

Mende, Vivien, Ritschel, Bernd, Freiberg, Sebastian, Palm, 

Harmut, and Gericke, Lutz, 2008, Directory Interchange 

Format (DIF) metadata and handling at the German 

Research Center for Geosciences’ Information Systems and 

Data Center, in Proceedings, Geoinformatics 2008—Data 

to Knowledge, Potsdam, Germany, June 11–13, 2008: U.S. 

Geological Survey Scientific Investigations Report 2008–

5172, p. 43–46 (this volume).

Mende, V., Ritschel, B., Palm, H., Gericke, L., and Freiberg, 

S., 2007, ISDC metadata management, in Program and 

abstracts, Observation of System Earth from Space, Status 

Seminar, Munich, Germany, November 22–23, 2007: Geotechnologien Science Report 11, p. 9–11. 

Reigber, C., Schwintzer, P., Lühr, H., Massmann, F., Galas, 

R., and Ritschel, B., 2003, CHAMP mission science data 

system operation and generation of scientific products, in

Program and abstracts, Observation of System Earth from 

Space, Status Seminar, Munich, Germany, June 12–13, 

2003: Geotechnologien Science Report 3, p. 129–131.

Ritschel, B., Behrends, K., Braune, S., Freiberg, S., 

Kopischke, R., Palm, H., and Schmidt, A., 2003, CHAMP/

GRACE-Information System and Data Center (ISDC)—The 

user interfaces for scientific products of the CHAMP and 

GRACE mission, in Program and abstracts, Observation of 

System Earth from Space, Status Seminar, Munich, Germany, June 12–13, 2003: Geotechnologien Science Report 

3, p. 132–133.

Ritschel, B., Bruhns, C., Burgess, P., Freiberg, S., Gericke, L., 

Kase, S., Kopischke, R., Loos, S., Lowisch, S., and Palm, 

H., 2006, The integration of CHAMP and GRACE products 

as well as associated scientific services in the new ISDC 

portal, in Observation of System Earth from Space, Status 

Seminar, Bonn, Germany, September 18–19, 2006: Potsdam, Germany, Geotechnologien, available only online at 

http://isdc.gfz-potsdam.de/. (Accessed August 11, 2008.)

Ritschel, B., Bruhns, C., Kopischke, R., Mende, V., Palm, H., 

Freiberg, S., and Gericke, L., 2007, The ISDC concept for 

long-term sustainability of geoscience data and information, in Proceedings, PV 2007 Conference—Ensuring the 

Long-Term Preservation and Value Adding to Scientific and 

Technical Data, Munich, Germany, October 9–11, 2007, 

available only online at http://www.pv2007.dlr.de/main/

results_en.htm/. (Accessed August 12, 2008.)

Ritschel, B., Mende, V., Palm, H., Kopischke, R., Bruhns, 

C., Gericke, L., and Freiberg, S., [2008], ISDC services—Data management, catalog interoperability and 

international cooperation: Potsdam, Germany, German 

Research Center for Geosciences, available only online at 

http://isdc.gfz-potsdam.de/. (Accessed August 12, 2008.)

Ritschel, Bernd, Pfeiffer, Sabine, Mende, Vivien, and Freiberg, 

Sebastian, 2008, Semantic web technologies for valueadded services at the German Research Center for Geosciences’ Information Systems and Data Center, in Proceedings, Geoinformatics 2008—Data to Knowledge, Potsdam, 

Germany, June 11–13, 2008: U.S. Geological Survey 

Scientific Investigations Report 2008–5172, p. 66–69 (this 

volume).

Voges, U., and Senkler, K., eds., 2005, OpenGIS Catalogue Services Specification 2.0—ISO19115/ISO19119 

Application Profile for CSW 2.0.: Wayland, Mass., Open 

Geospatial Consortium, Inc., available only online at 

http://www.opengeospatial.org/standards/deprecated/. 

(Accessed August 12, 2008.)
36 Geoinformatics 2008—Data to Knowledge

Scientific Application Portal Development for 

Research and Education in Cyberinfrastructure

By Choonhan Youn,1 Chaitan Baru,1 and Nancy 

Wilkins-Diehr1

1

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

La Jolla, Calif.

In recent years, the Internet has become an integral 

resource in the classrooms and homes of teachers and students. Widespread Web access to data and analysis tools by 

means of a cyberinfrastructure enhances the opportunities 

for teaching and learning. The concept of a cyberinfrastructure encompasses advanced scientific computing as well as 

a more comprehensive infrastructure for research and education, all of which are based on distributed federated networks 

of computers, data collections, information resources, online 

instruments, visualization tools, and human interfaces. Science 

communities increasingly are becoming dependent on such 

cyberinfrastructure for their research. In order to effectively 

train future scientists to make use of today’s cyberinfrastructure, educators must embrace the same technologies. Portals 

(or science gateways) provide tools for end users to use for 

online collaborations, access to computing resources, the 

ability to launch computational tasks, and sharing of data and 

other resources with others in a given community. The accessibility of Web interfaces means that students in a variety of 

locations and with a variety of backgrounds can all make use 

of an advanced cyberinfrastructure. Enhancing portals that we 

designed for high-end science so that they are also suitable 

in a variety of educational settings is a major contribution to 

workforce development.

The Geosciences Network (GEON, 

http://www.geongrid.org), which is a project funded by the 

United States’ National Science Foundation (NSF), is an 

Figure 1. Screen capture showing the Geosciences Network (GEON) Synthetic Seismogram (SYNSEIS) application being used to 

access data in the Incorporated Research Institutions for Seismology (IRIS) archives.
Geoinformatics 2008—Data to Knowledge 37

open collaborative project that is developing a cyberinfrastructure for the integration of three- and four-dimensional 

earth science data. The focus is on building data-sharing 

frameworks, developing tools and services, and identifying 

best practices with the objective of dramatically advancing 

geoscience research and education. These developments in 

infrastructure seek to extend access to data and to complex 

modeling tools from the hands of a few researchers to a 

much broader set of users. The GEON Synthetic Seismogram (SYNSEIS) application, for example, provides an 

easy-to-use interactive data-access and computing environment, using resources in TeraGrid (a network coordinated 

by the University of Chicago’s Grid Infrastructure Group) or 

GEON to study the three-dimensional lithospheric structure. 

SYNSEIS enables users to access the Incorporated Research 

Institutions for Seismology (IRIS) data archives by using an 

interactive map interface and retrieve data on earthquakes, 

seismic stations, and corresponding seismic waveforms 

(fig. 1). A three-dimensional crustal structure model is then 

obtained from a different Web service. The user defines 

earthquake source parameters and then generates a synthetic 

seismogram using validated software running on a remote 

supercomputer.

To support classroom use of this tool, we have developed 

a class account management system that instructors can use to 

easily create group accounts. Our goal is to combine Catalogue Service for Web (CSW) 2.0 concepts with conventional 

cyberinfrastructure to create virtual scientific and education 

communities. In this presentation, we will describe how the 

myProjects collaboration tools available in the GEON portal 

can be used, along with tagging, to allow users to review and 

vote on submitted contents (including parameter settings and 

job outputs) and support group discussions among the class. 

Sharing of such information may potentially help users avoid 

the unnecessary (and expensive) execution of computer coding 

and may provide them with a more effective way of sharing 

and testing possible solutions. As the use of collaboration tools 

and cyberinfrastructure matures, tools such as myProjects 

will have the potential for significant impact on education and 

research.

The TeraGrid project has recently launched the TeraGrid 

Pathways initiative. The goal of the initiative is to broaden 

the use of the high-end computing, data, and visualization 

resources provided by the NSF’s Office of Cyberinfrastructure. One component of this initiative is the adaptation of “science gateways” for use by educators. GEON has been selected 

to serve as a prototype for this work. The work will focus 

on the extension of GEON’s educational tools for use at the 

community college level, because there are many underserved 

communities who use community colleges as a springboard 

to higher education. The geosciences present tangible, visual 

concepts that lend themselves well to a variety of educational 

settings. In addition, the Native American population, through 

tribal colleges, has expressed interest in using science gateways for education on remote reservations. These connections 

will be pursued through the work with GEON.

Neptune—Developing a Digital Information 

Infrastructure for Micropaleontology in the 21st

Century

By David Lazarus,1 Cinzia Cervato,2 Douglas Fils,2 and 

Patrick Diver3

1

Paleontology Section, Museum of Natural History, Berlin, Germany.

2

Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa.

3

DivDat Consulting, Wesley, Ark.

Marine microfossil occurrences are used extensively 

for geologic age determination and for paleoceanographic or 

other paleoenvironmental research. They are less commonly 

used for studies of evolution, despite having one of the bestpreserved records of evolutionary change in the entire fossil 

record. Although marine microfossils have been widely 

studied from rock formations on land, several decades of 

scientific deep-sea drilling also have yielded a large archive 

of information on marine microfossil occurrences, particularly for the pelagic unicellular plankton groups: diatoms, 

radiolarians, coccolithophores (“nannofossils”), and 

planktonic foraminifera. Despite many published (mostly 

monographic) comparisons of occurrence information for 

selected individual, biostratigraphically important species, 

there has been no general synthesis of the fossil data collected by deep-sea drilling, nor any appropriate tools such 

as taxonomically and age-controlled occurrence databases, 

which are necessary for the effective synthesis of fossil 

occurrence data. There are databases (such as the Sepkoski 

database, developed at the University of Chicago, for marine 

invertebrate fossils) that have played a central role in the 

development of invertebrate paleontology for many years. 

Beginning in the early 1990s, a new database system—

Neptune—was developed to address the need for deep-sea 

marine microfossil synthesis tools. Neptune originally was 

developed at the Swiss Federal Institute of Technology—

Zürich and subsequently as part of the Chronos project 

(funded by the United States’ National Science Foundation) 

at Iowa State University. 

Neptune is a relational database and a set of external tools 

that link raw occurrence data for marine microfossils, as given 

in several hundred selected original range charts of deep-sea 

drilling science reports, to the essential scientific information 

needed to effectively retrieve and synthesize these data. These 

essential scientific data additions include (1) numeric geologic 

ages for every occurrence (which are based on quantitative 

age models for every drill hole in the system) and (2) master 

taxonomic name lists that link synonyms for the same taxa 

concepts to each other and distinguish different taxonomic 

data quality records from each other (for instance, records with 

clearly identified taxa versus records with “cf” or “?” observations). Neptune thus allows data to be retrieved from this 
38 Geoinformatics 2008—Data to Knowledge

important archive in a form suitable for large-scale syntheses 

of the deep-sea marine microfossil record and provides tools 

for summarizing the information. More recently, Neptune has 

been linked to the successor of the Sepkoski database—the 

Paleobiology Database (PBDB), which is an NSF-funded 

project currently hosted by University of California at Santa 

Barbara—thereby allowing microfossil data from land sections 

to be combined with data from marine sections. The system is 

currently being used to study large-scale patterns of Cenozoic 

evolutionary change in the plankton and as an age model and 

taxonomic reference library for other users of deep-sea drilling 

sections.

The current implementation of Neptune is as a relational 

database, which uses Post-Ingres Structured Query Language 

(PostgreSQL) and is hosted on the Chronos server stack at 

Iowa State University. Neptune is searchable through the 

Chronos portal and is seamlessly integrated with Chronos’ 

Java-based versions of the original Mac True Basic Age-Depth 

Plot and the Age-Range Chart applications written at the Swiss 

Federal Institute of Technology.

The analysis of large, heterogeneous datasets inevitably raises problems of mixed data quality, with data gaps, 

uneven sampling, age outliers, and incorrectly entered 

primary observations all affecting the validity of results. 

By linking with PBDB, Neptune analyses can make use of 

PBDB’s large library of paleobiologic tools, such as “rangethrough” and “subsampling” methods, for dealing with 

unevenly sampled data. Currently, tools are being developed 

for dealing with age outliers in taxon ranges, which were 

created due to taxonomic errors in the original data, reworking of fossils, or age model errors that resulted from poorly 

resolved or mutually inconsistent primary chronostratigraphic information.

Future development of Neptune is envisioned as part of 

a gradually evolving network of digital resources in marine 

micropaleontology (fig. 1, next page). These include stronger 

links to (1) primary deep-sea sediment core databases, such 

as the Janus system of the Integrated Ocean Drilling Program 

(IODP); (2) biostratigraphic and lithologic data from all 

IODP sites; (3) digital taxonomic catalogs of species images 

and descriptions (one such link, to Chronos’ own digital 

catalog system, has already been developed by Chronos); and 

(4) major collections of marine microfossil materials held in 

museums and other institutions around the world, such as the 

Micropaleontological Reference Centers’ network of deepsea marine microfossil slides. Effective networking of these 

resources will require the development of funding mechanisms to maintain and regularly update a central registry 

of the key shared field data: the taxonomic and age model 

information. The benefits for research, however, will be 

substantial and will include (1) major increases in data synthesis capacity, particularly for studies of long-term global 

processes, and (2) an improved efficiency in data retrieval 

and analysis for many other individual micropaleontologic 

research projects. 

The EarthScope Data Portal

By Ashraf Memon,1 Chaitan Baru,1 Knut Behrens,2

Rob Casey,3

 Ben Hoyt,4 Linus Kamb,3 Kai Lin,1 Bruce 

Weertman,3

 and Charley Weiland5

1

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

La Jolla, Calif.

2

International Continental Scientific Drilling Program, German Research

Center for Geosciences, Potsdam, Germany.

3

Incorporated Research Institutions for Seismology (IRIS), Data Management Center, Seattle, Wash.

4

University NAVSTAR (Navigation Signal Timing and Ranging) Consortium, Boulder, Colo.

5

Department of Geophysics, Stanford University, Palo Alto, Calif.

The EarthScope data portal, which is now in its alpha 

release, is being developed to provide a unified, single point 

of access to EarthScope data and products from (1) USArray 

(a continent-wide seismic observatory that is a component 

of EarthScope), (2) the University NAVSTAR (Navigation 

and Signal Timing and Ranging) Consortium’s (UNAVCO’s) 

Plate Boundary Observatory (PBO), and (3) the San Andreas 

Fault Observatory at Depth (SAFOD), which is funded by 

the National Science Foundation. The portal features basic 

search and data access capabilities to allow users to discover 

and access EarthScope data using spatial, temporal, and other 

metadata-based (data-type, station-specific) search conditions.

In this presentation, we will describe the features, design, 

and future improvements of the portal. This portal is being 

developed by a team consisting of the Geosciences Network 

(GEON, http://www.geongrid.org), Incorporated Research 

Institutions for Seismology (IRIS), NAVSTAR (Navigation 

and Signal Timing and Ranging) Consortium (UNAVCO), 

Stanford University, and the German Research Center for 

Geosciences’ International Continental Scientific Drilling 

Program. The portal search module invokes Web services 

developed by IRIS, UNAVCO, and Stanford to search for 

EarthScope data in the archives at each of these locations. The 

Web services provide information about all resources (data) 

that match the specified search conditions. Users can select 

from the returned datasets, add selected data to a “data cart,” 

and request the selected data to be packaged for download to 

the user. The services also are defined for “station discovery” 

(finding which stations are available for specified spatial and 

temporal parameters) and “data discovery” (finding the datasets that are available from the stations). The returned resulting datasets are organized in a hierarchical structure that is 

categorized based on the data type so that users can browse at 

ease and subsequently choose specific datasets, which are then 

assembled in a user “workspace” and available for download.

The EarthScope data portal has taken advantage of 

the significant portal development efforts of the GEON 
Geoinformatics 2008—Data to Knowledge 39

F-TNL IODP

F-DTD

Geologic 

Sample

Age Information

other Geology

data

N-TNL

N-DTD

R-TNL

R-DTD

D-TNL

D-DTD

other 

Biology

Research 

Portal

Example queries:

(colored lines)

taxa names 

in samples

Geologic 

Sample

other Geology

data

taxa names 

in samples Land

Geologic 

Sample

other Geology

data

taxa names Archive in samples

Query components:

Taxa, Geographic

Distribution, Geologic age

distribution

Taxonomy (paleontologic 

societies, museums)

Fossil occurrences 

(geologic surveys, 

archival centers)

Geologic age

(hosts?)

Name service

Foram DTD synthesis

Calcareous fossil

query }

Age Information

Age Information

Figure 1. Diagram showing structure of a possible future 

federated network of micropaleontology databases and related 

data sources, in which the current unitary content of Neptune is 

distributed between different science organizations. Individual 

databases or sections of databases shown by ovals, logical links 

between data types by lines. Organizations supporting each type 

of database shown in parentheses at bottom of figure. Examples 

of queries are given by colored text at top middle and right of 

figure. Earth science research questions about microfossils 

typically require information about the occurrences of species 

or other taxa in geologic samples, together with the geologic 

ages and geographic distribution of the samples in which they 

are found. Colored lines on left of figure show examples of how 

databases query each other according to different needs. Other 

than simple name service resolution (black), most queries involve 

several different data types (taxa, geologic sample information, 

and geologic age); queries are resolved into their components 

which are handled by different databases in the network. 

Taxonomy, databases (green, on left side of figure) for each 

different microfossil group, created and maintained by groups of 

specialist paleontologists, can resolve the complex way in which 

species and other taxonomic concepts are recorded by taxonomic 

names. The organizations that collect geologic materials (blue, 

middle of figure) can provide the needed geographic information 

about the samples, and they or a scientific data center’s 

archives store and query the actual reports of taxonomic name 

occurrences in geologic samples. “Land” refers to paleontologic 

databases from nonmarine sections, such as the Paleobiology 

Database. The geologic age of the samples (right side of figure) 

is a complex scientific interpretation linked to individual samples 

and sections. Most organizations do not maintain geologic age 

information with an accuracy appropriate for scientific research. 

Policy change or a dedicated chronology center may be needed to 

solve this problem. Abbreviations are as follows: IODP, Integrated 

Ocean Drilling Program; DTD, digital taxonomic dictionary or 

“catalog” that describes and illustrates taxonomic concepts; TNL, 

taxonomic name list that links names to concepts and serves as 

key field for linking database content together (name services). 

Microfossil groups are abbreviated as follows: F, foraminifera; N, 

calcareous nannofossils; R, radiolarians; D, diatoms.
40 Geoinformatics 2008—Data to Knowledge

project at the San Diego Supercomputer Center (SDSC, 

http://portal.geongrid.org), and the development of Web 

service interfaces at IRIS, UNAVCO, and Stanford. The portal 

is implemented using the open-source portal infrastructure 

software, GridSphere, which supports the well-known Java 

portlet interface, JSR 168, or the Portlet application programming interface (API). It uses a set of “core” portlets that have 

been developed in GEON for data registration, searches, and 

workspace services.

In this presentation, we will provide a report on the current state of development of the EarthScope data portal. So 

far, a preliminary deployment of the portal software has been 

conducted on systems at SDSC; initial designs have been 

accomplished for the StationDiscovery, DataDiscovery and 

DataPackaging services; and IRIS, UNAVCO, and Stanford 

have implemented the alpha version of the corresponding Web 

services, which runs on servers at their respective locations. 

The beta version of these Web services will be demonstrated 

during the presentation.

Enhancing Core Drilling Workflows Through 

Advanced Visualization Technology

By Yu-Chung Chen,1 Jason Leigh,1 Andrew Johnson,1

Luc Renambot,1

 Emi Ito,2 Paul Morin,3 Sean Higgins,4

Frank Rack,5

 Richard Levy,5 and Josh Reed6

1

Electronic Visualization Laboratory and the Department of Computer Science, University of Illinois—Chicago, Chicago, Ill.

2

Limnological Research Center, University of Minnesota, Minneapolis, 

Minn.

3

Department of Geology and Geophysics, University of Minnesota, Minneapolis, Minn.

4

Consortium for Ocean Leadership, Washington, D.C.

5

Department of Geosciences, University of Nebraska—Lincoln, Lincoln, 

Nebr.

6

Antarctic Geological Drilling Science Management Office, University of

Nebraska—Lincoln, Lincoln, Nebr.

Everywhere in the sciences, modern information technologies change the way people work. New tools and equipment 

are constantly being developed to help scientists to process a 

huge amount of data and to observe detailed phenomenon that 

they could not see before. We present the design and development of an initial visual core description tool with collaboration and annotation features that may be used for core drilling 

expeditions. By observing the use of the tool during real core 

drilling expeditions, we have learned how scientists make use 

of it and how it fits into modern core drilling workflows. 

The CoreWall Suite is a set of tools designed to aid realtime stratigraphic correlation, create initial core descriptions, 

and provide data visualization for various core-drilling communities. Corelyzer is the initial visual core description tool 

developed for the CoreWall suite. Corelyzer allows scientists 

to collaborate over huge data visualizations on a desktop 

workstation using one or more monitors with great interactivity and scalability. The main software architecture was developed using Java language with a native scene-graph library. 

The user-interface module and data-retrieval module were 

written in pure Java. The scene-graph library was developed 

in native C language with standard Open Graphics Library 

(OpenGL) for efficient rendering.

Scalability

Corelyzer was designed to be scalable. A graphics system 

that employs level-of-detail (LOD) control and texture paging 

(a method to conserve the amount of memory an image needs 

to load) was implemented inside Corelyzer. This graphics 

system allows scientists to load and interact smoothly with 

data representing thousands of meters of geological cores; one 

kilometer of core data produces roughly 30 gigabytes (GB) of 

raw imagery.

Visualization Capability

Corelyzer supports hardware setups that range from a 

single screen on a laptop computer to six liquid crystal display 

(LCD) panels connected to a single desktop workstation. The 

system scales core images with different formats and resolutions to match the physical core sample size. The main user 

interface provides major data visualization capabilities for 

core drilling, such as high-resolution core imagery, numerical 

core logging data, lithologic diagrams, smear slides, thin sections, and user-generated free-form or structured annotations.

Software extensibility

The Corelyzer source code was released under an opensource license and uses plain Extensible Mark-up Language 

(XML) file formats. Anyone can take this code and make 

modifications to fit his or her needs. For example, with a 

simple exporter module, the Drilling Information System 

(DIS) can export core data along with core imagery as a Corelyzer session file format, which enables all the core data to 

be loaded seamlessly into Corelyzer. Corelyzer also provides 

a plug-in framework, which allows third-party developers 

to extend its functionalities and capabilities; for example, 

support for lithologic diagrams was developed by Josh Reed, 

who is the information technology manager of the Antarctic 

Geological Drilling project (ANDRILL, a third-party entity). 

Moreover, for standardized core data (metadata) distribution, a 

“core feed” plug-in was designed to allow users to subscribe to 

core data description feeds defined in the standard syndication 

format. Users can look up the available feeds and subscribe to 

interesting core data, in the same manner as “podcasts.” The 
Geoinformatics 2008—Data to Knowledge 41

feed provides the metadata required to download and interpret 

actual imagery and numerical core log datasets.

Deployment and Usage

The CoreWall prototype has been used since 2006 by 

the National Lacustrine Core Repository at the University of 

Minnesota and by the Lamont-Doherty Earth Observatory 

at Columbia University. At the end of 2006 and 2007, Corelyzer was used in core drilling expeditions by the ANDRILL 

project. In the 2006 season, ANDRILL deployed with two 

CoreWall workstations with 30-inch LCD displays (fig. 1). 

The workstations mainly were used as follows:

1. During the night shift, a CoreWall workstation was placed 

alongside the physical cores on the tabletop to help with 

core description. The visualization capability that allows 

zooming into the high-resolution images beyond a core’s 

physical scale while still maintaining details made the 

setup act like an electronic microscope for the cores. This 

capability made it easier to do more accurate and detailed 

observations. For example, Dr. Franco Talarico (University of Siena, Italy) used to manually draw all of the core 

clasts on paper in order to classify them. With CoreWall, 

he now conducts research more efficiently with modern 

tools and techniques.

2. In the morning briefing, the other workstation was used 

for progress report explanations and tours of the core 

imagery alongside physical core samples. This workstation was set up in a public discussion area in order to help 

the researchers conduct context-sensitive discussions that 

benefited from being able to see the images on the screen.

Although there were only two CoreWall workstation 

setups for the entire science team, all involved personnel were 

encouraged to install Corelyzer on their laptop computers in 

order to easily access related data. The comments from the scientists have been positive, and in the 2007 season, ANDRILL 

increased the number of CoreWall workstations to six for the 

entire science team. One CoreWall workstation was set up 

right at the drill site to help the drillers make on-the-spot drilling decisions based on collected data.

An Analysis of Landscape Change Based on 

Remote Sensing and Geographic Information 

Systems in the Jinghe Basin, China

By Yonghua Zhao1

1

College of Earth Science and Land Resources Management, Chang’an 

University, Xi’an, China.

Using digital Landsat Thematic Mapper (TM) and 

Enhanced Thematic Mapper (ETM+) imagery from 1986, 

1995, and 2000 and a geographic information system (GIS), 

landscape changes were interpreted and analyzed in the Jinghe 

basin (a region in China that is experiencing serious soil erosion problems) in order to provide basic data for local decisionmaking and for sustainable land use and management. The 

results showed that between 1986 and 2000, most of the area 

covered in the basin was classified as grassland. The second 

largest area was cropland, the third was shrubland, and the 

fourth was forestland.

Because they cover the most area, grassland and crops 

probably have the most important effect on the direction of 

landscape change, ecological and environmental change, 

the safety of the regional ecology, and so on, in this region. 

Forests have an important function in maintaining environmental quality, preventing soil erosion, or maintaining 

ecological balance in the region; however, the combined area 

of all forests (including scattered forested areas) and shrubland 

was less than 11 percent of the total basin area. Only the area 

classified as built-up land always showed an increase from 

1986 to 2000. Areas of crops, forests and scattered forests, and 

unused land increased between 1986 and 1995 and decreased 

between 1995 and 2000. Bidirectional change of all landscape 

types was more obvious between 1995 and 2000 was more obvious between 1995 and 2000, and land- , and land- and land- and land- and landscape change was more obvious between 1986 and 1995. 

Above all, these changes showed that the landscape developed 

continuously and obviously was transformed before 1995; the 

landscape became regulated after 1995.

Among the types of areas that showed an increase, crop 

areas increased the most, by about 4,165 hectares (ha) from 

1986 to 1995. The second greatest increase was in areas classified as shrubland, by about 2,207 ha between 1995 and 2000. 

Unused land increased by about 849 ha between 1986 and 

1995, and grassland increased by about 816 ha between 1995 

and 2000. The increase in the amount of area covered by water 

was less than 94 ha from 1995 to 2000. 

Among the types of areas that showed a decrease, grasslands decreased the most, by about 3,125 ha from 1986 to 

Figure 1. A Corelyzer set-up, which is running on an Apple 

Mac Pro computer and uses two Apple 30-inch cinema-display 

monitors.
42 Geoinformatics 2008—Data to Knowledge

1995. The second was shrubland, by about 2,781 ha between 

1986 and 1995. Between 1995 and 2000, the area classified 

as crops decreased by about 2,097 ha, and scattered forests 

decreased by 1,086 ha. Other forested areas decreased by less 

than about 200 ha between 1995 and 2000.

The changes in the areas classified as crops, grassland, 

shrubland, and unused land dominated the changes in the 

Jinghe basin and influenced the direction and rate of the total 

landscape change. The GIS analysis further indicated that 

five land-use conversions were prominent: (1) crops to grassland, (2) crops to built-up land, (3) grassland to crops, (4) 

grassland to shrubland, and (5) grassland to scattered forest. 

The amount of change from crops to grassland was greater 

between 1986 and 1995 compared with the period between 

1995 and 2000. The above results showed that the landscape 

changed more between 1986 and 1995 than between 1995 

and 2000, and the landscape heterogeneity and fragmentation increased and the landscape connectivity decreased at 

regional scale.

Changes in land use or changes to the landscape can be 

measured by using either a land-use dynamic index (LUDI) 

(Liu and Buheaosier, 2000) or a landscape-departure index 

(LDI) (Wang and others, 2004), which quantify the rate of 

change and can help to predict the future trend of a change. 

The LUDI of cropland and grassland was lower and that of 

other forestland was higher during two periods. The LDI refers 

to the extent of departure from the original landscape resulting from of the effects of human activity. The index value is 

obtained by totaling the acreage covered by land classified as 

built-up land, crops, and other forested land in the research 

area and dividing that sum by the total acreage of the research 

area. In the Jinghe basin, the LDI increased from 0.436 in 

1986 to 0.448 in 1995 and then decreased to 0.443 by 2000, 

which showed that the effect of human activities on the landscape increased from 1986 to 1995, but then decreased after 

1995.

Remote-sensing data are widely used in many types of 

landscape-analysis studies. Data from the larger satellites such 

as Landsat’s TM, ETM+, Multispectral Scanner (MSS), and 

Moderate Resolution Imaging Spectroradiometer (MODIS) 

mostly have been used by researchers who were studying 

regional land-use and land-cover change, global environmental change, and other studies that required large areas to be 

covered. Data from the larger satellites had some disadvantages; for example, data were expensive, satellite costs were 

too high, run cycles took too long, resolution was too low, and 

so forth. With recent advances in technology, more and more 

smaller satellites have been launched and have yielded the 

following advantages: data are inexpensive, satellites costs are 

low, run cycles are shorter, resolution is higher, the satellite 

signal is easier to obtain, and more. Therefore, we thought that 

a small satellite would be a better choice to obtain data related 

to landscape ecology, land use, and other landscape-related 

issues. In future landscape-change research, data from smaller 

satellites will be more widely used by researchers because of 

these advantages.

Reference Cited

Liu, Jiyuan, and Buheaosier, 2000, Study on spatial-temporal 

feature of modern land use change in China—Using remote 

sensing techniques: Quaternary Sciences [China], v. 20, no. 

3, p. 229–239. 

Wang, Zongming, Zhang, Shuqing, and Zhang, Bai, 2004, 

Effects of land use change on values of ecosystem services 

of Sanjiang Plain, China: China Environmental Science, v. 

24, no. 1, p. 125–128.

The GEOROC Database as Part of a Growing 

Geoinformatics Network

By Baerbel Sarbas1

1

Department of Geochemistry, Max Planck Institute for Chemistry, Mainz, 

Germany.

Since its introduction in 1999, the geochemical database 

GEOROC (Geochemistry of Rocks of the Oceans and Continents, http://georoc.mpch-mainz.gwdg.de), which is hosted by 

the Max Planck Institute for Chemistry in Mainz, established 

itself as a major online resource available to the scientific 

community. GEOROC provides geochemical data that have 

been published for volcanic whole rocks and glasses, minerals 

and inclusions from ocean islands, large igneous provinces, 

convergent margins, Archean greenstone belts, and rift and 

intraplate volcanic regions. As of April 2008, the database provides about 350,000 analyses published in about 7,300 papers.

The Web interface of GEOROC allows the user to select 

samples by bibliographic, tectonic, geographic, petrologic, 

and chemical criteria. As part of the bibliographic query, the 

search for the GEOROC_Reference_Number permits an easier 

reproduction of published compilations created with the help 

of the database. A Google Maps-based search has been added 

which allows users to query by geographic location. A new 

service for GEOROC users is a discussion forum. It allows 

comments and suggestions to be entered while simultaneously 

working with the database and allows users to flag typographical errors that were made either by the database team or were 

in the original published report.

To get a quick idea of the geochemical signature of 

samples from certain localities or of a special rock type, 

GEOROC includes “ready-made” compilations of published 

data. In addition to these unvalued data, we offer so-called 

expert datasets. These datasets are compiled by non-GEOROC 

scientists and can include more than just the measured data 

(for example, normalized data or element ratios).

GEOROC joined with the Petrological Database of the 

Ocean Floor (PetDB, which is hosted by Columbia University) 

and the North American Volcanic and Intrusive Rock Database

(NAVDAT, administered by University of Kansas) to initiate 
Geoinformatics 2008—Data to Knowledge 43

the National Science Foundation-funded EarthChem consortium (http://www.earthchem.org/), with the goal of increasing 

the synergy between the three geochemical database efforts. 

The EarthChem portal (http://geoportal.kgs.ku.edu/earthchem/

jtest/) offers a seamless search across the three databases.

Publications cited in GEOROC are cross-linked 

with the geochemical database GeoReM (Geological and Environmental Reference Materials database; 

http://georem.mpch-mainz.gwdg.de/), which provides access 

to reference materials and isotopic standards. The detailed 

information about the analytical conditions that are described 

for the reference materials available in GeoReM enables users 

of GEOROC to estimate the quality of the analyzed rock 

samples. Similarly, it is possible to go directly from GeoReM 

to the respective reports in GEOROC to get the analyses of the 

studied samples.

Directory Interchange Format (DIF) Metadata 

and Handling at the German Research Center 

for Geosciences’ Information System and Data 

Center

By Vivien Mende,1 Bernd Ritschel,1 Sebastian 

Freiberg,1

 Hartmut Palm,1 and Lutz Gericke1

1

Information System and Data Center, German Research Center for Geosciences, Potsdam, Germany.

The Information System and Data Center (ISDC) is 

managing more than 11 terabytes (TB) of geoscience data and 

information. Currently, these data are coming from 11 missions, including the German Research Center for Geosciences’ 

Challenging Mini-Satellite Payload (CHAMP), the Gravity 

Recovery and Climate Experiment (GRACE, a joint partnership between the German Aerospace Center (DLR) and the 

National Aeronautics and Space Administration (NASA)), 

Germany’s TerraSAR-X (an X-band synthetic aperture radar 

satellite), the International Association of Geodesy’s Global 

Geodynamic Project (GGP), and others that have yielded 

nearly 300 product types and approximately 16 million 

products, which have been made available to more than 1,700 

users. This paper gives a short overview about the development and use of metadata in the ISDC. Each product type that 

results from a geoscience mission or project consists of a set 

of products. A product is composed of a data file (or files) and 

a metadata document. Figure 1 shows the three product types 

resulting from the TerraSAR-X. 

In order to describe and manage the products (data 

file(s) and metadata), we are using an evolution version 9.x 

of NASA’s Directory Interchange Format (DIF) standard. 

The manager of NASA’s Global Change Master Directory 

(GCMD), Lola Olsen, defines metadata as follows: “Descrip as follows: “Descriptive information that characterizes a set of quantitative and/or 

qualitative measurements and distinguishes that set from other 

similar measurement sets.” (See http://gcmd.nasa.gov/

Aboutus/standards/.) For the management of ISDC product 

types, DIF is an excellent choice. 

The ISDC base schema of the product type DIF XML 

documents is defined in the “base-dif.xsd” file. The ISDC 

Extensible Mark-up Language (XML) schema has been 

defined on the basis of the GCMD XML schema definition found online at http://gcmd.nasa.gov/Aboutus/xml/dif/

dif_v9.7.1.xsd. In order to describe single products, it was 

necessary to extend the DIF standard and to modify the 

GCMD XML schema. Although the structure of the ISDC 

DIF XML schema is different from the GCMD schema, 

the ISDC product type DIF XML documents are still valid 

in relation to the GCMD schema. Additionally, the ISDC 

is using a product type-data child DIF combination. The 

metadata of product types are described in associated product 

type DIF XML files according to the “base-dif.xsd” schema. 

The product type DIF XML files are validated and stored in 

an Oracle XML database. The product- (data file-) specific 

metadata are documented in data-child DIF XML files. Each 

product type has its own schema for the data-child DIF XML 

files. Data-child DIF documents are used to describe the 

data-file-specific properties. The complex XML type <Data_

Parameters> in the data-child DIF XML document provides 

the specific extension of the product type DIF XML structures. <Data_Parameters> includes specific metadata of the 

product, such as the data file name, data file size, revision, 

satellite identification, and other information. In order to 

implement this data model, we are using the redefined XML 

technique for the definition of complex XML types for the 

<Data_Parameters>. By redefining the ISDC “base-dif.xsd” 

schema, all data-child DIF XML documents are derived. 

Using the GCMD XML schema, this approach would not be 

possible because of the definition of XML reference structures.

The extended metadata in the data-child DIF XML documents are parsed by a Perl (an open-source software) script. If 

the data structure is correct, the extended metadata are stored 

in product-type-related tables in a relational database. The 

connection between the data-child DIF XML files and the 

product type DIF XML document is given by the equality of 

parts of the <Entry_ID> element in both the product type and 

the related product metadata documents. Additionally, the content of the <Parent_DIF> element in the data-child DIF XML 

document refers to the appropriate product-type DIF document. The relation between the schemata, the XML metadata 

files, and the storage structures is shown in figure 2.

Using the product-type DIF XML structures, it is possible 

to conduct a thematic content-based search of the different 

product-type documents as well as provide interoperability 

with other catalog systems. It is now possible to transform the 

XML DIF files into ISO 19115-standard documents (International Organization for Standardization, 2003) in order to use 

Open Geospatial Consortium-compliant Web services such as 

deegree’s (www.deegree.org) Catalogue Service for Web 2.0. 

Furthermore, achieving harmony with other catalog systems 
44 Geoinformatics 2008—Data to Knowledge

Chart showing the relations of mission-related product types at the German Research Center for Geosciences. Each product Figure 1.

type consists of a set of products. A product is composed of a data file(s) and metadata that is created by using DIF XML.
Geoinformatics 2008—Data to Knowledge 45

Figure 2. Chart showing the 

relation between schemata 

(base and the data-child 

schema) and the metadata 

(product-type metadata 

and child-metadata). The 

storage mechanisms, such 

as relational and XML-based 

databases, also are shown. 
46 Geoinformatics 2008—Data to Knowledge

is possible by using international standards. The structure 

of XML easily allows extending the DIF standard in future. 

Using the parent-child DIF concept, only a small amount of 

mandatory metadata must be included in both the product type 

and data-child DIF XML documents.

Reference Cited

International Organization for Standardization, 2003, Geographic information—Metadata: Geneva, Switzerland, 

International Organization for Standardization, 140 p.

Network of Research Infrastructures for 

European Seismology (NERIES)—Web Portal 

Developments for Interactive Access to 

Earthquake Data on a European Scale

By Alessandro Spinuso,1 Luca Trani,1 Sergio Rives,2

Phetaphone Thomy,2

 Fabian Euchner,3 Danijel 

Schorlemmer,4

 Joachim Saul,5 Andres Heinloo,5

Rémy Bossu,2

 and Torild van Eck1

1

Observatories and Research Facilities for European Seismology 

(ORFEUS) Data Center, Royal Netherlands Meteorological Institute, De Bilt, 

The Netherlands.

2

European-Mediterranean Seismological Center (EMSC), Bruyères-leChâtel, France.

3

Swiss Seismological Service, Swiss Federal Institute of Technology, 

Zurich, Switzerland.

4

Southern California Earthquake Center, University of Southern California, 

Los Angeles, Calif.

5

German Research Center for Geosciences, Potsdam, Germany.

The Concept

The Network of Research Infrastructures for European 

Seismology (NERIES) is a European Commission (EC) 

project whose focus is networking together seismological observatories and research institutes into one integrated 

European infrastructure that provides access to data and data 

products for research. One of the goals of NERIES is to design 

and develop a Web portal that acts as the uppermost layer of 

the infrastructure and provides rendering capabilities for the 

underlying sets of data 

Seismological institutes and organizations in European 

and Mediterranean countries maintain large, geographically 

distributed data archives. By using the NERIES portal, the 

broader earth science community and the general public will be 

able to access earthquake data archives from a single interactive 

Web site, which will make use of the proper tools and services. 

This scenario suggested a design approach based on the concept 

of an internet service oriented architecture (SOA) to establish 

a cyberinfrastructure for distributed and heterogeneous data 

streams and services. Recently, this approach has been tested 

and implemented in Europe by the NERIES consortium and in 

the United States by the EarthScope consortium. Here we present the current developments within NERIES.

Implementation

Key data formats

The Web services that are currently being designed 

and implemented will deliver data that have been adapted 

to appropriate formats. The parametric information about a 

seismic event is delivered using a seismology-specific Extensible Mark-up Language (XML) format called QuakeML 

(https://quake.ethz.ch/quakeml), which has been formalized and 

implemented largely within the NERIES project and in coordination with global earthquake-information agencies. Uniform 

Resource Identifiers (URIs) are used to assign identifiers to 

(1) seismic-event parameters described by QuakeML, and (2) 

generic resources such as authorities, location providers, location methods, adopted software, and so on, described by use of a 

data model constructed with the resource description framework 

(RDF) and accessible through a dedicated Web service.

In order to facilitate the exchange in Europe of the 

parametric information that is specific to seismic events, the 

European-Mediterranean Seismological Center (EMSC) has 

implemented a unique event identifier (UNID) that will create the seismic event URI used by the QuakeML data model. 

The UNIDs and URIs play an important role within the portal 

developments, and will be used to link and integrate the 

information retrieved from the different Web services. Access 

to data such as waveform files (broadband waveform and 

accelerometric data) will be provided through a dedicated set 

of Web services that will act as the Hypertext Transfer Protocol (http) interface, which would be the layer on top of the 

dedicated middleware used by the seismological observatory 

or institute that is supplying the data.

Software Technologies and Graphical User Interfaces

In order to achieve both the advantages of using a graphical user interface (GUI) and programmatic access to the data, 

all the Web services are integrated to create different interactive 

applications. Each single application consists of a Java-based 

JSR-168-standard portlet (often provided with interactive maps 

for data discovery) plugged into the centralized NERIES portal 

implemented through the use of open-source products belonging 

to the Apache Software Foundations’s Portals Project (http://

portals.apache.org), such as Jetspeed-2 and WSRP4J.

In specific cases, it will be possible to distribute the 

deployment of the portlets among the data providers, such as 

seismological agencies, because of the adoption within the 

distributed architecture of the NERIES portal of the Organization for the Advancement of Structured Information Stan-
Geoinformatics 2008—Data to Knowledge 47

dards’ (OASIS’) Web Services for Remote Portlets (WSRP) 

as the standard for presentation-oriented Web services. This 

approach has been already implemented between EMSC and 

Observatories and Research Facilities for European Seismology (ORFEUS)

The purpose of the GUI is to provide to the user his 

own environment where he can surf and retrieve the data of 

interest that are stored in the portal and managed by the user 

using the concept of the shopping cart found on some retail 

Web sites. This approach involves having the user interact 

with dedicated tools in order to compose personalized datasets 

that can be downloaded or combined with other information 

available either through the NERIES network of Web services 

or through the user`s own cart. For example, an event catalog composed by a user through a specific portlet also will 

be available to other applications found through the portal in 

order to obtain derivative measurements such as moment tensors or waveform data volumes. 

Administrative applications also are provided to perform monitoring tasks such as retrieving service statistics or 

scheduling submitted data requests. An administrative tool is 

included that allows the RDF model to be extended, within 

certain constraints, with new classes and properties.

Semantically Enabled Registration and 

Integration Engines (SEDRE and DIA) for the 

Earth Sciences

By Abdelmounaam Rezgui,1 Zaki Malik,1 and A. 

Krishna Sinha2

1

Department of Computer Sciences, Virginia Polytechnic Institute and State 

University, Blacksburg, Va.

2

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

We present both the justification and a development 

initiative to design and implement a pair of service engines 

that use ontologies for semantically enabled discovery and 

integration of structurally heterogeneous earth-science data. 

We also emphasize that the capabilities of these engines are 

likely to transform earth-science research and education. Our 

motivation in developing these engines is based on the recognized need to acquire knowledge through advanced semantic 

capabilities that are able to bridge the science disciplines.

Scientific studies of the Earth and solar system have 

resulted in massive volumes of data; however, most of the 

datasets are isolated from each other, and the ability to use 

these heterogeneous, discipline-specific data to generate 

“new knowledge” has been limited. In our ongoing research 

to facilitate the seamless exchange of heterogeneous data, 

we have developed a Web-based system called Discovery, 

Integration, and Analysis (DIA) that enables scientists to use 

ontologies to discover, integrate, and analyze earth-science 

data (Rezgui and others, 2007). In this paper, we also present 

the Semantically-Enabled Data Registration Engine (SEDRE), 

which is a system that complements DIA by enabling scientists to use ontologies to “advertise” their datasets so that they 

may be automatically discovered by others in the earth-science 

community. We first summarize our efforts in ontology 

development for the earth sciences and then present SEDRE to 

show how it uses ontologies for data registration.

Ontology Development for the Earth Sciences

The role of ontologies for enabling semantic integration 

is well established (Malik, Rezgui, Sinha, and others, 2007) 

as it allows a community to associate well-defined, commonly 

accepted definitions of scientific terms with data. In recent 

years, several research efforts have recognized the potential of 

ontologies in promoting data integration in the earth sciences 

(Sinha and others, 2007). Several ontologies currently are 

being developed, for instance, the National Aeronautics and 

Space Administration’s (NASA) Semantic Web for Earth and 

Environmental Terminology (SWEET) and the more dataoriented Earth and Planetary Ontology (EPONT) developed by 

Malik, Rezgui, and Sinha (2007). EPONT imports and inherits 

properties from existing ontologies. The availability of these 

data ontologies is likely to have significant impact in promoting intra- and interdisciplinary interoperability

Overview of DIA

Geoscientists have generated massive volumes of earth 

science data for decades. Most of the produced data, however, 

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

access, and properly interpret these large data repositories has 

been very limited because of the absence of data-sharing infrastructures that could be used to advertise the data and a common language to properly interpret other providers’ data. As a 

result, it is difficult to answer complex questions that require 

data from several sources. To address this problem, we have 

developed DIA, which provides a collaborative environment 

where scientists can share their resources for the discovery and 

integration of data by registering them through well-defined 

ontologies (Malik, Rezgui, and Sinha, 2007).

The DIA engine (Rezgui and others, 2007) provides three 

functions: discovery, integration, and analysis. Data discovery 

enables users to retrieve data sets, while data integration enables 

users to query multiple resources using common attributes to 

generate previously unknown information: a data product.

DIA’s architecture consists of five components:

•	 User interface—An ArcGIS Server .NET map viewer 

Web application.

•	 Two Web servers—The first 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. 
48 Geoinformatics 2008—Data to Knowledge

•	 Map server—An ArcGIS map server that provides 

maps to DIA’s query processor. 

•	 Registry servers—Provide directory functionalities 

(registration of data and tools, indexing, and so on) that 

providers use to advertise their resources on registry 

servers. 

•	 Query Processor—Produces the results for users’ queries and delivers them to the Web server. 

Overview of SEDRE

Semantic data registration is a precursor to improved data 

discovery and integration. To date, the majority of integrative solutions has been hindered because of the adoption of 

personal acronyms, notations, and so on, which makes it difficult for other scientists to correctly understand the semantics 

of the produced data. To address this concern, SEDRE was 

developed with the goal of allowing researchers to associate 

one or more ontologies with their data files. For instance, if a 

researcher wants to obtain data about carbon dioxide, SEDRE 

will access the data from any dataset where either “carbon 

dioxide,” or “CO2” was used. 

SEDRE facilitates discovery through resource registration at three levels:

•	 Keywords-based registration—Discovery of data 

resources (for instance, gravity meansurements, geologic maps, and so on) requires registering the use of 

high-level index terms.

•	 Ontological class-based registration—Discovering 

item-level databases requires registration at data-level 

ontologies.

•	 Item-level detail registration—Item-level detail or 

“fine-grained” registration consists of associating a 

column in a database to a specific concept or attribute 

of ontology, thus allowing the resource to be queried 

using concepts instead of actual values. This level of 

registration is a requirement for semantic integration 

(that is, for the automatic processing by tools of shared 

data).

Figure 1 shows the “wiring diagram” for SEDRE. We 

also show a small subsection of the Planetary Material package (part of EPONT) so that individual datasets containing 

geochemical analyses with locations (from Planetary Location ontology package of EPONT) can be mapped to terms 

defined in the ontologies. We recognize that data registration 

through ontologies is a time-consuming process, and as such, 

SEDRE has been developed as a downloadable service, where 

data owners can connect to SEDRE’s online repository only to 

upload the data-ontology mappings. This feature allows data 

owners to register their data to ontology mappings at their own 

convenience, while keeping ownership of their data.

SEDRE is designed to be used as a desktop application. Figure 2 shows an example using sulfur-dioxide data. 

As shown in the figure, sulfur-dioxide data gathered on any 

given date can be registered to the concept of sulfur dioxide 

in the EPONT ontology. The conceptual mapping of locations 

and the analyzed element abundances of liquids, gases, or 

solids can be captured through the SEDRE user interface. DIA 

accesses these semantically registered datasets for integration and analyses. We suggest that semantic interoperability 

challenges can be easily overcome through the deployment of 

SEDRE and DIA in a Web environment.

Acknowledgments

This research is supported by National Science Foundation award EAR 0225588 to A.K. Sinha and by a subcontract 

from a NASA award for SEDRE to Peter Fox (University 

Corporation for Atmospheric Research (UCAR).

References Cited

Baedecker, P.A., Grossman, J.N., and Buttleman, K.P., 1998, 

National geochemical data base: PLUTO geochemical data 

base for the United States: U.S. Geological Survey Digital 

Data Series DDS-47, 1 CD-ROM.

Malik, Z., Rezgui, A., Sinha, A.K., Lin, K., and Bouguettaya, 

A., 2007, DIA—A Web services-based infrastructure for 

semantic integration in geoinformatics, in Proceedings, 

International Conference on Web Services, Salt Lake City, 

Utah, July 9–13, 2007: New York, N.Y., Institute of Electrical and Electronics Engineers, p. 1,026–1,023.

Malik, Zaki, Rezgui, Abdelmounaam, and Sinha, A.K., 2007, 

Ontologic integration of geoscience data on the semantic 

web, in Brady, S.R., Sinha, A.K., and Gundersen, L.C., eds., 

Proceedings, Geoinformatics 2007—Data to Knowledge, 

San Diego Calif., May 17–18, 2007: U.S. Geological Survey Scientific Investigations Report 2007–5199, p. 41–43.

Rezgui, Abdelmounaam, Malik, Zaki, and Sinha, A.K., 2007, 

DIA engine—Discovery, integration, and analysis of earth 

science data, in Brady, S.R., Sinha, A.K., and Gundersen, 

L.C., eds., Proceedings, Geoinformatics 2007—Data to 

Knowledge, San Diego Calif., May 17–18, 2007: U.S. Geological Survey Scientific Investigations Report 2007–5199, 

p. 15–18. 
Geoinformatics 2008—Data to Knowledge 49

Figure 1. Flow diagram showing the main components of SEDRE. When the data provider publishes the data, SEDRE tracks their origin, 

assigns a registration number, and monitors their provenance. Data with a corresponding location can be registered through SEDRE. 

Geochemical data sets such as PLUTO (Baedecker and others, 1998) can be registered through a graphical user interface (linked to 

ontologies) that allows a user to map a geochemical measurement (for example, sulfur dioxide) to a corresponding concept shown in 

Ontological Class Level Mapping. The mapped product is also shown in the bottom-right corner.
50 Geoinformatics 2008—Data to Knowledge

Figure 2. Schematic representation of showing the registration of data through SEDRE and their discovery and integration through 

DIA. To answer a query about sulfur dioxide, a user (data provider) registers sulfur-dioxide measurements (tons per day) to create an 

association (a map) between the ontologic concept and the data (SEDRE). The mapped data can be discovered by the DIA system, 

which uses the same ontological infrastructure.
Geoinformatics 2008—Data to Knowledge 51

Oral Session III

Semantic Provenance for Image Data 

Processing

Peter Fox,1 Deborah L. McGuinness,2,3 Paulo Pinheiro 

da Silva,4

 Stephan Zednik,1 Jose Garcia,1 Li Ding,2

Nicholas Del Rio,4

 and Cynthia Chang2

1

High Altitude Observatory, Earth and Sun Systems Laboratory, National 

Center for Atmospheric Research, Boulder, Colo.

2

Department of Computer Science, Rensselaer Polytechnic Institute, Troy, 

N.Y.

3

McGuinness Associates, Latham, N.Y.

4

Department of Computer Science, University of Texas—El Paso, Tex.

Introduction

In some virtual observatories, many of the functions that 

an end-user wishes to perform (in our terminology: use cases) 

are difficult to implement. This is a limiting factor when trying 

to make diverse image datasets available to a broad user base. 

Some real-life use cases are as follows:

1. What algorithms or methods were used in the process to 

obtain the Coronal Helium I Imaging Photometer (CHIP) 

solar limb “image of the day” for January 26, 2005 at the 

Mauna Loa Solar Observatory?

2. What were the cloud cover and atmospheric seeing conditions during the local morning of January 26, 2005 at the 

Mauna Loa Solar Observatory?

3. Why does this image look bad?

In these sample use cases, it is important to note that 

the required provenance information was either not collected 

from different stages in the data-processing pipeline, or it was 

collected but it was not propagated (because the pipeline is 

not fully integrated and does not have the necessary software 

instrumentation, or because some information was generated 

manually and was not annotated). In a Semantic Web context, 

the metadata about information acquisition is called “knowledge provenance.” We describe the provenance requirements 

that have emerged in our previous work on virtual observatories as well as requirements that we identified from a series of 

use cases collected from scientific data users and instrument 

scientists. We also describe our progress in the context of solar 

physics image-data-processing pipelines and discuss the general applicability of our work to data ingest in general.

Virtual observatories are examples of the growing trend 

of supporting distributed interdisciplinary scientific research; 

with a wider range of users come additional requirements and 

changing priorities. We present the provenance requirements 

from work on virtual observatories (described by Del Rio and 

others, 2007, and McGuinness and others, 2007) in several 

scientific communities.

We use knowledge provenance in a broad sense to 

include the origins of knowledge in any virtual system. 

Knowledge provenance includes sources of raw data, experiments used to generate data, processing applied to the data, 

and so on. In this work, which was conducted as part of 

the Semantic Provenance Capture in Data Ingest Systems 

project (SPCDIS, http://spcdis.hao.ucar.edu), we provide 

an extensible representation for provenance in data systems 

in general and for the Virtual Solar-Terrestrial Observatory 

(http://www.vsto.org) in particular. To understand which elements we need to capture, along with the use cases, we also 

need to collect and assemble relevant documentation.

An example of how this documentation is collected is 

shown in figure 1 for the CHIP instrument. The central part of 

the diagram indicates the specific artifacts created from previous 

artifact(s) by the processes (left). The left side of the diagram 

indicates processes that are performed and additional information that is available or added. The right side indicates the 

specific people who have primary responsibility for the stage, as 

well as which portion takes place in Hawaii or in Boulder.

Inference Web and the Proof Markup Language

Inference Web (McGuinness and others, 2003, 2004) is 

a knowledge provenance infrastructure that supports comprehensive explanation capabilities. Those capabilities include 

interoperable explanations of sources (that is, sources published on the Web or accessible from files), assumptions, 

learned information, and answers (for example, scientific 

results) that are associated with inferred or stated conclusions. This knowledge provenance information may be used to 

improve users’ trust regarding those conclusions and thus may 

make systems that include knowledge provenance support 

upon which we are able to provide answers to questions that 

a user wants to ask. The tools provided by the Inference Web 

suite are focused on scientific information and knowledge 

provenance for scientific workflow; its data may include datasets, visualizations, and simulations, all of which expand the 

range of data types that the IW infrastructure must support.

Inference Web provides the Proof Markup Language 

(PML) (Pinheiro da Silva and others, 2004; McGuinness 

and others, 2007) to encode justification information about 

any kind of response produced by agents. PML justifications 

are graphs with the edges always pointing towards the final 

justification conclusion and they store provenance about the 

associated information sources.

Probe-It! is a browser that graphically renders the provenance information associated with results coming from both 

inference engines and scientific workflows. Probe-It! assumes 

that a user has provided existing provenance resources that 

may be viewed. Here our main interest is to display in text or 

visualize the provenance for scientists to better understand 

several aspects of the quality of CHIP images.
52 Geoinformatics 2008—Data to Knowledge

Because provenance associated with results from small scientific workflows can become large and incomprehensible as a 

whole, Probe-It! provides multiple views suited to different elements of provenance: query, global, and local. The conclusion 

is encoded in an Extended QuickLook (EQL) format, which we 

have developed, and the views show how the conclusion was 

derived. The resultant PML documents contain both workflow 

provenance and workflow lineage (workflow results generated by scientific Web services); thus the browser is capable of 

rendering maps, tables, and other scientific artifacts.

Provenance—Identification and Mark-Up

The provenance information we collect depends on the 

processing stage. For instance, in figure 1, the middle part of 

the pipeline from the site observer to Alice Lecinski involves 

artifacts and processes in Hawaii and Boulder for the creation 

of quick-look (QL) images. We needed specific augmentation 

of the artifacts that were obtained while producing the QL 

images. The images are used (1) on the mountain in Hawaii 

for observers to judge image quality and instrument perforSite

Observer

general comments, 

weather comments, 

instrument

comments

Image File with 

Header

Quick Look

Observer Logs

Alice

Lecinski

David

Elmore

Instrument

Capture

Site/Instrument

Configuration

Instrument

Calibration

site calibration, 

configuration modes,

instrument

calibration

Ship to 

NCAR

Don

Kolinski

Images with 

Headers, and 

Observer Logs

Quick Look GIF

Hawaii UCAR

Accounting

+ Quality 

Rating Data Calibrated Data 

with Quality 

value

Quality field

specifying 'Good', 

'Bad', or 'Ugly' data; 

added by Don

Flatten,

Re-scale,

Rotate

Image

Processed Data 

Images (FITS) 

in archive-ready 

state

CHIP

Pipeline

Figure 1. Graphical 

representation of the CHIP data 

pipeline, including key artifacts, 

actors and the roles they play, 

algorithmic processes applied 

along the pipeline, and ancillary 

data added to the artifacts 

as they progress through the 

pipeline.
Geoinformatics 2008—Data to Knowledge 53

mance, or to note features of interest on the Sun; and (2) in 

Boulder for placement on the Mauna Loa Solar Observatory 

(MLSO) home page (http://mlso.hao.ucar.edu).

Figure 2 shows a result from the browser application 

Probe-It! when it is applied to the EQL images. Information 

has been extracted from the observer log and presented in a 

structured form to indicate who added the comments, when 

they were added, and that the sky was clear.

Discussion and Conclusion

We have provided a valuable addition to one part of an 

image-processing data pipeline for solar images taken by an 

instrument at the MLSO. The creation of EQL images in support 

of answering questions such as, “What were the weather and 

observing conditions for this QL image?” is providing significant 

added value to those users who monitor the data pipeline and 

instrument performance. We have introduced structured observer 

log information into the explanation documentation (not previously available) and created PML instances by using two sets of 

tools to browse and search these explanations. The next stage of 

development will involve other parts of the pipeline to benefit 

instrument designers, project scientists, analysts, and end users.

Acknowledgments

The Semantic Provenance Capture in Data Ingest 

Systems project is funded by the National Science Foundation (NSF) Office of Cyberstructure’s Software Development 

for Cyberinfrastructure program grant OCI-0721943. The 

National Center for Atmospheric Research is operated by the 

University Corporation for Atmospheric Research with substantial sponsorship from the NSF. We also received support 

from NSF grant HRD-0734825 to the Center of Excellence for 

Sharing Resources for the Advancement of Research and Education (Cyber-ShARE) at the University of Texas—El Paso.

References Cited

Del Rio, Nick, and Pinheiro da Silva, Paulo, 2007, Probe-it! 

Visualization support for provenance, in Bebis, George, 

Boyle, Richard, Parvin, Bahram, Darko, Koracin, Paragios, 

Nikos, Syeda-Mahmood, Tanveer, Ju, Tao, Liu, Zicheng, 

Coquillart, Sabine, Cruz, Neira, Carolina, Müller, Torsten, 

and Malzbender, Tom, eds., Advances in Visual Computing, 

Proceedings of the Third International Symposium on Visual 

Computing, Lake Tahoe, Nev., November 26–28, 2007: Lecture Notes in Computer Science, v. 4842, p. 732–741.

Del Rio, Nick, Pinheiro da Silva, Paulo, Gates, A.Q., and 

Salayandia, L., 2007, Semantic annotation of maps through 

knowledge provenance, in Fonseca, Frederico, Rodríguez, 

M.A., and Levashkin, Sergei, eds., Geospatial semantics, Proceedings of the Second International Conference 

on Geospatial Semantics (GeoS), Mexico City, Mexico, 

November 29–30, 2007: Lecture Notes in Computer 

Science, v. 4853, p. 20–35.

Figure 2. An example of the use of the Probe-It! browser application. The full provenance trace is in the lower left corner. The main 

part of screen shows an enlarged view of the lower portion of the provenance trace. The image of the Sun is enlarged and allows

annotations by the data provider.
54 Geoinformatics 2008—Data to Knowledge

McGuinness, D.L., Ding, Li, Pinheiro da Silva, Paulo, and 

Chang, Cynthia, 2007, PML 2—A modular explanation 

interlingua, in Roth-Berghofer, Thomas, Schulz, Stefan, 

Bahls, Daniel, and Leake, D.B., eds., Explanation-aware 

computing, Papers from the 2007 Association for the 

Advancement of Artificial Intelligence Workshop, Vancouver, British Columbia, Canada, July 22–23, 2007: Association for the Advancement of Artificial Intelligence Technical 

Report WS–07–06, p. 49–55.

McGuinness, D.L., Fox, Peter, Cinquini, Luca, West, Patrick, Garcia, Jose, and Benedict, J.L., 2007, The Virtual 

Solar-Terrestrial Observatory—A deployed semantic web 

application case study for scientific research, in Cheetham, 

William, and Goker, Mehmet, eds., Proceedings of the 

Nineteenth Conference on Innovative Applications of 

Artificial Intelligence, Vancouver, British Columbia, 

Canada, July 22–26, 2007: Menlo Park, Calif., Association for the Advancement of Artificial Intelligence Press, 

p. 1,730–1,737.

McGuinness, D.L., and Pinheiro da Silva, Paulo, 2004, 

Explaining answers from the Semantic Web—The Inference 

Web approach, in Sycara, Katia, and Mylopoulous, John, 

eds., Proceedings, Second International Semantic Web Conference, Sanibel Island, Fla., October 20–23, 2003: Journal 

of Web Semantics, v. 1 no. 4, p. 397–413.

Pinheiro da Silva, Paulo, McGuinness, D.L., and Fikes, 

Richard, 2006, A proof markup language for Semantic Web 

services, in Bell, David, Bussler, Christoph, and Yang, Jian, 

eds., The Semantic Web and Web services: Information 

Systems, v. 31, no. 4-5, p. 381–395.

Effective Future Use of Current Remotely Sensed 

Data Sets to Study Long-Term Climate Changes

By Albert J. Fleig1 and Curt Tilmes2

1

PITA Analytic Sciences, Bethesda, Md.

2

Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, Md.

Determining changes of a geophysical parameter over 

time requires having a way to estimate the parameter values 

at both ends of the time range under consideration. Measurements being made now will provide the beginning of the data 

record for studies to be done in the future. Unfortunately, 

present standards for archiving remotely sensed datasets do 

not cover all the information that future scientists will need to 

precisely understand and effectively use the current data. Even 

more unfortunately, the scientists who made the current data 

sets will be long gone and unable to answer the resulting questions. The problem is that any given measurement system has 

both a relative accuracy or precision and an absolute accuracy. 

Typically the precision of a “validated” measurement system 

is much better known than the absolute accuracy. Trends can 

be estimated from precise measurements as long as the same 

measurement system is continued in use and the fundamental geophysical situation does not change from that present 

when the dataset was validated; however, no instrument lasts 

forever and improved measurement techniques often lead to 

fundamentally different ways of measuring the same parameter. Over long time periods, geophysical values that were 

assumed as inputs for a given measurement system (input as 

static climate values rather than measured quantities) may 

also change. These changes in instruments, measurement and 

algorithm technology, geophysical situations, and knowledge 

of ancillary data might not be a great problem if the absolute accuracy of both systems is known and if the trend to be 

investigated involves changes that are substantially larger than 

the difference between the absolute accuracy of the current 

and future measurements. If this is not the case, then it would 

be possible for a future scientist to estimate the size of these 

changes and their impact on the resulting long-term trend if 

(but only if) he or she knew exactly how the original dataset 

was made. Unfortunately, current practice does not require 

this information to be archived. Journal articles, subject to 

page limitations, describe the approach but not the details of 

the implementation. Algorithm Theoretical Basis Documents 

currently produced for many of the measurement systems at 

the National Aeronautics and Space Administration (NASA) 

are longer and more detailed; however, they are written before 

launch and are not updated at the end of the mission to document the changes between what was intended and what was 

actually done. The only thing that is sure to be consistent with 

the archived measurements is the source code that was used to 

make the measurements. If the source code is augmented with 

specific identification and archiving of all of the inputs to the 

processing (for every production run) and the exact details of 

the processing system itself, then it becomes possible to replicate and understand the results. 

We have developed and are currently running a production system, described by Tilmes and Fleig (2008, this 

volume), that automates the collection and storage of all the 

provenance information described above. In order for a future 

scientist to do sensitivity studies of the impact of various input 

and algorithm changes, there are several more things that need 

to be automatically collected and stored. They include detailed 

information about how tables used in the algorithm were 

developed and an explanation of what the code was doing at 

every step of the process. There are structural changes in the 

current data-management approach and best-practice procedures that can minimize the above problems. 

Scientists who understand and have made current datasets will not be available to explain them forever. It is time 

to decide whether the information necessary to understand 

exactly how a dataset was produced should be provided as a 

general matter of science policy. Decisions about requiring the 

necessary changes and best practices should be made as the 

result of conscious evaluation of their cost and a consideration 
Geoinformatics 2008—Data to Knowledge 55

of the impact of being unable to understand exactly how the 

past data were made, rather than being left by default to the 

current approach.

Reference Cited

Tilmes, Curt, and Fleig, A.J., 2008, Standardizing interfaces 

for external access to data and processing for the National 

Aeronautics and Space Administration’s Ozone Product 

Evaluation and Test Element (PEATE), in Brady, S.R., 

Sinha, A.K., and Gundersen, L.C., eds., Geoinformatics 2008—Data to Knowledge: U.S. Geological Survey 

Scientific Investigations Report 2008–5172, p. 21–22 (this 

volume).

Peer-Reviewed, Open Data Publication as a 

Means of Data Quality Management in Research

By Hans Pfeiffenberger1 and David Carlson2

1

Alfred Wegener Institute, Bremerhaven, Germany.

2

International Program Office, International Polar Year, Cambridge, United

Kingdom.

A few years ago in the earth-science data community, a 

discussion began that sought to answer the following questions: How can data be cited, should the data be published, 

and, if so, how could they be published? At the same time, the 

community debated whether and why data can or should be 

openly accessible. The newly launched journal entitled “Earth 

System Science Data” is possibly the first journal devoted 

exclusively to the peer-reviewed publication of datasets. The 

journal’s publication policy attempts to provide some specific 

answers to these questions in order to address data-quality 

management (especially in publicly funded research).

Organizations (such as exploration companies or public 

meteorological services) that collect data as an essential part 

of their business have instituted internal instruments and procedures for data-quality management. This is to be expected 

where financial or other success directly depends on data quality, especially if the collected data can be reused and can thus 

constitute “capital.”

By contrast, publicly funded research is almost exclusively driven by the requirement to “publish or perish.” In 

many public organizations, there is no financial or other type 

of bonus for data-quality management. Consequently, in 

most disciplines and institutions, data-quality management 

is regarded as overhead—and that’s if an institutional awareness or a policy about data-quality management even exists. 

At the level of an individual researcher, there are more facets 

to the problem: the researcher will regard collected data as 

the basis for the next, or even many more publications, and 

thus the data become his personal “capital.” This motivation, 

however, will not result in a systematic drive towards neutral 

data-quality management practices that must typically involve 

third parties.

The way that public institutions traditionally handle 

quality management led to the belief that publishing data by 

means of peer-reviewed journal articles as well is a concept 

that ideally unites a well-known and respected instrument 

with the necessary incentive (namely, an increase in the 

publication count of individuals or institutions by publishing a 

dataset). The special form of two-stage open peer review has 

been practiced by a number of high-impact journals in earth 

science topics for some years. Owing to this specific method, 

it is necessary that the published datasets be openly available 

to reviewers, commentators and, finally, the journal readers. 

Public peer review combines the power of peer review with 

the well-known purging effect seen elsewhere in the world of 

open-source software.

We will discuss the emerging rules and practices (for 

example, authors’ and reviewers’ guidelines) of Earth 

System Science Data and try to predict their effectiveness 

and limits regarding data-quality management in research. 

Finally, we will discuss (1) the role of this journal with 

respect to data centers or data repositories (and the people 

who contribute to and operate them) and (2) the relations 

and technical links between the journal articles and the datasets they are about.

Long-Term Availability of Geoscience Data

By Jens Klump1

1

German Research Center for Geosciences, Potsdam, Germany.

Introduction

In the last decade, research in the geological sciences has 

produced vast amounts of new data. In some cases, it is the 

enormous volume of data that poses a technical challenge; in 

other cases, it is their semantic complexity. Regardless of the 

volume and format, geoscience data are characterized by their 

origin in a heterogeneous and dynamic research environment. 

Workflows in a business or administrative context are characterized by their transactional behavior, whereas scientific 

workflows may require ad-hoc changes that become necessary 

through the incorporation of new results into experimental 

working hypotheses (Barga and Gannon, 2007).

To the individual scientist, data curation is not at the 

focus of scientific work and there are few incentives for 

scientists to make data accessible for re-use or re-purposing. 

Only few science-funding agencies ask grant recipients to 

make their data accessible, and even fewer journals make 

data access a prerequisite for publication. Furthermore, the 

roles and responsibilities in long-term curation of scientific 

data still need to be resolved (Lyon, 2007). This situation 

leads to deficits in data management that put large portions 
56 Geoinformatics 2008—Data to Knowledge

of our scientific heritage at risk of loss. The inaccessibility of 

data might have a negative impact on the quality of research 

(Nature, 2006).

Achieving sustainable, long-term accessibility and reusability of research data requires a combination of organizational and technical measures. On the organizational side, data 

curation needs to become an integral part of good scientific 

practice; at the same time, the geoinformatics community has 

to develop tools that facilitate the tasks needed for efficient 

and sustainable data curation.

Organizational Strategies

Several declarations by governmental, nongovernmental, and scientific bodies have called for open access to data 

and for better accountability for the long-term preservation of 

data, but with little success. Several studies (Lyon, 2007, and 

Klump, 2008, among others) have investigated the requirements needed for this effort. These studies also report on bestpractice examples from existing data repositories.

A key to devising effective and sustainable strategies 

for the long-term preservation and accessibility of research 

data is to define “levels of persistence” in the data-curation 

process and its supporting technical architecture. The domains 

of collaboration and publication, with respect to research data, 

are not discrete but rather form the end-points of a “curation 

continuum.” The implementation of data-curation processes, 

however, requires the definition of a boundary between the 

two domains in order to distinguish the roles and responsibilities of the actors in the data-curation processes. The 

idea is to distinguish the domain of active research, where 

curation is the responsibility of the scientists (collaboration 

domain), from the domain of long-term preservation (publication domain), where responsibility and expertise lie with 

the “memory institutions” such as a library or data center 

(Treloar and others, 2007). The diagram in figure 1 shows the 

two data-curation domains and the “curation boundary” with 

its interface between a university’s research groups and its 

memory institution.

Figure 1. Schematic diagram showing data curation in the collaboration domain and in the publication domain. Note that objects in 

the publication domain require comprehensive metadata; however, in the collaboration domain, metadata are available only as implicit 

information. From Treloar and others (2007, used with permission).
Geoinformatics 2008—Data to Knowledge 57

Technological Strategies

Some scientific disciplines already have repositories for 

their data; however, for the majority of researchers, no data 

repositories exist. One example where best practices for disciplinary data repositories are used is at the World Data Centers 

(WDC) of the International Council for Science (ICSU). The 

use of repositories for data curation on an institutional level is 

still a relatively recent idea (Lyon, 2007; Treloar and others, 

2007).

In most cases, the existing disciplinary data repositories 

are not integrated into the scientific workflow, which leads to 

only a small proportion of the data being archived in disciplinary repositories. This break in the workflow also is reflected in 

the problems observed in the generation and curation of metadata. More research needs to be done to determine (1) which 

kind of metadata are needed at which level of data curation 

(Treloar and others, 2007) and (2) how metadata can be generated automatically in the data-curation processes (Robertson, 

2006).

The heterogeneity of data in the geological sciences 

requires special attention to data and file formats. Not all 

formats that are popular among scientists are suitable for longterm preservation (Lormant and others, 2005). This also means 

that preservation metadata need to encode more of the data 

format than, for instance, just their Multipurpose Internet Mail 

Extension (MIME).

Re-use and Re-purposing of Data

Data curation and long-term preservation of digital 

research data are motivated by the goal of both scientists and 

institutions to re-use and re-purpose research data that already 

exist. This goal will be achieved only if the use and citation 

of data become part of scientific culture. Without demand 

from scientists, none of the data repositories can be operated 

on a sustainable basis. This effort requires that a scientist’s or 

institution’s data holdings can be found through catalogs and 

portals and that the published data can be cited in future work 

(Klump and others, 2006).

Uniform Resource Locators (URLs) are transient and, 

therefore, are not suitable as a means of referencing data for 

the purpose of citation. The shortcomings of URLs are overcome by the use of persistent identifiers, such as the Digital 

Object Identifier (DOI) and Uniform Resource Names (URNs) 

(Altman and King, 2007).

Conclusions

The introduction to the Open Archival Information Systems reference model (International Organization for Standardization, 2003) describes a digital archive as “an organization of people and systems, that has accepted the responsibility 

to preserve information and make it available for a Designated 

Community.” Data curation and long-term preservation of scientific data are, therefore, not only technical issues; they also 

need an appropriate organizational framework.

Successful approaches to long-term availability of data 

need to recognize the roles and responsibilities in the datacuration process. The identification of actors in the process 

is needed in order to identify the right tools and incentives 

that are necessary components of technical and organizational 

strategies for long-term availability of data.

References Cited

Altman, Micah, and King, Gary, 2007, A proposed standard 

for the scholarly citation of quantitative data: D-Lib Magazine, v. 13, nos. 3-4, available only online at http://dlib.org/

dlib/march07/altman/03altman.html/. (Accessed August 18, 

2008.) (doi:10.1045/march2007-altman)

Barga, Roger, and Gannon, D.B., 2007, Scientific versus business workflows, in Taylor, I.J., Deelman, E., Gannon, D.B., 

and Shields, M., eds., Workflows for e-science: London, 

United Kingdom, Springer-Verlag, p. 9–16.

International Organization for Standardization, 2003, ISO 

14721:2003, Space data and information transfer systems—

Open archival information system—Reference model: 

Geneva, Switzerland, International Organization for Standardization, 141 p.

Klump, Jens, 2008, Requirements of e-science and grid 

technology for scientific data archiving: Nestor Studies 9, 

50 p.

Klump, Jens, Bertelmann, Roland, Brase, Jan, Diepenbroek, 

Michael, Grobe, Hannes, Höck, Heinke, Lautenschlager, 

Michael, Schindler, Uwe, Sens, Irina, and Wächter, 

Joachim, 2006, Data publication in the Open Access Initiative: Data Science Journal, v. 5, p. 79–83. (doi:10.2481/

dsj.5.79)

Lormant, Nicolas, Huc, Claude, Boucon, Daniele, and Miquel, 

Christine, 2005, How to evaluate the ability of a file format 

to ensure long-term preservation for digital information?, in 

Proceedings, Ensuring Long-term Preservation and Adding 

Value to Scientific and Technical Data (PV 2005), Edinburgh, United Kingdom: Bath, United Kingdom, United 

Kingdom Office for Library and Information Networking, 

11 p., available only online at http://www.ukoln.ac.uk/

events/pv-2005/pv-2005-final-papers/003.pdf. (Accessed 

August 18, 2008.)

Lyon, Liz, 2007, Dealing with data—Roles, rights, responsibilities and relationships; consultancy report: Bath, 

United Kingdom, United Kingdom Office for Library and 

Information Networking, 65 p., available only online at 

http://www.ukoln.ac.uk/ukoln/staff/e.j.lyon/reports/dealing_

with_data_report-final.pdf/. (Accessed August 18, 2008.)
58 Geoinformatics 2008—Data to Knowledge

Nature, 2006, A fair share (editorial): Nature, v. 444, no. 7120, 

p. 653–654.

Robertson, R.J., 2006, Evaluation of metadata workflows for 

the Glasgow ePrints and DSpace services: Glasgow, United 

Kingdom, University of Strathclyde, 48 p., available only 

online at http://eprints.cdlr.strath.ac.uk/2374/01/

Robertson_DAEDALUS_Metadata_Workflow_Evaluation.

pdf. (Accesed August 18, 2008.)

Treloar, Andrew, Groenewegen, David, and Harboe-Ree, 

Catherine, 2007, The data curation continuum—Managing 

data objects in institutional repositories: D-Lib Magazine, 

v. 13, nos. 9-10, available only online at http://dlib.org/dlib/

september07/treloar/09treloar.html/. (Accessed August 18, 

2008.) (doi:10.1045/september2007-treloar)

Towards an OpenEarth Framework (OEF)

By Chaitan Baru,1 G. Randy Keller,2 David R. Nadeau,1

and John L. Moreland1

1

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

La Jolla, Calif.

2

School of Geology and Geophysics, University of Oklahoma, Norman, 

Okla.

Integrating Data and Toolkits

In 2002, the Geosciences Network (GEON) brought 

together 16 institutions to develop an infrastructure for managing 

distributed collections of large, heterogeneous, multidisciplinary 

datasets. In the years since then, this infrastructure has expanded 

to include open-source software for integrating, analyzing, and 

visualizing these datasets. We call this software suite the OpenEarth Framework (OEF) because it is a community-driven set 

of open standards for data models and services. A principal focus 

of this work is integration that spans the following:

•	 Data types—Light detection and ranging (LiDAR), 

satellite, and other types of imagery; digital elevation 

models (DEMs), borehole samples, velocity models 

from seismic tomography, gravity measurements, and 

simulation results.

•	 Data storage schemes—File systems, databases, and 

archiving systems such as the Storage Resource Broker 

(SRB).

•	 Data delivery methods—Local files, database queries, 

Web services (such as Web Map Service (WMS) and 

Web Feature Service (WFS)), and services for new 

data types, such as large tomographic volumes.

•	 Data formats—Shapefiles, Network Common Data 

Format (NetCDF), Geostationary Earth Orbit Tagged 

Image File Format (geoTIFF), and other formal and 

commonly practiced standard formats.

•	 Data models—From two- and three-dimensional 

geometry to semantically richer models of features and 

relationships between those features.

•	 Coordinate systems—Including two- and three-dimensional spatial representations as well as coordinate 

systems for time scales that may span hundreds of millions of years.

There are several good toolkits that address portions of 

this space, including GeoTools, GeoTIFF libraries, Shapefile 

parsers, and many Web services toolkits. The OEF seeks to 

integrate them within a common framework. By spanning 

multiple toolkits, the OEF can grow as the sum of these toolkits grows. The OEF can also remain “toolkit agnostic” and 

expand to support additional toolkits as they emerge.

Visualizing Data

The OEF also is addressing gaps in these toolkits. Chief 

among these is a lack of support for interactive three-dimensional visualization. While many three-dimensional visualization tools exist, they often have built-in assumptions that 

limit their use within geoscience contexts. Google Earth, for 

instance, is a fascinating tool for exploring surface features, 

but it has no support for diving beneath the surface. Tools 

tuned for time lines on the scale of earthquake cycles may be 

insufficient to handle deep time for exploring the evolution of 

the lithosphere and linkages between such phenomena as orogeny and climate. Other tools may do very well when datasets 

are small enough to fit entirely in memory, but they stumble 

on large high-resolution data spanning continents and millions 

of years.

With the OEF’s focus on integrating data that span the 

geosciences, it is important to develop an open software 

architecture and corresponding software that can properly 

manipulate and visualize the integrated data. Because of this 

requirement, the OEF’s software stack extends from deep 

within data archives available through the Web outwards to 

interactive visualization tools running on the user’s desktop or 

laptop computer.

Figure 1 shows three types of services that have been 

developed to accomplish interactive three-dimensional visualization and analysis. At the deepest level, Dataset Access 

Services manage and deliver stored data and metadata. These 

services hide storage details, such as the storage medium, 

Internet location, administrative domain, access authentication, data replication, and storage optimization. Stored 

metadata characterizes registered data, including its spatial 

and temporal extent, resolution, and history. Of particular 

importance is a data derivation tree that links together original 
Geoinformatics 2008—Data to Knowledge 59

data and data derived through format conversion, subsetting, 

resampling, or other analysis.

At the next higher level, Data Modeling Services provide 

on-demand and preprocessing operations on archived data. 

These operations may automatically subset data to extract 

only the specific data that would satisfy a Web services query. 

Services may cache the extracted data for future re-use, preextract data of expected interest, and otherwise manage the 

data to enable fast and fluid data delivery. It is at this level that 

the OEF implements Web services for the delivery of images, 

features, or volumetric data.

Above this level are Data Interaction Services, which 

are designed to support rapid visualization of integrated 

data sets. For instance, services here create multiresolution 

models that enable visualization tools to zoom smoothly into 

data by swapping low-resolution data for higher resolution 

data “on the fly.” These services also subdivide data to better 

support progressive changes to the display as the user pans 

through large data or reveals additional details of interest. 

Derived data may be cached, staged at closer network locations, or downloaded in the background to the user’s computer. Although three-dimensional rendering and interaction 

is performed on the user’s own computer, these services help 

reduce delays as the user explores the deeper parts of the 

data archives.

Finally, the OEF’s visualization tools run on the user’s 

computer and use three-dimensional graphics acceleration 

hardware to display points, lines, polygons, volumetric data, 

animations, isosurfaces, cutting planes, and so forth. In keeping with the spirit of the inclusive style of the OEF, the open 

architecture supports multiple visualization tools authored 

throughout the community. GEON’s Integrated Data Viewer, 

for example, provides an existing mature platform that is 

being extended to use OEF’s layered data services. Additional 

visualization tools are being developed to drive the creation of 

higher level OEF data services and to explore new visualization techniques and user interface styles for interacting with 

integrated datasets.

OEF visualization tools will provide user interfaces that 

support spatial and temporal queries sent off to one or more 

data archives. Query results will be presented within integrated views that combine, for instance, surface elevations 

Figure 1. Layered service 

components of the OpenEarth 

Framework consisting of a 

Data Access Service, Data 

Modeling Service, and Data 

Interaction Service. The 

external visualization tools 

and the modeling and analysis 

packages can gain access 

to remote, multidimensional 

geoscience data using this 

framework. Abbreviations 

are as follows: ASCII, 

American Standard Code 

for Information Interchange; 

GEON1, Geosciences Network 

Project—Phase 1; GEON IDV, 

the Geosciences Network’s 

Integrated Data Viewer; 

geoTIFF, Geostationary Earth 

Orbit Tagged Image File 

Format; GML, Geography 

Mark-up Language; GeoSciML, 

Geoscience Mark-up 

Language; netCDF, network 

Common Data Form; WFS, 

Web Feature Service; WMS, 

Web Mapping Service, WCS, 

Web Coverage Service; 

RDBMS, Relational Database 

Management System; XML, 

Extensible Mark-up Language.
60 Geoinformatics 2008—Data to Knowledge

derived from LiDAR, surface colors from satellite imagery, 

borehole paths, cutting planes through seismic tomography 

data, and other subsurface structures derived from analysis and 

simulations.

Although the various OEF data services described above 

will certainly be used, they are optional. Not all data of interest are in a published data archive. OEF visualization tools also 

must be able to visualize new unpublished data that are locally 

stored. These local datasets may be loaded along with published data from academic archives, thus enabling comparisons 

between new and established views of subsurface structure.

Developing Open-Source Software

Admirable visualization tools already exist in specific 

commercial domains. These tools can produce beautiful 

imagery that is tuned to the needs of those domains; however, 

the tools may be less suitable for supporting current research 

because they cannot provide a flexible test bed for new data 

models and visualization ideas, nor can they be integrated with 

academic data archives. Open-source software is needed that 

can provide the necessary flexibility for academic research. 

Open source software benefits also include community participation and contribution and the creation of a robust developer 

and user community. In the end, developing open-source software ensures both the flexibility and longevity of the software 

base, thereby creating a lasting community asset.

Project Plans

We plan to begin with a sample set of heterogeneous 

datasets for a given geographic region of interest where a 

variety of data is currently available. Using these data as a test 

case, we will develop software to enable visualization of the 

combined information and the ability to interactively access 

and manipulate the underlying data.

Integration of Hydrologic Observations from 

Government and Academic Data Collections 

with the Consortium of Universities for the 

Advancement of Hydrologic Sciences (CUAHSI) 

Hydrologic Information System

By Ilya Zaslavsky,1 David Valentine,1 and David 

Maidment2

1

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

La Jolla, Calif.

2

Department of Civil Engineering, University of Texas, Austin, Tex.

The Consortium of Universities for the Advancement of 

Hydrologic Sciences, Inc. (CUAHSI) Hydrologic Information 

System (HIS) project is a multi-year, multi-university effort 

to develop a cyberinfrastructure for advanced hydrologic 

research and education. The HIS creates a virtual organization 

and a technical foundation that enables publication, discovery, 

retrieval, analysis, and integration of hydrologic information 

across multiple distributed sources to generate a comprehensive picture of hydrologic observations for the entire United 

States. As a university-based effort, the HIS project develops 

an infrastructure for publishing academic data collections. At 

the same time, several recent surveys (for example, Bandaragoda and others, 2005) have shown that hydrologic research 

often relies on selected Federal data sources: the National 

Water Information System (NWIS) from the U.S. Geological Survey (USGS), the Storage and Retrieval (STORET) 

system from the Environmental Protection Agency (EPA), 

the Automated Surface Observing System (ASOS) from the 

National Climatic Data Center (NCDC), Snowpack Telemetry 

(SNOTEL) from the Department of Agriculture (USDA), and 

others; some State data repositories also house large collections of hydrologic data. Integration of governmental data 

collections into the HIS has been an important direction of the 

project.

The main challenges to integrating observational data 

across agencies and academic projects include (1) heterogeneity across the information systems; (2) a lack of standard 

and widely adopted information models, data exchange 

protocols, and agreed-upon semantics for data interchange; 

and (3) incompatible policies for data serving, data retention, security, funding, and so on. Within the CUAHSI HIS 

project, these challenges have been addressed by the following activities:

•	 Developing a common information model for observation data collected at stationary points (measurement 

stations) that would be uniform across government and 

academic sources.

•	 Implementing the common information model as (1) 

a relational schema, the Observations Data Model 

(ODM), that supports publication of observational 

data collections developed as part of academic projects 

(Horsburgh and others, 2008), (2) a series of databases 

storing observation data catalogs describing agency 

repositories, and (3) a standard Extensible Mark-up 

Language (XML) schema for exchanging water observations, called Water Mark-up Language, or WaterML 

(Zaslavsky and others, 2007).

•	 Developing Web services with a common set of 

method signatures to retrieve WaterML-compliant 

information about observation stations (GetSites, 

GetSiteInfo), variables (GetVariables, GetVariableInfo), and values (GetValues). These services, 

called WaterOneFlow services, are implemented as 

XML wrappers over Web-based data access systems 

maintained by Federal and State agencies, such as 

NWISWeb. In the last year, these services have been 
Geoinformatics 2008—Data to Knowledge 61

recoded to take advantage of the Web services developed by our partner agencies. For example, the USGS 

NWIS team has published a beta version of a Web 

service that provides programmatic access to NWIS 

Daily Values data in a WaterML-compliant form. 

A similar effort has been undertaken at NCDC to 

publish ASOS data following the WaterML schema, 

while the EPA has developed the Water Quality 

Exchange (WQX) framework for sharing waterquality data and submitting them to the STORET data 

warehouse. Mapping of WQX elements to WaterML 

is the basis for recoding WaterOneFlow services for 

the STORET repository.

•	 Managing varying semantics by mapping water quantity, water quality, and other parameters collected at 

government agencies to a common vocabulary. This 

task is essential owing to the size and heterogeneity of 

available parameter codes (for example, both USGS 

NWIS and EPA STORET have more than 15,000 

listed parameter codes) and the differences in naming 

conventions adopted at different agencies and research 

groups. The mapping supports cross-database searches; 

users navigate a parameter ontology and find variables 

at each observation network that have been associated 

with the search term. For example, a search for nitrate 

measurements in a given area may uncover a range of 

stations maintained by USGS, EPA, other agencies, 

and academic projects where nitrate-related variables 

were measured. The online mapping system for crossdatabase searching and retrieval is called Hydroseek 

(Beran and Piasecki, in press).

•	 Creating an observation-data publication environment 

where local data managers can load observation data 

they collected into ODM, validate them, publish them 

as Web services, configure them to be presented via 

an online mapping interface (Data Access System for 

Hydrology, or DASH), associate variable names with 

common ontology terms, and register the Web services 

at a central HIS site to make the data available through 

Hydroseek. The components of the data publication 

workflow are part of the HIS server, which has been 

deployed over the last year at hydrologic observatory 

test beds to support publication of local observation 

data.

•	 Developing online user interfaces that combine disparate data into common spatial and temporal representations.

The components mentioned above are organized in 

a service-oriented architecture (fig. 1). The HIS includes 

software stacks for HIS Server and HIS Server Lite (the latter 

is based on free software components only), which are being 

deployed to the 11 National Science Foundation-supported 

hydrologic observatory test beds in order to enable uniform 

publication of local observational data from mostly academic 

sources. The central HIS site at the San Diego Supercomputer 

Center (SDSC) serves observation data catalogs that contain 

sufficient information for formulating data retrieval requests 

made to agency data repositories.

Until recently, CUAHSI Web services mostly have 

worked by wrapping respective agency Web sites (NWIS, 

STORET, and so on) into XML wrappers. This caused a major 

bottleneck as the services were sensitive to changes in page 

layout, and the information had to be relayed via SDSC servFigure 1. Main components 

of the CUAHSI HIS serviceoriented architecture. 

Abbreviations are as 

follows: DASH, Data Access 

System for Hydrology; ETL, 

Extract-Transform-Load; 

HIS, Hydrologic Information 

System; HTML, Hypertext 

Mark-up Language; IDL, 

Interactive Data Language; 

ODM, Observations Data 

Model; OpenMI, Open 

Modeling Interface; SOAP, 

Simple Object Access 

Protocol; WaterML, Water 

Mark-up Language; WSDL, 

Web Service Description 

Language; XML, Extensible 

Mark-up Language.
62 Geoinformatics 2008—Data to Knowledge

ers. In addition, the use of Web service wrappers to harvest 

observation data catalogs from agency Web sites was an errorprone process. As collaboration with these agencies on Web 

services development intensified in the last year, this situation 

has changed. The HIS project now receives database snapshots 

for building observation data catalogs, and connects to newly 

developed WaterML-compliant or other Web services that are 

hosted at agency servers, which enables faster data discovery 

and retrieval. The same model is being extended now to State 

agencies, as the States of Florida, Texas, and Idaho are implementing their HIS systems.

Acknowledgments

We gratefully acknowledge the support of National Science Foundation award EAR-0622374 (David R. Maidment, 

principal investigator). We gratefully acknowledge cooperation, insightful discussions, and help provided by partner 

agency personnel from USGS (R. Hirsch, K. Lins, D. Briar, D. 

Coyle, M. Hamill, and other members of the Water Resources 

Discipline), EPA (C. Spooner, M. Hamilton, D. Young and the 

STORET team), and NCDC (R. Baldwin).

References Cited

Bandaragoda, C.J., Tarboton, D.G., and Maidment, D.R., 

2005, User needs assessment, chapter 4 in Maidment, 

D.R., ed., Hydrologic Information System status report, 

version 1—September 15, 2005: Washington, D.C., 

Consortium of Universities for the Advancement of 

Hydrologic Sciences, Inc., p. 48–87, available only online 

at http://www.cuahsi.org/docs/HISStatusSept15.pdf/. 

(Accessed August 19, 2008.)

Beran, Bora, and Piasecki, Michael, in press, Engineering new 

paths to water data: Computers and Geosciences.

Horsburgh, J.S., Tarboton, D.G., Maidment, D.R., and 

Zaslavsky, Ilya, 2008, A relational model for environmental and water resources data: Water Resources 

Research, v. 44, no. 5, citation number W05406, 

available only online at http://www.agu.org/pubs/

crossref/2008/2007WR006392.shtml/. (Subscription may be 

required.) (doi:10.1029/2007WR006392)

Zaslavsky, Ilya, Valentine, David, and Whiteaker, Tim, eds., 

2007, CUAHSI WaterML: Wayland, Mass., Open Geospatial Consortium, Inc., document OGC 07–041, available 

only online at http://www.opengeospatial.org/standards/dp/. 

(Accessed August 19, 2008.)

Metadata and Semantics in the Astronomical 

Virtual Observatory

By Norman Gray1

1

Department of Physics and Astronomy, University of Leicester, Leicester, 

United Kingdom.

The astronomical virtual observatory (VO) shares many 

of the goals of the geophysics and space-science VO, but has 

a different history and faces different challenges. I will review 

the astronomical VO’s present situation and its current solutions, discuss the roadmap for this VO’s “standards body” (the 

International Virtual Observatory Alliance, or IVOA), and pay 

particular attention to the semantic technologies which are 

being used or anticipated in this domain.

The Astronomical VO—Contexts and Goals

The astronomical VO—which we may take to cover 

night-time astronomy, plus radio, X-ray and solar astronomy 

(plus a little solar terrestrial physics)—is characterized by 

a large number of independent data and image archives, 

which contain collections ranging in size from megabytes 

to (soon) petabytes and with a mixture of curation styles 

ranging from semiformal to highly professional. Despite 

this heterogeneity, these archives have significant overlaps 

in terms of file formats, coordinate systems, and objects 

of interest; for instance, both X-ray and radio astronomers 

will be interested in a supernova remnant, both will refer to 

it with a right ascension and declination, and both will be 

able to produce a flexible image transport system (FITS) 

file containing relevant data. The range of resources available is large enough that scientists may not be aware of all 

the resources that might be of use to them, or they may not 

know how to use interesting resources in different wavelength ranges.

This shared technology and interest means that the astronomical VO should be in a prime position to take advantage 

of processes already developed by VOs in other scientific 

disciplines and to make significant progress towards domainwide interoperability. The range of archive sizes means that 

this VO has notable data discovery problems and significant 

social problems in bringing a wide range of actors to common 

agreement.

IVOA Responses

The International Virtual Observatory Alliance (IVOA) 

is a consortium of consortia and acts as a coordinator for 

multiple national astronomy VO projects. Its processes are 

explicitly modeled on those of the World Wide Web Consortium (W3C).
Geoinformatics 2008—Data to Knowledge 63

The IVOA has been successful, both in brokering highand low-level agreements on protocols and formats and (a 

softer, but equally challenging process) in establishing itself as 

the single forum which coordinates the astronomical VO, thus 

acting as a “nursery” for other spin-off VO developments. I 

will review the history of this process.

Semantic Technologies Within the IVOA

The IVOA’s principal accomplishments in coordinating 

metadata output have been the establishment of a VO-wide 

resource registry, the development of a small set of common and extensible data models, and support for a few more 

sophisticated semantic experiments. The registry consists of 

references to image servers, catalog servers, and Web services, along with their associated metadata. The data models 

that IVOA has agreed to so far cover coordinate systems and 

catalog coverage information. Both models were substantially 

harder to agree upon than was initially expected, for interesting and informative reasons.

The semantic technologies explored to date include a 

basic system for describing astronomical data (Unified Content Descriptors, or UCDs), an ontology of astronomical object 

types, the early stages of a system for linking serialized data to 

data models, and the development of interoperable controlled 

vocabularies.

Other Projects

Several other semantics-oriented projects are being developed using IVOA technologies:

•	 The Explicator project’s goal is to avoid the expense 

and complication of creating consensus data models 

by helping data centers first make their data available in a data model that is natural to them (and thus 

inexpensive to define and maintain) and then formally 

declare mappings to well-known data models.

•	 The Semantic Knowledge Underpinning Astronomy 

(SKUA) project is creating a prototype of a distributed network of semantically aware, shared, annotated 

services in the form of resource description framework 

(RDF) databases. This semantic layer will support a 

cluster of applications, which will either directly support users in finding and recovering useful resources, 

or indirectly support them by supporting user-facing 

applications, including a Facebook-like astronomical 

virtual research environment (VRE).

Comparison of Different Land-Use Object 

Classes by Means of Semantic Similarity 

Measurements

By Chris Schubert,1 Ilonka Wolpert,1 and Ingrid 

Christ1

1

Delphi IMM, GmbH, Potsdam, Germany.

Introduction

Land-use and land-cover data are often required for 

public tasks on a regional, a national, and even on a European level. Currently, some datasets using different thematic 

and geometric accuracy are available for these tasks, for 

example, the Coordination of Information on the Environment (CORINE) Land Cover (CLC) project, the Authoritative 

Topographic-Cartographic Information System (ATKIS), or 

the recently established Global Monitoring for Environment 

and Security (GMES) services.

The CLC and GMES classification systems contain 

substantial, object-oriented descriptions of land-use and 

land-cover classes, which are produced via satellite in order 

to provide environmental monitoring on a European level. In 

contrast, ATKIS is an information system whose goal is to provide a German topographic land survey with a high geometric 

accuracy and progressive actualization degree for urban areas; 

however, for many specialized applications in environmental 

studies, this nationwide dataset does not provide the required 

information accuracy. Moreover, for mapping and updating 

services, it is necessary to combine these heterogeneous and 

already existing datasets. This action is technically feasible, 

but an important semantic problem remains. To tackle this 

problem, two steps must be taken: (1) formalize the description of object classes, and (2) set up a similarity measurement 

by using a knowledge-based model.

These approaches are developed by Delphi Information 

Model Management GmbH (Delphi IMM) in order to contribute to the German research project DeCOVER (Deutschland 

(Germany) Land Cover), which is a joint project of 11 

partners, funded by the Federal Ministry of Economics and 

Technology and managed by the German Aerospace Center. 

DeCOVER was initiated to conceptualize and demonstrate 

innovative and cost-efficient geoinformation services based 

on semantic interoperability and remote-sensing data with the 

goal of updating the existing land-cover datasets in Germany. 

Knowledge Representation

Datasets consisting of different objects, which are based 

on heterogeneous classification systems, can be exchanged 

and integrated only if they are comparable. Figure 1 depicts 

sport and leisure objects classified in ATKIS and CLC. These 

objects are nearly congruent, although the terms of the object 

classes are different; for instance, compare “ATKIS-2201 
64 Geoinformatics 2008—Data to Knowledge

Sports facilities” with “CLC-1.4.2 Sport and leisure facilities.” 

Instead, other objects in the catalog are clearly deviating from 

each other; for instance, compare “ATKIS-4108 Grove” with 

“CLC-3.2.1 Natural grassland.”

In fact, these differences are caused by distinct conceptualizations and object descriptions. Consequently, to make 

object classes comparable, they have to be set at the same definition level by means of ontologies. An ontology is an explicit 

description of a common “world outlook” (Gruber, 2003). In 

order to make the knowledge of classification systems explicit, 

Delphi IMM created a multilevel process and applied it to the 

ATKIS and CLC catalogs as follows:

•	 Extracting the required information from the existing 

mapping instructions of the catalogs,

•	 Creating a basic knowledge model, and

•	 Defining all object classes as an application ontology.

The basic model deals with concepts, object properties, 

and relations for a general object class. For each object class 

of a classification system, there has to be a more specific 

application ontology based on taxonomy and relation of the 

basic knowledge model (Lutz and others, in press). Concept 

expressions and property restrictions characterize application ontologies. The main components—“land cover” (for 

example, vegetation, water, or urban area) and “land use”—

are universal properties. Other parameters are the “location” 

with respect to the sea, the “characteristic neighborhood,” or 

the “genesis” (natural or manmade). In addition, each object 

class can be expressed by specific properties depending on 

land cover. A vegetated object, for instance, can be specified 

by information about soil moisture, which actually implies that 

there might be a swamp present.

The following process of reasoning is an automated process, which is lead by logical conclusions and classifying to an 

automatic subsumption—a depiction of the hierarchy of object 

classes. The ontology-based reasoning serves as a validation of 

formalized application ontologies within one domain or catalog; for example, the object class “mixed forest” consequently 

is a subclass of “forest.” The opposite of this very simple and 

easily realized automated reasoning (comparing the object 

classes from different catalogs) is significantly more difficult. 

Here, only equal or more comprehensive properties have to be 

taken into account; however, the similarity between different object classes must also be considered. For this reason, a 

similarity measurement was developed. 

Similarity Measurement

The similarity of two object classes can be qualified 

in either a network model or a feature model. The network 

model describes the distance between nodes in a hierarchical 

tree structure where special attention is given to the number of edges between two nodes within a multidimensional 

area (Rada and others, 1989).The feature model measures 

the similarity by comparing the common properties of two 

object classes (Tversky, 1977). We used a combination of 

both models: the feature model permits a rough estimation 

of similarity regarding the number of common properties 

and the network model outlines the refinement. The distance 

between the two similar properties is based on the knowledge model.

We distinguish between substantial similarity and 

mapping similarity and therefore developed two different 

algorithms. Both are based on a combined feature-network 

model. Substantial similarity considers two object classes 

that need to be compared as symmetric objects; therefore, a 

one-time evaluation has to be executed for each pair of properties, regardless of which of the two object classes presents 

the origin or the destination (table 1). Finally, the calculated 

similarity shows the proximity of two object classes, but not 

the opportunity to correlate one object with another object. 

The intent of the mapping similarity is to show the potential 

transfer from an origin object class to a destination object 

Figure 1. Land-cover maps 

derived from Spot Image 

satellite imagery. A, Landcover map using the ATKIS 

classification system. B, Landcover map using the CLC 2000 

system. The minimum mapping 

unit for ATKIS is 1 hectare 

and for CLC is 25 hectares. A 

comparison of A and B shows 

different feature demarcations, 

which are based on different 

feature contents. 
Geoinformatics 2008—Data to Knowledge 65

class (tables 2 and 3). This asymmetric examination only 

considers properties from the source class and gives the 

opportunity to map a source object class into a destination 

object class.

The following example shows how substantial and similarity mapping works, using the information found in tables 

1 through 3. The substantial similarity of object class “CLC 

141 Green urban areas” and “ATKIS 2201 Sport facilities” 

measures 88 percent (table 1). This high value indicates 

that the two classes are very similar, but this method does 

not show whether the class “CLC 141 Green urban areas” 

could be mapped into “ATKIS 2201 Sport facilities.” To 

determine whether it could, we use the method of mapping similarity. Table 2 shows an opportunity of 65 percent 

to map “CLC 141 Green urban areas” into “ATKIS 2201 

Sport facilities” and obversely the validation is 79 percent. 

In contrast to substantial similarity, the values of mapping 

similarity are lower. Actually, these differing similarity 

values can be explained by means of the properties. Figure 2 

shows a section of a knowledge-based model which is used 

for all object classes. “ATKIS 2201 Sport facilities” has the 

property called “Sport” and ”CLC 141 Green urban areas” 

has the property called “Recreation” or ”Culture” for the 

land-use parameter in this model, connected respectively 

with one edge.

Conclusions

The results are run in a Web application where heterogeneous catalogs of land use and land cover are automatically 

compared. The framework, Jena (developed by SourceForge, 

Inc.; see http://sourceforge.net for more information), supports 

the extraction of several ontology concepts.

Delphi IMM has achieved a semantic comparison of different land-use classes at the catalog level by means of ontologies 

and a similarity measurement algorithm. We developed our 

basic knowledge model as well as the similarity measurement 

algorithm in an open-ended way so that any other land-use 

catalog can be added and compared. Future work will focus on a 

feature-based search and especially on the mapping at an object 

level, which eventually will ensure the opportunity to map 

spatial data classified in one catalog to spatial data of another 

catalog without needing to consider geometry and topology.

Table 1. Substantial similarity of “green spaces” object classes from the CLC and ATKIS systems.

Object class ATKIS_2201_Sport 

facilities (percent)

ATKIS_2202_Leisure 

facilities (percent)

ATKIS_4108_Grove 

(percent)

CLC_141_Green_urban_areas (percent) 88 92 63

CLC_142_Sport_and_leisure_facilities (percent) 92 92 63

CLC_321_Natural_grassland (percent) 83 83 70

Table 2. Mapping similarity of “green spaces” object classes from the CLC system to the ATKIS system.

Destination class ATKIS_2201_Sport ATKIS_2202_Leisure ATKIS_4108_Grove 

Origin class facilities (percent) facilities (percent) (percent)

CLC_141_Green_urban_areas (percent) 65 67 31

CLC_142_Sport_and_leisure_facilities (percent) 76 76 36

CLC_321_Natural_grassland (percent) 57 57 28

Table 3. Mapping similarity of “green spaces” object classes from the ATKIS system to CLC system.

Destination class CLC_141_Green_urban_ CLC_142_Sport_and_leisure_ CLC_321_Natural 

Origin class areas (percent) facilities (percent) grassland (percent)

ATKIS_2201_Sport facilities (percent) 79 86 20

ATKIS _2202_Leisure facilities (percent) 86 86 20

ATKIS_4108_Grove (percent) 21 50 28
66 Geoinformatics 2008—Data to Knowledge

References Cited

Gruber, Tom, 2003, It is what it does—The pragmatics of ontology for knowledge sharing, in Proceedings, Sharing the Knowledge, International CIDOC 

CRM Symposium, Washington, D.C. March 26–27, 

2003: International Committee for Museum Documentation Conceptual Reference Model Web page at 

http://cidoc.ics.forth.gr/docs/symposium_presentations/

gruber_cidoc-ontology-2003.pdf. (Accessed September 9, 

2008.) 

Lutz, M., Christ, I., Schubert, C., Klien, E. and Hübner, S., in 

press, Overcoming semantic heterogeneity in spatial data 

infrastructures: Computers and Geoscience.

Rada, R., Mili, H., Bicknell, E., and Blettner, M., 1989, Development and application of a metric on semantic nets: IEEE 

Transactions on Systems, Man and Cybernetics, v. 19, no. 1, 

p. 17–30.

Tversky, A., 1977, Features of similarity: Psychological 

Review, v. 84, no. 4, p. 327–352.

Semantic Web Technologies for Value-Added 

Services at the German Research Center for 

Geosciences’ Information System and Data 

Center

By Bernd Ritschel,1 Sabine Pfeiffer,1 Vivien Mende,1

and Sebastian Freiberg1

1

Information System and Data Center, German Research Center for Geosciences, Potsdam, Germany

The German Research Center for Geosciences’ Information System and Data Center (ISDC) portal provides retrievable earth observation data, information, and knowledge 

about the geosciences using a metadata-based catalog system. Although searchable metadata related to a data product 

are stored in tables, the metadata that are dependent on the 

product type are represented using the National Aeronautics 

and Space Administration’s (NASA’s) Global Change Master 

Directory (GCMD) Directory Interchange Format (DIF) documents, which are written in Extensible Mark-up Language 

Figure 2. Diagram showing 

the section of the knowledgebased-model that demonstrates 

the distance between a 

property pair.
Geoinformatics 2008—Data to Knowledge 67

(XML). General and project-specific information on ISDC’s 

portal (including information about its architecture, operation, 

and the philosophy behind the use of the metadata) is found in 

a companion paper (Ritschel and others, 2008, this volume).

Semantic Web technology (Matthews, 2005) is used in 

order to provide new and extended access to data, information, and knowledge at the ISDC; it also makes references and 

correlations between different classes of metadata (documents) 

visible. In addition to these functions, interoperable ISDC 

portal Web services, such as the Catalogue Service for Web 

(CSW), the Web Map Service (WMS), or Web-based discovery and registry services based on semantic relations, can be 

realized using standardized metadata documents, structures, 

concepts, and languages, such as Open Geospatial Consortium’s metadata based on ISO 19115 (International Organization for Standardization, 2003), XML, Resource Description 

Framework (RDF), Simple Knowledge Organization System 

(SKOS), or Web Ontology Language (OWL). Multi- and interdomain collaboration services are possible only with the addition of validated semantics in controlled vocabularies, such as 

NASA’s universal GCMD science keywords and associated 

directory keywords, or the mostly marine-science vocabulary 

developed by the Natural Environment Research Council’s 

Data Grid (NDG). In addition to organization- or committeedriven controlled vocabularies, generally accepted free vocabularies or “folksonomies” (community-driven classifications 

that have developed as the result of the transition to a Webbased culture) are becoming more and more important. (For 

more information on controlled vocabularies, consult David 

Rieckes’ Web site, http://www.controlledvocabulary.com. For 

an introduction to the Semantic Web, see Palmer, 2001.)

The Semantic Web is built using a layered architecture. 

The lowest layers are based on Universal Resource Identifiers (URIs) and XML. URIs are used in order to identify 

the semantic scope of the content that is to be modeled. The 

lowest layers support the RDF and related RDF schemas. They 

provide the techniques for the representation of semantics 

within structured information sources, which consist of relations between subjects, predicates, and objects. 

OWL and SKOS (Isaac and Summers, 2008) are appropriate and standardized languages for the representation and 

processing of knowledge. Whereas SKOS is used for the 

modeling of controlled vocabularies, different OWL specifications, such as OWL Lite or OWL Full, may be used to 

describe complex interdomain semantic relations. Protègè, 

Altova SemanticWorks, Semantic Web Ontology Overview 

and Perusal (SWOOP), and CmapTools are some of the tools 

for the creation, management, and processing of ontologies. 

CmapTools has been used for the modeling the new ISDC 

DIF-standard-compliant metadata concept.

As mentioned already, the ISDC product-type-dependent metadata are stored in DIF-standard-compliant XML 

documents. One product type is described by one metadata 

document from the metadata class product-type DIF. Inside 

this class, there are eight mandatory attributes (such as entry 

identification, entry title, and parameters) and more than 

25 optional attributes (such as a summary or a reference). 

The analysis of the content and the structure of the GCMD 

product-type DIF metadata class shows some attributes (such 

as project, platform, instrument, and institution) that also can 

be used to extend the simple concept of autonomous metadata classes. This means that, in addition to the product-type 

class, new metadata classes and relations can be created. For 

the description of the unique and discrete data products (data 

files), an extension of the DIF standard is used, which is 

described in detail in Mende and others (2008, this volume).

Figure 1 shows the proposed structure of the new ISDC 

DIF-standard-related metadata concept. The concept consists 

of concept nodes, which in our case are metadata classes or 

attributes of classes, and the relations between two concept 

nodes. These relations are visualized by concept arrows and 

the corresponding linking phrases in figure 1. Two concept 

nodes and the appropriate relation are always reflecting one of 

the different ISDC metadata schemes. Although the different shapes and colors of the concept nodes represent specific 

features of our concept nodes, not all ISDC metadata classes, 

attributes, and relations are represented in the concept diagram 

(fig. 1); for example, there is no way to distinguish between 

mandatory and optional classes and attributes.

The main ISDC metadata product-type class is located at 

the center of the concept diagram (fig. 1). Important features of the product type are referenced by attributes such as 

“parameter” (science keyword), “reference,” “citation,” “free_

keyword,” “entry_title,” “summary,” and “entry_id.” A relation, consisting of the two concept nodes “product_type” and 

“entry_id” and the associated linking phrase (“has unique”), 

can be read in the following way: “A product type has a 

unique identifier.” Another relation is the following: “A product type has a citation,” which reflects the relation between a 

producer of data products and the type of data products. The 

attribute “reference” is used for the relation between product 

type and the usage of related data products within scientific 

publications. Other major attributes are “project,” “platform” 

(observatory or instrument carrier), and “instrument” (sensor), 

which are represented by autonomous classes, too. In order to 

keep the concept clear and simple, attributes such as “data_

center,” “personnel,” “quality,” and others are not shown here 

but are part of the metadata concept.

In addition to the ISDC metadata classes and their 

relations, the concept also represents concept nodes for 

controlled and free vocabularies and the different sources of 

those vocabularies. The content of the product-type attribute 

“parameter” as well as the content of the main attributes and 

classes (such as “project,” “platform,” and “instrument”) are 

represented by controlled vocabularies. The ISDC metadata 

concept uses the GCMD’s science keywords and associated 

directory keywords as controlled vocabularies. Sources of free 

vocabularies are generated by data providers or users; these 

vocabularies can be used in addition to controlled vocabularies through the attribute “free_keyword.” In addition to the 

input of free keywords, a user-driven extension of the existing 

classification of keywords is possible. An example of a new 
68 Geoinformatics 2008—Data to Knowledge

non-DIF-standard-compliant keyword type is “application,” 

which describes the usage of data products in a specific application. Generally accepted free vocabularies can be supported 

via Web 2.0 technologies such as collaborative or social tagging. Other semantic information often is hidden in data about 

social navigation (the analysis of user activity). A proposed 

activity can be prompted, as in the following example: If user 

A always downloads product types X and Y together, then 

the proposal for user B, who only downloads product type Y, 

could be “Why not download product type X, too?

The new ISDC DIF metadata concept also can be used 

in order to create new and more abstract product-type classes, 

which, for example, have a different scope. As an example, 

ISDC product types related to the orbit of objects in space 

(such as Rapid Science Orbit, Predicted Orbit, or Precise 

Orbit, which are derived from different satellite missions or 

projects) can be described in a general way by the new product 

type “Orbit.” The name of the new product type not only 

allows users to network together the different unique product 

types, but also to create a more generalized, searchable index 

for orbit products within the ISDC portal in the future.

References Cited

International Organization for Standardization, 2003, Geographic information—Metadata: Geneva, Switzerland, 

International Organization for Standardization, 140 p.

Isaac, Aaron, and Summers, Ed, eds., 2008, SKOS Simple 

Knowledge Organization System primer—W3C working draft 21 February 2008: World Wide Web Consortium 

Web site at http://www.w3.org/TR/skos-primer/. (Accessed 

August 20, 2008.)

Figure 1. Diagram showing the relations between classes and attributes in the Information System and Data Center (ISDC) Directory 

Interchange Format (DIF) metadata concept.
Geoinformatics 2008—Data to Knowledge 69

Matthews, Brian, 2005, Sematic Web technologies: Joint 

Information Systems Committee Techwatch Report TSW 

05-02, 20 p., available only online at http://www.jisc.ac.uk/

media/documents/techwatch/jisctsw_05_02bpdf.pdf/. 

(Accessed August 20, 2008.)

Mende,Vivien, Ritschel, Bernd, Freiberg, Sebastian, Palm, 

Hartmut, and Gericke, Lutz, 2008, Directory Interchange 

Format (DIF) metadata and handling at the German 

Research Center for Geosciences’ Information System and 

Data Center, in Brady, S.R., Sinha, A.K., and Gundersen, 

L.C., eds., Proceedings, Geoinformatics 2008—Data to 

Knowledge, Potsdam, Germany, June 11–13, 2008: U.S. 

Geological Survey Scientific Investigations Report 2008–

5172, p. 43–46 (this volume).

Palmer, Sean, 2001, The Semantic Web—An introduction: 

Web site at http://infomesh.net/2001/swintro/. (Accessed 

August 20, 2008.)

Ritschel, Bernd, Mende, Vivien, Palm, Hartmut, Gericke, 

Lutz, Freiberg, Sebastian, Kopischke, Ronny, and Bruhns, 

Christian, 2008, The German Research Center for Geosciences’ Information System and Data Center—Portal to 

geoscientific data, information, and knowledge, in Brady, 

S.R., Sinha, A.K., and Gundersen, L.C., eds., Proceedings, 

Geoinformatics 2008—Data to Knowledge, Potsdam, Germany, June 11–13, 2008: U.S. Geological Survey Scientific 

Investigations Report 2008–5172, p. 33–35 (this volume).

Sensor-Based Landslide Early Warning System 

(SLEWS)—Development of a Spatial Data 

Infrastructure With Integrated Real-Time Sensor 

Data as a Basis for Early Warning Systems 

Exemplifying Landslides

By Stefanie Hass,1 Kai Walter,2 Frank Niemeyer,2

Christian Arnhardt,3

 Kristine Asch,1 Raffig Azzam,3

Ralf Bill,2

 Tomas Fernandez-Steeger,3 Stefan Daniel 

Homfeld,4

 and Hartmut Ritter4

1

Federal Institute for Geosciences and Natural Resources, Hannover, 

Germany.

2

Department of Geodesy and Geoinformatics, Rostock University, Rostock, 

Germany.

3

Department of Engineering Geology and Hydrogeology, RWTH Aachen 

University, Aachen, Germany.

4

ScatterWeb GmbH, Berlin, Germany.

Accessible geographic information becomes more and 

more important for making decisions. Spatial data infrastructures (SDIs) allow the distribution and access of sensor 

and geographic data by using Web services developed by 

the Open Geospatial Consortium (OGC). The Sensor-based 

Landslide Early Warning System (SLEWS) is a joint project 

of the RWTH Aachen University, University of Rostock, 

the Federal Institute for Geosciences and Natural Resources 

(Hannover), and ScatterWeb GmbH (Berlin) that uses open 

standardized Web service specifications and data formats 

provided by OGC’s Sensor Web Enablement (SWE) initiative 

to establish a monitoring and warning system for landslides. 

SLEWS is built around a self-organizing wireless sensor network, hosting sensors such as inclinometers, accelerometers, 

and displacement and pressure detectors to obtain real-time 

data (fig. 1).

The Sensor Web Enablement initiative offers methods 

to discover, access, and filter sensor data, which allows for 

effective data management and analysis. One major requirement of early warning and risk management systems is the 

ability to immediately extract reliable information from the 

collected data. This process takes place in the information 

tier (fig. 1) by data modeling, which involves complex series 

of algorithms and analytic processes. The sensor data are 

collected and standardized within the data tier and then forwarded to the information tier where the data are analyzed, 

modeled, and specifically processed for various end-user 

applications.

When a critical event occurs, OGC services such as the 

Sensor Alert Service (SAS) and Web Notification Service 

(WNS) automatically send a warning message. The Sensor 

Mark-up Language, SensorML, is used to formalize and standardize the sensor processes and hardware. SWE services can 

be combined with already existing OGC Web services such 

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

(WCS), and Web Map Service (WMS). When long-term planning is needed, digital maps are automatically created that provide support for decisions and resource deployment planning 

to respond to a critical event. Moreover, within an SDI, the 

functions of classical geoinformation systems can be used by 

the Web Processing Service (WPS). These functions allow the 

intersection of themes, such as detailed and visualized information about roads, buildings, or other crucial infrastructures 

inside the hazard zone. The geographic and sensor information 

is made available by means of an internet browser or other 

mobile applications such as a personal digital assistant (PDA) 

or a cell phone with advanced features.

The development of an SDI that uses OGC Web Services is an innovative technology that meets the requirements 

of early warning and risk management systems. The SDI is 

able to encapsulate heterogeneous data and to transform the 

measured data into relevant information. OGC Web Services 

(OWS) and Sensor Web Enablement open up new possibilities 

for real-time monitoring and provide information for decisionmaking by enabling the user to collect data stored in different 

locations. This newly developed, advanced technology provides local authorities with essential hazard information that 

may protect life and help mitigate potential damage caused by 

landslides and rockfalls. 
70 Geoinformatics 2008—Data to Knowledge

Figure 1. Diagram showing the planned 

system infrastructure for the Sensorbased Landslide Early Warning System 

(SLEWS), from data measurement to 

information distribution. Section A 

shows the sensor network and data 

management; section B shows data 

analysis, information management, 

and distribution; and section C shows 

user management, visualization, and 

discovery. Abbreviations are as follows: 

AJAX, asynchronous JavaScript and XML 

(Extensible Mark-up Language); HTTP, 

Hypertext Mark-up Language; WCS, Web 

Coverage Service; WFS, Web Feature 

Service; WMS, Web Map Service.
Geoinformatics 2008—Data to Knowledge 71

Ontological Geosciences

By Kangping Sun1

1

Reserch Center for Space Science and Technology, China University of 

Geosciences, Wuhan, China.

The study of geosciences may be viewed as two separate 

activities: (1) gaining knowledge about the Earth through the 

traditional geoscience disciplines of geophysics, geochemistry 

and so forth and (2) determining how to seek, obtain, and infer 

new geoscience knowledge. Since the end of 2007, the author 

has led a team of graduates and senior undergraduates at China 

University of Geosciences (CUG) to experiment with the use 

of geoscience concept models to represent theme-oriented 

concepts in geoscience literature. One of the objectives of this 

project is to integrate geoscience data using a subject-oriented 

concept model. Another objective is to explicitly characterize, 

within the concept model, the knowledge that is related to or 

implied in the theme-oriented concept but is extracted from 

published literature (other than the literature that was used in 

order to develop the concept model) in order to gain further 

insight of the mechanisms a concept model should possess. 

This project is still ongoing; however, there are two conclusions we have been able to draw from the progress made so 

far:

1. The knowledge related to and implied in any single geoscience term, conclusion, or theory is vast, as it relates in 

some way to almost every other concept in geosciences; 

and

2. Geosciences contain various recognition patterns, including those that can be used to seek or infer new geoscientific knowledge. 

Conventional automated computer systems may not 

be able to correctly comprehend and operate a geoscience 

concept model that is capable of recognizing new geoscientific 

knowledge patterns. Furthermore, it does not seem feasible 

that one can just use the limited number of geoscience concept 

patterns already known to us to consistently provide adequate 

representations of such complex and ever-evolving knowledge. For geoscientists, the knowledge represented by the new 

concept models derived only from the limited patterns already 

known to us may be especially hard to accept. For these reasons, we propose to develop a subdiscipline of the geosciences 

called “ontological geosciences.”

We refer to ontological geosciences as that which is 

based on geoscience data and encompasses all the contents 

of the traditional geosciences. The essential characteristic of 

the ontological geosciences is that it is composed of formal 

discrete geoscience elements. Hence, the ontological geosciences possess better correlation, integrity, and consistency 

than the traditional geosciences. Although they are similar to 

the traditional geosciences, the ontological geosciences must 

first be recognizable and operable by people who want to use 

it, especially the geoscientists; second, the content that makes 

up the basis for the ontological geosciences must also be 

programmable (that is, recognizable and operable insofar as 

computers are concerned, which is unique for the data-based 

geosciences). Ontological geosciences, however, should be 

neither considered nor developed as an ordinary computer 

system; instead, they ultimately should be developed as a 

programmable knowledge system.

The traditional geosciences are mainly concerned with 

the knowledge of the structure, composition, and evolution of 

the Earth. Ontological geosciences focus on the way the structure, composition, and evolution of the Earth are represented 

on computers, thus providing an underlying structure for the

internally integrated representation of geoscience knowledge. 

The main challenges to the ontological geosciences are as follows:

1. Extraction and standardization of geoscience knowledge 

patterns from the traditional geoscientific literature and 

the use of those derived knowledge patterns to represent 

that part of geoscience knowledge that has not been conclusively proven or generally recognized.

2. A determination of the extent to which one can surmise, 

generalize, and unify these knowledge patterns.

3. The knowledge about the Earth may not be mature 

enough to be integrated into a consistent model.

4. The structure, composition, and evolution of the Earth 

are beyond the control of human beings. Our knowledge 

of the Earth, therefore, has not been and will never be 

complete; all the geoscience knowledge we have acquired 

through the gradual process of studying the Earth is, in 

theory, incomplete. When scientists observe new natural 

phenomena, they often provide explanations by proposing theories or hypotheses, which are often personal and 

subjective. The verification process of these theories and 

hypotheses may take a long time because the Earth is an 

extraordinarily complex entity and scientific observational 

tools and studies are very expensive. On the other hand, 

in order to get a consistent interface to access geosci get a consistent interface to access geoscience data, ontologies or “standard dictionaries” must be 

defined. Consequently, the geoscience principles should 

be included because the meaning of geoscientific terms 

are often significant within a given context, or theory. In 

short, if we assume that we know the geoscience structures, then we can use them to “teach” computers to 

understand geoscientific concepts. We hope to use general 

or commonly recognized knowledge patterns in order to 

develop just one computer system to support the whole of 

the geosciences.

Our solution to the aforementioned challenges is to 

design a hierarchical ontological geosciences computer system 

that is developed by users through collaboration with others. The architecture of the proposed system is illustrated in 

figure 1. In order to construct such a system, two concepts 

must be accepted. First, the ontological geosciences consist of 
72 Geoinformatics 2008—Data to Knowledge

a system of hierarchies of geoscience knowledge. The hierarchy of the architecture is not limited within the geoscience 

concept model; it extends to the geoscience knowledge levels, 

including the levels of the knowledge base that can be used to 

design the model or applications. Second, it is a system that 

is self-improving through the collaborative efforts between 

system users and computers. The computer network facilities provide a convenient mechanism for communication and 

sharing knowledge among the system users. The users gain 

knowledge from the system and improve it according to their 

knowledge of geosciences. Some of the most important learning and improving processes take place between the hierarchical levels. The users learn the knowledge patterns from the 

processes occurring in the upper layer and transform them 

into the lower-level model or applications. Last, the middle 

layer of the architecture can be regarded as “dictionary-based 

geosciences,” which is that aspect of the geosciences that is 

only represented by the terminologies provided in the dictionary. The dictionary provides the meaning of terms for geoscience data in the databases as well; therefore, the ontological 

geosciences can be built on the basis of geoscience data via 

the dictionary.

In summary, ontological geosciences is a structured 

geoscience subdiscipline that is based on geoscience data and 

is recognizable and operable on computer systems. In the current environment of research and development, we regard it 

as being essentially a “human-being-centered” geoscience. It 

is the people who learn and generalize the knowledge patterns, who determine what geoscience content is appropriate 

for computers to represent, and who teach the computers to 

understand the ontological geosciences that in turn support 

geoscientific research.

Acknowledgments

The author would like to thank China Geological Survey 

for their support and financial contribution to this research 

project.

A Volcano Erupts—Semantic Data Registration 

and Integration

By Peter Fox,1 A. Krishna Sinha,2 Deborah L. 

McGuinness,3,4 Rob Raskin,5

 and Abdelmounaam 

Rezgui2

1

High Altitude Observatory, Earth and Sun Systems Laboratory, National 

Center for Atmospheric Research, Boulder, Colo.

2

Departmtent of Geosciences, Virginia Polytechnic Institute and State 

University, Blacksburg, Va.

3

Department of Computer Science, Rensselaer Polytechnic Institute, Troy, 

N.Y.

4

McGuinness Associates, Latham, N.Y. 

5

Jet Propulsion Laboratory, Pasadena, Calif.

We present a progress report in a research effort (Semantically-Enabled Scientific Data Integration, or SESDI) into the 

application of Semantic Web methods and technologies to the 

challenging problem of integrating heterogeneous volcanic 

and atmospheric chemical-compound data, which are used 

to assess the atmospheric effects of a volcanic eruption. One 

requirement to accomplish this is the semantic registration 

of datasets to domain and integrative ontologies. We demonstrate how ontologies are implemented by leveraging existing 

distributed semantic technology frameworks. 

Introduction

The goal of our project is to enable the next generation of 

interdisciplinary and discipline-specific data and information 

systems to answer many challenging science questions requiring data from widely distinct fields. Our initial focus was on 

the integration of volcanic and atmospheric data sources in 

learning and formalization

provides meaning leaning and

formalization

Geosciences represented by natural language

consists of Geoscience

concept set

Geoscience

concept model

Databases query data Computer

applications

1 Dictionary

2 Controlled

terminology

3 Geoscience

ontology

.

.

.

Figure 1. Diagram showing the architecture of the ontological geosciences.

similar

terms

Figure 1. Diagram showing 

the architecture of the 

ontological geosciences.
Geoinformatics 2008—Data to Knowledge 73

support of investigations into relationships between volcanic 

activity and global climate (McGuinness and others, 2006, 

2007; Fox, McGuinness, and others, 2007; Fox, Sinha, and 

others, 2007; Sinha and others, 2007). Another goal was to 

facilitate search and retrieval using an underlying framework 

that contains information about the semantics of the scientific 

terms that are used in the search. We also focused on the registration of disciplinary datasets in order to fully facilitate the 

integration of the volcanic and atmospheric data sources. We 

developed a tool to aid data providers with registering the data 

without explicitly knowing about the underlying ontologies.

Semantic Data Integration Methodology

We followed a methodology reported in previous work 

(Benedict and others, 2007) because our effort depended on 

machine-processable specifications of the scientific terms that 

are used in the study of volcanoes and the atmosphere. We 

identified specific ontology modules that need construction in 

the areas of volcanoes, plate tectonics, atmosphere, and climate, 

which draw heavily on existing ontologies. We used ontologies in the form of modules from the Jet Propulsion Laboratory’s Semantic Web for Earth and Environmental Terminology 

(SWEET), Virtual Solar-Terrestrial Observatory (VSTO), and 

Geosciences Network (GEON). Our attention has been focused 

on the “Atmosphere-Volcano Use Case,” whose goal is as follows: To determine the statistical signatures of both volcanic 

and solar forcings on the height of the tropopause. 

We convened small workshop groups along these topic 

lines. We started with use cases and elements of the existing 

vocabularies or ontologies (where available) and proceeded to 

develop the knowledge representation. We used CmapTools, a 

concept mapping tool from Institute for Human and Machine 

Cognition (IHMC, http://cmap.ihmc.us/coe) that reads and 

writes Ontology Web Language- (OWL-) based ontologies and 

provides OWL-based predicate assistance for adding relations 

between concepts. Figure 1 shows how the ontologies were 

packaged, indicating the direction of importing and package 

dependency (see figure caption for more details).

We leveraged the VSTO framework (Fox and others, 2006, 

McGuinness and others, 2007) by replacing the solar-terrestrialFigure 1. Schematic diagram 

showing the packaging (that 

is, referencing and importing 

of ontologies) to make 

the necessary knowledge 

concepts and relations known 

to the application program. 

The concept-mapping tool 

CMAP was used to generate 

this figure. CMAP allows the 

embedding of more detailed 

concepts within a grouping (for 

example, the SemanticFilter 

and SWEETOntologyPackage). 

Abbreviations and symbols 

are as follows: SWEET, 

Semantic Web for Earth and 

Environmental Terminology; 

<< (to the right of the boxes), 

allows the box to be “closed” 

and only display a single box 

with the higher level name; 

boxes can be expanded by 

clicking on a corresponding 

>> icon.
74 Geoinformatics 2008—Data to Knowledge

specific ontology and data sources with appropriate volcano and 

atmospheric ontologies and data and catalog sources.

Our work required an even more modular approach to 

ontology re-use; the result was that we were able to conceive 

a new conceptual decomposition starting with SWEET 1.2 

(http://sweet.jpl.nasa.gov). This effort will lead to the next version of the framework, which was based on broad community 

input and participation guided by the principle of re-use by 

other applications.

Data Registration

We base our data registration effort on the work 

from GEON (http://www.geongrid.org) and VSTO 

(http://www.vsto.org). The data registration sensibly consists 

of three levels:

1. Discovery of data resources, which requires registration 

through use of high-level index terms.

2. Discovery of item-level databases, which requires registration at data-type-level ontologies.

3. Item-detail-level registration (required for semantic integration).

We developed a software application known as the 

Semantically-Enabled Data Registration Engine (SEDRE) 

to implement the registrations. The tool is intended to be 

used by people with a variety of skill levels. We are using an 

ontology for the registration workflow for SEDRE for levels 

1, 2, and 3 and the two “disciplines (+ sub-disciplines)”: 

Geosphere+Geochemistry and Atmosphere+Atmospheric 

Chemistry.

Figure 2 shows one phase of registration of sulfur-dioxide 

data from a level-2 swath product from the European satellitemounted Scanning Imaging Absorption Spectrometer for 

Atmospheric Chartography (SCIAMACHY). A user opens a 

file and selects Atmosphere > Atmospheric Chemistry and the 

screen in figure 2 is displayed.

Discussion and Conclusion

We have presented the latest progress in an effort that 

uses modular ontologies to capture meanings of terms in distinct but related science domains with the goal of facilitating 

research into relationships between the domains and re-use of 

the modular ontologies. We have leveraged the existing starting points for reference ontologies in atmospheric science and 

partly developed ontologies for volcanoes and plate tectonics. 

We have held workshops to vet the ontologies among the multiple communities and completed a series of ontology mapping 

and merging exercises to arrive at the current modularization. 

The key element of data registration using developed ontologies is a new capability within the scientific Semantic Web 

community. The SEDRE tool we have developed is still evolving and we have only limited experience with user testing, but 

to date, the results and feedback are encouraging.

Acknowledgments

This work is supported by the National Aeronautics 

and Space Administration’s (NASA’s) Advancing CollaboraFigure 2. The SEDRE software 

tool as shown on the computer. 

There are four main panes in 

the window: (1) the upper left 

shows key level-1 and level-2 

concepts such as location 

and related metadata for 

observations; (2) the lower left 

shows common compounds, 

the periodic table, and oxides 

(those that the user selects 

and associates with elements 

in the data table); (3) the upper 

right shows a preview of the 

data, where column headers 

are selectable so as to be 

associated with the concepts 

on the left two panes; and (4) 

the lower right is a display 

of the accumulated set of 

mapped relations (for example, 

SCD (Slant Column Density) is 

mapped to sulfur-dioxide as 

well as units, and so on).
Geoinformatics 2008—Data to Knowledge 75

tive Connections for Earth System Science (ACCESS) and 

Advanced Information Systems Technology (AIST) programs.

References Cited

Benedict, J.L., McGuinness, D.L., and Fox, Peter, 2007, A 

Semantic Web-based methodology for building conceptual 

models of scientific information: Eos, Transactions of the 

American Geophysical Union, v. 88, no. 52, Fall Meeting 

Supplement, Abstract IN53A–0950, p. F774.

Fox, Peter, McGuinness, D.L., Middleton, Don, Cinquini, 

Luca, Darnell, J.A., Garcia, Jose, West, Patrick, Benedict, 

James, and Solomon, Stan, 2006, Semantically-enabled 

large-scale science data repositories, in Cruz, Isabel, 

Decker, Stefan, Allemang, Dean, Proest, Chris, Schwabe, 

Daniel, Mika, Peter, Uschold, Mike, and Arroyo, Lora, eds., 

The Semantic Web—ISWC 2006, Proceedings, Fifth International Semantic Web Conference, Athens, Ga., November 

5–9, 2006: Lecture Notes in Computer Science, v. 4273, 

p. 792–805.

Fox, Peter, McGuinness, D.L., Raskin, Rob, and Sinha, 

Krishna, 2007, A volcano erupts—Semantically mediated 

integration of heterogeneous volcanic and atmospheric data, 

in Proceedings of the ACM First Workshop on Cyberinfrastructure—Information Management in eScience, Lisbon, 

Portugal, November 9, 2007: New York, N.Y., Association 

for Computing Machinery, p. 1–6.

Fox, Peter, Sinha, Krishna, Raskin, Rob, McGuinness, D.L., 

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Scientific Investigations Report 2007–5199, p. 43–46. 
Manuscript approved for publication September 25, 2008.

Prepared by Reston Publishing Service Center.

Editing by Elizabeth D. Koozmin.

Photocomposition and design by Cathy Y. Knutson and Anna N. Glover.

For more information concerning this report, please contact 

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

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